UNLOCKING ARTIFICIAL INTELLIGENCE POTENTIAL: A REVIEW OF NIGERIA'S TRANSPORTATION SYSTEMS

Volume-09 Issue 12, December -2025 ISSN: 2456-9348 Impact Factor: 8.232 International Journal of Engineering Technology Research & Management (IJETRM) https://ijetrm.com/ IJETRM (http://ijetrm.com/) [355] UNLOCKING ARTIFICIAL INTELLIGENCE POTENTIAL: A REVIEW OF NIGERIA'S TRANSPORTATION SYSTEMS Aderonke J. Ojo https://orcid.org/0009-0001-7123-4045 Department of Procurement and Supply Chain Management, Osun State Polytechnic, Iree, Osun State, Nigeria. Adedayo D. Ojo Department of Computer Science, Osun State Polytechnic, Iree, Osun State, Nigeria. Adeyinka P. Ajayi Department of Transport Management, Redeemer's University, Ede, Osun State, Nigeria. ABSTRACT Artificial Intelligence (AI) is increasingly acknowledged as a transformative force in transportation systems worldwide. In Nigeria, where rapid urbanization intensifies mobility challenges, AI offers practical pathways to improve traffic management, logistics, public transport, and road safety. This study employed a systematic review to examine how AI is being applied within Nigeria’s transportation sector, its benefits, and the barriers to adoption. A comprehensive search of academic databases, government reports, and scholarly literature published between 2015 and 2025 was conducted. Guided by PRISMA protocols, 1,245 records were identified, 1,000 screened after duplicates were removed, and 150 full-text articles assessed for eligibility. Seventy-seven studies met the inclusion criteria and were analysed thematically. The evidence highlights diverse applications of AI, including intelligent traffic systems, predictive logistics, smart scheduling for public transport, and safety monitoring. These innovations demonstrate clear benefits: greater efficiency, enhanced safety, improved commuter experiences, and contributions to environmental sustainability. Yet, significant challenges remain. Infrastructural limitations, policy and regulatory gaps, data quality concerns, financial constraints, and issues of public trust continue to hinder widespread adoption. This review concludes that AI holds substantial promise for advancing Nigeria’s transportation systems. Realising this potential will require deliberate investments in infrastructure, robust governance frameworks, and the cultivation of a skilled workforce. With these foundations in place, AI can become a catalyst for safer, more efficient, and more sustainable mobility across Nigeria’s urban centres. Keywords: Public Transportation; Artificial Intelligence; Urban Centres; Smart Mobility; Traffic Management INTRODUCTION Nigeria’s transport system functions as the pillar of its economy, yet it continues to struggle with congestion, inefficiency, and underdevelopment. These challenges slow industrial growth, limit access to goods and services, and weaken trade competitiveness. As cities expand and mobility demands intensify, the urgency for innovative solutions becomes clearer. Artificial Intelligence (AI) has emerged globally as a powerful tool to address such systemic issues. In many developed countries, AI is already reshaping transportation by easing congestion, improving safety, reducing costs, and enhancing service reliability. Intelligent traffic management, predictive logistics, smart public transport scheduling, and infrastructure maintenance are no longer futuristic concepts but practical realities. According to the American Society of Civil Engineers (ASCE), outdated infrastructure and congestion cost drivers over $1,000 annually in vehicle wear and fuel inefficiencies [77]. AI technologies such as machine learning and predictive analytics underpin intelligent transport systems that adapt to real-time conditions and support data-driven decision-making [35]. These innovations are central to the broader vision of smart cities, where AI, the Internet of Things (IoT), and big data analytics converge to manage resources efficiently, improve Volume-09 Issue 12, December -2025 ISSN: 2456-9348 Impact Factor: 8.232 International Journal of Engineering Technology Research & Management (IJETRM) https://ijetrm.com/ IJETRM (http://ijetrm.com/) [356] public safety, and enhance quality of life [21]. In Nigeria, however, AI adoption in transportation remains limited and fragmented. Persistent challenges—including inadequate infrastructure, ineffective logistics management, and chronic traffic congestion—continue to constrain efficiency [9, 51, 60]. While government and private sector initiatives have attempted to modernise transport systems, progress has been slowed by the absence of integrated, intelligent approaches. Lagos State, Nigeria’s commercial hub, illustrates this tension: despite rapid urban growth and mounting transport challenges, AI applications remain underutilised, with limited empirical research guiding implementation [21]. Nonetheless, emerging initiatives such as AI-powered planning and scheduling for the Bus Rapid Transit (BRT) network signal a shift toward intelligent transport solutions. Studies also highlight the potential of Intelligent Transportation Systems (ITS) to reduce congestion and improve road network efficiency [10, 72]. This paper provides a Nigeria-specific systematic review of AI applications in transportation, examining current practices, benefits, limitations, and future possibilities. By synthesising evidence from academic research, government publications, and grey literature, it aims to give transport practitioners and policymakers a clearer picture of how AI can be harnessed to reform Nigeria’s transportation systems. In doing so, it situates Nigeria’s experience within global smart mobility trends while identifying context-specific opportunities and barriers to adoption. RESEARCH OBJECTIVES The main aim of this study is to provide a comprehensive, Nigeria-specific synthesis of how Artificial Intelligence (AI) is being applied in the transportation sector, and to evaluate its potential for addressing systemic challenges. Building on global insights and local realities, the review seeks to bridge the gap between theoretical potential and practical application. Therefore, the specific objectives are to: • Identify currently deployed AI applications in Nigeria’s transportation systems. • Assess the benefits of AI adoption, such as cost reduction and improved safety. • Examine the challenges of AI integration, including infrastructural limitations and financial constraints. • Highlight opportunities for future development in smart and sustainable urban mobility. • Provide evidence-based recommendations for policymakers, practitioners, and researchers. LITERATURE REVIEW Artificial Intelligence (AI) is increasingly transforming transportation systems worldwide, offering innovative solutions for traffic congestion, logistics inefficiencies, and public transport optimisation. Nigeria, despite infrastructural limitations, has begun adopting AI-driven technologies to address pressing mobility challenges. This review synthesises existing research on AI applications in Nigeria’s transport and distribution sectors, highlighting benefits, challenges, and lessons from global best practices. AI in Traffic Management Traffic congestion is a persistent problem in Nigeria’s major cities, including Lagos, Abuja, and Port Harcourt, with significant impacts on productivity and quality of life [64]. AI-powered traffic management systems, leveraging machine learning and predictive analytics, provide real-time data analysis to optimise traffic signals, reduce bottlenecks, and enhance safety [63, 72]. Lagos has piloted AI-driven traffic control centres in partnership with technology firms [21, 53]. Global examples underscore the potential of such systems: Singapore achieved a 15% reduction in congestion and a 30% decrease in travel times through AI traffic optimisation [31], while Columbus, Ohio, recorded a 25% reduction in crashes and a 12% improvement in travel times [15]. AI in Public Transport AI applications in public transport have improved efficiency and passenger experience. In Lagos, the Bus Rapid Transit (BRT) system employs AI for route planning, scheduling, and fleet management, resulting in reduced wait times and enhanced service reliability [21]. Machine learning enables dynamic scheduling, adjusting bus frequencies and headways in real time to match passenger demand [4, 2]. Centralised planning with optimisation software has demonstrated measurable gains when agencies integrate data pipelines and commit to unified scheduling and rostering. AI in Logistics and Distribution Nigeria’s distribution sector faces inefficiencies in supply chain management and logistics [36, 58]. AI technologies strengthen inventory management, streamline delivery routes, and improve demand forecasting [29]. E-commerce platforms such as Jumia and Konga employ AI-driven logistics solutions to analyse consumer data, forecast purchasing trends, and optimise delivery schedules [37, 46]. AI-powered inventory systems reduce stockouts and surplus inventory, while logistics platforms provide real-time shipment tracking, enhancing transparency and customer satisfaction [17, 48]. By automating processes such as order processing and restocking, Volume-09 Issue 12, December -2025 ISSN: 2456-9348 Impact Factor: 8.232 International Journal of Engineering Technology Research & Management (IJETRM) https://ijetrm.com/ IJETRM (http://ijetrm.com/) [357] AI improves warehouse productivity and reduces holding costs [67]. Route optimisation algorithms further cut fuel usage and delivery times, benefiting freight companies operating in Nigeria’s challenging road conditions [54]. AI in Predictive Maintenance Predictive maintenance is a critical application of AI in Nigeria’s transport sector. AI-enabled systems use sensors and machine learning to monitor vehicle conditions in real time, forecasting mechanical issues before they occur [28]. Data on tyre pressure, brake wear, and engine temperature can be analysed to predict maintenance needs, reducing downtime and costs while improving reliability [69, 71]. Ride-hailing services such as Uber and Bolt also integrate AI to match drivers with passengers, optimise routes, and provide real-time traffic information [12, 63]. These applications enhance customer experience, while predictive analytics extend vehicle longevity and minimise breakdowns [62]. Global Perspective and Indigenous Challenges International experiences demonstrate AI’s capacity to revolutionise transportation, from self-driving cars in San Francisco to AI-optimised traffic systems in Singapore [31, 15]. Nigeria’s adoption, however, is constrained by limited infrastructure, inadequate data availability, and a shortage of skilled professionals [5, 8]. These challenges present opportunities for innovation through investment in AI talent, improved data infrastructure, and publicprivate partnerships. Early initiatives in Lagos demonstrate feasibility, and lessons from global successes suggest that Nigeria can achieve significant improvements in efficiency, safety, and reliability by scaling pilot projects, fostering collaboration, and investing in technological capacity. METHODOLOGY This study employed a systematic review to synthesise existing research on the applications and implications of Artificial Intelligence (AI) in Nigeria’s transportation sector. Searches were conducted across academic databases including Scopus, Web of Science, Google Scholar, and JSTOR, as well as government publications and grey literature. The review covered the period 2015–2025 to capture recent developments in AI adoption. Keywords such as “Artificial Intelligence,” “Transportation,” “Nigeria,” “Traffic Management,” “Logistics,” and “Public Transport” were combined with Boolean operators and truncations to refine results. Reference lists of included studies were also screened to identify additional publications. Inclusion and Exclusion Criteria Studies were included if they: • Focused on AI applications within Nigeria’s transportation sector. • Reported empirical findings or expert insights. • Were published in English between 2015 and 2025. Exclusion criteria applied to studies that: • Did not focus on Nigeria. • Addressed transportation without AI relevance. • Were conceptual or opinion pieces lacking empirical evidence. • Were published in languages other than English. Study Selection and PRISMA Flow The selection process followed PRISMA 2020 guidelines to ensure transparency. A total of 1,245 records were identified. After removing 245 duplicates, 1,000 records were screened by title and abstract. Of these, 850 were excluded as irrelevant. The remaining 150 full-text articles were assessed for eligibility, with 73 excluded for reasons such as insufficient AI focus, lack of empirical evidence, or being outside the Nigerian context. Ultimately, 77 studies met all criteria and were incorporated into the qualitative synthesis. Data Extraction and Thematic Analysis Data from the included studies were extracted using a structured template capturing study objectives, AI applications, transportation domain (e.g., traffic management, logistics, public transport), reported benefits, and challenges. A thematic analysis was then conducted to identify recurring patterns and insights. Codes were developed inductively and refined iteratively. Themes were organised into three overarching categories: • Applications of AI (e.g., intelligent traffic management, predictive logistics, smart public transport systems). • Benefits of AI adoption (e.g., efficiency gains, reduced congestion, improved safety). • Challenges and barriers (e.g., infrastructural limitations, policy gaps, data quality issues). Volume-09 Issue 12, December -2025 ISSN: 2456-9348 Impact Factor: 8.232 International Journal of Engineering Technology Research & Management (IJETRM) https://ijetrm.com/ IJETRM (http://ijetrm.com/) [358] PRISMA flowchart methodology Source: PRISMA 2020 guidelines. RESULTS AND DISCUSSIONS The review highlights the transformative potential of Artificial Intelligence (AI) in Nigeria’s road transportation systems, with applications spanning traffic management, logistics optimisation, predictive maintenance, and safety enhancement. Evidence from both domestic and international studies demonstrates AI’s capacity to streamline procedures, improve decision-making, and enhance operational efficiency [66]. And the result of the discoveries from the review can be explicitly presented as follows; Efficiency Gains in Transportation and Logistics AI-powered ride-hailing services such as Uber and Bolt have reduced passenger wait times and improved route optimisation, thereby increasing service efficiency [20]. Similarly, AI-driven logistics solutions have enhanced delivery networks by minimising delays and maximising resource utilisation [67]. These innovations have contributed to lower operating costs in both distribution and transportation sectors through automation and resource optimisation [29]. Importantly, the integration of AI into logistics platforms has improved customer satisfaction by ensuring timely deliveries and providing real-time tracking updates, as demonstrated by ecommerce platforms such as Jumia and Konga [59, 70]. Included 77 studies met criteria and were included Identification Records identified from; • Databases (e.g. Scopus, Google scholar, JSTOR, Research gate) • Other sources: Government Publications, Literatures, Reference lists Duplicate removal Screening 1000 record screened by titles and abstracs 850 were excluded as irrelevant Records ecluded by irrelevance Eligibility 150 full-text articles were assessed for eligibility, with 73 excluded for reasons such as insufficient AI focus, lack of empirical evidence 150 records and 73 excluded: for not meeting the inclusion criteria Volume-09 Issue 12, December -2025 ISSN: 2456-9348 Impact Factor: 8.232 International Journal of Engineering Technology Research & Management (IJETRM) https://ijetrm.com/ IJETRM (http://ijetrm.com/) [359] Predictive Maintenance and Safety Predictive maintenance systems represent a critical advancement in Nigeria’s transport sector. By anticipating mechanical issues before they escalate, these systems reduce downtime and repair costs, while improving vehicle reliability [22]. Beyond maintenance, AI contributes to road safety by detecting hazardous driving behaviours and risky road conditions, enabling proactive accident prevention [75, 76]. This is particularly relevant in Nigeria, where road accidents remain a pressing public safety concern. The development of autonomous vehicle technologies and AI-enabled traffic management systems further underscores AI’s role in reducing human error and enhancing safety [61]. Demand Forecasting and Inventory Management AI-driven demand forecasting and inventory management systems have reduced inefficiencies in Nigeria’s distribution industry by lowering costs associated with excess inventory and stockouts [20]. These systems analyse consumer behaviour and market trends to optimise stock levels, thereby improving supply chain resilience. The ability to forecast demand accurately not only reduces waste but also strengthens the reliability of Nigeria’s distribution networks. Synthesis and Analytical Insights The findings collectively reveal that AI’s integration into Nigeria’s transport and distribution systems is not merely a technological upgrade but a structural transformation. Efficiency gains in ride-hailing and logistics demonstrate AI’s capacity to address urban mobility challenges, while predictive maintenance and safety applications highlight its potential to mitigate systemic risks. However, the benefits are contingent upon overcoming persistent barriers related to infrastructure, policy frameworks, and equity. Limited data availability and a shortage of skilled professionals constrain the scalability of AI solutions, while policy gaps hinder coordinated adoption. From a broader perspective, AI’s role in Nigeria’s transport sector should be understood as both incremental and disruptive. Incremental improvements, such as route optimisation and predictive maintenance, which deliver immediate operational benefits. Disruptive innovations—such as autonomous vehicles and integrated traffic management centres—hold the potential to redefine mobility paradigms but require long-term investment and regulatory adaptation. Implications for Nigeria The synthesis of domestic and global evidence suggests that Nigeria’s path to unlocking AI potential lies in strategic scaling of pilot projects, fostering public-private partnerships, and investing in AI talent development. By addressing infrastructural and policy barriers, Nigeria can leverage AI to enhance urban living, reduce congestion, and improve safety. The challenge is not whether AI can transform Nigeria’s transport systems, but how quickly and effectively these innovations can be institutionalised within the country’s unique socio-economic context. CHALLENGES OF AI ADOPTION IN NIGERIA Nigeria's distribution and transportation networks benefit significantly from artificial Intelligence (AI), but several obstacles prevent widespread deployment [21]. These include a shortage of skilled workers, expensive overhead, insufficient infrastructure, and worries about the security and privacy of data. One major obstacle to the successful application of AI technology in Nigeria is the country's inadequate infrastructure, especially roads and energy [57]. Furthermore, for AI systems to operate at their best, dependable power supplies and well-maintained road networks are necessities, and both are deficient in many Nigerian regions, especially the rural areas [7]. Additionally, the full potential of AI in distribution and transportation can only be achieved with the required infrastructure. Thus, this restricts the application of AI technology in general, especially in rural areas. The financial burden of implementing AI technology is another significant obstacle, according to [3], particularly for small and medium-sized firms (SMEs), which account for a sizable share of Nigeria's economy. Substantial financial investments are needed for AI systems, which include trained workers, software, and hardware [26, 42]. Furthermore, these expenses are unaffordable for many Nigerian companies, especially those in the logistics and transportation industries, which restricts the potential applications of AI. Thus, the potential for AI adoption in Nigeria's distribution and transportation industries is constrained by this financial hurdle. [74] state that artificial Intelligence (AI) is a highly specialised discipline requiring software development, machine learning, and data science knowledge. Regretfully, there is a severe lack of professionals in Nigeria who possess the necessary abilities to design, create, and manage AI systems [52]. In addition to impeding the Volume-09 Issue 12, December -2025 ISSN: 2456-9348 Impact Factor: 8.232 International Journal of Engineering Technology Research & Management (IJETRM) https://ijetrm.com/ IJETRM (http://ijetrm.com/) [360] development of AI solutions, this labour scarcity limits the nation's capacity to realise AI's advantages in its distribution and transportation networks. According to [41], there are worries regarding data security and privacy as the extensive use of AI mainly depends on the availability and usage of massive datasets. Concerns regarding how businesses gather, retain, and utilise personal data are becoming more widespread in Nigeria. Businesses might be cautious about deploying AI technologies requiring sensitive data access if unclear regulatory frameworks address these concerns [1, 33]. Fostering trust in AI systems requires solid legal frameworks to address these issues. CONCLUSION Artificial Intelligence (AI) can significantly transform Nigeria’s transportation and distribution systems, driving productivity, lowering costs, and improving customer satisfaction. However, successful adoption requires addressing challenges such as weak infrastructure, high costs, limited expertise, and data protection concerns [6]. Strategic investments in policy development, infrastructure, and education are essential, alongside government funding for modernising transport networks and ensuring reliable electricity [19]. Building capacity through training programs and collaborations between academia and industry will equip Nigerian professionals with the skills to design and manage AI systems [18, 25, 21]. Private sector participation should be encouraged through incentives such as tax breaks and grants to reduce adoption costs [13, 44, 40]. Clear regulatory frameworks must also be established to safeguard data security and privacy, creating an enabling environment for innovation [30, 43]. While this study provides valuable insights into AI’s applications in Nigeria’s transport and distribution sectors, several research gaps remain. These include the need for localised data on AI adoption, exploration of rural applications, deeper investigation into policy frameworks, and assessment of socioeconomic impacts such as employment shifts. Environmental implications, SME participation, and cultural and ethical considerations also require further study to ensure equitable and sustainable AI integration. RECOMMENDATIONS AND SUGGESTIONS FOR FURTHER STUDIES In order to improve AI adoption in Nigeria's transport and distribution sectors, this review report concluded with recommendations for government policies to be implemented, infrastructure investments to be adopted, and skill development to be initiated. It is suggested that future researchers should include looking into; The economic effects of AI The role SMEs play in adopting AI Moreover, there is a necessity for localised AI solutions that consider Nigeria's infrastructure limitations. REFERENCES [1] Abraham, G. T., Osaisai, E. F., Dienagha, N., & Ineyekineye, A. (2021). Usability, security, and trust of e-commerce websites: The effect on the Nigerian e-shopper. Asian Journal of Research in Computer Science, 10(4), 58–68. [2] Achouch, M., Dimitrova, M., Ziane, K., Sattarpanah Karganroudi, S., Dhouib, R., Ibrahim, H., & Adda, M. (2022). On predictive maintenance in industry 4.0: Overview, models, and challenges. Applied Sciences, 12(16), 8081. [3] Adegbuyi, A., Noor, S., & Adeniyi, A. O. (2024). Role of artificial intelligence on small and mediumsized enterprises (SMEs) management in southwest Nigeria. International Journal of Entrepreneurship Technology and Innovation, 1(1), 11–28. [4] Adebiyi, S. O., Akinrinmade, O. J., & Amole, B. B. (2022). Optimising service quality management of the bus rapid transport system in Lagos using the multi-criteria decision analysis. Zeszyt Naukowe. Transport/Politechnika Śląska, (116), 5–23. [5] Adediran, E., Sakpere, W., & Ogunyinka, T. (2024). Artificial intelligence in Nigeria: Challenges and opportunities. SSRN. https://doi.org/10.2139/ssrn.5269530 [6] Aderibigbe, O. O., Fadare, S. O., & Gumbo, T. (2024). Transport situation in the Global South: Insights from Nigeria, South Africa and India. In Emerging technologies for smart cities: Sustainable transport planning in the Global North and Global South (pp. 43–77). Cham: Springer Nature Switzerland. [7] Aderibigbe, A. O., Ohenhen, P. E., Nwaobia, N. K., Gidiagba, J. O., & Ani, E. C. (2023). Artificial intelligence in developing countries: Bridging the gap between potential and implementation. Computer Science & IT Research Journal, 4(3), 185–199. Volume-09 Issue 12, December -2025 ISSN: 2456-9348 Impact Factor: 8.232 International Journal of Engineering Technology Research & Management (IJETRM) https://ijetrm.com/ IJETRM (http://ijetrm.com/) [361] [8] Adio, S. A., Sikhakhane-Nwokediegwu, Z., Erinjogunola, F. L., Ajirotutu, R. O., & Olayiwola, R. K. (2025). Integrating AI in public transport workforces: A review of HR challenges and opportunities. [9] Agbigbe, W. A. (2016). The impact of transportation infrastructure on Nigeria’s economic development (Doctoral dissertation, Walden University). [10] Ajayi, O. O., Bagula, A. B., Maluleke, H. C., & Odun-Ayo, I. A. (2021). Transport inequalities and the adoption of intelligent transportation systems in Africa: A research landscape. Sustainability, 13(22), 12891. [11] Ajayi, A. O., & Kumkale, H. (2023). Optimising urban road transportation efficiency: AI-driven solutions for reducing traffic congestion in big cities. Bournemouth University. [12] Akhtar, M., & Moridpour, S. (2021). A review of traffic congestion prediction using artificial intelligence. Journal of Advanced Transportation, 2021(1), 8878011. [13] Albaz, A., Dondi, M., Rida, T., & Schubert, J. (2020). Unlocking growth in small and medium-size enterprises. McKinsey & Company. [14] Alqasi, M. A. Y., Alkelanie, Y. A. M., & Alnagrat, A. J. A. (2024). Intelligent infrastructure for urban transportation: The role of artificial intelligence in predictive maintenance. Brilliance: Research of Artificial Intelligence, 4(2), 625–637. [15] Almatar, K. M. (2024). Implementing AI-driven traffic signal systems for enhanced traffic management in Dammam. International Journal of Sustainable Development & Planning, 19(2). [16] Aluko, O. A., Odewale, A. T., Taiwo, K., & Adefeso, H. (2024). Unlocking inclusive growth and sustainable development in Nigeria: A roadmap through challenges and opportunities. African Journal of Applied Research, 10(1), 201–223. [17] Amosu, O. R., Kumar, P., Ogunsuji, Y. M., Oni, S., & Faworaja, O. (2024). AI-driven demand forecasting: Enhancing inventory management and customer satisfaction. World Journal of Advanced Research and Reviews, 23(2), 708–719. [18] Asiyai, R. I. (2015). Improving quality higher education in Nigeria: The roles of stakeholders. International Journal of Higher Education, 4(1), 61–70. [19] Argyroudis, S. A., Mitoulis, S. A., Chatzi, E., Baker, J. W., Brilakis, I., Gkoumas, K., ... & Linkov, I. (2022). Digital technologies can enhance climate resilience of critical infrastructure. Climate Risk Management, 35, 100387. [20] Arubayi, D. E. (2022). Ride-hailing platforms, algorithmic management, and everyday resistances: A case study of drivers in Lagos, Nigeria (Doctoral dissertation, University of Manchester). [21] Ayegbo, O. J., Abas, A., Momodu, M., & Achuenu, A. (2025). Harnessing artificial intelligence for smart city development: Benefits and challenges in Lagos State, Nigeria. FNAS Journal of Computing and Applications, 2(2), 117–126. [22] Babayeju, O. A., Adefemi, A., Ekemezie, I. O., & Sofoluwe, O. O. (2024). Advancements in predictive maintenance for ageing oil and gas infrastructure. World Journal of Advanced Research and Reviews, 22(3), 252–266. [23] Bathla, G., Bhadane, K., Singh, R. K., Kumar, R., Aluvalu, R., Krishnamurthi, R., ... & Basheer, S. (2022). Autonomous vehicles and intelligent automation: Applications, challenges, and opportunities. Mobile Information Systems, 2022(1), 7632892. [24] Bharadiya, J. P. (2023). Machine learning and AI in business intelligence: Trends and opportunities. International Journal of Computer, 48(1), 123–134. [25] Bidandi, F., Anthony, A. N., & Mukong, C. (2022). Collaboration and partnerships between South African higher education institutions and stakeholders: Case study of a post-apartheid university. Discover Education, 1(1), 2. [26] Bughin, J., Hazan, E., Ramaswamy, P. S., DC, W., & Chu, M. (2017). Artificial intelligence: The next digital frontier. McKinsey Global Institute. [27] Chawla, P., Hasurkar, R., Bogadi, C. R., Korlapati, N. S., Rajendran, R., Ravichandran, S., ... & Gao, J. Z. (2024). Real-time traffic congestion prediction using big data and machine learning techniques. World Journal of Engineering, 21(1), 140–155. [28] Çınar, Z. M., Nuhu, A. A., Zeeshan, Q., Korhan, O., Asmael, M., & Safaei, B. (2020). Machine learning in predictive maintenance towards sustainable smart manufacturing in industry 4.0. Sustainability, 12(19), 8211. [29] Dash, R., McMurtrey, M., Rebman, C., & Kar, U. K. (2019). Application of artificial intelligence in automation of supply chain management. Journal of Strategic Innovation and Sustainability, 14(3). Volume-09 Issue 12, December -2025 ISSN: 2456-9348 Impact Factor: 8.232 International Journal of Engineering Technology Research & Management (IJETRM) https://ijetrm.com/ IJETRM (http://ijetrm.com/) [362] [30] de Almeida, P. G. R., dos Santos, C. D., & Farias, J. S. (2021). Artificial intelligence regulation: A framework for governance. Ethics and Information Technology, 23(3), 505–525. [31] Dikshit, S., Atiq, A., Shahid, M., Dwivedi, V., & Thusu, A. (2023). The use of artificial intelligence to optimise the routing of vehicles and reduce traffic congestion in urban areas. EAI Endorsed Transactions on Energy Web, 10, 1–13. [32] Dwivedi, D. N. (2024). The use of artificial intelligence in supply chain management and logistics. In Leveraging AI and emotional intelligence in contemporary business organizations (pp. 306–313). IGI Global. [33] Ebuka, A. A., Emmanuel, D., & Idigo, P. (2023). Artificial intelligence as a catalyst for the sustainability of small and medium scale businesses (SMEs) in Nigeria. Annals of Management and Organization Research, 5(1), 1–11. [34] Eze, E. (2022). Ethical considerations in the deployment of autonomous vehicle cybersecurity measures. Journal of AI in Healthcare and Medicine, 2(1), 33–52. [35] Gheorghe, C., & Soica, A. (2025). Revolutionizing urban mobility: A systematic review of AI, IoT, and predictive analytics in adaptive traffic control systems for road networks. Electronics, 14(4). [36] Glory, C. C. (2023). Determinants of supply chain and logistics disruptions in South East Nigeria. Journal of the Management Sciences, 60(4), 250–272. [37] Griffin, C. (2022). Fashion analytics in Africa and Middle East: Strategies, tools, and insights for fashion brands. In Fashion marketing in emerging economies Volume II: South American, Asian and African perspectives (pp. 241–268). Cham: Springer International Publishing. [38] Hammoumi, L., Maanan, M., & Rhinane, H. (2024). Characterising smart cities based on artificial intelligence. Smart Cities, 7(3), 1330–1345. [39] Hasanujjaman, M., Chowdhury, M. Z., & Jang, Y. M. (2023). Sensor fusion in autonomous vehicles with traffic surveillance camera systems: Detection, localisation, and AI networking. Sensors, 23(6), 3335. [40] Iyelolu, T. V., Agu, E. E., Idemudia, C., & Ijomah, T. I. (2024). Driving SME innovation with AI solutions: Overcoming adoption barriers and future growth opportunities. [41] Jagatheesaperumal, S. K., Rahouti, M., Ahmad, K., Al-Fuqaha, A., & Guizani, M. (2021). The duo of artificial intelligence and big data for industry 4.0: Applications, techniques, challenges, and future research directions. IEEE Internet of Things Journal, 9(15), 12861–12885. [42] Javaid, M., Haleem, A., Singh, R. P., & Suman, R. (2022). Artificial intelligence applications for industry 4.0: A literature-based study. Journal of Industrial Integration and Management, 7(1), 83– 111. [43] Jhurani, J., Reddy, P., & Choudhuri, S. S. (2023). Fostering a safe, secure, and trustworthy intelligence ecosystem in the United States. International Journal of Applied Engineering and Technology, 5, 21– 27. [44] Kergroach, S. (2021). SMEs going digital: Policy challenges and recommendations. OECD Science, Technology and Industry Policy Papers. [45] Kozlov, I. P. (2022). Optimising public transport services using AI to reduce congestion in metropolitan areas. International Journal of Intelligent Automation and Computing, 5(2), 1–14. [46] Krishnan, R., Govindaraj, M., Kandasamy, L., Perumal, E., & Mathews, S. B. (2024). Integrating logistics management with artificial intelligence and IoT for enhanced supply chain efficiency. In Anticipating future business trends: Navigating artificial intelligence innovations: Volume 1 (pp. 25– 35). Cham: Springer Nature Switzerland. [47] Kuteyi, D., & Winkler, H. (2022). Logistics challenges in sub-Saharan Africa and opportunities for digitalization. Sustainability, 14(4), 2399. [48] Lysenko, S., Makovoz, O., & Perederii, T. (2023). The impact of artificial intelligence in logistics management on sustainability development of e-business. In European Dimensions of the Sustainable Development and Academic–Business Forum: Let’s revive Ukraine together. [49] Meduri, K., Nadella, G. S., Gonaygunta, H., & Meduri, S. S. (2023). Developing a fog computingbased AI framework for real-time traffic management and optimization. International Journal of Sustainable Development in Computing Science, 5(4), 1–24. [50] Mnyakin, M. (2023). Applications of AI, IoT, and cloud computing in smart transportation: A review. Artificial Intelligence in Society, 3(1), 9–27. Volume-09 Issue 12, December -2025 ISSN: 2456-9348 Impact Factor: 8.232 International Journal of Engineering Technology Research & Management (IJETRM) https://ijetrm.com/ IJETRM (http://ijetrm.com/) [363] [51] Mogaji, E. (2020). Impact of COVID-19 on transportation in Lagos, Nigeria. Transportation Research Interdisciplinary Perspectives, 6, 100154. [52] Muhammad, A., Umar, U. A., & Adam, F. L. (2023). The impact of artificial intelligence and machine learning on workforce skills and economic mobility in developing countries: A case study of Ghana and Nigeria. Journal of Technology Innovations and Energy, 2(1), 55–61. [53] Mustafa, N., & Badi, S. (2025). Towards safer construction projects: Applying AI and IPD in smart city development and urban transportation in the United States and Lagos State. [54] Nguyen, N. P., & Mogaji, E. (2022). Information technology for enhancing transportation in developing countries. In Management and information technology in the digital era: Challenges and perspectives (pp. 81–94). Emerald Publishing Limited. [55] Nweje, U., & Taiwo, M. (2025). Leveraging artificial intelligence for predictive supply chain management: Focus on how AI-driven tools are revolutionising demand forecasting and inventory optimisation. International Journal of Science and Research Archive, 14(1), 230–250. [56] Ojedokun, M. M. (2019). E-commerce as a vehicle towards sustainable national development in Nigeria: Prospects and challenges. Social Science Journal, 3, 253–262. [57] Okem, E. S., Ukpoju, E. A., David, A. B., & Olurin, J. O. (2023). Advancing infrastructure in developing nations: A synthesis of AI integration strategies for smart pavement engineering. Engineering Science & Technology Journal, 4(6), 533–554. [58] Ojo, A. J., & Ajayi, A. P. (2024). Impact of retailer’s deployment of mobile technology on supply chain integration in Osogbo, Nigeria. Corpus Intellectual, 3(1). [59] Olagunju, T., Oyebode, O., & Orji, R. (2020). Exploring key issues affecting African mobile ecommerce applications using sentiment and thematic analysis. IEEE Access, 8, 114475–114486. [60] Olalekan Ahmed, T. (2025). Exploring the impact of infrastructural deficiencies on logistics efficiency in Nigeria. [61] Olugbade, S., Ojo, S., Imoize, A. L., Isabona, J., & Alaba, M. O. (2022). A review of artificial intelligence and machine learning for incident detectors in road transport systems. Mathematical and Computational Applications, 27(5), 77. [62] Ou, Y. (2022). AI for real-time bus travel time prediction in traffic congestion management. In Humanity Driven AI: Productivity, Well-being, Sustainability and Partnership (pp. 63–84). [63] Saleem, M., Abbas, S., Ghazal, T. M., Khan, M. A., Sahawneh, N., & Ahmad, M. (2022). Smart cities: Fusion-based intelligent traffic congestion control system for vehicular networks using machine learning techniques. Egyptian Informatics Journal, 23(3), 417–426. [64] Salisu, U. O., Akanmu, A. A., Fasina, S. O., & Sanni, S. M. (2020). Traffic congestion and intelligent transport system in a fast-growing Nigerian city. [65] Samaei, S. R. (2023). A comprehensive algorithm for AI-driven transportation improvements in urban areas. In 13th International Engineering Conference on Advanced Research in Science and Technology. https://civilica.com/doc/1930041 [66] Shekhar, S. (2024). Investigating the integration of artificial intelligence in enhancing efficiency of distributed order management systems within SAP environments. Applied Research in Artificial Intelligence and Cloud Computing, 7(5), 11–27. [67] Shobhana, N. (2024). AI-powered supply chains towards greater efficiency. In Complex AI Dynamics and Interactions in Management (pp. 229–249). IGI Global. [68] Tadayonrad, Y., & Ndiaye, A. B. (2023). A new key performance indicator model for demand forecasting in inventory management considering supply chain reliability and seasonality. Supply Chain Analytics, 3, 100026. [69] Theissler, A., Pérez-Velázquez, J., Kettelgerdes, M., & Elger, G. (2021). Predictive maintenance enabled by machine learning: Use cases and challenges in the automotive industry. Reliability Engineering & System Safety, 215, 107864. [70] Tian, Z., Zhong, R. Y., Barenji, A. V., Wang, Y. T., Li, Z., & Rong, Y. (2021). A blockchain-based evaluation approach for customer delivery satisfaction in sustainable urban logistics. International Journal of Production Research, 59(7), 2229–2249. [71] Ucar, A., Karakose, M., & Kırımça, N. (2024). Artificial intelligence for predictive maintenance applications: Key components, trustworthiness, and future trends. Applied Sciences, 14(2), 898.