Deliverable 2.3 Understanding and benchmarking the existing travel and destination selection behaviours

Project number: F-DUT-2022-0088 Driving Equitable and Accessible 15 Minute Neighbourhood Transformations WP2. Review and comparative analysis T2.3. Understanding and benchmarking the existing travel and destination selection behaviours Deliverable 2.3 Understanding and benchmarking the existing travel and destination selection behaviours Date: 17/03/2025 Responsible partner: Universität für Bodenkultur Wien (BOKU) Authors: Georgia Charalampidou, Universität für Bodenkultur Wien (BOKU) Roman Klementschitz, Universität für Bodenkultur Wien (BOKU) Yusak Susilo, Universität für Bodenkultur Wien (BOKU) 2 DOCUMENT CHANGE RECORD Version Date Status Author Description 0.1 04/07/2024 Draft Georgia Charalampidou (BOKU) First results 0.2 23/07/2024 Draft Georgia Charalampidou (BOKU) Adding results from population segments 0.3 15/11/2024 Draft Roman Klementschitz (BOKU), Yusak Susilo (BOKU) Internal review, edit, revise 0.4 09/12/2024 Draft Anna Grigolon (UT), Jelten Baguet (Mpact) Daniela Arias Molinares (UT), Domokos Esztergár-Kiss (BME) First round of review 0.5 7/02/2025 Draft Georgia Charalampidou (BOKU) Final version after first round of review 0.6 17/02/2025 Draft Daniela Arias Molinares (UT), Baran Ulak (UT), Domokos Esztergár-Kiss (BME), Ana Clara Szymanski (TUM), Charlotte van Vessem (VUB) Second round of review 1.0 17/03/2025 Final version Georgia Charalampidou (BOKU) Final version after the review process The DUT Partnership is supported by the European Comission and funded under the Horizon Europe co-funded Partnership scheme (Topic HORIZON-CL5-2021-D2-01-16) 3 TABLE OF CONTENTS DOCUMENT CHANGE RECORD ...................................................................................................... 2 TABLE OF CONTENTS ....................................................................................................................... 3 LIST OF FIGURES ............................................................................................................................... 5 LIST OF TABLES ............................................................................................................................... 10 1. EXECUTIVE SUMMARY ......................................................................................................... 14 2. INTRODUCTION .................................................................................................................... 15 3. METHODOLOGY .................................................................................................................... 16 4. VIENNA ................................................................................................................................... 19 4.1. General characteristics of Vienna and LL location ....................................................................................... 19 4.2. Descriptive statistical analysis ............................................................................................................................. 20 4.3. Modal split .................................................................................................................................................................... 21 4.4. Trip Characteristics – Vienna ................................................................................................................................ 22 4.5. Trip Characteristics – Liesing ............................................................................................................................... 28 4.6. Travel behaviour across different socioeconomic groups ........................................................................ 34 5. UTRECHT ............................................................................................................................... 36 5.1. General characteristics of Utrecht and LL location...................................................................................... 36 5.2. Descriptive statistical analysis ............................................................................................................................. 37 5.3. Modal split .................................................................................................................................................................... 38 5.4. Trip Characteristics – Utrecht .............................................................................................................................. 39 5.5. Trip Characteristics – Overvecht ......................................................................................................................... 46 5.6. Travel behaviour across different socioeconomic groups ........................................................................ 50 6. BRUSSELS .............................................................................................................................. 52 6.1. General characteristics of Brussels and LL location .................................................................................... 52 6.2. Descriptive statistical analysis ............................................................................................................................. 53 6.3. Modal split .................................................................................................................................................................... 54 6.4. Trip Characteristics – Brussels ............................................................................................................................ 55 6.5. Trip Characteristics – Brussels City ................................................................................................................... 61 6.6. Travel behaviour across different socioeconomic groups ........................................................................ 66 7. BUDAPEST ............................................................................................................................. 68 7.1. General characteristics of Budapest and LL location .................................................................................. 68 7.2. Descriptive statistical analysis ............................................................................................................................. 68 7.3. Modal split .................................................................................................................................................................... 70 7.4. Trip Characteristics – Budapest........................................................................................................................... 71 7.5. Trip Characteristics – 16th and 17th district .................................................................................................... 76 7.6. Travel behaviour across different socioeconomic groups ........................................................................ 79 4 8. ÎLE-DE-FRANCE ..................................................................................................................... 81 8.1. General characteristics of Île-de-France and LL location.......................................................................... 81 8.2. Descriptive statistical analysis ............................................................................................................................. 82 8.3. Modal split .................................................................................................................................................................... 83 8.4. Trip Characteristics - Île-de-France ................................................................................................................... 84 8.5. Trip Characteristics – Essonne ............................................................................................................................. 91 8.6. Travel behaviour across different socioeconomic groups ........................................................................ 97 9. MUNICH ................................................................................................................................ 99 9.1. General characteristics of Munich and LL location ...................................................................................... 99 9.2. Descriptive statistical analysis ...........................................................................................................................100 9.3. Modal split ..................................................................................................................................................................101 9.4. Trip Characteristics – Munich ............................................................................................................................102 9.5. Trip Characteristics – Dense Town at S-Bahn Termini ............................................................................108 9.6. Travel behaviour across different socioeconomic groups ......................................................................114 10. COMPARISON ..................................................................................................................... 116 10.1. City level ..................................................................................................................................................................116 10.2. Outskirts (Living Lab) level .............................................................................................................................119 11. CONCLUSIONS .................................................................................................................... 122 12. REFERENCES ......................................................................................................................... 124 APPENDIX A ................................................................................................................................... 125 APPENDIX B ................................................................................................................................... 130 APPENDIX C ................................................................................................................................... 135 APPENDIX D ................................................................................................................................... 139 APPENDIX E ................................................................................................................................... 142 APPENDIX F ................................................................................................................................... 147 APPENDIX G ................................................................................................................................... 152 APPENDIX H ................................................................................................................................... 158 5 LIST OF FIGURES Figure 1: Location of the Liesing district (orange line) and the Vienna Living Lab (red pin) (Source: OSM (2024)) ......................................................................................................................................................... 19 Figure 2: Modal split by trip purpose - Vienna ........................................................................................... 21 Figure 3: Modal split by trip purpose - Liesing ........................................................................................... 22 Figure 4: Density plot of work trip duration (a) and distance (b) by transport mode – Vienna .... 23 Figure 5: Cumulative distribution function (CDF) of work trip duration (a, b) and distance (c, d)– Vienna .................................................................................................................................................................... 23 Figure 6: Density plot of educational trip duration (a) and distance (b) by transport mode – Vienna ................................................................................................................................................................................. 24 Figure 7: Cumulative distribution function (CDF) of educational trip duration (a, b) and distance (c, d)– Vienna ....................................................................................................................................................... 25 Figure 8: Density plot of shopping trip duration (a) and distance (b) by transport mode – Vienna ................................................................................................................................................................................. 26 Figure 9: Cumulative distribution function (CDF) of shopping trip duration (a, b) and distance (c, d)– Vienna ............................................................................................................................................................. 26 Figure 10: Density plot of leisure trip duration (a) and distance (b) by transport mode – Vienna 27 Figure 11: Cumulative distribution function (CDF) of leisure trip duration (a, b) and distance (c, d)– Vienna .................................................................................................................................................................... 28 Figure 12: Density plot of work trip duration (a) and distance (b) by transport mode – Liesing ... 29 Figure 13: Cumulative distribution function (CDF) of work trip duration (a, b) and distance (c, d)– Liesing .................................................................................................................................................................... 29 Figure 14: Density plot of educational trip duration (a) and distance (b) by transport mode – Liesing .................................................................................................................................................................... 30 Figure 15: Cumulative distribution function (CDF) of educational trip duration (a, b) and distance (c, d)– Liesing ....................................................................................................................................................... 31 Figure 16: Density plot of shopping trip duration (a) and distance (b) by transport mode – Liesing ................................................................................................................................................................................. 31 Figure 17: Cumulative distribution function (CDF) of shopping trip duration (a, b) and distance (c, d)– Liesing ............................................................................................................................................................. 32 Figure 18: Density plot of leisure trip duration (a) and distance (b) by transport mode – Liesing 33 Figure 19: Cumulative distribution function (CDF) of leisure trip duration (a, b) and distance (c, d)– Liesing .................................................................................................................................................................... 33 Figure 20: Location of the Utrecht Living Lab “Overvecht” (orange dot) (Source: OSM (2024)) ... 36 Figure 21: Modal split by trip purpose – Utrecht ........................................................................................ 38 Figure 22: Modal split by trip purpose - Overvecht ................................................................................... 39 Figure 23: Density plot of work trip duration (a) and distance (b) by transport mode – Utrecht . 39 6 Figure 24: Cumulative distribution function (CDF) of work trip duration (a, b) and distance (c, d)– Utrecht ................................................................................................................................................................... 40 Figure 25: Density plot of educational trip duration (a) and distance (b) by transport mode – Utrecht ................................................................................................................................................................... 41 Figure 26: Cumulative distribution function (CDF) of educational trip duration (a, b) and distance (c, d)– Utrecht ...................................................................................................................................................... 42 Figure 27: Density plot of shopping trip duration (a) and distance (b) by transport mode – Utrecht ................................................................................................................................................................................. 43 Figure 28: Cumulative distribution function (CDF) of shopping trip duration (a, b) and distance (c, d)– Utrecht ............................................................................................................................................................ 43 Figure 29: Density plot of leisure trip duration (a) and distance (b) by transport mode – Utrecht .................................................................................................................................................................................44 Figure 30: Cumulative distribution function (CDF) of leisure trip duration (a, b) and distance (c, d)– Utrecht ................................................................................................................................................................... 45 Figure 31: Density plot of work trip duration (a) and distance (b) by transport mode – Overvecht ................................................................................................................................................................................. 46 Figure 32: Cumulative distribution function (CDF) of work trip duration (a) and distance (b)– Overvecht .............................................................................................................................................................. 46 Figure 33: Density plot of shopping trip duration (a) and distance (b) by transport mode – Overvecht .............................................................................................................................................................. 47 Figure 34: Cumulative distribution function (CDF) of shopping trip duration (a, b) and distance (c, d)– Overvecht ....................................................................................................................................................... 48 Figure 35: Density plot of leisure trip duration (a) and distance (b) by transport mode – Overvecht ................................................................................................................................................................................. 49 Figure 36: Cumulative distribution function (CDF) of leisure trip duration (a, b) and distance (c, d)– Overvecht .............................................................................................................................................................. 49 Figure 37: Location of the City of Brussels district (orange line) and the Brussels’ Living Lab (red pins) (Source: OSM (2024)) .............................................................................................................................. 52 Figure 38: Modal split by trip purpose – Brussels ...................................................................................... 54 Figure 39: Modal split by trip purpose – Brussels City .............................................................................. 55 Figure 40: Density plot of work trip duration (a) and distance (b) by transport mode – Brussels 55 Figure 41: Cumulative distribution function (CDF) of work trip duration (a, b) and distance (c, d)– Brussels .................................................................................................................................................................. 56 Figure 42: Density plot of educational trip duration (a) and distance (b) by transport mode – Brussels .................................................................................................................................................................. 57 Figure 43: Cumulative distribution function (CDF) of educational trip duration (a, b) and distance (c, d)– Brussels ..................................................................................................................................................... 57 Figure 44: Density plot of shopping trip duration (a) and distance (b) by transport mode – Brussels ................................................................................................................................................................................. 58 Figure 45: Cumulative distribution function (CDF) of shopping trip duration (a, b) and distance (c, d)– Brussels ........................................................................................................................................................... 59 7 Figure 46: Density plot of leisure trip duration (a) and distance (b) by transport mode – Brussels ................................................................................................................................................................................. 60 Figure 47: Cumulative distribution function (CDF) of leisure trip duration (a, b) and distance (c, d)– Brussels .................................................................................................................................................................. 60 Figure 48: Density plot of work trip duration (a) and distance (b) by transport mode – Brussels City .......................................................................................................................................................................... 61 Figure 49: Cumulative distribution function (CDF) of work trip duration (a, b) and distance (c, d)– Brussels City ......................................................................................................................................................... 62 Figure 50: Density plot of educational trip duration (a) and distance (b) by transport mode – Brussels City ......................................................................................................................................................... 62 Figure 51: Cumulative distribution function (CDF) of educational trip duration (a) and distance (b)– Brussels City ......................................................................................................................................................... 63 Figure 52: Density plot of shopping trip duration (a) and distance (b) by transport mode – Brussels City .......................................................................................................................................................................... 64 Figure 53: Cumulative distribution function (CDF) of shopping trip duration (a, b) and distance (c, d)– Brussels City .................................................................................................................................................. 64 Figure 54: Density plot of leisure trip duration (a) and distance (b) by transport mode – Brussels City .......................................................................................................................................................................... 65 Figure 55: Cumulative distribution function (CDF) of leisure trip duration (a, b) and distance (c, d)– Brussels City ......................................................................................................................................................... 66 Figure 56: Location of the Rákosmente district (orange line) (Source: OSM (2024)) ....................... 68 Figure 57: Modal split by trip purpose – Budapest .................................................................................... 70 Figure 58: Modal split by trip purpose – 16th & 17th District ..................................................................... 70 Figure 59: Density plot of work trip duration by transport mode – Budapest ................................... 71 Figure 60: Cumulative distribution function (CDF) of work trip duration– Budapest ....................... 71 Figure 61: Density plot of educational trip duration by transport mode – Budapest ....................... 72 Figure 62: Cumulative distribution function (CDF) of educational trip duration– Budapest .......... 73 Figure 63: Density plot of shopping trip duration by transport mode – Budapest ........................... 73 Figure 64: Cumulative distribution function (CDF) of shopping trip duration– Budapest .............. 74 Figure 65: Density plot of leisure trip duration by transport mode – Budapest ................................ 75 Figure 66: Cumulative distribution function (CDF) of leisure trip duration– Budapest .................... 75 Figure 67: Density plot of work trip duration by transport mode – 16th & 17th District .................... 76 Figure 68: Cumulative distribution function (CDF) of work trip duration– 16th & 17th District........ 76 Figure 69: Density plot of shopping trip duration by transport mode – 16th & 17th District ............ 77 Figure 70: Cumulative distribution function (CDF) of shopping trip duration– 16th & 17th District78 Figure 71: Density plot of leisure trip duration by transport mode – 16th & 17th District .................. 78 Figure 72: Cumulative distribution function (CDF) of leisure trip duration– 16th & 17th District ..... 79 Figure 73: Location of the Essonne district (orange line) (Source: OSM (2024)) ............................... 81 8 Figure 74: Modal split by trip purpose - Île-de-France ............................................................................. 83 Figure 75: Modal split by trip purpose - Essonne ....................................................................................... 84 Figure 76: Density plot of work trip duration (a) and distance (b) by transport mode – Île-deFrance ..................................................................................................................................................................... 84 Figure 77: Cumulative distribution function (CDF) of work trip duration (a, b) and distance (c, d)– Île-de-France ........................................................................................................................................................ 85 Figure 78: Density plot of educational trip duration (a) and distance (b) by transport mode – Îlede-France .............................................................................................................................................................. 86 Figure 79: Cumulative distribution function (CDF) of educational trip duration (a, b) and distance (c, d)– Île-de-France ........................................................................................................................................... 87 Figure 80: Density plot of shopping trip duration (a) and distance (b) by transport mode – Île-deFrance ..................................................................................................................................................................... 88 Figure 81: Cumulative distribution function (CDF) of shopping trip duration (a, b) and distance (c, d)– Île-de-France ................................................................................................................................................. 88 Figure 82: Density plot of leisure trip duration (a) and distance (b) by transport mode – Île-deFrance ..................................................................................................................................................................... 89 Figure 83: Cumulative distribution function (CDF) of leisure trip duration (a, b) and distance (c, d)– Île-de-France ........................................................................................................................................................ 90 Figure 84: Density plot of work trip duration (a) and distance (b) by transport mode – Essonne 91 Figure 85: Cumulative distribution function (CDF) of work trip duration (a, b) and distance (c, d)– Essonne .................................................................................................................................................................. 92 Figure 86: Density plot of educational trip duration (a) and distance (b) by transport mode – Essonne .................................................................................................................................................................. 93 Figure 87: Cumulative distribution function (CDF) of educational trip duration (a, b) and distance (c, d)– Essonne ..................................................................................................................................................... 93 Figure 88: Density plot of shopping trip duration (a) and distance (b) by transport mode – Essonne ................................................................................................................................................................................. 94 Figure 89: Cumulative distribution function (CDF) of shopping trip duration (a, b) and distance (c, d)– Essonne ........................................................................................................................................................... 95 Figure 90: Density plot of leisure trip duration (a) and distance (b) by transport mode – Essonne ................................................................................................................................................................................. 96 Figure 91: Cumulative distribution function (CDF) of leisure trip duration (a, b) and distance (c, d)– Essonne .................................................................................................................................................................. 96 Figure 92: Location of the Munich Living Labs (red pins) (Source: OSM (2024)) ............................... 99 Figure 93: Modal split by trip purpose – Munich ...................................................................................... 101 Figure 94: Modal split by trip purpose – Dense Towns at S-Bahn Termini ....................................... 102 Figure 95: Density plot of work trip duration (a) and distance (b) by transport mode – Munich 102 Figure 96: Cumulative distribution function (CDF) of work trip duration (a, b) and distance (c, d)– Munich ................................................................................................................................................................. 103 9 Figure 97: Density plot of educational trip duration (a) and distance (b) by transport mode – Munich ................................................................................................................................................................. 104 Figure 98: Cumulative distribution function (CDF) of educational trip duration (a, b) and distance (c, d)– Munich .................................................................................................................................................... 104 Figure 99: Density plot of shopping trip duration (a) and distance (b) by transport mode – Munich ............................................................................................................................................................................... 105 Figure 100: Cumulative distribution function (CDF) of shopping trip duration (a, b) and distance (c, d)– Munich .................................................................................................................................................... 106 Figure 101: Density plot of leisure trip duration (a) and distance (b) by transport mode – Munich ............................................................................................................................................................................... 107 Figure 102: Cumulative distribution function (CDF) of leisure trip duration (a, b) and distance (c, d)– Munich .......................................................................................................................................................... 107 Figure 103: Density plot of work trip duration (a) and distance (b) by transport mode – Dense Towns at S-Bahn Termini ................................................................................................................................ 108 Figure 104: Cumulative distribution function (CDF) of work trip duration (a, b) and distance (c, d)– Dense Towns at S-Bahn Termini ................................................................................................................... 109 Figure 105: Density plot of educational trip duration (a) and distance (b) by transport mode – Dense Towns at S-Bahn Termi ........................................................................................................................ 110 Figure 106: Cumulative distribution function (CDF) of educational trip duration (a, b) and distance (c, d)– Dense Towns at S-Bahn Termini........................................................................................................ 110 Figure 107: Density plot of shopping trip duration (a) and distance (b) by transport mode – Dense Towns at S-Bahn Termi .................................................................................................................................... 111 Figure 108: Cumulative distribution function (CDF) of shopping trip duration (a, b) and distance (c, d)– Dense Towns at S-Bahn Termini........................................................................................................ 112 Figure 109: Density plot of leisure trip duration (a) and distance (b) by transport mode – Dense Towns at S-Bahn Termi .................................................................................................................................... 113 Figure 110: Cumulative distribution function (CDF) of leisure trip duration (a, b) and distance (c, d)– Dense Towns at S-Bahn Termini ............................................................................................................. 113 Figure 111: Cumulative distribution function (CDF) of trip duration per trip purpose– City level 116 Figure 112: Percentage of trips completed within 15 minutes by trip purpose - City level ............ 116 Figure 113: Cumulative distribution function (CDF) of trip duration per trip purpose– Outskirts (Living Lab) level ................................................................................................................................................ 119 Figure 114: Percentage of trips completed within 15 minutes by trip purpose – Outskirts (Living Lab) level .............................................................................................................................................................. 119 16 3. METHODOLOGY For the comparative data analysis, we used the existing household travel surveys from all the Living Lab (LL) locations. Each LL had access to at least one household travel survey dataset. To gain a clearer understanding of the scope and content of these surveys, a structured questionnaire was developed (Appendix H). This questionnaire helped in collecting all the relevant details and selecting the most suitable dataset for the analysis. The specific information we sought to gather included the following: 1. General information about the survey (e.g. name, year, sample size, description of the purpose) 2. Dataset information (e.g. availability restrictions, format type, language) 3. Information about the attributes of households and people 4. Information about the attributes of each reported trip After collecting and evaluating the relevant information from all, available to use, household travel surveys, we selected the datasets presented in Table 1 for the analysis. Table 2 provides an overview of the dataset characteristics and variables. Table 1: Household travel survey selection Country/Region Travel Survey Austria Österreich Unterwegs 2013-14 (ÖU) Netherlands Onderweg in Nederland 2021-22 (ODiN) Brussels Onderzoek Verplaatsingsgedrag 2021-22 (OVG) Budapest Household Survey for Unified Macroscopic Transport Model 2019 Île - de - France Enquête Globale Transport 2018-20 (EGT) Germany Mobilität in Deutschland 2017 (MiD) The goal of the comparative analysis, as previously outlined in this document, is to assess and enhance the understanding of current travel patterns and behaviors among individuals living in the LL locations. Therefore, the analysis focused on the travel behaviour of individuals in their local environment. Accordingly, a specific methodology has been implemented. Each database has been filtered to match with the following criteria: 1. All the reported trips have as origin the residential district of the respondent. 2. All the reported trips have as destination the same country, to avoid long trips that could influence the final output of the analysis. 3. The purpose of the reported trips is work, education, shopping, and leisure. The selection is based on the priorities set out in the 15-minute-city concept and identified in Deliverable 2.1 (1). However, Deliverable 2.1 identifies 6 core functions called: work, commerce (here, shopping), education, healthcare and services. As not all datasets include trips from the healthcare category, and trips related to services cannot be accurately identified, these two trip purpose categories are not included in the analysis. 4. The travel mode of the reported trips is pedestrian, bicycle, car (driver or passenger), or public transport. The criteria were shaped based on the mutual characteristics of the datasets in use. As illustrated in Table 2, it becomes evident that the aggregation level of the resident region of the respondents varies across the datasets. Consequently, the district level was selected as it is consistent across all datasets. It is noteworthy that the Austrian and German national household datasets are the most outdated; however, they were the only available datasets for analysis, thus necessitating their use. With regard to socioeconomic-related variables on individual level, it is observed that the Brussels dataset does not include a substantial number of variables pertinent to the analysis. Finally, in the Budapest dataset, the trip duration and distance variables were not included, and thus, they had to be calculated for the analysis. The trip duration variable was successfully calculated using the recorded start and end times of the trip; however, the trip distance variable could not be calculated due to an absence of relevant data. A more thorough exposition of the data analysis characteristics of each city can be found in the following sections. Table 2: Overview of the national household travel survey characteristics and variables Austria The Netherlands Brussels Budapest Île-de-France Germany A. General Information Name Österreich Unterwegs (ÖU) Onderweg in Nederland (ODiN) Onderzoek Verplaatsingsgedrag (OVG) Household survey for Unified Macroscopic Transport model Enquête Globale Transport 2020 (EGT H2020) Mobilität in Deutschland (MiD) Year 2013 - 2014 2021 - 2022 2021 - 2022 2019 2018 - 2020 2017 Number of reported days per respondent 2 1 1 1 1 1 Type of reported days All the days All the days All the days Weekdays Weekdays All the days Survey period October 2013– December 2014 January 2021 – December 2021 March 2021 – January 2022 October – November 2019 January 2018 – March 2020 May 2016 – October 2017 Net sample size 17.070 Households – 38.220 Individuals 48.000 Individuals 2.685 Individuals 4800 Households in Budapest, 120 households in Agglomeration 4.800 Households, 10.470 Individuals 156.420 Households, 316.361 Individuals Survey area Austria The Netherlands Brussels Capital Region Budapest and Agglomeration Île-de-France Germany Definition of reported trip Every trip that uses the public space/network Individual report on activity for the requested day - Door to door by addresses for every trips during one workday Individual report of all trips made during the day before the interview All trips made by the members of a household through the reported day Aggregation level District (Bezirk) Postal code Postal code District & Address Couronne/ District Address, Postal code, Municipality, District B. Household Characteristics Living Location ✓ ✓ ✓ ✓ ✓ ✓ Size ✓ ✓ ✓ ✓ ✓ ✓ Composition ✓ ✓ - ✓ ✓ - Income ✓ ✓ - ✓ ✓ ✓ Num. and type of private transport means ✓ ✓ ✓ ✓ ✓ ✓ C. Individuals Characteristics Age ✓ ✓ ✓ ✓ ✓ ✓ Gender ✓ ✓ ✓ ✓ ✓ ✓ Academic level ✓ ✓ - ✓ ✓ ✓ Occupation ✓ ✓ - ✓ ✓ ✓ Income ✓ ✓ - ✓ ✓ - PT Subscriptions ✓ ✓ - ✓ ✓ ✓ Driving licence ✓ ✓ - ✓ ✓ ✓ Available means of transport ✓ ✓ - ✓ ✓ ✓ 18 Total travel distance per person ✓ ✓ - - - - Total travel time per person ✓ ✓ - - ✓ - D. Trips Characteristics Aggregation level of origin and destination Postal code, Municipality, District Postal code Postal code Address Couronne/ District Address, Postal code, Municipality, District Trip purpose Work ✓ ✓ ✓ ✓ ✓ ✓ Business trip ✓ ✓ ✓ ✓ ✓ ✓ School/Education ✓ ✓ ✓ ✓ ✓ ✓ Dropping off/picking up/accompanying people ✓ ✓ ✓ ✓ ✓ ✓ Shopping ✓ ✓ ✓ ✓ ✓ ✓ Running errands ✓ ✓ ✓ ✓ ✓ ✓ Leisure ✓ ✓ ✓ ✓ ✓ ✓ Visiting friends ✓ ✓ ✓ ✓ ✓ - Other - Recreational walking Recreational walking - - - Trip mode ✓ ✓ ✓ ✓ ✓ ✓ Trip length ✓ ✓ ✓ - ✓ ✓ Trip duration ✓ ✓ ✓ - ✓ ✓ 4. VIENNA 4.1. General characteristics of Vienna and LL location Vienna is the capital and one of the nine federal states of Austria and consists of 23 districts (“Bezirke”). With more than 2 million inhabitants in 2024, it is the largest city in Austria and one of the fastest growing major cities in Europe (2). Vienna has an extensive public transport infrastructure consisting of 6 underground lines, railway, suburban railway, tram, bus and bicycle infrastructure. It also offers car and micromobility sharing services. The Viennese Living Lab is Wiener Flur and its surrounding neighbourhoods, which are located in the 23rd District of Vienna, known as "Liesing". Liesing is situated in the south-west of Vienna and has a population of approximately 118.000 inhabitants. Wiener Flur is proximate to the border of Lower Austria and constitutes the largest public housing development in the former village of Siebenhirten in Liesing. The area is served by the U6 underground line, a tram line, bus lines and car/bike sharing services. Figure 1 shows the map of Vienna and the location of the Viennese Living Lab “Wiener Flur” (red pin). Figure 1: Location of the Liesing district (orange line) and the Vienna Living Lab (red pin) (Source: OSM (2024)) 20 4.2. Descriptive statistical analysis The analysis begins with an overview of the socioeconomic characteristics of households and individuals. A short list of variables has been selected from the dataset “Österreich Unterwegs 2013/14” (2), to help us picture and understand the characteristics of residents, both at the city level and at the district level of the Living Lab location. Table 3 shows the socioeconomic characteristics of households, while Table 4 outlines the socioeconomic characteristics of individuals. It should be noted that the information on the respondent's place of residence is provided by the dataset ÖU at the district level (Bezirke), and therefore for the case of the LL location "Wiener Flur" we analyse the whole area of Liesing. The sample used for this part of the analysis is the total resident population within the area (Vienna or Liesing), with no filtering for specific trip purposes. Furthermore, the sample of survey respondents who did not provide a valid response to one or more of the selected questions was excluded from the sample. Table 3: Socioeconomic characteristics of households (Vienna & Liesing) – Source: ÖU 2013/14 Socioeconomic Characteristics of Households Vienna LL Location (Liesing) n % n % Household size 1 Person 970 31.9 52 27.4 2 Persons 1230 40.4 79 41.6 3 Persons 446 14.6 22 11.6 4+ Persons 399 13.1 37 19.5 Economic situation (Self estimation of respondents) Very poor 43 1.5 2 1.1 Poor 183 6.2 5 2.7 Average 1334 45.1 95 51.1 Good 1089 36.8 69 37.1 Very good 307 10.4 15 8.1 Car ownership 0 620 23.1 20 11.5 1 1594 59.5 110 63.2 2 399 14.9 39 22.4 3 57 2.1 5 2.9 4+ 10 0.4 - - Table 4: Socioeconomic characteristics of individuals (Vienna & Liesing) - – Source: ÖU 2013/14 Socioeconomic Characteristics of Individuals Vienna LL Location (Liesing) n % n % Gender Male 2734 46.3 181 44.9 Female 3173 53.7 222 55.1 Age Category (y.o.) 6 – 14 368 6.2 35 8.7 15 – 19 324 5.5 30 7.4 20 – 24 332 5.6 16 4.0 25 – 34 813 13.8 46 11.4 35 – 44 783 13.3 47 11.7 45 – 54 1060 17.9 64 15.9 55 – 64 837 14.2 56 13.9 65+ 1390 23.5 109 27.0 Occupation Student 1011 17.1 85 21.1 Employed 2606 44.1 154 38.2 Pensioner 1650 27.9 127 31.5 Other 640 10.8 37 9.2 Car Driving License Yes 4256 80.9 295 84.8 No 1004 1 9.1 53 15.2 Availability of bike Yes 3195 62.5 252 71.2 No 1919 3 7.5 102 28.8 Availability of car Always 2797 57.4 222 67.3 Occasionally 806 16.5 42 12.7 Never 1272 26.1 66 20.0 21 The descriptive statistical analysis provides a comprehensive insight into the demographic characteristics of the residents of Vienna and Liesing. Firstly, the sample of women is slightly larger than that of men. Comparing the distribution with the official statistics (51% females in Vienna and 52% in Liesing), both for Vienna and Liesing there is only a small oversampling of women in the sample (3, 4). This demonstrates that the survey's sample represents sufficiently the actual population. Secondly, residents of Liesing have a greater availability of cars and bicycles, while slightly larger proportion of households in Liesing belong to the average income category. In addition, middle-aged and older adults, as well as people in work and retirement, are more strongly represented in the sample. 4.3. Modal split Figure 2 illustrates the modal split by trip purpose for the city of Vienna. It is evident that Viennese citizens prefer by far the use of public transport for their educational and work trips. Regarding leisure and shopping trips, the most prevalent travel modes are walking and public transport. Car use is also popular, particularly for shopping and work-related trips, while bike use is among the least popular modes of transport for all trip purposes. Figure 2: Modal split by trip purpose - Vienna In Liesing, the modal split results show a preference in car use and public transport, particularly for education and work-related trips (Figure 3). However, Liesing’s residents prefer to drive more than using public transport for their work commutes. This is largely due to Liesing’s location on the Viennese outskirts, where the public transport network may be less extensive, and many residents work in areas outside of Liesing, which are not easily accessible by modes other than car. As for shopping-related trips, the car is also the dominant mode of transport, highlighting the lack of shopping opportunities accessible by other more sustainable modes. Bike use remains low on the list of preferences among Liesing’s residents. It is important to highlight that the sample size for the area of Liesing is smaller than that of Vienna, and this may affect the representativeness of the results. This is also reflected in the error bars shown in the plots. 22 Figure 3: Modal split by trip purpose - Liesing 4.4. Trip Characteristics – Vienna 4.4.1. Work trips The following density plots (Figure 4) illustrate the distribution of the trip duration and trip distance across the 5 different transport modes, while Table 5 presents the working trip duration and distance statistics across modes in Vienna. To gain a more comprehensive understanding of the trip characteristics, the Cumulative Distribution Function (CDF) has been calculated for both the travel distance and duration. This function enables the calculation of the percentage (%) of working trips conducted up to a certain amount of time or distance, and helps the reader understand whether or not the travel behaviour of the people living in Vienna is in line with the 15mC concept. Figure 5 illustrates the CDF functions for working trip duration and distance in Vienna. In Vienna, the average work trip duration is approximately 30 minutes, with an average distance of almost 10 kilometres. According to Table 6 only the 27% of work trips in Vienna are completed in less than 15 minutes. The high standard deviation (SD) suggests significant variability in the sample, indicating that some trips are considerably longer or shorter than the average. Work-related trips in Vienna conducted by public transport, the most preferred travel mode of the Viennese residents for this trip purpose, have an average duration of 37 minutes while less than 10% of these trips are completed in under 15 minutes (see Table 6). Work related car trips in Vienna, either as driver or passenger, are shorter in duration than the average, although the distances covered are greater. Furthermore, more than 50% of the trips undertaken by car as a passenger lasted less than 15 minutes, while for the trips conducted as a driver this figure approaches 30%. Work related walking trips in Vienna are the shortest in both duration and distance, and more than 80% of them last up to 15 minutes. 23 (a) Duration by mode of transport (b) Distance by mode of transport Figure 4: Density plot of work trip duration (a) and distance (b) by transport mode – Vienna Table 5: Work trip statistics by transport mode - Vienna Transport Modes Sample (n) Duration (min) Distance (km) Mean Median SD Mean Median SD All Modes 1823 29.45 30.0 19.81 9.56 6.7 12.77 Pedestrians 197 11.87 10.0 10.33 0.93 0.5 1.04 Bicycle 114 18.48 15.0 11.18 3.77 3.0 2.75 Car (as Driver) 532 25.34 25.0 13.63 12.01 9.0 10.69 Car (as Passenger) 47 23.38 15.0 21.92 15.64 6.0 36.23 Public Transport 933 37.15 34.0 21.19 10.40 7.6 12.73 (a) Duration across all modes (b) Duration by mode of transport (c) Distance across all modes (d) Distance by mode of transport Figure 5: Cumulative distribution function (CDF) of work trip duration (a, b) and distance (c, d)– Vienna 24 Table 6: Percentage of work trips conducted for different time stamps and modes - Vienna Transport modes Share of trips with a duration less than (min) 10 15 20 30 All Modes 14.3% 26.9% 38.6% 65.3% Pedestrians 62.4% 83.2% 90.4% 96.4% Bicycle 33.3% 56.1% 68.4% 88.6% Car (as Driver) 14.1% 29.9% 46.8% 76.3% Car (as Passenger) 21.2% 53.2% 65.9% 85.1% Public Transport 1.6% 8.5% 18.0% 48.6% 4.4.2. Educational trips According to Table 7, the average duration of an educational trip in Vienna is approximately 25 minutes, with an average distance of 6.2 kilometres. A high proportion (41%) of education trips are completed in less than 15 minutes (see Table 8). Similarly to the work-related trips, the high standard deviation (SD) of education-related trips suggests significant variability in the sample. This is due to the fact that this category includes not only trips to educational institutions but also to universities, which are mostly located in the city centre of Vienna, necessitating longer travel distances. Trips conducted by public transport, which is the most preferred mode of transport for this purpose, have an average duration of 32 minutes, while less than 20% of these trips are made in less than 15 minutes. Walking trips are also popular and are much shorter (11 minutes) than average. Moreover, more than 90% of walking trips are completed in less than 15 minutes, indicating a high level of accessibility to educational facilities. (a) Duration by mode of transport (b) Distance by mode of transport Figure 6: Density plot of educational trip duration (a) and distance (b) by transport mode – Vienna Table 7: Educational trip statistics by transport mode - Vienna Transport Modes Sample (n) Duration (min) Distance (km) Mean Median SD Mean Median SD All Modes 674 24.79 20.0 17.08 6.15 3.9 9.17 Pedestrians 149 10.67 10.0 6.21 0.81 0.5 0.66 Bicycle 24 15.37 15.0 8.57 3.09 2.0 2.47 Car (as Driver) 37 24.22 20.0 13.45 9.93 8.0 8.57 Car (as Passenger) 51 14.96 10.8 10.69 5.25 3.0 5.79 Public Transport 413 31.70 30.0 17.08 8.02 5.0 10.52 25 (a) Duration across all modes (b) Duration by mode of transport (c) Distance across all modes (d) Distance by mode of transport Figure 7: Cumulative distribution function (CDF) of educational trip duration (a, b) and distance (c, d)– Vienna Table 8: Percentage of education trips conducted for different time stamps and modes - Vienna Transport Modes Share of trips with a duration less than (min) 10 15 20 30 All Modes 24.6% 40.5% 53.0% 74.0% Pedestrians 67.8 % 92.6 % 96.6 % 99.3 % Bicycle 33.3% 66.7% 83.3% 95.8% Car (as Driver) 21.6 % 35.1 % 54.1 % 75.7 % Car (as Passenger) 49.0% 70.6% 86.3% 94.1% Public Transport 5.8 % 16.9 % 31.2 % 61.0 % 4.4.3. Shopping trips Table 9, reveals that on average Viennese citizens make short trips (16.5 minutes) to fulfil their shopping needs. According to Table 10, 70% of shopping trips are completed within 15 minutes, indicating a dense network of shopping locations across Vienna. Walking is the most common mode for shopping trips, with an average duration of 13 minutes, while more than 80% lasting up to 15 minutes. In addition, shopping trips made by car or public transport are frequent in the sample, with an average trip duration of 16 and 26 minutes respectively. It should be noted that the shopping trips included in this category are not only for daily needs but also for additional purchases, such as clothing or home equipment. This may influence the average trip duration as people may travel longer to reach bigger retail facilities. 32 Table 17: Shopping trip statistics by transport mode - Liesing Transport Modes Sample (n) Duration (min) Distance (km) Mean Median SD Mean Median SD All Modes 96 20.52 10.0 43.62 5.47 2.5 15.58 Pedestrians 27 32.06 15.0 77.17 1.15 1.0 0.92 Bicycle Insufficient sample size for analysis Car (as Driver) 42 14.94 10.0 16.29 6.66 3.5 16.61 Car (as Passenger) 22 17.77 10.0 21.41 9.0 3.5 22.68 Public Transport Insufficient sample size for analysis (a) Duration across all modes (b) Duration by mode of transport (c) Distance across all modes (d) Distance by mode of transport Figure 17: Cumulative distribution function (CDF) of shopping trip duration (a, b) and distance (c, d)– Liesing Table 18: Percentage of shopping trips conducted for different time stamps – Liesing Transport Modes Share of trips with a duration less than (min) 10 15 20 30 All Modes 52.1% 71.9% 79.2% 93.8% Pedestrians 44.4% 70.4% 74.1% 92.6% Bicycle Insufficient sample size for analysis Car (as Driver) 57.1% 76.2% 83.3% 95.2% Car (as Passenger) 54.5% 72.7% 81.8% 90.9% Public Transport Insufficient sample size for analysis 4.5.4. Leisure trips For leisure trips, the average trip duration is 34 minutes and distance 10 kilometres (Table 19). These results do not differ vastly from the ones of Vienna, where the average trip duration is 33 minutes. In Liesing, walking is the most preferred mode for this trip purpose with an average trip duration of 42 minutes. However, according to Table 20 only the 28% of walking trips last up to 15 minutes (but important to mention, in this category walking trips for pleasure itself and walking to a specific venue for leisure activity (e.g. public pool, gym) are included). Due to the location of Liesing, people may prefer to either visit a natural park nearby or use their private vehicle to access a leisure facility outside of Vienna. 33 (a) Duration by mode of transport (b) Distance by mode of transport Figure 18: Density plot of leisure trip duration (a) and distance (b) by transport mode – Liesing Table 19: Leisure trip statistics by transport mode - Liesing Transport Modes Sample (n) Duration (min) Distance (km) Mean Median SD Mean Median SD All Modes 92 34.03 30.0 24.43 10.13 4.7 14.78 Pedestrians 32 41.72 37.5 26.97 2.79 2.0 2.17 Bicycle Insufficient sample size for analysis Car (as Driver) 22 24.68 20.0 14.73 13.27 9.0 14.69 Car (as Passenger) Insufficient sample size for analysis Public Transport (a) Duration across all modes (b) Duration by mode of transport (c) Distance across all modes (d) Distance by mode of transport Figure 19: Cumulative distribution function (CDF) of leisure trip duration (a, b) and distance (c, d)– Liesing 34 Table 20: Percentage of leisure trips conducted for different time stamps – Liesing Transport Modes Share of trips with a duration less than (min) 10 15 20 30 All Modes 10.6% 35.1% 42.6% 58.5% Pedestrians 6.3% 28.1% 28.1% 46.8% Bicycle Insufficient sample size for analysis Car (as Driver) 18.2% 45.5% 54.5% 77.3% Car (as Passenger) Insufficient sample size for analysis Public Transport 4.6. Travel behaviour across different socioeconomic groups The present section of the analysis is concerned with an examination of modal split and trip characteristics of different socioeconomic groups in Vienna. The main findings of the analysis are presented here, but the detailed results of the analysis can be found in Appendix A and G. 4.6.1. Gender  Work trips:  Public transport is the most utilised mode for both genders.  Men have a marginally higher share of car trips compared to women.  Women travel shorter distances than men but with similar travel times.  Educational trips:  Public transport is the leading mode for both genders.  Travel patterns for education trips are nearly identical between men and women.  Shopping trips:  Walking is the dominant mode for both genders.  Women are more likely to travel as car passengers for shopping.  Leisure trips:  Walking and public transport are the two most used modes for both genders. 4.6.2. Income The dataset categorizes the economic situation of households as very poor, poor, average, good, and very good. For the purpose of the analysis, we have been merged the first two categories (very poor and poor) into a single category called low income, and the last two categories (good and very good) into a single category called high income.  Work trips:  Low-income individuals rely more on public transport and walking.  High-income individuals use cars more frequently for work trips.  Travel times are similar, but low-income individuals travel shorter distances.  Educational trips:  Low-income individuals demonstrate a greater reliance on walking and public transport.  Car use is low across all income groups for education trips.  Shopping trips:  Low-income individuals use walking and public transport more frequently.  High-income individuals prefer using cars for shopping trips.  Walking and public transport remain prevalent across all income groups  Leisure trips:  Low-income individuals prefer walking, public transport, and bicycles.  High-income individuals travel longer distances and use cars more frequently for leisure activities. 35 4.6.3. Age In order to analyse travel behaviour across age in Vienna, the sample has been divided into four groups. The groups are the following: 1. Children/Students (up to 18 years old) 2. Young adults (19 to 39 years old) 3. Middle-aged adults (40 to 59 years old) 4. Old adults (over 60 years old)  Work trips:  Children/Students allocate the most time to commuting yet travel shorter distances.  Young adults demonstrate a higher preference for car use, yet they also utilize public transport and walking.  Middle-aged adults exhibit a strong preference for car use for work-related trips.  For older adults, the car is the dominant mode for work-related trips.  Educational trips:  Children/Students rely heavily on public transport and walking.  Young and middle-aged adults travel longer distances for education.  Shopping trips:  Walking is the dominant mode across all age groups.  Young and middle-aged adults have the highest proportion of shopping trips made on foot.  Middle-aged adults tend to travel slightly longer distances for shopping.  Leisure trips:  Young adults exhibit a higher preference for car use in relation to leisure trips.  Middle-aged adults allocate the most time to leisure travel.  Older adults demonstrate a significantly reduced propensity to travel for leisure activities. 36 5. UTRECHT 5.1. General characteristics of Utrecht and LL location The Municipality of Utrecht is located in the Province of Utrecht, which is one of the 12 provinces that constitute the Netherlands. The city of Utrecht is the fourth most populous city in the Netherlands, with an estimated population of approximately 375,000 inhabitants. The city's reputation is largely built on its extensive cycling infrastructure and public transport system, offering seamless connections to all major cities and towns in the country. Utrecht’s Living Lab is “Overvecht Noord” and is located in the Northeast of Utrecht. The area has a population of approximately 35,000 inhabitants, and it is served by the local public transport system. In addition, it offers a wide range of cycling-related services. Figure 20 shows the map of Utrecht and the location of the Utrecht Living Lab “Overvecht” (orange dot). Figure 20: Location of the Utrecht Living Lab “Overvecht” (orange dot) (Source: OSM (2024)) 37 5.2. Descriptive statistical analysis For the descriptive statistical analysis for the city of Utrecht and Utrecht’s LL location, the dataset “Onderweg in Nederland 2021 (ODiN)” (5) was used. A list of socioeconomic variables, both for households and individuals, was selected and presented in Tables Table 21 and Table 22 respectively. It is important to acknowledge the limitations imposed by the relatively modest sample size of trips conducted by the residents of the "Overvecht Noord" region. This restricts the scope and generalisability of the analysis' results. To mitigate these limitations, a comprehensive analysis of the entire Overvecht area is conducted, encompassing both the northern and southern region. The sample used for this part of the analysis is the total resident population within the area (Utrecht or Overvecht), with no filtering for specific trip purposes. Furthermore, the sample of survey respondents who did not provide a valid response to one or more of the selected questions was excluded from the sample. Table 21: Socioeconomic characteristics of households (Utrecht & Overvecht) – Source: ODiN 2021 Socioeconomic Characteristics of Households Utrecht LL Location ( Overvecht ) n % n % Household size 1 Person 1 302 17.2 48 30.8 2 Persons 2 587 34.1 49 31.4 3 Persons 1074 14.2 17 10.9 4+ Persons 2615 34.5 42 26.9 Car ownership 0 1178 15.5 59 37.8 1 3653 48.2 75 48.1 2 2145 28.3 18 11.5 3 449 5.9 3 1.9 4+ 153 2.0 1 0.6 Number of car driving license per household 0 425 5.6 28 17.9 1 1920 25.4 56 35.9 2 4398 58.0 63 40.4 3 605 8.0 5 3.2 4+ 230 3.0 4 2.6 Table 22: Socioeconomic characteristics of individuals (Utrecht & Overvecht) – Source: ODiN 2021 Socioeconomic Characteristics of Individuals Utrecht LL Location ( Overvecht ) n % n % Gender Male 3750 49.5 85 54.5 Female 3828 50.5 71 45.5 Age Category (y.o.) 6 – 14 775 10.2 12 7.7 15 – 19 461 6.1 9 5.8 20 – 24 486 6.4 12 7.7 25 – 34 1323 17.5 48 30.8 35 – 44 1070 14.1 23 14.7 45 – 54 1202 15.9 18 11.5 55 – 64 903 11.9 14 9.0 65+ 1358 17.9 20 12.8 Occupation Student 1558 20.6 31 19.9 Employed 4126 54.4 82 52.6 Pensioner 1184 15.6 18 11.5 Other 710 9.4 25 16.0 38 The descriptive statistical analysis offers a detailed overview of the demographic profiles of residents in Utrecht and Overvecht region. With respect to gender, the population in the sample is almost equally distributed in Utrecht, in Overvecht however, the sample of men is larger than that of women by 10%. Furthermore, a significant disparity is observed in the ownership of vehicles between the two areas. While the majority of households in Utrecht possess at least one car, in Overvecht, the proportion of households without cars is higher. Regarding occupation, both samples are predominantly comprised of employed individuals and students. 5.3. Modal split As illustrated in Figure 21, the modal split by trip purpose for the city of Utrecht reveals distinct patterns in transportation choices. For commuting to work, driving is the most preferred mode for Utrecht residents, followed by cycling. When it comes to educational trips, cycling stands as the clear favourite. As for shopping trips, cycling is the dominant mode, but there is also a high share of trips made by foot or car. With respect to leisure trips, here walking emerges as the predominant mode with 43%, followed by bicycle and car usage. It is important to be noted that in the leisure trips category are included walking trips for recreation, so this might strongly affect modal split. Furthermore, the findings indicate that public transportation is remarkably underutilized for daily travel in Utrecht. Figure 21: Modal split by trip purpose – Utrecht The results of the modal split analysis in Overvecht, presented in Figure 22, indicate a preference for cycling and walking, particularly for shopping and leisure trips. Conversely, commutes are predominantly performed by car and bicycle, mirroring the modal split observed in Utrecht. Cycling emerges as the predominant mode for educational trips, followed by public transportation. However, the small sample size for the Overvecht area may impact the representativeness of the results. This limitation is also reflected in the error bars shown in the plots. 39 Figure 22: Modal split by trip purpose - Overvecht 5.4. Trip Characteristics – Utrecht 5.4.1. Work trips The mean duration of a work trip in Utrecht is approximately 26 minutes, with an average distance of 15 kilometres. As indicated in Table 24, nearly 41% of the work trips in Utrecht are completed in less than 15 minutes. Driving is the most prevalent travel mode for work-related trips, with an average duration of 27 minutes, while only 30% of these trips are completed in under 15 minutes. This might be attributed to the fact that a high share of work facilities is not located in the city centre, necessitating longer commutes. Cycling is also a popular trip mode for work trips, with an average duration of 20 minutes, and approximately 57% of these commutes lasting up to 15 minutes. In the case of public transport commutes, the average duration is close to 56 minutes, with nearly 3% of these trips are completed in less than 15 minutes. Finally, the travel mode with the highest proportion of completed trips in less than 15 minutes is walking (82.3%). (a) Duration by mode of transport (b) Distance by mode of transport Figure 23: Density plot of work trip duration (a) and distance (b) by transport mode – Utrecht 40 Table 23: Work trip statistics by transport mode - Utrecht Transport Modes Sample (n) Duration (min) Distance (km) Mean Median SD Mean Median SD All Modes 2579 26.01 20.00 22.52 15.29 8.00 18.56 Pedestrians 130 11.18 5.00 12.44 0.94 0.55 1.20 Bicycle 913 20.04 15.00 19.78 4.65 3.40 4.26 Car (as Driver) 1266 27.31 25.00 21.01 22.20 17.00 20.47 Car (as Passenger) 77 26.47 20.00 17.36 16.23 12.00 16.96 Public Transport 193 55.50 50.00 24.87 29.62 23.60 21.58 (a) Duration across all modes (b) Duration by mode of transport (c) Distance across all modes (d) Distance by mode of transport Figure 24: Cumulative distribution function (CDF) of work trip duration (a, b) and distance (c, d)– Utrecht Table 24: Percentage of work trips conducted for different time stamps – Utrecht Transport Modes Share of trips with a duration less than (min) 10 15 20 30 All Modes 23.3% 40.5% 52.2% 75.8% Pedestrians 70.0% 82.3% 89.2% 95.4% Bicycle 34.5% 56.7% 68.2% 85.4% Car (as Driver) 14.6% 30.9% 44.2% 75.6% Car (as Passenger) 13.0% 31.2% 51.9% 81.8% Public Transport 0.5% 2.6% 4.1% 15.5% 41 5.4.2. Educational trips In Utrecht, the average duration of the educational trips is close to 22 minutes and the most prevalent travel mode for this category is cycling. Cycling trips last on average 18 minutes and more than 60% of these trips are completed in less than 15 minutes. According to Table 25, modes such as public transport and car are preferred for longer trips to educational institutions, while the share of trips of less than 15 minutes is 3.5% and 21% respectively. Moreover, walking is the mode with the highest share of trips completed up to 15 minutes. Overall, the results show a high degree of proximity to educational institutions a fact that allows the use of sustainable modes such as walking and cycling. (a) Duration by mode of transport (b) Distance by mode of transport Figure 25: Density plot of educational trip duration (a) and distance (b) by transport mode – Utrecht Table 25: Educational trip statistics by transport mode - Utrecht Transport Modes Sample (n) Duration (min) Distance (km) Mean Median SD Mean Median SD All Modes 1208 21.58 15.00 23.02 6.99 2.50 12.53 Pedestrians 155 8.97 5.00 7.94 0.77 0.50 0.87 Bicycle 755 17.69 15.00 19.10 3.38 2.30 3.20 Car (as Driver) 55 31.02 25.00 20.77 21.64 14.00 21.59 Car (as Passenger) 106 17.74 13.50 15.67 8.05 3.05 11.58 Public Transport 137 56.47 54.00 26.66 27.25 21.50 20.74 48 (a) Duration across all modes (b) Duration by mode of transport (c) Distance across all modes (d) Distance by mode of transport Figure 34: Cumulative distribution function (CDF) of shopping trip duration (a, b) and distance (c, d)– Overvecht Table 34: Percentage of shopping trips conducted for different time stamps – Overvecht Transport Modes Share of trips with a duration less than (min) 10 15 20 30 All Modes 62.8% 81.9% 89.4% 97.9% Pedestrians 73.3% 88.9% 93.3% 97.8% Bicycle 74.1% 77.8% 88.9% 88.9% Car (as Driver) Insufficient sample size for analysis Car (as Passenger) Public Transport 5.5.4. Leisure trips The mean duration of a leisure trip in Overvecht is approximately 53 minutes, while the mean distance is almost 10 kilometres. Walking is again the prevalent mode for leisure trips, followed by cycling. A leisure trip made by foot lasts on average 57 minutes, while trips made by bicycle are shorter in duration but slightly longer in distance. Half of the leisure trips made by bicycle last up to 15 minutes, whereas for walking trips this percentage is relatively low (22%). It is noteworthy, that this trip purpose category includes trips for either social recreation or recreational walking, which might cause longer trips. In general, fewer leisure trips in Overvecht are completed in less than 15 minutes (32.5%) than in Utrecht as a whole (43%). 49 (a) Duration by mode of transport (b) Distance by mode of transport Figure 35: Density plot of leisure trip duration (a) and distance (b) by transport mode – Overvecht Table 35: Leisure trip statistics by transport mode - Overvecht Transport Modes Sample (n) Duration (min) Distance (km) Mean Median SD Mean Median SD All Modes 120 53.87 30.0 67.1 9.51 3.5 20.97 Pedestrians 63 57.29 45.0 64.63 3.56 2.0 3.52 Bicycle 28 42.86 17.5 61.8 4.88 2.95 4.44 Car (as Driver) Insufficient sample size for analysis Car (as Passenger) Public Transport (a) Duration across all modes (b) Duration by mode of transport (c) Distance across all modes (d) Distance by mode of transport Figure 36: Cumulative distribution function (CDF) of leisure trip duration (a, b) and distance (c, d)– Overvecht 50 Table 36: Percentage of leisure trips conducted for different time stamps – Overvecht Transport Modes Share of trips with a duration less than (min) 10 15 20 30 All Modes 17.5% 32.5% 40.0% 55.0% Pedestrians 17.5% 22.2% 31.8% 46.0% Bicycle 17.9% 50.0% 57.1% 67.9% Car (as Driver) Insufficient sample size for analysis Car (as Passenger) Public Transport 5.6. Travel behaviour across different socioeconomic groups The present section of the analysis is concerned with an examination of modal split and trip characteristics of different socioeconomic groups in Utrecht. The main findings of the analysis are presented here, but the detailed results of the analysis can be found in Appendix B and G. 5.6.1. Gender  Work trips:  Men travel significantly longer distances than women for work-related trips, despite similar travel times, and use their cars more.  Women cycle to work more than men.  Educational trips:  Both sexes travel similar distances, probably due to the proximity to educational institutions.  Men cycle and walk slightly more than women for educational trips.  Women are more likely to use public transport for education.  Shopping trips:  Travel times and distances are similar for both sexes.  Men are more likely than women to use a car for shopping, while women are more likely to use a bicycle.  Walking is a popular mode of transport for shopping trips.  Leisure trips:  Men and women spend roughly the same amount of time and cover similar distances on leisure trips.  Walking is the dominant mode of transport for leisure trips for both sexes.  Men are more likely than women to cycle and drive for leisure trips. 5.6.2. Income The dataset categorizes the economic situation of households in Utrecht based on the standardised disposable income of the household (10% groups). In the Netherlands, households are devided by income level into ten groups (deciles) with an equal number of households. The income limits between these 10% groups vary from year to year. (https://opendata.cbs.nl/statline/?dl=D4D1#/CBS/nl/dataset/83931NED/table ). For the purpose of the analysis, we have been merged the first five groups (first 10% group – fifth 10% group) into a single category called low income, and the last five groups (sixth 10% group – tenth 10% group) into a single category called high income.  Work trips:  High income travellers cover longer distances for work-related trips.  High-income individuals predominantly opt for car, low-income individuals demonstrate a higher preference for bicycles.  Educational trips:  The mean trip distance for educational purposes is found to be similar across different income groups. 51  Bicycle is the most utilised travel mode across both income groups.  Public transport usage is slightly higher for low-income individuals compared to highincome individuals.  Shopping trips:  High-income individuals travel significantly longer distances than low-income individuals.  Low-income individuals usually walk or cycle for their shopping trips, while highincome individuals use their car and bicycle.  Leisure trips:  Individuals of a lower socio-economic status tend to undertake significantly longer journeys to engage in leisure activities in comparison to their high-income counterparts.  There are no notable differences in terms of modal split across the two income groups. 5.6.3. Age In order to analyse travel behaviour across age in Utrecht, the sample has been divided into four groups. The groups are the following: 1. Children/Students (up to 18 years old) 2. Young adults (19 to 39 years old) 3. Middle-aged adults (40 to 59 years old) 4. Old adults (over 60 years old)  Work trips:  Children/Students travel the least for work.  Middle-aged adults and old adults travel shorter distances and less time than young adults.  The bicycle is the predominant mode for work trips undertaken by children/students, while older adults are more likely to use the car.  Educational trips:  Young adults travel the longest and farthest for education-related trips.  Young adults use public transport more than individuals in other age groups.  Shopping trips:  Children/Students have the highest travel time and the longest distance.  Older age groups have similar travel times and distances.  Young adults like to walk more when they go shopping. People in other age groups tend to use cars and bicycles more.  Leisure trips:  Children/Students spend more time traveling but cover shorter distances.  The predominant mode of transportation for leisure trips among age groups 1, 2, and 3 is walking. For children and students, the predominant mode of transportation is bicycle. 52 6. BRUSSELS 6.1. General characteristics of Brussels and LL location The Brussels Capital Region (hereafter, Brussels) is one of three Regions of Belgium, the others being Flanders and Wallonia. It is comprised of 19 municipalities and has a population of over 1.2 million inhabitants. It is located at the core of the Belgian transport network, offering numerous highways and railways for connections with other European cities. Moreover, Brussels boasts an extensive public transport infrastructure, comprising underground lines, trams, buses and bicycle infrastructure. Additionally, the city offers car, micromobility and cargo bike sharing services, thereby promoting sustainable urban mobility options. The Living Labs of Brussels are located in two peripheral neighborhoods in the northern part of Brussels, namely Neder-over-Heembeek and Haren. Administratively, they are part of the municipality of the City of Brussels, but they are located on the northern periphery of the region, extending beyond the immediate central zone. Access to these Living Labs is facilitated by the local public transport system, predominantly through bus services. Finally, they offer limited car and micromobility sharing services. Figure 37 shows the map of Brussels and the location of the Living Labs “Neder-over-Heembeek” and “Haren” (red pins) Figure 37: Location of the City of Brussels district (orange line) and the Brussels’ Living Lab (red pins) (Source: OSM (2024)) 53 6.2. Descriptive statistical analysis For the descriptive statistical analysis for Brussels Capital Region (hereafter, Brussels) and the LL location, the dataset “Onderzoek Verplaatsingsgedrag (OVG) 2021” (6) was used. A list of socioeconomic variables, both for households and individuals, was selected and presented in Tables Table 37 and Table 38 respectively. It is important to acknowledge the limitations imposed by the relatively modest sample size of trips undertaken by the residents of the “Neder-over-Heembeek” and “Haren” regions. This restricts the scope and generalisability of the analysis' results. To address these limitations, a comprehensive analysis of the entire municipality City of Brussels (hereafter, Brussels City) is conducted. The sample used for this part of the analysis is the total resident population within the area (Brussels or Brussels City), with no filtering for specific trip purposes. Furthermore, the sample of survey respondents who did not provide a valid response to one or more of the selected questions was excluded from the sample. Table 37 Socioeconomic characteristics of households (Brussels & Brussels City) – Source: OVG 2021 Socioeconomic Characteristics of Households Brussels LL Location (Brussels City) n % n % Household size 1 Person 557 20.7 84 23.9 2 Persons 673 25.1 87 24.7 3 Persons 442 16.5 50 14.2 4 Persons 507 18.9 59 16.8 5+ Persons 506 18.8 72 20.5 Car ownership 0 914 34 128 36.4 1 1367 50.9 187 53.1 2 360 13.4 35 9.9 3 33 1.2 2 0.6 4+ 11 0.4 - -0 Table 38: Socioeconomic characteristics of individuals (Brussels & Brussels City) – Source: OVG 2021 Socioeconomic Characteristics of Individuals Brussels LL Location (Brussels City) N % n % Gender Male 1234 46.1 179 51.3 Female 1443 53.9 170 48.7 Age Category (y.o.) 6 – 11 259 9.6 36 10.2 12 – 17 232 8.6 26 7.4 18 – 24 201 7.5 23 6.5 25 – 34 460 17.1 80 22.7 35 – 44 440 16.4 70 19.9 45 – 54 380 14.2 45 12.8 55 – 64 304 11.3 37 10.5 65+ 409 15.2 35 9.9 The descriptive statistical analysis provides a valuable perspective on the demographic characteristics of the Brussels and Brussels City populations. With respect to car ownership, the majority of households in both regions possess at least one car. The Brussels sample reveals a slight preponderance of female representation, while the Brussels City sample shows a more balanced gender distribution, with males and females represented equally. With respect to age, individuals older than 25 years old are more represented in the sample of both regions. However, the OVG survey lacks variables related to occupation type and income, a limitation that would enhance the results. 54 6.3. Modal split Figure 38 illustrates the modal split by trip purpose for Brussels. For commuters, public transportation is the predominant mode of transportation, followed by walking and driving. For educational-related trips, public transportation emerges as the predominant mode of transportation, followed by walking. Conversely, shopping trips are predominantly undertaken by walking, though car use and public transportation also emerge as popular modes. In regard to leisure trips, walking emerges as the predominant mode. The analysis indicates a preference among Brussels residents for walking and public transportation when undertaking daily commutes. Additionally, bicycle usage is not as prevalent in Brussels. Figure 38: Modal split by trip purpose – Brussels An analysis of the modal split in the Brussels City area reveals that there are no notable disparities when compared with the modal split of Brussels itself. According to the results illustrated in Figure 39, walking and public transport are the dominant modes for all trip categories, while the utilisation of bicycles remains low in preference. However, a decline in car usage is evident for workand shoppingrelated trips, suggesting that the residents of the Brussels City area are able to satisfy their daily needs through the utilisation of more sustainable transportation modes. 55 Figure 39: Modal split by trip purpose – Brussels City 6.4. Trip Characteristics – Brussels 6.4.1. Work trips The average commute in Brussels lasts 26 minutes and is 8.5 kilometres long. Table 40 shows that almost 36% of the commutes in Brussels are made in less than 15 minutes. Public transport is the most used travel mode for commuting, with an average commute time of 40 minutes, while only 5% of these commutes are completed in 15 minutes. Walking is also a popular trip mode for commuting to work, with an average duration of 11 minutes, and approximately 80% of these commutes last up to 15 minutes. For car trips, the average duration is approximately 28 minutes, with nearly 24% of these trips lasting up to 15 minutes. This might be attributed to the fact that a high share of work facilities is not located in the city centre, requiring longer commutes. (a) Duration by mode of transport (b) Distance by mode of transport Figure 40: Density plot of work trip duration (a) and distance (b) by transport mode – Brussels 56 Table 39: Work trip statistics by transport mode - Brussels Transport Modes Sample (n) Duration (min) Distance (km) Mean Median SD Mean Median SD All Modes 604 26.07 21.0 20.86 8.51 4.5 17.65 Pedestrians 155 11.12 10.0 8.86 0.81 0.6 0.71 Bicycle 93 18.58 15.0 11.32 4.70 3.5 3.75 Car (as Driver) 144 27.67 24.0 23.38 17.09 9.0 30.07 Car (as Passenger) Insufficient sample size for analysis Public Transport 202 39.56 36.0 19.97 9.28 6.3 11.90 (a) Duration across all modes (b) Duration by mode of transport (c) Distance across all modes (d) Distance by mode of transport Figure 41: Cumulative distribution function (CDF) of work trip duration (a, b) and distance (c, d)– Brussels Table 40: Percentage of work trips conducted for different time stamps – Brussels Transport Modes Share of trips with a duration less than (min) 10 15 20 30 All Modes 23.5% 35.9% 49.5% 68.5% Pedestrians 63.9% 79.4% 89.0% 98.1% Bicycle 28.0% 51.6% 72.0% 86.0% Car (as Driver) 9.7% 23.6% 44.4% 72.9% Car (as Passenger) Insufficient sample size for analysis Public Transport 1.0% 5.0% 13.4% 34.7% 6.4.2. Educational trips On average, educational trips in Brussels last approximately 24 minutes and are 5.4 kilometres long. In this trip purpose category, almost 50 percent of trips last up to 15 minutes, suggesting a rather high level of proximity to all types of educational institutions. Public transport is the most used mode, followed by walking. Public transport trips have an average duration of 39.2 minutes, while only 10% completed within 15 minutes. Walking trips to educational institutions last on average 11 minutes and 81.5% are completed within 15 minutes. This underscores the accessibility of nearby educational institutions for pedestrians. Cycling trips are similar to walking trips in terms of duration, although they cover longer distances. 57 (a) Duration by mode of transport (b) Distance by mode of transport Figure 42: Density plot of educational trip duration (a) and distance (b) by transport mode – Brussels Table 41: Educational trip statistics by transport mode - Brussels Transport Modes Sample (n) Duration (min) Distance (km) Mean Median SD Mean Median SD All Modes 353 24.2 17.0 22.43 5.44 2.2 16.58 Pedestrians 119 11.2 10.0 7.63 0.82 0.7 0.72 Bicycle 29 11.3 10.0 7.43 2.09 1.6 1.72 Car (as Driver) Insufficient sample size for analysis Car (as Passenger) 54 18.1 15.0 12.84 3.78 2.5 3.74 Public Transport 142 39.2 36.0 23.12 10.10 5.2 24.76 (a) Duration across all modes (b) Duration by mode of transport (c) Distance across all modes (d) Distance by mode of transport Figure 43: Cumulative distribution function (CDF) of educational trip duration (a, b) and distance (c, d)– Brussels 64 (a) Duration by mode of transport (b) Distance by mode of transport Figure 52: Density plot of shopping trip duration (a) and distance (b) by transport mode – Brussels City Table 51: Shopping trip statistics by transport mode – Brussels City Transport Modes Sample (n) Duration (min) Distance (km) Mean Median SD Mean Median SD All Modes 121 16.65 12.0 13.81 2.87 1.0 5.01 Pedestrians 76 12.04 10.0 10.84 0.86 0.6 0.83 Bicycle Insufficient sample size for analysis Car (as Driver) Car (as Passenger) Public Transport 21 30.14 30.0 10.31 5.28 3.2 3.78 (a) Duration across all modes (b) Duration by mode of transport (c) Distance across all modes (d) Distance by mode of transport Figure 53: Cumulative distribution function (CDF) of shopping trip duration (a, b) and distance (c, d)– Brussels City 65 Table 52: Percentage of shopping trips conducted for different time stamps – Brussels City Transport Modes Share of trips with a duration less than (min) 10 15 20 30 All Modes 48.8% 60.3% 71.1% 86.8% Pedestrians 63.2% 80.3% 88.2% 94.7% Bicycle Insufficient sample size for analysis Car (as Driver) Car (as Passenger) Public Transport - 4.8% 14.3% 57.1% 6.5.4. Leisure trips The duration of leisure trips in Brussels City last on average 27 minutes, while according to Table 54, 42% of them are completed within 15 minutes. The most frequently utilised mode of transportation for this purpose is walking, with an average duration of 20 minutes and a distance covered of 1.3 kilometres. In comparison to leisure trips undertaken by foot in Brussels, those in this area are shorter on average, indicating a high level of accessibility to leisure facilities in the area. Unfortunately, the findings of the analysis are limited due to an inadequate sample size. (a) Duration by mode of transport (b) Distance by mode of transport Figure 54: Density plot of leisure trip duration (a) and distance (b) by transport mode – Brussels City Table 53: Leisure trip statistics by transport mode – Brussels City Transport Modes Sample (n) Duration (min) Distance (km) Mean Median SD Mean Median SD All Modes 107 26.93 20.0 24.99 7.26 1.9 17.71 Pedestrians 60 20.37 12.0 23.56 1.28 0.8 1.62 Bicycle Insufficient sample size for analysis Car (as Driver) Car (as Passenger) Public Transport 66 (a) Duration across all modes (b) Duration by mode of transport (c) Distance across all modes (d) Distance by mode of transport Figure 55: Cumulative distribution function (CDF) of leisure trip duration (a, b) and distance (c, d)– Brussels City Table 54: Percentage of leisure trips conducted for different time stamps – Brussels City Transport Modes Share of trips with a duration less than (min) 10 15 20 30 All Modes 29.9% 42.1% 54.2% 73.8% Pedestrians 46.7% 61.7% 71.7% 85.0% Bicycle Insufficient sample size for analysis Car (as Driver) Car (as Passenger) Public Transport 6.6. Travel behaviour across different socioeconomic groups The present section of the analysis is concerned with an examination of modal split and trip characteristics of different socioeconomic groups in Brussels. The main findings of the analysis are presented here, but the detailed results of the analysis can be found in Appendix C and G. 6.6.1. Gender  Work, education, and shopping trips show no significant gender differences in travel behaviour.  Leisure trips are significantly longer for males, both in terms of distance and time travelled.  Women rely more on public transport across all trip purposes compared to men.  Men use bicycles more than women, especially for leisure and shopping trips.  Car mode is more dominant among men for work and shopping trips.  Pedestrian travel is slightly more common among women, especially for education and leisure trips. 6.6.2. Age In order to analyse travel behaviour by age in Brussels, the sample has been divided into four age groups. The age distribution in each group is differs slightly from the one used for the other cities. This is due to 67 the fact that the dataset is already categorising the respondents in these groups. The groups are the following: 1. Children/Students (up to 17 years old) 2. Young adults (18 to 34 years old) 3. Middle-aged adults (35 to 64 years old) 4. Old adults (over 65 years old)  Education trips show the strongest variation across age groups, with young adults traveling significantly longer distances.  Leisure trips are significantly longer for older adults, indicating a shift in travel patterns.  Work and shopping trips show no significant variation across age groups.  Variability in travel times increases with age, especially for education and leisure trips. 68 7. BUDAPEST 7.1. General characteristics of Budapest and LL location Budapest is the capital of Hungary and consists of 23 districts. With approximately 1.685 million inhabitants (7), it is the largest city in Hungary. Budapest has an efficient and public transport network that includes underground lines, tram, trolleybuses, and suburban railway lines. It also offers car and micromobility sharing services. The Budapest Living Lab is located in the 17th district of Budapest called Rákosmente. Rákosmente is a suburban district of Budapest in the eastern part of the city, and has a population of about 90,000 ihabitants. In Rákosmente, people rely heavily on private car use, while the frequency of public transport is low. Figure 56 shows the map of Budapest and the location of the Rákosmente district. Figure 56: Location of the Rákosmente district (orange line) (Source: OSM (2024)) 7.2. Descriptive statistical analysis For the descriptive statistical analysis for the city of Budapest and the Budapest’s LL region the dataset “Household Survey for Unified Macroscopic Transport Model 2019” (8) was used. A list of socioeconomic variables, both for households and individuals, was selected and presented in Tables Table 55 and Table 56 respectively. It is important to acknowledge the limitations imposed by the relatively modest sample size of trips made by residents of the Rákosmente region. This limits the scope and generalisability of the results of the analysis. In order to address these limitations, a comprehensive analysis of the 16th and 17th districts is carried out. The 16th district of Budapest is located next to the 17th district of Budapest (Rákosmente) and shares common socio-economic and travel characteristics. The sample used for this part of the analysis is the total resident population within the area (Budapest or Budapest’s LL region), with no filtering for specific trip purposes. Furthermore, the sample of survey 69 respondents who did not provide a valid response to one or more of the selected questions was excluded from the sample. Table 55: Socioeconomic characteristics of households (Budapest & Budapest LL location) – Source: HSUMTM 2019 Socioeconomic Characteristics of Households Budapest LL Location (16 th & 17 th District) n % n % Household size 1 Person 1307 38 101 28.5 2 Persons 988 28.7 100 28.2 3 Persons 604 17.6 77 21.7 4+ Persons 540 15.7 77 21.7 Economic situation Less than 50.000 Ft 19 0.8 14 4.5 50.001 - 100.000 Ft 163 7 17 5.5 100.001 - 150.000 Ft 647 27.9 54 17.4 150.001 - 250.000 Ft 982 42.3 185 59.5 250.001 - 350.000 Ft 338 14.6 31 10 350.001 - 450.000 Ft 108 4.7 8 2.6 More than 450.000 Ft 64 2.8 2 0.6 Car ownership 0 1240 36.1 171 48.2 1 1781 51.8 162 45.6 2 396 11.5 21 5.9 3 19 0.6 1 0.3 Bike availability 0 2047 59.5 242 68.2 1 814 23.7 58 16.3 2 380 11.0 27 7.6 3 124 3.6 14 3.9 3+ 74 2.2 14 3.9 Table 56: Socioeconomic characteristics of individuals (Budapest & Budapest LL location) – Source: HSUMTUM 2019 Socioeconomic Characteristics of Individuals Budapest LL Location (16 th & 17 th District) n % n % Gender Male 2123 61.7 242 68.2 Female 1316 38.3 113 31.8 Age Category (y.o.) 6 – 14 - - - - 15 – 19 10 0.3 - - 20 – 24 106 3.1 1 0.3 25 – 34 563 16.4 29 8.2 35 – 44 783 22.8 73 20.6 45 – 54 795 23.1 74 20.8 55 – 64 543 15.8 82 23.1 65+ 639 18.6 96 27 Occupation Employed 2733 79.5 321 90.4 Student - - - - Pensioner 675 19.6 34 9.6 Other 31 0.9 - - Car Driving License No 1035 30.1 160 45.1 Yes 2404 69.9 195 54.9 The descriptive statistical analysis provides a detailed overview of the demographic profile of residents in Budapest and the Budapest LL region. With respect to gender, males are overrepresented in the sample. However, it is evident that the gender distribution within the sample deviates from the official statistics of Budapest. According to the statistics provided by the Hungarian Central Statistical Office (9) for the year of 2019, males represented 47% of the total population of Budapest. With regard to age, the sample is predominantly composed of individuals over the age of 25, and especially of people in the age group 34 to 54 years old. n relation to the subjects' occupations, the sample consists primarily of those currently in employment and retired individuals, as well as those in receipt of a state pension. It is noteworthy that the sample is lacking in students, a phenomenon that is also associated with the age distribution of the sample. Concerning the availability of bicycles, it is observed that many households in Budapest do not possess a bicycle, in contrast to the prevalence of car ownership, where over 60% of households own at least one car. 70 7.3. Modal split As demonstrated in Figure 57, the modal split results for the city of Budapest reveal that the majority of commuters utilise private vehicles and public transport. This trend extends to educational trips as well, however, the limited number of educational trips included in the dataset may have influenced the analysis' outcomes. In contrast, when it comes to shopping and leisure trips, walking emerges as the predominant mode, followed by driving and public transport. Notably, bicycle usage remains minimal in Budapest. Figure 57: Modal split by trip purpose – Budapest The results of the modal split analysis in the 16th and 17th District of Budapest, as presented in Figure 58, indicate a preference for driving and public transport, particularly for shopping and work-related trips. Furthermore, it is observed that leisure trips are predominantly undertaken by car, on foot, and by public transport, which mirrors the modal split observed in Budapest. However, an increase in car usage is observed in shopping and leisure trips, while a decrease is observed for work-related trips. It is also noted that bicycle usage remains low. Moreover, the lack of data from educational trips limits the analysis of the modal split for this trip category. Figure 58: Modal split by trip purpose – 16th & 17th District 71 7.4. Trip Characteristics – Budapest 7.4.1. Work trips In Budapest, the average commute lasts approximately 36 minutes, while only the 17.4% of these trips are completed within 15 minutes. The high standard deviation (SD) suggests considerable variability in the sample, indicating that some trips are considerably longer or shorter than the average. The majority of the work-related trips in Budapest are made by car. The average duration of these journeys is 36 minutes, with only 12.3% of trips lasting up to 15 minutes. This suggests a limited level of proximity to work-related facilities, particularly for those who opt for sustainable transportation modes. Additionally, commutes undertaken by public transport are prevalent, though they tend to be significantly longer than those made by car. Furthermore, only the 4.4% of these commutes are completed within 15 minutes. Consequently, the analysis indicates a high degree of car dependency among individuals in Budapest, which contributes to the overall length of commutes. Figure 59: Density plot of work trip duration by transport mode – Budapest Table 57: Work trip statistics by transport mode – Budapest Transport Modes Sample (n) Duration (min) Mean Median SD All Modes 2366 35.88 32.0 26.90 Pedestrians 233 11.74 10.0 9.18 Bicycle 110 27.12 20.0 23.77 Car (as Driver) 1187 35.91 30.0 26.23 Car (as Passenger) 43 38.23 40.0 15.94 Public Transport 793 44.01 40.0 27.67 (a) Duration across all modes (b) Duration by mode of transport Figure 60: Cumulative distribution function (CDF) of work trip duration– Budapest 72 Table 58: Percentage of work trips conducted for different time stamps – Budapest Transport Modes Share of trips with a duration less than (min) 10 15 20 30 All Modes 10.5% 17.4% 27.8% 49.6% Pedestrians 67.8% 82.8% 93.1% 98.3% Bicycle 10.9% 28.2% 51.8% 86.4% Car (as Driver) 5.2% 12.3% 23.4% 50.7% Car (as Passenger) 7.0% 14.0% 18.6% 30.2% Public Transport 1.8% 4.4% 12.4% 29.6% 7.4.2. Educational trips The mean duration of educational trips in Budapest is 31.4 minutes, and according to Table 60, almost 22% of these trips are completed within 15 minutes. Public transport is the predominant mode for education-related trips in Budapest, followed by car. Public transport trips last on average 35 minutes, while almost 8% of them last up to 15 minutes. It is important to note that the age group of individuals younger than 19 y.o. is not represented successfully in the sample, therefore, the majority of educational trips in Budapest have as a destination tertiary educational facilities. The results indicate an overall low level of proximity to educational institutions in Budapest. This has resulted in a shift away from more sustainable means of transport like walking or cycling. Figure 61: Density plot of educational trip duration by transport mode – Budapest Table 59: Educational trip statistics by transport mode – Budapest Transport Modes Sample (n) Duration (min) Mean Median SD All Modes 74 31.39 30.0 15.34 Pedestrians Insufficient sample size for analysis Bicycle Car (as Driver) 26 25.42 20.0 18.08 Car (as Passenger) Insufficient sample size for analysis Public Transport 38 35.84 33.5 12.14 73 (a) Duration across all modes (b) Duration by mode of transport Figure 62: Cumulative distribution function (CDF) of educational trip duration– Budapest Table 60: Percentage of educational trips conducted for different time stamps – Budapest Transport Modes Share of trips with a duration less than (min) 10 15 20 30 All Modes 8.1% 21.6% 35.1% 56.8% Pedestrians Insufficient sample size for analysis Bicycle Car (as Driver) 19.2% 38.5% 65.4% 73.1% Car (as Passenger) Insufficient sample size for analysis Public Transport 7.9% 13.2% 47.4% 7.4.3. Shopping trips Shopping trips in Budapest last on average 20 minutes, while 55% of them are completed within 15 minutes. The most used transport mode for shopping-related trips in Budapest is walking, followed by car and public transport. According to the results presented in Table 61 and Table 62, walking trips to shopping facilities last on average 11.8 minutes and almost 82% of them last up to 15 minutes. In addition, car journeys to shopping facilities are on average longer than walking journeys, and the high variance in the sample shows that car journeys are preferred for shopping destinations that are far from home and that may offer a wide variety of products. The same applies to shopping trips made by public transport. However, only 24% of public transport trips are completed within 15 minutes, compared to 48% of car trips. Overall, individuals in Budapest are choosing more sustainable modes of transport to meet their daily shopping needs, a finding that indicates a good level of proximity to shopping facilities. Figure 63: Density plot of shopping trip duration by transport mode – Budapest 80 1. Young adults (19 to 39 years old) 2. Middle-aged adults (40 to 59 years old) 3. Old adults (over 60 years old)  Work travel times are relatively stable across age groups with no significant difference.  Individuals from all age groups usually drive for work-related trips.  Middle-aged adults have the longest shopping trips, but with high variability.  Car is the predominant mode for shopping and leisure trips conducted by middle-aged adults.  Older adults tend to spend more time on leisure trips, but not significantly.  Both young and old adults walk more than middle-aged adults when travelling for leisure. 81 8. ÎLE-DE-FRANCE 8.1. General characteristics of Île-de-France and LL location Île-de-France region consists of 8 districts (départements) and has over 1,200 municipalities. (10). With more than 12,4 million inhabitants in 2022, it is the most populous region and the most densely populated in France. Île-de-France has a highly developed public transport network consisting of 16 metro lines, regional train lines, tram lines and bus lines. The Île-de-France Living Lab is centred around the T12 Express tramway corridor, which links several municipalities in the Essonne region. This tramway, which will be inaugurated in 2023, serves around 280,000 inhabitants. The transport network in the Living Lab area already includes improved cycling infrastructure linking the tram line to the surrounding neighbourhoods and regional trains. Figure 73 shows the map of Île-de-France and the location of the Essonne region. Figure 73: Location of the Essonne district (orange line) (Source: OSM (2024)) 82 8.2. Descriptive statistical analysis For the descriptive statistical analysis for the Île-de-France region and the Essonne region, the dataset “Enquête Globale Transport (EGT) 2020” (11) was used. A list of socioeconomic variables, both for households and individuals, was selected and presented in Tables Table 71 and Table 72 respectively. The sample used for this part of the analysis is the total resident population within the area (Île-deFrance or Essonne), with no filtering for specific trip purposes. Furthermore, the sample of survey respondents who did not provide a valid response to one or more of the selected questions was excluded from the sample. Table 71: Socioeconomic characteristics of households (Île-de-France & Essonne) – Source: EGT 2020 Socioeconomic Characteristics of Households Île - de - France LL Location ( Essonne ) n % n % Household size 1 Person 1779 37.3 161 32.0 2 Persons 1512 31.7 168 33.4 3 Persons 602 12.6 75 14.9 4+ Persons 873 18.3 99 19.7 Economic situation Less than 800 € 87 2.2 8 1.9 From 800 to 1200 € 200 5.0 15 3.5 From 1200 to 1600 € 324 8.1 32 7.5 From 1600 to 2000 € 389 9.8 53 12.5 From 2000 to 2400 € 411 10.3 44 10.4 From 2400 to 3000 € 491 12.3 55 12.9 From 3000 to 3500 € 398 10.0 50 11.8 From 3500 to 4500 € 582 14.6 74 17.4 From 4500 to 5500 € 414 10.4 37 8.7 5500€ and more 683 17.2 57 13.4 Car ownership 0 839 19.4 30 6.4 1 2292 53.0 253 54.1 2 1033 23.9 165 35.3 3 140 3.2 18 3.8 4+ 20 0.5 2 0.4 Table 72: Socioeconomic characteristics of individuals (Île-de-France & Essonne) – Source: EGT 2020 Socioeconomic Characteristics of Individuals Île - de - France LL Location ( Essonne ) n % n % Gender Male 4993 47.7 554 48.2 Female 5477 52.3 596 51.8 Age Category (y.o.) 6 – 14 1972 18.8 203 17.7 15 – 19 615 5.9 81 7.0 20 – 24 359 3.4 40 3.5 25 – 34 1061 10.1 112 9.7 35 – 44 1462 14.0 142 12.3 45 – 54 1608 15.4 181 15.7 55 – 64 1381 13.2 157 13.7 65+ 2012 19.2 234 20.3 Occupation Student 2064 22.1 225 22.3 Employed 4433 47.4 463 45.9 Pensioner 2181 23.3 251 24.9 Other 673 7.2 70 6.9 Car driving license Yes 6397 79.8 156 17.7 No 1617 20.2 724 82.3 The descriptive statistical analysis provides a comprehensive insight into the demographic characteristics of the inhabitants of Île-de-France and Essonne. Firstly, the sample of women is slightly larger than that of men. In addition, children and older adults are more strongly represented in the sample. Regarding the type of employment, the majority of the sample is employed. In addition, most of 83 the people in Île-de-France have a driving licence, in contrast to the people in Essonne, where only 17.7% of the inhabitants have a driving licence. 8.3. Modal split As illustrated in Figure 74, and analysis of the modal split by trip purpose for the Île-de-France region reveals that for commuters, driving is the predominant mode of transportation, followed by public transport and walking. With respect to educational-related trips, walking emerges as the predominant mode of transportation, followed by public transport. In the case of shopping trips, walking is the most prevalent mode, though car use and public transportation also emerge as popular modes. With regard to leisure trips, walking emerges as the predominant mode. The analysis indicates a preference among Île-de-France residents for walking and public transportation when fulfilling daily needs. Furthermore, bicycle usage is not as prevalent in Île-de-France. Figure 74: Modal split by trip purpose - Île-de-France The results of the modal split analysis in the Essonne department, as presented in Figure 75, indicate a clear preference for driving and public transport for work-related trips. A notable increase in the utilisation of cars for daily commutes is observed, accompanied by a decline in walking. A similar trend is observed in the context of shopping, with an increase in car use for this purpose. Leisure trips are predominantly undertaken on foot, and by car, which mirrors the modal split observed in Île-de-France. Additionally, an increase in public transportation usage and car usage (as passenger) is observed for educational trips. It is also noted that bicycle usage remains low. The modal split results in Essonne department indicate a strong reliance on motorized transport, contrasting with the results of Île-deFrance modal split. 84 Figure 75: Modal split by trip purpose - Essonne 8.4. Trip Characteristics - Île-de-France 8.4.1. Work trips The average duration of a work trip in Île-de-France is approximately 34 minutes, with an average distance of 9 kilometres. According to Table 74, nearly 85% of work trips in the region are completed within 15 minutes. Driving is the most prevalent mode for commuting, with an average duration of 32 minutes. However, only 30.8% of these trips last up to 15 minutes. Public transport is also a popular mode of commuting, especially for longer trips, with the average duration of a commute undertaken by public transport being 55 minutes, while covering almost 14 kilometres. This may be attributed to the fact that a significant proportion of Île-de-France residents reside in areas distant from the Paris region, necessitating longer commutes. Finally, walking trips are the shortest both in duration and distance. Despite the high degree of proximity to work-related facilities indicated by the results, residents of the Île-de-France region nevertheless remain highly dependent on motorised transport for their daily commutes. (a) Duration by mode of transport (b) Distance by mode of transport Figure 76: Density plot of work trip duration (a) and distance (b) by transport mode – Île-de-France 85 Table 73: Work trip statistics by transport mode – Île-de-France Transport Modes Sample (n) Duration (min) Distance (km) Mean Median SD Mean Median SD All Modes 4141 34.19 30.0 28.30 8.99 5.0 13.06 Pedestrians 1008 10.06 7.5 11.40 0.57 0.4 0.69 Bicycle 145 25.83 20.0 23.79 4.61 2.8 5.91 Car (as Driver) 1477 31.69 30.0 22.48 10.76 7.3 10.61 Car (as Passenger) 67 19.42 15.0 16.36 5.63 2.7 7.38 Public Transport 1444 55.10 50.0 27.47 13.66 9.4 17.05 (a) Duration across all modes (b) Duration by mode of transport (c) Distance across all modes (d) Distance by mode of transport Figure 77: Cumulative distribution function (CDF) of work trip duration (a, b) and distance (c, d)– Île-de-France Table 74: Percentage of work trips conducted for different time stamps – Île-de-France Transport Modes Share of trips with a duration less than (min) 10 15 20 30 All Modes 26.8% 36.2% 43.5% 58.5% Pedestrians 24.8% 43.4% 60.0% 80.0% Bicycle 26.8% 36.2% 43.5% 58.5% Car (as Driver) 18.5% 30.8% 41.2% 63.8% Car (as Passenger) 43.3% 59.7% 77.6% 85.1% Public Transport 0.5% 3.5% 7.5% 22.4% 86 8.4.2. Educational trips Trips to educational institutions in Île-de-France region last on average 20 minutes, while covering almost 3 kilometres. The results presented in Table 76 indicate a high degree of proximity to educational institutions, since almost 90% of the educational trips reported are completed within 15 minutes. The dominant mode in this trip purpose category is walking, followed by public transport and car (as passenger). Walking trips are the shortest in both duration and distance, and 72% of them last up to 15 minutes. In contrast, public transport trips are the longest, with an average duration of 43 minutes and an average distance of almost 8 kilometres. The predominance of short trips in the Île-de-France region is indicative of a high degree of dependency on motorised transport. This phenomenon may be attributed to the fact that a significant proportion of university students who do not reside in close proximity to Paris, where the majority of universities are located, are compelled to undertake longer journeys. (a) Duration by mode of transport (b) Distance by mode of transport Figure 78: Density plot of educational trip duration (a) and distance (b) by transport mode – Île-de-France Table 75: Educational trip statistics by transport mode – Île-de-France Transport Modes Sample (n) Duration (min) Distance (km) Mean Median SD Mean Median SD All Modes 2267 19.65 10.0 20.47 2.95 0.8 5.62 Pedestrians 1234 10.02 10.0 6.35 0.60 0.4 0.61 Bicycle 43 19.95 10.0 25.93 3.28 1.5 5.15 Car (as Driver) 28 29.50 25.0 13.01 10.30 8.0 8.33 Car (as Passenger) 351 12.44 10.0 9.70 2.37 1.0 3.44 Public Transport 611 42.77 35.0 24.69 7.68 4.4 8.28 87 (a) Duration across all modes (b) Duration by mode of transport (c) Distance across all modes (d) Distance by mode of transport Figure 79: Cumulative distribution function (CDF) of educational trip duration (a, b) and distance (c, d)– Île-de-France Table 76: Percentage of educational trips conducted for different time stamps – Île-de-France Transport Modes Share of trips with a duration less than (min) 10 15 20 30 All Modes 73.3% 89.7% 95.9% 99.4% Pedestrians 51.2% 72.1% 81.4% Bicycle 3.6% 7.1% 42.9% 67.9% Car (as Driver) 51.9% 64.7% 73.7% 83.3% Car (as Passenger) 64.7% 77.5% 90.0% 95.4% Public Transport 3.6% 8.8% 20.5% 44.5% 8.4.3. Shopping trips On average, shopping trips in Île-de-France region last approximately 16 minutes, with a distance covered of 3 kilometres. The predominant mode of transportation is walking, followed by car and public transport. On average, individuals in the Île-de-France region walk for 10 minutes to access shopping facilities, with almost 90% of these trips lasting up to 15 minutes. Car journeys, on the other hand, are comparatively lengthy, although they do cover greater distances. The high proportion of trips completed within 15 minutes underscores a high degree of proximity to shopping amenities in the Île-de-France region. 88 (a) Duration by mode of transport (b) Distance by mode of transport Figure 80: Density plot of shopping trip duration (a) and distance (b) by transport mode – Île-de-France Table 77: Shopping trip statistics by transport mode – Île-de-France Transport Modes Sample (n) Duration (min) Distance (km) Mean Median SD Mean Median SD All Modes 4109 16.21 10.0 17.82 3.05 0.9 8.01 Pedestrians 1954 9.73 5.0 8.50 0.49 0.3 0.56 Bicycle 68 16.47 15.0 15.50 2.42 1.3 3.42 Car (as Driver) 1291 17.45 15.0 18.31 4.89 2.4 7.84 Car (as Passenger) 295 16.57 15.0 13.09 4.23 2.5 6.40 Public Transport 501 38.05 30.0 26.13 7.68 4.1 16.86 (a) Duration across all modes (b) Duration by mode of transport (c) Distance across all modes (d) Distance by mode of transport Figure 81: Cumulative distribution function (CDF) of shopping trip duration (a, b) and distance (c, d)– Île-de-France 89 Table 78: Percentage of shopping trips conducted for different time stamps – Île-de-France Transport Modes Share of trips with a duration less than (min) 10 15 20 30 All Modes 54.4% 72.0% 79.9% 90.1% Pedestrians 75.0% 89.4% 94.1% 98.1% Bicycle 48.5% 67.6% 75.0% 92.6% Car (as Driver) 45.0% 68.2% 78.5% 91.4% Car (as Passenger) 41.7% 69.8% 83.1% 94.2% Public Transport 6.6% 15.6% 27.3% 52.9% 8.4.4. Leisure trips The average travel time for residents of Île-de-France to reach a leisure facility is 23 minutes, with a distance of 4 kilometres being covered. According to Table 80, almost 60% of these journeys are completed within 15 minutes. Walking is the most used mode for leisure trips, followed by car (as driver) and public transport. On average, walking trips are brief in both distance and duration, with 75% of them lasting up to 15 minutes. The car is also a popular transport mode in this trip category for longer distances. Bicycle trips are characterised by their extended duration, with an average duration of 34 minutes. The high standard deviation in the sample indicates that the bicycle is used for both short and long leisure trips by Île-de-France residents, with 75% of trips completed within 15 minutes. Finally, trips made by public transport are on average the longest (46 minutes) and cover longer distances than those made by other travel modes. Therefore, a relatively limited proportion (10.3%) of these trips last up to 15 minutes. (a) Duration by mode of transport (b) Distance by mode of transport Figure 82: Density plot of leisure trip duration (a) and distance (b) by transport mode – Île-de-France Table 79: Leisure trip statistics by transport mode – Île-de-France Transport Modes Sample (n) Duration (min) Distance (km) Mean Median SD Mean Median SD All Modes 5400 23.10 15.0 25.95 4.02 1.0 13.34 Pedestrians 2830 15.53 10.0 18.58 0.60 0.4 0.76 Bicycle 157 33.99 15.0 43.50 8.03 1.8 26.80 Car (as Driver) 1086 24.98 15.0 26.71 7.80 3.3 15.97 Car (as Passenger) 533 21.91 15.0 23.18 6.39 2.7 13.46 Public Transport 794 46.17 40.0 29.64 8.63 4.8 22.54 96 (a) Duration by mode of transport (b) Distance by mode of transport Figure 90: Density plot of leisure trip duration (a) and distance (b) by transport mode – Essonne Table 87: Leisure trip statistics by transport mode – Essonne Transport Modes Sample (n) Duration (min) Distance (km) Mean Median SD Mean Median SD All Modes 428 22.82 15.0 24.06 4.61 1.6 7.69 Pedestrians 174 18.12 15.0 14.33 0.54 0.3 0.77 Bicycle Insufficient sample size for analysis Car (as Driver) 139 21.91 15.0 18.02 7.06 3.8 7.92 Car (as Passenger) 72 16.31 15.0 12.20 4.73 2.6 5.68 Public Transport 32 62.13 60.0 32.29 14.84 16.0 9.69 (a) Duration across all modes (b) Duration by mode of transport (c) Distance across all modes (d) Distance by mode of transport Figure 91: Cumulative distribution function (CDF) of leisure trip duration (a, b) and distance (c, d)– Essonne 97 Table 88: Percentage of leisure trips conducted for different time stamps – Essonne Transport Modes Share of trips with a duration less than (min) 10 15 20 30 All Modes 39.3% 58.2% 69.4% 82.2% Pedestrians 46.0% 64.4% 70.7% 86.8% Bicycle Insufficient sample size for analysis Car (as Driver) 34.5% 56.8% 71.9% 84.2% Car (as Passenger) 48.6% 65.3% 87.5% 94.4% Public Transport 6.3% 6.3% 6.3% 18.8% 8.6. Travel behaviour across different socioeconomic groups The present section of the analysis is concerned with an examination of modal split and trip characteristics of different socioeconomic groups in Île-de-France. The main findings of the analysis are presented here, but the detailed results of the analysis can be found in Appendix E and G. 8.6.1. Gender  Males travel significantly longer than women, for work and leisure trip purposes.  Females tend to walk more often for shopping trips than males, who are more likely to use their cars for this purpose.  Walking is the predominant mode of transportation for educational excursions, irrespective of gender.  Females utilize public transport for commutes than males, who generally prefer private transportation. 8.6.2. Income The dataset categorizes the economic situation of households in Île-de-France based on the monthly net income class of the household. For the purpose of the analysis, we have been merged the first six groups (less than 800€ – up to 3000€) into a single category called low income, and the last five groups (more than 3000€ - more than 5500€) into a single category called high income. This categorisation is based on the national statistics that state that for the year of 2021 the average net monthly income in Île-deFrance was approximately 3,128€ (https://www.statista.com/statistics/1440766/average-netmonthly-income-in-france-by-gender-and-by-region/).  Work and education trips have no significant variation both in duration and distance across income groups.  Shopping trips are significantly longer in terms of distance for the low-income group.  The trip duration of leisure trips is significantly longer for individuals from the low-income group. 8.6.3. Age In order to analyse travel behaviour across age in Île-de-France, the sample has been divided into four groups. The groups are the following: 1. Children/Students (up to 18 years old) 2. Young adults (19 to 39 years old) 3. Middle-aged adults (40 to 59 years old) 4. Old adults (over 60 years old)  Work travel distance differs significantly across age groups, but duration does not.  Education trips show the largest variation, with young adults traveling significantly farther and longer.  Shopping and leisure trips increase slightly with age but remain relatively short. 98  Older adults take the longest leisure trips, while younger adults have the longest education trips.  Car usage increases with age, especially for shopping and leisure trips.  Public transport is the predominant mode for work and education trips for young adults.  Middle-aged and older adults rely more on cars for non-work trips. 99 9. MUNICH 9.1. General characteristics of Munich and LL location The capital of the southern German state of Bavaria is the city Munich, which has a population of 1,48 million inhabitants and is the third largest city in Germany. Munich's public transport network consists of suburban railways, eight metro lines, tram lines, bus lines and an extensive cycling infrastructure. Furthermore, the city offers a plethora of active and shared mobility options. The Munich Living Lab constitutes the two municipalities of Geretsried and Wolfratshausen, which are located in the southern part of Munich. Geretsried has an approximate population of 25,705, while Wolfratshausen has a total population of 19,115 inhabitants. Both municipalities are walkable and cyclable, and they are connected with the city of Munich via suburban railways and buses. Figure 92 shows the map of Munich and the location of the Munich Living Lab (red pins). Figure 92: Location of the Munich Living Labs (red pins) (Source: OSM (2024)) 100 9.2. Descriptive statistical analysis For the descriptive statistical analysis for the Munich region and the Munich Living Lab region, the dataset “Mobilität in Deutschland (MiD) 2017” (12) was used. A list of socioeconomic variables, both for households and individuals, was selected and presented in Tables Table 89 and Table 90 respectively. It is important to acknowledge the limitations imposed by the relatively modest sample size of trips made by residents of the of Geretsried and Wolfratshausen municipalities. This limits the scope and generalisability of the results of the analysis. In order to address these limitations, a comprehensive analysis of all the dense towns at S-Bahn termini is conducted, since all these towns share common socio-economic and travel characteristics. The sample used for this part of the analysis is the total resident population within the area (Munich or Munich LL), with no filtering for specific trip purposes. Furthermore, the sample of survey respondents who did not provide a valid response to one or more of the selected questions was excluded from the sample Table 89: Socioeconomic characteristics of households (Munich & Dense Towns at S-Bahn Termini) – Source: MiD 2017 Socioeconomic Characteristics of Households Munich LL Location (Dense Towns at S - Bahn Ter.) n % n % Household size 1 Person 2582 31.4 101 17.2 2 Persons 3547 43.2 262 44.6 3 Persons 1051 12.8 88 15 4+ Persons 1038 12.6 137 23.3 Economic situation Very poor 220 2.7 21 3.6 Poor 497 6 35 6 Average 3255 39.6 225 38.3 Good 2970 36.1 239 40.6 Very good 1276 15.5 68 11.6 Car ownership 0 2013 24.5 24 4.1 1 4598 56 293 49.8 2+ 1607 19.6 271 46.1 Table 90: Socioeconomic characteristics of individuals (Munich & Dense Towns at S-Bahn Termini) – Source: MiD 2017 Socioeconomic Characteristics of Individuals Munich LL Location (Dense Towns at S - Bahn Ter.) n % n % Gender Male 7755 49.1 666 49.2 Female 8026 50.9 687 50.8 Age Category (y.o.) 0 - 17 2088 13.2 223 16.5 18 - 29 1777 11.3 153 11.4 30 - 39 2352 14.9 123 9.1 40 - 49 2152 13.6 194 14.4 50 - 59 2419 15.3 236 17.5 60 - 69 2040 12.9 221 16.4 70 - 79 2140 13.6 155 11.5 80+ 804 5.1 43 3.2 Occupation Student 2203 14 261 19.3 Employed 7865 49.9 630 46.7 Pensioner 3974 25.2 327 24.2 Other 1735 11 132 9.8 Car driving license Yes 10831 91.7 886 94.2 No 982 8.3 55 5.8 Car availability Always 8672 74.9 792 85.7 Occasionally 1701 14.7 81 8.8 Never 1210 10.4 51 5.5 Bicycle availability Yes 12865 81.6 1125 83.2 No 2897 18.4 227 16.8 101 The descriptive statistical analysis provides a comprehensive overview of the demographic profiles of residents in Munich and the Munich Living Lab region. With respect to gender, the population in the sample is almost equally distributed. However, a marked disparity emerges with respect to vehicle ownership, with the Munich Living Lab region exhibiting a significantly higher rate compared to the Munich sample. While the majority of households in Munich LL possess at least one car, in Munich, the proportion of households without cars is higher. With respect to occupation, both samples are predominantly comprised of employed individuals and pensioners. With respect to income, the majority of households in both regions report an average or good economic situation. 9.3. Modal split Figure 93 shows the modal split by trip purpose for the city of Munich. For working commutes, public transport is the most preferred mode for Munich residents, followed by driving and cycling. For educational trips, public transport is once again the most prevalent mode of transport, though the share of cycling and walking trips is also high. In contrast, shopping trips are predominantly undertaken on foot, while car usage remains notably high. With respect to leisure trips, here walking emerges as the predominant mode with 43%, followed by public transport and car usage. The modal split in Munich demonstrates a clear preference for public transport for work and educational purposes, and for walking for shopping and leisure purposes. Nevertheless, car usage and bicycle usage persist as prevalent modes for work-related and shopping-related trips. Figure 93: Modal split by trip purpose – Munich The results of the modal split analysis in the Munich Living Lab area, as presented in Figure 94, indicate a clear preference for driving for work-related trips. In comparison with the results for the wider Munich area, where public transport is the predominant mode of transport, there is a marked increase in car usage for daily commutes, accompanied by a decline in cycling and public transport usage. A similar trend is observed in the context of shopping, with an increase in car use for this purpose. Furthermore, it is observed that leisure trips are predominantly undertaken by car or on foot. The modal split results in Munich Living Lab area indicate a reliance on motorized transport, particularly for work and shopping trips, contrasting with the results of the modal split in Munich. 102 Figure 94: Modal split by trip purpose – Dense Towns at S-Bahn Termini 9.4. Trip Characteristics – Munich 9.4.1. Work trips The mean travel time for commuters in Munich is 32 minutes, with a distance of 12 kilometres being covered. According to Table 92, 26% of the commutes in Munich are completed within 15 minutes. The most common modes of transportation for commuting are public transport, private vehicle and bicycle. The mean duration of public transport commutes is 42 minutes, while the mean distance travelled is 14.5 kilometres. In contrast, commutes by car are comparatively brief, yet cover extended distances. Bicycle commutes, on average, last approximately 23 minutes, and 44% of these are completed within 15 minutes. The analysis indicates that individuals in Munich allocate a substantial portion of their daily time to commuting and they are dependent on motorized transport and the public transportation system. (a) Duration by mode of transport (b) Distance by mode of transport Figure 95: Density plot of work trip duration (a) and distance (b) by transport mode – Munich 103 Table 91: Work trip statistics by transport mode – Munich Transport Modes Sample (n) Duration (min) Distance (km) Mean Median SD Mean Median SD All Modes 6773 31.90 30.00 26.91 12.06 7.20 28.93 Pedestrians 521 16.89 10.00 18.02 1.50 0.98 2.35 Bicycle 1459 22.73 20.00 14.32 5.10 3.92 4.11 Car (as Driver) 2166 29.28 25.00 22.88 15.98 9.50 28.56 Car (as Passenger) 140 38.34 20.00 50.36 20.17 5.87 50.26 Public Transport 2487 42.34 35.00 31.19 14.49 9.00 36.64 (a) Duration across all modes (b) Duration by mode of transport (c) Distance across all modes (d) Distance by mode of transport Figure 96: Cumulative distribution function (CDF) of work trip duration (a, b) and distance (c, d)– Munich Table 92: Percentage of work trips conducted for different time stamps – Munich Transport Modes Share of trips with a duration less than (min) 10 15 20 30 All Modes 13.7% 26.2% 37.4% 63.7% Pedestrians 54.5% 70.4% 78.1% 89.4% Bicycle 22.5% 44.1% 58.5% 82.8% Car (as Driver) 11.2% 26.9% 41.8% 72.1% Car (as Passenger) 18.6% 35.7% 50.7% 65.0% Public Transport 2.1% 5.3% 11.9% 39.8% 9.4.2. Educational trips Trips to educational institutions in Munich last on average 23.5 minutes, while covering almost 6 kilometres. According to Table 94, almost 55% of educational trips in Munich are completed within 15 minutes. The predominant mode of transportation in this category is public transport, followed by bicycle and walking. Educational trips undertaken by public transport have an average duration of 37 minutes (see Table 93: Educational trip statistics by transport mode – Munich), while covering 9 kilometres. Bicycle and walking trips are also popular, although the cover much shorter distances. Walking emerges as one of the prevalent modes, accounting for 82.5% of all education-related trips completed within the stipulated time frame. The results indicate a high degree of proximity to educational institutions in Munich. 104 (a) Duration by mode of transport (b) Distance by mode of transport Figure 97: Density plot of educational trip duration (a) and distance (b) by transport mode – Munich Table 93: Educational trip statistics by transport mode – Munich Transport Modes Sample (n) Duration (min) Distance (km) Mean Median SD Mean Median SD All Modes 2298 23.50 15.00 23.98 5.93 2.35 20.58 Pedestrians 543 14.10 10.00 15.80 0.88 0.64 0.95 Bicycle 612 16.04 15.00 10.41 2.51 1.96 2.29 Car (as Driver) 86 44.52 25.00 69.60 34.08 8.71 81.42 Car (as Passenger) 308 16.79 14.50 21.12 6.25 2.43 28.23 Public Transport 749 36.77 30.00 20.20 9.03 6.30 9.32 (a) Duration across all modes (b) Duration by mode of transport (c) Distance across all modes (d) Distance by mode of transport Figure 98: Cumulative distribution function (CDF) of educational trip duration (a, b) and distance (c, d)– Munich 105 Table 94: Percentage of educational trips conducted for different time stamps – Munich Transport Modes Share of trips with a duration less than (min) 10 15 20 30 All Modes 33.4% 54.2% 63.7% 79.5% Pedestrians 58.7% 82.5% 89.7% 96.5% Bicycle 41.7% 71.2% 83.2% 94.0% Car (as Driver) 11.6% 39.5% 48.8% 69.8% Car (as Passenger) 48.7% 75.0% 83.1% 92.5% Public Transport 4.5% 12.8% 22.7% 51.0% 9.4.3. Shopping trips The mean duration of shopping trips in Munich is 17 minutes, with a total distance covered of almost 4 kilometres. The predominant mode of transportation is walking, followed by car and cycling. Furthermore, almost 72% of shopping trips in Munich are completed within 15 minutes. Walking and cycling trips are brief, both in distance and duration, while approximately 80% of them last up to 15 minutes. Car trips (as driver) tend to cover longer distances, although they are brief in duration. Finally, shopping trips undertaken by public transport cover similar distances to those made by car, yet they are characterised by a longer duration. The findings of this study demonstrate that individuals in Munich predominantly utilise sustainable transport modes for shopping trips; however, they are still dependent on motorised transport when they wish to cover longer distances. (a) Duration by mode of transport (b) Distance by mode of transport Figure 99: Density plot of shopping trip duration (a) and distance (b) by transport mode – Munich Table 95: Shopping trip statistics by transport mode – Munich Transport Modes Sample (n) Duration (min) Distance (km) Mean Median SD Mean Median SD All Modes 8888 17.12 10.00 20.65 3.69 1.47 12.76 Pedestrians 3126 14.28 10.00 19.36 0.80 0.49 1.22 Bicycle 1752 13.34 10.00 15.28 2.08 1.27 2.83 Car (as Driver) 2264 17.22 10.00 21.17 6.44 2.95 21.18 Car (as Passenger) 662 20.25 15.00 21.04 7.59 3.80 12.93 Public Transport 1084 29.25 25.00 25.20 6.49 4.50 14.78 112 (a) Duration across all modes (b) Duration by mode of transport (c) Distance across all modes (d) Distance by mode of transport Figure 108: Cumulative distribution function (CDF) of shopping trip duration (a, b) and distance (c, d)– Dense Towns at S-Bahn Termini Table 104: Percentage of shopping trips conducted for different time stamps – Dense Towns at S-Bahn Termini Transport Modes Share of trips with a duration less than (min) 10 15 20 30 All Modes 62.3% 77.3% 83.9% 90.7% Pedestrians 50.9% 68.3% 79.8% 91.3% Bicycle 66.2% 85.9% 92.9% 93.7% Car (as Driver) 66.1% 79.2% 84.3% 91.4% Car (as Passenger) 60.2% 75.3% 83.9% 91.4% Public Transport Insufficient sample size for analysis 9.5.4. Leisure trips The residents of Munich's LL location typically travel an average of 31.5 minutes for leisure purposes. According to Table 104, almost 55% of these leisure trips are completed within 15 minutes. The predominant mode of transportation is the automobile, followed by walking. The duration of car journeys is on average 30 minutes, with almost 57% of these lasting up to 15 minutes. In contrast, walking trips are characterised by their duration, albeit covering shorter distances, with almost 49% of them lasting up to 15 minutes. This figure, however, may not necessarily signify a lack of proximity to leisure facilities, but rather, it could be indicative of a distinct preference among Munich's LL for walking as a leisure activity in itself. 113 (a) Duration by mode of transport (b) Distance by mode of transport Figure 109: Density plot of leisure trip duration (a) and distance (b) by transport mode – Dense Towns at S-Bahn Termi Table 105: Leisure trip statistics by transport mode – Dense Towns at S-Bahn Termini Transport Modes Sample (n) Duration (min) Distance (km) Mean Median SD Mean Median SD All Modes 1384 31.43 15.00 39.77 14.90 3.46 42.96 Pedestrians 367 34.93 20.00 37.09 2.08 1.19 2.37 Bicycle 209 26.27 10.00 36.80 4.62 1.96 9.91 Car (as Driver) 438 29.18 15.00 40.88 24.45 7.60 63.96 Car (as Passenger) 272 22.92 15.00 30.73 17.00 5.70 33.98 Public Transport 98 63.02 45.00 54.37 36.26 27.90 51.91 (a) Duration across all modes (b) Duration by mode of transport (c) Distance across all modes (d) Distance by mode of transport Figure 110: Cumulative distribution function (CDF) of leisure trip duration (a, b) and distance (c, d)– Dense Towns at S-Bahn Termini 114 Table 106: Percentage of leisure trips conducted for different time stamps – Dense Towns at S-Bahn Termini Transport Modes Share of trips with a duration less than (min) 10 15 20 30 All Modes 36.9% 55.2% 63.0% 73.4% Pedestrians 36.2 % 48.8 % 54.8 % 66.8 % Bicycle 51.7% 66.9% 74.2% 82.3% Car (as Driver) 33.1 % 56.4 % 68.0 % 76.9 % Car (as Passenger) 43.0% 69.1% 75.4% 84.9% Public Transport 8.2% 10.2% 13.3% 31.6% 9.6. Travel behaviour across different socioeconomic groups The present section of the analysis is concerned with an examination of modal split and trip characteristics of different socioeconomic groups in Munich. The main findings of the analysis are presented here, but the detailed results of the analysis can be found in Appendix F and G. 9.6.1. Gender  Work trips are significantly longer for males than for females, however, for other trip purposes no significant differences have been found.  In terms of trip duration, there are no statistically significant differences in trip durations between genders for any trip purpose.  Males tend to drive cars more often, especially for work and shopping trips.  Females rely more on public transport and walking for their shopping and leisure trips.  Bicycle usage is fairly balances between genders, but slightly higher of males in education trips. 9.6.2. Income The dataset categorizes the economic situation of households as very poor, poor, average, good, and very good. For the purpose of the analysis, we have been merged the first two categories (very poor and poor) into a single category called low income, and the last two categories (good and very good) into a single category called high income.  Low-income individuals spend more time on education and shopping trips, while high-income individuals cover longer distances for work and leisure.  Work trips are longer in distance for high-income individuals; however, trip duration does not differ significantly.  Bike usage is higher among high-income individuals, especially for work and education trips.  High-income individuals use cars more for their shopping and work trips.  Low-income individuals rely heavily on public transport for all trip purposes.  People with higher incomes are more likely to walk to educational institutions. 9.6.3. Age In order to analyse travel behaviour across age in Munich, the sample has been divided into four groups. The groups are the following: 1. Children/Students (up to 17 years old) 2. Young adults (18 to 39 years old) 3. Middle-aged adults (40 to 59 years old) 4. Old adults (over 60 years old)  Trip duration shows significant variations across age groups for education, shopping and leisure trips.  Trip distance shows significant differences for education and leisure trips, but no significant differences for shopping and work trips.  Middle-aged adults and old adults use cars more for their work and shopping trips. 115  Children and young adults rely more on public transportation for their education trips.  The age group with the highest share of walking trips to shopping facilities is the one of young adults.  Bike usage is higher among children/students and middle-aged adults. 116 10. COMPARISON This chapter undertakes a comparative analysis of the travel behaviour exhibited by the inhabitants of the cities and Living Labs under review. 10.1. City level Figure 111 presents the Cumulative Distribution Functions (CDF) for the city level, categorized according to trip purpose, and Tables 107 to 110 present the percentage of work, educational, shopping, and leisure trips conducted for different time stamps. (a) Work trips (b) Educational trips (c) Shopping trips (d) Leisure trips Figure 111: Cumulative distribution function (CDF) of trip duration per trip purpose– City level Figure 112: Percentage of trips completed within 15 minutes by trip purpose - City level 117 Work trips The results of the study demonstrate a divergence in the alignment of work trips with the 15-Minute City (15mC) concept. The city of Utrecht exhibits the strongest alignment with this concept, with 40.5% of work-related trips lasting less than 15 minutes and 75.8% taking less than 30 minutes. This suggests that the urban design of Utrecht is more compact, facilitating efficient access to workplaces. Brussels is a close second with 35.9% of trips being completed within 15 minutes, though a greater proportion of these trips are completed within the 30-minute time range. Île-de-France, in spite of its status as a vast metropolitan zone, demonstrates that 36.2% of journeys are executed within a time span of 15 minutes, whilst a notable proportion of 58.5% falls within a 30-minute time frame. Munich and Vienna demonstrate moderate alignment with the concept, with approximately 26-27% of trips falling under 15 minutes, and substantial proportions extending beyond 30 minutes (63.7% and 65.3%, respectively). Budapest, with its 17.4% of short-duration trips (under 15 minutes) and 49.6% of trips under 30 minutes, appears to be the furthest from fully embracing the 15mC concept. Overall, Utrecht stands out as the closest match, while Budapest shows the least alignment. In regard to the modal split, individuals in Vienna, Brussels and Munich demonstrate a greater reliance on public transport and cars for their commutes, in comparison to individuals in Utrecht, Budapest, and the Île-de-France region, where there is a preference for motorized transport. Table 107: Percentage of work trips conducted for different time stamps – City level City Share of work trips with a duration less than (min) 10 15 20 30 Vienna 14.3% 26.9% 38.6% 65.3% Utrecht 23.3% 40.5% 52.2% 75.8% Brussels 23.5% 35.9% 49.5% 68.5% Budapest 10.5% 17.4% 27.8% 49.6% Île-de-France 26.8% 36.2% 43.5% 58.5% Munich 13.7% 26.2% 37.4% 63.7% Educational trips A comparison of these cities in relation to the 15-Minute City concept reveals Île-de-France to be the most accomplished region in this regard, with a noteworthy 89.7% of trips taking under 15 minutes and almost all (99.4%) taking under 30 minutes. This finding suggests the presence of a well-distributed educational infrastructure in the whole region. Utrecht also reveals notable performance, with 57.5% of trips completed within 15 minutes and 80.8% within 30 minutes. Munich and Brussels demonstrate moderate alignment, with approximately 50% of educational trips completed within 15 minutes and around 70-80% within 30 minutes. Vienna also displays a comparable tendency, with 40.5% of trips lasting less than 15 minutes and 74% lasting less than 30 minutes. On the other hand, Budapest exhibits the least degree of alignment with only 21.6% of educational trips falling within the under 15 minutes bracket and just 56.8% within the under 30 minutes bracket. Overall, Île-de-France and Utrecht appear closest to the ideal of 15-minute city centres (15mC), where the majority of students can access educational facilities efficiently, whereas Budapest falls behind. However, the absence of students under the age of 18 in the Budapest dataset precludes the possibility of deriving any meaningful insights regarding the degree of proximity to primary and secondary educational institutions in Budapest. With respect to the modal split, students in Vienna, Brussels, Budapest and Munich demonstrate a greater propensity for utilising public transportation to access educational institutions, in contrast to their counterparts in Utrecht and the Île-de-France region, where cycling and walking are preferred modes of transportation, respectively. 118 Table 108: Percentage of educational trips conducted for different time stamps – City level City Share of educational trips with a duration less than (min) 10 15 20 30 Vienna 24.6% 40.5% 53.0% 74.0% Utrecht 44.3% 57.5% 67.1% 80.8% Brussels 33.1% 48.7% 56.9% 71.7% Budapest 8.1% 21.6% 35.1% 56.8% Île-de-France 73.3% 89.7% 95.9% 99.4% Munich 33.4% 54.2% 63.7% 79.5% Shopping trips In terms of shopping trips, Utrecht once again exhibits a high degree of alignment with the 15mC concept, with 84.1% of trips being completed within 15 minutes and 96.1% of trips taking no more than 30 minutes. This finding suggests a high level of proximity to shopping facilities in Utrecht. Vienna, Munich, the Île-de-France region and Brussels follow closely behind, with approximately 70% of shopping trips completed within 15 minutes and around 90% within 30 minutes. This finding indicates that these cities also exhibit a high degree of commercial proximity. Budapest, while maintaining a majority of trips within 30 minutes (87.4%), exhibits deficiencies in shorter trip durations, with only 55% under 15 minutes and 37.7% under 10 minutes. Consequently, it is evident that Utrecht exhibits optimal proximity for shopping, whilst Budapest remains the least aligned, although all cities demonstrate relatively satisfactory access to shopping opportunities. Furthermore, with respect to modal split, individuals in all cities except Utrecht demonstrate a preference for walking when accessing shopping facilities. In Utrecht, however, the preferred options are cycling, driving or walking. Table 109: Percentage of shopping trips conducted for different time stamps – City level City Share of shopping trips with a duration less than (min) 10 15 20 30 Vienna 52.1% 69.9% 79.5% 90.1% Utrecht 67.8% 84.1% 90.0% 96.1% Brussels 55.0% 69.3% 78.3% 90.0% Budapest 37.7% 55.0% 72.6% 87.4% Île-de-France 54.4% 72.0% 79.9% 90.1% Munich 52.05 71.3% 79.4% 90.6% Leisure trips In terms of leisure trips, the Île-de-France region aligns most closely with the 15-Minute City (15mC) concept, with 59.2% of trips completed within 15 minutes and 80.8% within 30 minutes. This suggests a high degree of proximity to recreational spaces. Budapest also performs satisfactorily, where 47% of leisure trips are completed within 15 minutes and 75% within 30 minutes. Brussels and Utrecht exhibit analogous trends, with approximately 42-43% of trips falling within the 15-minute mark and around two-thirds of all trips taking up to 30 minutes. Conversely, Vienna and Munich exhibit the lowest proportions of short leisure trips, with approximately 36-40% of trips falling under 15 minutes and around 67% within 30 minutes. In conclusion, Île-de-France demonstrates the highest degree of proximity to recreational facilities among the other cities. With respect to the modal split, the predominant mode of transportation for leisure travel in all cities is walking. Table 110: Percentage of leisure trips conducted for different time stamps – City level City Share of leisure trips with a duration less than (min) 10 15 20 30 Vienna 20.1% 36.0% 46.6% 67.0% Utrecht 27.1% 43.0% 50.1% 66.6% Brussels 27.9% 42.5% 52.4% 70.3% Budapest 35.1% 47.0% 57.7% 75.0% Île-de-France 42.1% 59.2% 66.4% 80.8% Munich 24.3% 39.7% 48.9% 67.8% 119 10.2. Outskirts (Living Lab) level Figure 113 presents the Cumulative Distribution Functions (CDF) for the outskirts (Living Lab) level, categorized according to trip purpose, and Tables 111 to 114 present the percentage of work, educational, shopping, and leisure trips conducted for different time stamps. (a) Work trips (b) Educational trips (c) Shopping trips (d) Leisure trips Figure 113: Cumulative distribution function (CDF) of trip duration per trip purpose– Outskirts (Living Lab) level Figure 114: Percentage of trips completed within 15 minutes by trip purpose – Outskirts (Living Lab) level 120 Work trips An analysis of work trip durations in the Living Labs (LL) reveals that Munich LL exhibits the closest alignment with the 15mC concept, with 37.7% of trips lasting less than 15 minutes and 62.7% occurring within 30 minutes, suggesting enhanced local job accessibility. A similar performance is observed in Brussels LL, with 29.7% of work trips falling under the 15-minute threshold and 74.3% within the 30minute range. The Utrecht LL exhibits moderate alignment, with 26.7% of work trips falling under 15 minutes and a substantial 76.7% within 30 minutes. In contrast, the Île-de-France LL and Vienna LL exhibit comparatively lower percentages of short-duration commutes, with approximately 19-28% falling under the 15-minute category and around 50% within a 30-minute radius. This indicates a more dispersed distribution of employment centers in these regions. The Budapest LL exhibits the most lacking performance, with only 4.9% of trips occurring under 15 minutes and a mere 37.2% under 30 minutes, underscoring substantial challenges in local job accessibility. In conclusion, the Munich LL demonstrates the strongest alignment with the 15mC vision, while the Budapest LL exhibits the least alignment. Table 111: Percentage of work trips conducted for different time stamps – Outskirts (Living Lab) level Living Lab Share of work trips with a duration less than (min) 10 15 20 30 Vienna 11.8% 19.1% 30.9% 50.0% Utrecht 13.3% 26.7% 40.0% 76.7% Brussels 18.9% 29.7% 54.1% 74.3% Budapest 1.7% 4.9% 10.1% 37.2% Île-de-France 18.1% 28.1% 36.7% 51.8% Munich 26.3% 37.7% 46.0% 62.7% Educational trips In the Living Labs, Île-de-France and Munich exhibit the most robust alignment with the 15mC concept for educational excursions. Île-de-France distinguishes itself with 52.5% of trips completed within 15 minutes and 74% within 30 minutes, reflecting a well-distributed network of schools and universities. Munich exhibits a similar performance, with 51.4% of trips completed in under 15 minutes and 80.5% within 30 minutes, suggesting a comparable level of accessibility. Brussels also demonstrates a positive performance with 48.6% of trips completed within 15 minutes and 80% within 30 minutes. Conversely, Vienna exhibits a comparatively lower level of accessibility, with only 31.8% of educational trips falling within 15 minutes and 56.8% within 30 minutes, suggesting a less evenly distributed educational infrastructure. Notably, data concerning Utrecht and Budapest is unavailable due to insufficient sample size, impeding comprehensive comparative analyses. Overall, the Île-de-France LL and Munich LL appear to best support the 15mC vision for education. Table 112: Percentage of educational trips conducted for different time stamps – Outskirts (Living Lab) level Living Lab Share of educational trips with a duration less than (min) 10 15 20 30 Vienna 20.5% 31.8% 40.9% 56.8% Utrecht - - - - Brussels 34.3% 48.6% 54.3% 80.0% Budapest - - - - Île-de-France 43.6% 52.5% 67.2% 74.0% Munich 31.9% 51.4% 65.6% 80.5% 121 Shopping trips Among the Living Labs, Utrecht and Munich demonstrate optimal accessibility for shopping trips in accordance with the 15mC concept. Utrecht demonstrates a noteworthy distinction with 81.9% of trips completed within 15 minutes and an impressive 97.9% within 30 minutes, underscoring its robust retail accessibility. Munich exhibits a comparable performance, with 77.3% of trips completed in under 15 minutes and 90.7% within 30 minutes, suggesting a well-distributed commercial network. Vienna and Île-de-France also demonstrate commendable performance, with approximately 70% of trips occurring within 15 minutes and over 90% within 30 minutes, indicating convenient access to shopping destinations. Conversely, Brussels exhibits a slightly lower performance, with only 60.3% of trips falling under 15 minutes and 86.8% within 30 minutes. Budapest exhibits the least efficient performance, with only 44.8% of shopping trips under 15 minutes and 81% within 30 minutes, indicating a less compact retail infrastructure. In conclusion, it is evident that the Utrecht LL and the Munich LL most closely align with the 15mC principles for shopping accessibility. Table 113: Percentage of shopping trips conducted for different time stamps – Outskirts (Living Lab) level Living Lab Share of shopping trips with a duration less than (min) 10 15 20 30 Vienna 52.1% 71.9% 79.2% 93.8% Utrecht 62.8% 81.9% 89.4% 97.9% Brussels 48.8% 60.3% 71.1% 86.8% Budapest 17.2% 44.8% 58.6% 81.0% Île-de-France 49.5% 69.3% 80.2% 93.9% Munich 62.3% 77.3% 83.9% 90.7% Leisure trips For leisure trips in the Living Labs, Île-de-France and Munich show the strongest alignment with the 15mC concept. Île-de-France leads with 58.2% of trips within 15 minutes and 82.2% within 30 minutes, indicating widespread access to recreational opportunities. Munich follows closely, with 55.2% of trips less than 15 minutes and 73.4% within 30 minutes, suggesting a well-distributed network of leisure destinations. Brussels and Budapest show moderate accessibility, with about 42% and 37.5% of trips under 15 minutes, respectively, and nearly 70% within 30 minutes. Utrecht LL and Vienna LL rank lowest, with only about a third of leisure trips within 15 minutes and just over half within 30 minutes, suggesting a more limited proximity to leisure destinations. Overall, the Île-de-France LL and the Munich LL best support the 15mC vision for leisure accessibility. Table 114: Percentage of leisure trips conducted for different time stamps – Outskirts (Living Lab) level Living Lab Share of leisure trips with a duration less than (min) 10 15 20 30 Vienna 10.6% 35.1% 42.6% 58.5% Utrecht 17.5% 32.5% 40.0% 55.0% Brussels 29.9% 42.1% 54.2% 73.8% Budapest 25.0% 37.5% 48.9% 69.3% Île-de-France 39.3% 58.2% 69.4% 82.2% Munich 36.9% 55.2% 63.0% 73.4% 128 Age A9: Modal split across age groups - Vienna A10: Trip duration density across age groups and trip purposes - Vienna 129 A11: Trip duration statistics across age groups – Vienna Trip Purpose Age Group N Mean (min) SD Pr(>F) Work 1 25 38.4 17.5 0.009** 2 679 30.8 22.2 3 1026 28.4 18.1 4 93 29.0 18.6 Education 1 411 21.1 15.3 <0.001*** 2 219 30.5 18.1 3 37 33.5 18.6 4 Insufficient sample size for analysis Shopping 1 65 16.2 13.5 0.056 2 388 14.4 13.4 3 579 16.4 18.0 4 711 17.7 23.0 Leisure 1 220 27.5 29.1 <0.001*** 2 516 28.6 23.9 3 577 35.4 41.5 4 588 17.7 23.0 Significance level ( p - value): * ≤0.05, **≤0.01, ***≤0.001 A12: Trip distance statistics across age groups – Vienna Trip Purpose Age Group N Mean (km) SD Pr(>F) Work 1 25 9.1 6.6 0.476 2 679 10.1 15.0 3 1026 9.2 10.3 4 93 9.9 19.8 Education 1 411 5.1 9.7 0.003** 2 219 7.4 7.7 3 37 10.9 9.9 4 Insufficient sample size for analysis Shopping 1 65 3.6 3.6 0.044* 2 388 3.3 6.6 3 579 4.6 10.8 4 711 3.4 7.6 Leisure 1 220 7.3 14.1 0.013* 2 516 7.5 12.3 3 577 12.6 39.3 4 588 3.4 7.6 Significance level ( p - value): * ≤0.05, **≤0.01, ***≤0.001 130 APPENDIX B Utrecht Gender B1: Modal split across genders – Utrecht B2: Trip duration density across genders and trip purposes - Utrecht 131 B3: Trip duration statistics across genders – Utrecht Trip Purpose Gender N Mean (min) SD t-score p-value Work Male 1336 26.52 21.20 1.183 0.237 Female 1243 25.46 23.86 Education Male 564 20.55 24.56 -1.443 0.149 Female 644 22.48 21.57 Shopping Male 2478 11.87 11.82 -1.184 0.236 Female 2951 12.33 16.74 Leisure Male 4082 37.61 48.67 -0.179 0.857 Female 4288 37.80 47.29 Significance level ( p - value): * ≤0.05 B4: Trip distance statistics across genders – Utrecht Trip Purpose Gender N Mean (km) SD t-score p-value Work Male 1336 16.92 20.19 4.669 <0.05* Female 1243 13.54 16.47 Education Male 564 6.66 13.06 -0.848 0.397 Female 644 7.28 12.06 Shopping Male 2478 3.50 6.74 1.109 0.268 Female 2951 3.29 7.52 Leisure Male 4082 8.98 17.17 1.099 0.272 Female 4288 8.56 17.68 Significance level ( p - value): * ≤0.05 Income B5: Modal split across income groups - Utrecht 132 B6: Trip duration density across income groups and trip purposes - Utrecht B7: Trip duration statistics across income groups – Utrecht Trip Purpose Income Group N Mean (min) SD t-score p-value Work Low 543 26.32 26.51 0.375 0.708 High 2013 25.86 21.29 Education Low 342 23.07 23.03 1.459 0.145 High 841 20.91 23.12 Shopping Low 1516 12.57 12.23 1.512 0.131 High 3831 11.96 15.67 Leisure Low 1850 40.27 56.97 2.455 0.014* High 6411 36.74 44.86 Significance level ( p - value): * ≤0.05 (5%) B8: Trip distance statistics across income groups – Utrecht Trip Purpose Income Group N Mean (km) SD t-score p-value Work Low 543 11.68 14.71 -6.042 <0.05* High 2013 16.30 19.39 Education Low 342 6.79 11.56 -0.369 0.712 High 841 7.08 12.95 Shopping Low 1516 3.02 5.89 -2.809 <0.05* High 3831 3.57 7.68 Leisure Low 1850 8.57 17.16 -0.565 0.572 High 6411 8.83 17.54 Significance level ( p - value): * ≤0.05 (5%) 133 Age B9: Modal split across age groups - Utrecht B10: Trip duration density across age groups and trip purposes – Utrecht 134 B11: Trip duration statistics across age groups – Utrecht Trip Purpose Age Group N Mean (min) SD Pr(>F) Work 1 132 16.89 14.34 <0.001*** 2 1020 27.80 21.59 3 999 26.13 24.98 4 380 23.61 19.97 Education 1 963 18.03 20.39 <0.001*** 2 181 38.19 26.59 3 43 30.19 31.45 4 21 23.57 16.52 Shopping 1 345 13.93 14.18 0.018* 2 1503 12.05 20.39 3 1747 11.45 11.30 4 1762 12.49 11.69 Leisure 1 1512 30.13 42.99 <0.001*** 2 1020 27.80 21.59 3 181 38.19 26.59 4 1503 12.05 20.39 Significance level ( p - value): * ≤0.05, **≤0.01, ***≤0.001 B12: Trip distance statistics across age groups – Utrecht Trip Purpose Age Group N Mean (km) SD Pr(>F) Work 1 132 4.94 6.65 <0.001*** 2 1020 16.66 19.61 3 999 16.09 18.44 4 380 12.44 16.57 Education 1 963 4.47 7.57 <0.001*** 2 181 19.14 22.09 3 43 11.23 16.40 4 21 8.97 9.22 Shopping 1 345 4.54 10.11 0.006** 2 1503 3.09 6.78 3 1747 3.53 6.94 4 1762 3.28 7.04 Leisure 1 1512 6.08 12.99 <0.001*** 2 1020 9.10 18.26 3 181 9.53 19.25 4 1503 9.39 16.67 Significance level ( p - value): ** ≤0.01, ***≤0.001 135 APPENDIX C BRUSSELS Gender C1: Modal split across genders – Brussels C2: Trip duration density across genders and trip purposes - Brussels 136 C3: Trip duration statistics across genders – Brussels Trip Purpose Gender N Mean (min) SD t-score p-value Work Male 265 24.25 21.46 -0.107 0.915 Female 339 27.49 20.30 Education Male 146 24.34 22.67 -0.947 0.349 Female 206 24.10 22.35 Shopping Male 501 14.33 15.79 0.144 0.886 Female 685 16.09 17.37 Leisure Male 531 33.21 42.79 2.724 0.007* Female 560 28.07 27.24 Significance level ( p - value): * ≤0.05 (5%) C4: Trip distance statistics across income genders – Brussels Trip Purpose Gender N Mean (km) SD t-score p-value Work Male 265 16.15 34.86 0.402 0.688 Female 339 14.29 20.18 Education Male 146 4.91 7.22 -0.419 0.677 Female 206 5.97 11.24 Shopping Male 501 7.64 26.55 0.578 0.564 Female 685 6.08 14.14 Leisure Male 531 15.65 30.00 2.319 0.021* Female 560 9.45 20.89 Significance level ( p - value): * ≤0.05 (5%) Age C5: Modal split across age groups - Brussels 137 C6: Trip duration density across age groups and trip purposes - Brussels C7: Trip duration statistics across age groups – Brussels Trip Purpose Age Group N Mean (min) SD Pr(>F) Work 1 Insufficient sample size for analysis 0.518 2 197 27.95 16.24 3 302 25.00 15.48 4 Insufficient sample size for analysis Education 1 246 19.91 2.50 <0.001*** 2 70 36.46 9.64 3 28 22.57 10.29 4 Insufficient sample size for analysis Shopping 1 116 16.29 10.04 0.156 2 262 14.45 8.27 3 397 14.75 6.61 4 251 17.20 3.78 Leisure 1 193 28.63 12.70 0.02* 2 285 28.78 19.86 3 325 29.33 9.81 4 149 39.21 12.18 Significance level ( p - value): * ≤0.05, **≤0.01, ***≤0.001 144 E6: Trip duration density across income groups and trip purposes - Île-de-France E7: Trip duration statistics across income groups – Île-de-France Trip Purpose Income Group N Mean (min) SD t-score p-value Work Low 1048 34.2 31.1 0.200 0.841 High 2480 34.0 27.3 Education Low 620 19.1 21.0 -0.332 0.740 High 1288 19.4 19.8 Shopping Low 1446 16.4 18.5 0.459 0.647 High 2013 16.1 18.1 Leisure Low 1686 25.5 29.5 4.726 <0.05* High 2915 21.5 23.7 Significance level ( p - value): * ≤0.05 (5%) E8: Trip distance statistics across income groups – Île-de-France Trip Purpose Income Group N Mean (km) SD t-score p-value Work Low 1048 34.2 31.1 -1.552 0.121 High 2480 34.0 27.3 Education Low 620 19.1 21.0 -1.355 0.176 High 1288 19.4 19.8 Shopping Low 1446 16.4 18.5 -3.979 <0.05* High 2013 16.1 18.1 Leisure Low 1686 25.5 29.5 1.306 0.192 High 2915 21.5 23.7 Significance level ( p - value): * ≤0.05 (5%) 145 Age E9: Modal split across age groups - Île-de-France E10: Trip duration density across age groups and trip purposes - Île-de-France 146 E11: Trip duration statistics across age groups – Île-de-France Trip Purpose Age Group N Mean (min) SD Pr(>F) Work 1 Insufficient sample size for analysis 0.267 2 1387 34.8 29.4 3 2286 34.3 28.0 4 318 31.4 26.7 Education 1 2021 16.2 15.4 <0.001*** 2 236 48.5 32.0 3 Insufficient sample size for analysis 4 Insufficient sample size for analysis Shopping 1 191 12.5 15.8 <0.001*** 2 659 17.0 17.4 3 1334 17.7 20.7 4 1874 15.3 15.8 Leisure 1 792 17.5 18.2 <0.001*** 2 1175 24.5 29.0 3 1462 21.6 27.5 4 1903 25.7 25.2 Significance level ( p - value): * ≤0.05, **≤0.01, ***≤0.001 E12: Trip distance statistics across age groups – Île-de-France Trip Purpose Age Group N Mean (km) SD Pr(>F) Work 1 Insufficient sample size for analysis 0.007** 2 1387 9.0 11.7 3 2286 9.4 14.4 4 318 6.8 8.8 Education 1 2021 2.0 3.5 <0.001*** 2 236 11.2 10.9 3 Insufficient sample size for analysis 4 Insufficient sample size for analysis Shopping 1 191 2.6 15.1 <0.001*** 2 659 3.3 5.8 3 1334 3.7 7.7 4 1874 2.5 7.9 Leisure 1 792 2.4 8.2 0.004** 2 1175 4.5 16.3 3 1462 4.2 15.9 4 1903 4.2 10.6 Significance level ( p - value): * ≤0.05, **≤0.01, ***≤0.001 147 APPENDIX F MUNICH Gender F1: Modal split across genders - Munich F2: Trip duration density across genders and trip purposes - Munich 148 F3: Trip duration statistics across genders – Munich Trip Purpose Gender N Mean (min) SD t-score p-value Work Male 3595 32.4 27.5 1.740 0.082 Female 3178 31.3 26.2 Education Male 1172 23.2 26.2 -0.692 0.489 Female 1124 23.9 21.5 Shopping Male 4134 16.8 21.6 -1.294 0.196 Female 4752 17.4 19.7 Leisure Male 7432 37.3 47.4 -0.955 0.340 Female 8120 38.0 49.6 Significance level ( p - value): * ≤0.05 (5%) F4: Trip distance statistics across genders – Munich Trip Purpose Gender N Mean (km) SD t-score p-value Work Male 3595 13.4 31.9 4.108 <0.05* Female 3178 10.6 25.1 Education Male 1172 6.3 24.7 0.881 0.379 Female 1124 5.6 15.1 Shopping Male 4134 4.0 17.4 1.927 0.054 Female 4752 3.4 6.4 Leisure Male 7432 15.3 47.9 -1.213 0.225 Female 8120 16.4 56.4 Significance level ( p - value): * ≤0.05 (5%) Income F5: Modal split across income groups - Munich 149 F6: Trip duration density across income groups and trip purposes - Munich F7: Trip duration statistics across age groups – Munich Trip Purpose Income Group N Mean (min) SD t-score p-value Work Low 301 30.1 18.9 -1.100 0.272 High 4751 31.4 25.9 Education Low 236 29.6 30.4 3.577 <0.05* High 1686 22.2 23.1 Shopping Low 669 19.0 27.9 2.886 <0.05* High 4733 15.8 18.8 Leisure Low 1038 37.9 51.2 0.858 0.391 High 9316 36.5 47.8 Significance level ( p - value): * ≤0.05 (5%) F8: Trip distance statistics across age groups – Munich Trip Purpose Income Group N Mean (km) SD t-score p-value Work Low 301 9.2 13.0 -3.766 <0.05* High 4751 12.5 31.7 Education Low 236 8.0 13.4 2.587 <0.05* High 1686 5.4 20.2 Shopping Low 669 3.3 6.6 -1.644 0.100 High 4733 3.8 15.9 Leisure Low 1038 9.8 27.3 -6.829 <0.05* High 9316 16.8 56.2 Significance level ( p - value): * ≤0.05 (5%) 150 Age F9: Modal split across age groups - Munich F10: Trip duration density across age groups and trip purposes – Munich 151 F10: Trip duration statistics across age groups – Munich Trip Purpose Age Group N Mean (min) SD Pr(>F) Work 1 Insufficient sample size for analysis 0.413 2 2666 32.5 28.1 3 3389 31.5 24.1 4 706 31.8 34.3 Education 1 1714 19.1 16.2 <0.001*** 2 527 35.6 31.1 3 42 45.0 74.0 4 Insufficient sample size for analysis Shopping 1 272 17.7 18.8 <0.001*** 2 1970 15.8 16.4 3 2869 15.7 21.3 4 3777 18.8 22.1 Leisure 1 2401 29.5 40.5 <0.001*** 2 4200 37.1 49.3 3 4224 38.2 50.7 4 4741 41.9 49.1 Significance level ( p - value): * ≤0.05, **≤0.01, ***≤0.001 F11: Trip distance statistics across age groups – Munich Trip Purpose Age Group N Mean (km) SD Pr(>F) Work 1 Insufficient sample size for analysis 0.731 2 2666 12.3 28.6 3 3389 12.0 29.0 4 706 11.5 30.0 Education 1 1714 3.4 5.9 <0.001*** 2 527 12.8 36.4 3 42 23.0 64.1 4 Insufficient sample size for analysis Shopping 1 272 4.0 6.1 0.151 2 1970 3.6 8.1 3 2869 4.1 19.1 4 3777 3.4 8.3 Leisure 1 2401 12.2 45.5 <0.001*** 2 4200 17.5 55.5 3 4224 17.3 54.7 4 4741 15.0 50.9 Significance level ( p - value): * ≤0.05, **≤0.01, ***≤0.001 APPENDIX G G1: Gender-Based Comparison of Travel Time (min) at the City Level Pedestrian Trip Purpose Vienna Utrecht Brussels Budapest Île-de-France Munich Male Female Male Female Male Female Male Female Male Female Male Female Work 12.37 11.44 12.8 9.4 10.2 11.8 11.4 12.2 10.1 10.0 16.1 17.8 Education 11.07 10.28 8.8 9.1 12.1 10.6 - - 9.7 10.3 13.2 15.1 Shopping 13.21 12.75 10.1 10.2 9.7 11.4 12.0 11.7 9.5 9.9 13.8 14.7 Leisure 34.06 35.32 41.3 42.1 32.9 27.1 12.5 13.0 15.8 15.3 35.1 35.2 Bicycle Work 19.10 17.60 20.0 20.1 19.6 17.5 23.7 32.3 26.2 25.3 23.7 21.6 Education - - 18.5 16.9 - - - - 19.8 - 15.9 16.2 Shopping 12.12 10.42 11.0 10.6 10.8 9.4 18.8 20.3 15.4 17.7 13.2 13.5 Leisure 43.83 29.00 36.7 34.8 25.6 29.9 19.5 - 31.9 37.6 29.7 26.0 Car (as Driver) Work 26.45 23.22 27.5 27.0 28.3 26.8 36.1 35.0 33.1 30.0 29.7 28.6 Education 23.15 - 35.0 28.4 - - - - - - 46.1 42.5 Shopping 17.12 14.77 12.8 13.1 18.1 15.6 20.6 31.4 18.0 16.8 18.2 16.0 Leisure 31.09 25.57 31.3 26.3 35.0 24.1 32.4 32.4 26.8 23.0 39.0 36.7 Car (as Passenger) Work - 21.57 29.3 24.5 - - 40.0 - 20.3 18.4 38.7 38.1 Education 17.60 12.41 17.1 18.3 21.3 15.5 - - 13.7 11.3 16.4 17.3 Shopping 19.61 16.59 16.2 16.9 - 15.1 - - 16.6 16.5 19.5 20.5 Leisure 24.08 31.56 34.2 35.1 34.3 21.7 - 33.2 20.0 23.1 36.6 41.3 Public Transport Work 38.08 36.53 60.6 51.5 37.6 40.4 44.4 43.5 57.1 53.4 44.5 40.3 Education 31.43 31.94 53.1 58.2 40.3 38.5 - 33.5 41.9 43.6 36.5 37.0 Shopping 26.17 25.39 - 49.4 27.0 32.0 27.4 28.2 41.0 36.4 29.0 29.5 Leisure 34.42 34.49 69.9 73.9 37.5 37.5 32.7 39.3 48.8 44.1 45.9 49.6 153 G2: Gender-Based Comparison of Travel Distance (km) at the City Level Walk Trip Purpose Vienna Utrecht Brussels Budapest Île-de-France Munich Male Female Male Female Male Female Male Female Male Female Male Female Work 1.00 0.88 1.1 0.8 1.5 1.4 0.6 0.6 1.6 1.4 Education 0.95 0.68 0.9 0.7 1.2 1.2 0.6 0.6 0.8 1.0 Shopping 0.88 0.78 0.7 0.7 1.5 1.6 0.5 0.5 0.8 0.8 Leisure 2.31 2.19 3.0 2.9 3.5 2.5 0.6 0.6 2.1 2.0 Bicycle Work 4.09 3.31 4.7 4.6 6.4 6.6 5.2 3.8 5.7 4.5 Education - - 3.6 3.2 - - 3.2 - 2.6 2.5 Shopping 1.96 1.39 2.0 1.8 2.4 2.8 2.2 2.7 2.1 2.0 Leisure 9.78 4.50 6.5 5.3 10.6 8.9 5.0 13.1 5.4 4.2 Car (as Driver) Work 13.07 9.98 23.5 20.5 22.0 22.1 11.9 9.4 16.7 14.9 Education 9.51 - 26.7 18.2 - - - - 36.4 31.1 Shopping 7.87 5.80 6.0 5.7 13.0 7.4 5.2 4.6 7.4 5.3 Leisure 22.28 16.28 18.4 16.1 29.0 15.0 8.8 6.7 29.4 24.2 Car (as Passenger) Work - 13.49 18.1 14.9 - - 5.4 5.9 16.6 22.9 Education 6.62 3.94 7.7 8.3 5.1 2.1 2.7 2.1 7.1 5.2 Shopping 8.01 8.23 8.6 8.3 - 12.4 3.1 4.6 7.5 7.6 Leisure 13.74 21.56 18.4 22.0 37.1 24.3 6.4 6.4 27.8 32.4 Public Transport Work 11.66 9.56 34.9 25.5 7.7 11.6 15.1 12.5 17.5 11.8 Education 8.05 7.99 30.0 25.8 17.0 7.6 7.4 8.0 9.3 8.8 Shopping 6.15 5.94 - 15.9 2.7 4.7 9.0 7.0 7.4 5.9 Leisure 12.06 11.28 38.2 37.3 14.1 2.3 10.9 6.8 19.5 26.1