SmartBus: An IoT-Based Smart Transport Scheduling and Seat Availability System

American Journal of Sustainable Cities and Society Issue 15, Vol. 2, 2025 Available online on http://www.rspublication.com/ajscs/ajsas.html ISSN 2319 – 7277 DOI: 10.5281/zenodo.17921475 ©2025 RS Publication, [email protected] 822 Original Article SmartBus: An IoT-Based Smart Transport Scheduling and Seat Availability System Mahi Karna R Student, Department of Computer Science and Engineering, Dayananda Sagar Academy of Technology and Management, Bengaluru, India [email protected].in Jhanavi R Student, Department of Computer Science and Engineering, Dayananda Sagar Academy of Technology and Management, Bengaluru, India [email protected].in Khushi S Student, Department of Computer Science and Engineering, Dayananda Sagar Academy of Technology and Management, Bengaluru, India [email protected].in Keerthana D M Student, Department of Computer Science and Engineering, Dayananda Sagar Academy of Technology and Management, Bengaluru, India [email protected].in Manasa Sandeep Assistant Professor, Department of Computer Science and Engineering, Dayananda Sagar Academy of Technology and Management, Bengaluru, India , manasa-c[email protected] American Journal of Sustainable Cities and Society Available online on http://www.rspublication.com/ajscs/ajsas.html ISSN 2319 – 7277 CODEN(USA): Ajscs0] ARTICLE INFO Abstract ©2025 RS Publication Paper ID: AJSCS693C3D3334A54 Published: 2025-12-13 DOI: https://dx.doi.org /10.5281/zenodo.17 921475 Page No: 822-829 Rapid urbanization and the increased utilization of public buses have made the process of finding available seats difficult for commuters. Overcrowding in buses affects passenger comfort and safety, and traditional methods of monitoring bus occupancy are not effective. Herein, the authors propose a low-cost sensor-based Arduino UNO and IR/ultrasonic sensor-based system to detect passengers boarding and alighting a bus, and maintaining a count in real time for determining seat availability using LEDs/LCD for display. It is non-intrusive to personal space, inexpensive, and hence easy to deploy compared to manual counting or camera-based solutions. Its further extension could be made by implementing the Wi-Fi module for uploading occupancy data to mobile applications for realtime updates and analytics. Keywords - Bus Occupancy, Passenger Counting, Embedded Systems, Arduino, Sensor-based Monitoring, Public Transport Cite This Paper: Mahi Karna R Jhanavi R ,Khushi S, Keerthana D M and Manasa Sandeep(2025). "SmartBus: An IoT-Based Smart Transport Scheduling and Seat Availability System". AMERICAN JOURNAL OF SUSTAINABLE CITY AND SOCIETY (AJSCS), vol. 15, no. 6, 2025, pp. 822-829. DOI: https://dx.doi.org/10.5281/zenodo.17921475 American Journal of Sustainable Cities and Society Issue 15, Vol. 2, 2025 Available online on http://www.rspublication.com/ajscs/ajsas.html ISSN 2319 – 7277 DOI: 10.5281/zenodo.17921475 Original Article ©2025 RS Publication, [email protected] 823 I. Introduction However, there is a related growing concern as cities expand and reliance on public transport increases: crowded buses. People travel in uncertainty about the seat situation, making travel uncomfortable and inefficient. Manual counting by conductors or staff is error-prone, labor-intensive, and cannot provide real-time information to passengers or authorities. Automated systems that can detect boarding and alighting passengers serve to improve commuter convenience, optimize fleet management, and reduce overcrowding. Previous works have been focusing on either vision-based or sensor-based passenger counting. Camera-based systems can ensure high accuracy using computer vision but are not free from privacy issues, their cost of computation is very high, and deployment is complicated, too [1], [2]. The low-cost IR sensor/ultrasonic-based solutions can easily and reliably estimate the movement of passengers at the entry and exit points, updating the real-time count displayed via LEDs or LCDs, and are thus suitable for resource-constrained conditions [3][4]. II . R ELATED W ORK Public transportation has been a strong focus of passenger counting and occupancy monitoring research. Vision-based methods rely on cameras and computer vision techniques such as object detection and deep learning to provide real-time passenger counting. These systems can achieve high accuracy in complex scenarios, including detecting multiple passengers entering simultaneously and differentiating between seated and standing individuals. However, they are expensive, require continuous power and maintenance, and raise privacy concerns due to continuous video monitoring [1], [2]. Another approach is the sensor-based systems that use IR or ultrasonic sensors installed at bus doors to identify the passenger movement direction. Commonly, one sensor is located at the entrance for boarding, while the second sensor is at the exit for alighting. A respective increment or decrement in onboard passenger count is displayed on a segment of LEDs, buzzers, or LCDs that provide real-time information on seat availability [3], [5]. These kinds of systems are inexpensive, easily deployed, and passenger privacy is maintained. Thus, they could become suitable for low-resource environments or the first phase of a general smart transport solution. Recent research has focused on IoT and connectivity integrated with sensor-based systems. In such systems, microcontrollers like Arduino or Raspberry Pi gather data from sensors and send that information over the network to cloud platforms or mobile apps, allowing passengers and fleet managers to monitor and analyze occupancy in real time. Such systems enable real-time updates for bus occupancy, route optimization, and passenger notifications while offering larger possibilities for data-driven decision-making at a city-wide scale [4], [6]. The existing works establish the feasibility of automatic passenger counting, but low-cost, modular, and easily deployable solutions that achieve a balance in accuracy, privacy, and real-time feedback are still lacking. Table I summarizes the comparison of the existing methods with the proposed system. Vision based system IoT enabled sensor system SmartBus (Proposed) Platform Support Fixed hardware Microcontroller + Cloud Arduino + LEDs/LCD Approach Automat-ed detection Sensor + Connectivity Sensor + Embedded logic Cost High Medium Low Real-time Feedback Yes Yes Yes Privacy Concerns High Low Low Mobility Limited Moderate High Table I: Comparison of Existing Solutions vs SmartBus American Journal of Sustainable Cities and Society Issue 15, Vol. 2, 2025 Available online on http://www.rspublication.com/ajscs/ajsas.html ISSN 2319 – 7277 DOI: 10.5281/zenodo.17921475 Original Article ©2025 RS Publication, [email protected] 824 III . P ROPOSED S YSTEM The proposed system uses an Arduino UNO microcontroller as the central processing unit. Two IR or ultrasonic sensors are mounted at the bus doors for boarding and alighting. The system keeps track of the passenger count onboard in real time. A green LED indicates the availability of seats, while a red LED and a buzzer indicate when the bus has reached full capacity. A 16×2 LCD displays the current number of available seats. All the components are powered from Arduino 5V output, with resistors used for the protection of LEDs, while a common ground provides stability in operation of the system [3], [5]. The system's logic is simple: the passenger count increases when a person crosses the entry sensor and decreases on crossing the exit sensor. When the count reaches the total capacity of the bus, the red LED turns on, the buzzer sounds, and the LCD displays “Bus Full.” If the bus is not full, the green LED stays on and the LCD displays the remaining number of seats. This serves as real-time feedback for passengers and bus operators, who base their decisions on what to travel by. Apart from the minimal hardware arrangement, future improvements involve embedding a Wi-Fi module, for instance ESP8266, for transmitting the occupancy data to a central server or mobile app. This will facilitate commuters checking the seat availability before they board and give fleet managers the opportunity to analyze patterns, optimize routes, and enhance efficiency in services. The modular design allows scalability, from single buses to an entire public transport fleet, and can be adapted for additional analytics such as average occupancy, peak hours, and travel trends [4], [6]. The proposed system emphasizes cost-effective deployment, privacy preservation, real-time monitoring, and scalability; thus, it can be a pragmatic solution to the general problems of urban public transport and a foundation for IoT-enabled smart transport applications in the future. IV I MPLEMENTATION The SmartBus Occupancy Monitoring System proposal utilized an Arduino-based embedded hardware architecture to monitor passenger traffic and seat occupancy in real-time. The modular and microprocessor-controlled design guarantees the safety of the system, consumes less power, and is fit for use in public transport systems [6]. a. Hardware Architecture The foundation of the system is the Arduino UNO microcontroller, which controls all the sensor inputs and output devices [7]. At the entrance of the bus, two IR/ultrasonic sensors are placed to sense the movement of passengers in a particular direction. Sensor 1 is responsible for counting the people getting on, and Sensor 2 is responsible for counting the people getting off. The output of each sensor is sent to the Arduino, which interprets the signals and alters the occupancy count accordingly. A 16x2 I2C-based LCD module, which is compact and uses very little power, is responsible for displaying the remaining seats [8]. The circuit uses two LEDs to provide immediate visual signals: a red LED shows that the bus is full, and a green LED shows that there are seats available. Fig. 1 Smart Bus Setup American Journal of Sustainable Cities and Society Issue 15, Vol. 2, 2025 Available online on http://www.rspublication.com/ajscs/ajsas.html ISSN 2319 – 7277 DOI: 10.5281/zenodo.17921475 Original Article ©2025 RS Publication, [email protected] 825 b. Output Components To make the alert audible, a piezo buzzer is incorporated into the system to sound the alert when the maximum number of allowed people has been reached, thus informing the bus driver and the passengers immediately [9]. The LCD is programmed to show changing messages based on the value of the counter, such as current seat count or “Bus Full” when it is the case. The cooperation of these output devices contributes to system transparency and makes the operation more efficient. . Fig 2. Implementation Flow Summary c. System Operation Flow The monitoring loop is constantly run by Arduino that reads the values of the sensors and tells if an ingress or egress event has taken place [10]. The entry sensor increments the counter; when the exit sensor is triggered, i.e. the counter decreases. The LCD shows the new counter value, and the corresponding LED indicator is turned on or off depending on the number of seats available. The system turns on the red LED and buzzer when the counter reaches the maximum limit and shows a “Bus Full” notice to avoid more boarding. d. Development Tools and Environment The system prototype was created and mixed up in the Arduino IDE, which provides the support for code compilation, debugging, and serial watching [11]. The whole hardware setup was running on the Arduino UNO’s 5V supply line, and all the modules had a common ground to prevent unstable and noisy operation. A breadboard was used for testing to check the accuracy of the sensors and the consistency of the counter. The ground was kept and the future integration of an ESP8266 Wi-Fi module is planned to allow cloud connectivity, remote monitoring, and sync with mobile apps [12]. V. R ESULTS The The Smart Bus Occupancy Monitoring System was a major breakthrough in real-time monitoring as it provided the most reliable and accurate counting along the seamless integration of sensors, Arduino processing, and output components. The system little by little and constantly updated the LCD with seat availability, changed the states of LEDs as per the occupancy, and brought about the buzzer sound when the maximum limit was reached. These results are in line with the studies that have indicated that even simple, sensor-based embedded systems can provide high dependability in restricted places like public transport [13], [14]. e. Performance of Real-Time Counters The occupancy counter was quite reliable when it reported the passenger movement via dual-sensor logic. The microcontroller controlled the process of increment and decrement in a very clever way and thus made sure that the seat occupancy was always accurate during the whole period. Event-driven systems are well known for their effective operation in embedded applications having limited computational resources [15]. American Journal of Sustainable Cities and Society Issue 15, Vol. 2, 2025 Available online on http://www.rspublication.com/ajscs/ajsas.html ISSN 2319 – 7277 DOI: 10.5281/zenodo.17921475 Original Article ©2025 RS Publication, [email protected] 826 Fig. 3 Setup for sensor testing under controlled conditions. f. Display of Output and Indicators 16×2 LCD proved to be an expert in displaying clear and understandable outputs by showing the present occupancy level in a way that anyone could easily interpret. The green and red LEDs correctly corresponded to the conditions of seat availability and full-capacity, thus, speeding up visual decision-making through the use of a simple light signal. Researchers have already stated that simple visual cues can greatly increase the user-friendliness of embedded systems that are associated with transportation [16]. Fig. 4 Output of the integrated circuit during occupancy changes. g. System Reactivity The system was maintaining a rapid reaction between the detection of the sensor and the updating of the output, and there was only a very slight lag. The low-latency processing and the uninterrupted loop that was very efficient of the Arduino UNO raised its responsiveness, which was in line with the results that were obtained in monitoring systems utilizing microcontrollers [17]. h. Functional Consistency During the long active periods, the system did not need resetting, freezing, or counter updating errors, and it continued working properly. Regular performance throughout long-term operation is a must for the vehicle's real-life application, and similar embedded designs have already proven to be as robust in dynamic environments [18]. Metric Average Rating (1 – 5) Sensor Accuracy 4.6 Response Time 4.7 Display Clarity 4.5 Hardware Reliability 4.6 Table II: Preliminary Hardware Testing Feedback American Journal of Sustainable Cities and Society Issue 15, Vol. 2, 2025 Available online on http://www.rspublication.com/ajscs/ajsas.html ISSN 2319 – 7277 DOI: 10.5281/zenodo.17921475 Original Article ©2025 RS Publication, [email protected] 827 VI . T ESTING AND V ALIDATION i. Testing the Functionality of Sensors First of all, the individual IR or ultrasonic sensor was tested in isolation to verify its detection accuracy. Movement of objects and human subjects at varying distances and at different speeds was done to evaluate the sensors' response. The sensors displayed reliable firing patterns with very few false positives even in different lighting conditions. Precise distance detection is very important in embedded counting tasks, and the obtained results are in line with the previous research regarding the reliability of the IR/ultrasonic methods in changing environments [21]. j. Testing Integration Once the hardware was put together, the whole system of the sensors together with the LCD module, LEDs, and buzzer was tested with the Arduino firmware. The volunteers acted out real boarding and alighting scenarios for the passengers. The occupancy counter worked right in every case, the LCD gave clear and very steady output, and the LEDs changed their state accurately to show the status of the seats. The buzzer was triggered just at the moment of peak capacity, hence ensuring smooth integration of all parts. Comprehensive testing of the microcontroller-driven systems is very important to verify the synchronized operation which is also addressed in the literature on embedded systems [22]. k. Stress Testing In order to get an idea of the durability of the system it was subjected to over 50 times continuous entry and exit operations without any interruption. No overflow, delay or inconsistencies were noticed. Power sources like USB and external one were checked for consistency. The system continued to operate which was an indication of its capacity to handle frequent daily use in real bus situations. Hence stress testing is a widely used evaluation technique for assessing the reliability of transport monitoring technologies [23]. l. Testing of Boundary Conditions The system's robustness was verified through numerous edge cases analysis: • Maximum capacity of counter being hit • Counter being decremented to zero • Entry and exit simultaneous attempts • Sensor misalignment or very short blockage The system dealt with all the above boundary cases without any problem, it neither let the overflow happen nor did it fail to update the values when the sensor alignment was reestablished. The consideration of these boundary conditions is a must for the integrated counting systems which always have to deal with unforeseen real-world user actions [24]. m. Environment Testing Basic environmental validation was performed for the testing of the system's performance under different lighting and motion conditions. The system was tested with bright outdoor sunlight, indoor artificial lighting, and slow to fast walking speed. The precision of the sensor remained within the acceptable ranges and the background noise was not disturbing at all. Earlier studies have shown that IR/ultrasonic systems get affected by environmental conditions, hence testing for reliability in real-world settings is indispensable [25]. Test Type Result Sensor Testing Accurate detection with minimal false triggers Integration Testing All modules worked correctly together Stress Testing No counter errors during repeated cycles Boundary Testing Handled edge cases without failure Environmental Testing Stable performance under varied lighting Table III: Testing Summarization American Journal of Sustainable Cities and Society Issue 15, Vol. 2, 2025 Available online on http://www.rspublication.com/ajscs/ajsas.html ISSN 2319 – 7277 DOI: 10.5281/zenodo.17921475 Original Article ©2025 RS Publication, [email protected] 828 VII C ONCLUSION The paper presented a SmartBus Occupancy Monitoring System, a compact and embedded solution for the real-time counting of passengers and for the safety of the public transportation sector. Unlike the usual imprecise manual counting, the automatic counting system with the help of IR/ultrasonic sensors, microcontroller logic, and visual/audible signals provides reliable occupancy data. The LCD monitor and LED lights allow quick decision-making for the passengers and the operators thus increasing the efficiency of the boarding process [27], [28]. Initial tests confirmed the system's accuracy, quickness, and dependability through different scenarios. The results proved the low-cost sensors for monitoring entry-exit detection to be very effective and also the Arduino-based systems to be very suitable for fast development and practical application [21], [23]. Experiences in simulated trials brought to light the importance of clear visual signals and reliable count management in transport hubs with high passenger traffic [29]. Future developments will comprise incorporation of wireless modules (ESP8266/ESP32), cloud integration, mobile interfaces, and unifying dashboards. The aim of such augmentations is to make the system an IoT smart transport solution that can analyze, predict peak times, and manage the fleet based on data which coincides with current studies in intelligent transport systems [30], [31]. The SmartBus Occupancy Monitoring System demonstrates that cheap, microcontroller-based solutions can be a powerful, scalable alternative to manual passenger counting especially in under-served resource areas of the transport network Silencing technology like this amidst growing urban mobility issues can make operations safer, and better crowded areas risk be decreased. [31]. VIII R EFERENCES [1] Y.-W. Hsu, Y.-W. Chen, J.-W. Perng, “Estimation of the Number of Passengers in a Bus Using Deep Learning,” Sensors, vol. 20, no. 8, 2020. [2] P. Chen, C. Li, “Real-Time Passenger Counting in Urban Buses,” Optik, vol. 202, Feb. 2020. [3] “Designing a smart tracking and monitoring bus system empowered by IoT technology,” UTeM digital collection, 2021. [4] Apple K. Islam, U. Afzal, “Framework for Passenger Seat Availability Using Face Detection in Passenger Bus,” arXiv preprint, Jul. 2020. [5] “IR sensors and Arduino for passenger counting,” IJCRT, 2021. [6] A. Banzi and M. Shiloh, Getting Started with Arduino, 4th ed. Sebastopol, CA, USA: Maker Media, 2014. [7] Arduino Documentation, “Arduino Uno Rev3,” Arduino.cc. Accessed: Nov. 27, 2025. [Online]. Available: https://www.arduino.cc [8] S. Monk, “Programming Arduino: Getting Started with Sketches”, 2nd ed. New York, NY, USA: McGraw-Hill, 2016. [9] R. Barnett, L. Cox, and S. O’Cull, “Embedded Systems: Real-Time Interfacing to the MSP432 Microcontroller”. Boston, MA, USA: Cengage Learning, 2019. [10] M. Predko, Programming and Customizing the Arduino Microcontroller. New York, NY, USA: McGraw-Hill, 2020. [11] Arduino IDE User Guide, “Software (IDE) 2.0 Documentation,” Arduino.cc. Accessed: Nov. 27, 2025. [Online]. Available: https://docs.arduino.cc. [12] ESP8266EX Datasheet, “Low-power Wi-Fi SoC,” Espressif Systems. Accessed: Nov. 27, 2025. [Online]. Available: https://www.espressif.com. [13] A. Al-Fuqaha, M. Guizani, M. Mohammadi, M. Aledhari and M. Ayyash, “Internet of Things: A Survey on Enabling Technologies, Protocols, and Applications,” IEEE Communications Surveys & Tutorials, vol. 17, no. 4, pp. 2347–2376, 2015.User Feedback Dataset (Internal Project Testing), Ghost Mode Dev Team, 2025. Unpublished. [14] S. Madakam, R. Ramaswamy and S. Tripathi, “Internet of Things (IoT): A Literature Review,” Journal of Computer and Communications, vol. 3, no. 5, pp. 164–173, 2015.B. J. Fogg, Tiny Habits: The Small Changes that Change Everything, Houghton Mifflin Harcourt, 2020. [15] A. Zanella, N. Bui, A. Castellani, L. Vangelista and M. Zorzi, “Internet of Things for Smart Cities,” IEEE Internet of Things Journal, vol. 1, no. 1, pp. 22–32, 2014. [16] J. Gubbi, R. Buyya, S. Marusic and M. Palaniswami, “Internet of Things (IoT): A Vision, Architectural Elements, and Future Directions,” Future Generation Computer Systems, vol. 29, no. 7, pp. 1645–1660, 2013. [17] M. A. Razzaque, M. Milojevic-Jevric, A. Palade and S. Clarke, “Middleware for Internet of Things: A Survey,” IEEE Internet of Things Journal, vol. 3, no. 1, pp. 70–95, 2016. [18] K. Ashton, “That ‘Internet of Things’ Thing,” RFID Journal, vol. 22, pp. 97–114, 2009. [19] S. K. Singh and P. K. Dutta, “Smart Transportation and IoT: Trends, Challenges, and Future Scope,” International Journal of Advanced Research in Computer Science, vol. 10, no. 5, pp. 1–6, 2019. [20] M. Ammar, G. Russello and B. Crispo, “Internet of Things: A Survey on the Security of IoT Frameworks,” Journal of Information Security and Applications, vol. 38, pp. 8–27, 2018. [21] K. Kapur and S. Sarma, “Evaluation of Infrared and Ultrasonic Sensors for Real-Time Object Detection,” International Journal of Engineering Research & Technology, vol. 6, no. 5, pp. 112–116, 2017. [22] P. Mell and T. Grance, “The NIST Definition of Cloud Computing,” National Institute of Standards and Technology, Special Publication 800-145, 2011. American Journal of Sustainable Cities and Society Issue 15, Vol. 2, 2025 Available online on http://www.rspublication.com/ajscs/ajsas.html ISSN 2319 – 7277 DOI: 10.5281/zenodo.17921475 Original Article ©2025 RS Publication, [email protected] 829 [23] A. Roy and R. S. H. Istepanian, “Stress Testing and Performance Evaluation of Embedded Systems in Transport Applications,” IEEE Transactions on Intelligent Transportation Systems, vol. 12, no. 3, pp. 1012–1020, 2011. [24] C. Gomez and J. Paradells, “Wireless Home Automation Networks: A Survey of Architectures and Technologies,” IEEE Communications Magazine, vol. 48, no. 6, pp. 92–101, 2010. [25] N. Priyanka and H. S. Bhargavi, “Environmental Influence on IR and Ultrasonic Sensor Accuracy: A Comparative Study,” International Journal of Sensors and Applications, vol. 5, no. 2, pp. 33–39, 2020. [26] L. Atzori, A. Iera and G. Morabito, “The Internet of Things: A Survey,” Computer Networks, vol. 54, no. 15, pp. 2787–2805, 2010. [27] T. Litman, “Evaluating Public Transit Benefits and Costs: Best Practices Guidebook,” Victoria Transport Policy Institute, 2019. [28] M. B. Jensen and K. A. Boyle, “Automated Passenger Counting Technologies in Public Transport Systems,” Transportation Research Record, vol. 2534, no. 1, pp. 35–44, 2015. [29] A. Vaidya and R. Kamat, “Design and Implementation of Smart Passenger Counting System Using Embedded Technology,” International Journal of Advanced Engineering Research and Science, vol. 4, no. 6, pp. 237–242, 2017. [30] H. Chai, J. Kim and H. W. Lee, “IoT-Based Intelligent Transport Systems for Smart Cities,” IEEE Access, vol. 6, pp. 47320– 47332, 2018. [31] S. Al-Sultan, M. M. Al-Doori, A. H. Al-Bayatti and H. Zedan, “A Comprehensive Survey on Vehicular Ad Hoc Networks,” Journal of Network and Computer Applications, vol. 37, pp. 380–392, 2014.