THE CONVERGENCE OF MACHINE LEARNING AND TRANSPORTATION INFRASTRUCTURE: CRITICAL PERSPECTIVES ON AUTONOMOUS VEHICLES AND SMART TRAFFIC MANAGEMENT
Keywords:
Autonomous Transportation Systems, Data Engineering Infrastructure, Traffic Management Optimization, Transportation Workforce TransformationAbstract
The integration of artificial intelligence and data engineering in transportation systems represents a transformative shift in modern mobility infrastructure. This comprehensive article examines the technological frameworks, implementation challenges, and societal implications of AI-driven transportation solutions, with particular emphasis on autonomous vehicles and smart traffic management systems. The article analyzes the complex interplay between machine learning models, sensor networks, and data processing pipelines that enable real-time decision-making in autonomous vehicles while exploring the architectural requirements for effective smart traffic management. Critical attention is given to safety protocols, ethical considerations, and bias mitigation strategies essential for reliable system deployment. The article further investigates the socioeconomic implications of widespread AI adoption in transportation, including workforce transformation, economic impacts, and emerging industry paradigms. Through systematic analysis of current implementations and future projections, this article provides actionable insights for technical development, policy formulation, and strategic planning. The findings highlight the need for balanced integration of AI technologies while addressing safety concerns, ethical considerations, and societal readiness. This article contributes to the growing body of knowledge on intelligent transportation systems by offering a comprehensive framework for sustainable AI integration in transportation infrastructure, accompanied by specific recommendations for stakeholders across technical, regulatory, and policy domains.
References
Garikapati, D., & Shetiya, S. S. (2024). "Autonomous Vehicles: Evolution of Artificial Intelligence and Learning Algorithms". IEEE Xplore. doi: 10.1109/JAS.2020.1003021. https://arxiv.org/pdf/2402.17690
Sethi, I. K. (2023). "Autonomous Vehicles and Systems: A Technological and Societal Perspective". IEEE Xplore. ISBN: 978-1-5386-0022-1. https://ieeexplore.ieee.org/book/10266928
Jurgen, R. K. (2012). "Distributed System Architecture of Autonomous Vehicles and Real-Time Path Planning Based on the Curvilinear Coordinate System". IEEE Xplore.
doi: 10.1109/JAS.2012.1003021. https://ieeexplore.ieee.org/abstract/document/8505534/authors#authors
Paret, D., & Rebaine, H. (2022). "Autonomous and Connected Vehicles: Network Architectures from Legacy Networks to Automotive Ethernet". IEEE Xplore. ISBN:
-1-111-98161-3. https://ieeexplore.ieee.org/book/9770851
Wang, N., Teng, Y., Hu, G., & Yu, F. R. (2023). "Importance-Driven Data Collection for Efficient Online Learning in Wireless Edge Networks". IEEE International Conference on Communications (ICC). doi: 10.1109/ICC.2023.10278679. https://ieeexplore.ieee.org/document/10278679
Ma, Y., Wang, Z., Yang, H., & Yang, L. (2020). "Artificial Intelligence Applications in the Development of Autonomous Vehicles: A Survey". IEEE/CAA Journal of Automatica Sinica, vol. 7, no. 2, pp. 315-329. doi: 10.1109/JAS.2020.1003021. https://www.ieee-jas.net/en/article/doi/10.1109/JAS.2020.1003021
Goodall, N. (2014). "Machine Ethics and Autonomous Vehicles," In Road Vehicle Automation, Springer, Cham, pp. 93-102. https://link.springer.com/chapter/10.1007/978-3-319-05990-7_9
Terefe Tucho, G. (2022). "A review on the socio-economic impacts of informal transportation and its complementarity to address equity and achieve sustainable development goals," Journal of Engineering and Applied Science. doi: 10.1186/s44147-022-00074-8. https://jeas.springeropen.com/articles/10.1186/s44147-022-00074-8
Wolniak, R., & Stecuła, K. (2024). "Artificial Intelligence in Smart Cities—Applications, Barriers, and Future Directions: A Review," Smart Cities, vol. 7, no. 3, pp. 1346-1389. https://www.mdpi.com/2624-6511/7/3/57
Yan, Z., Jiang, L., Huang, X., Zhang, L., & Zhou, X. (2023). "Intelligent urbanism with artificial intelligence in shaping tomorrow's smart cities: current developments, trends, and future directions," Journal of Cloud Computing, vol. 12, Article number: 179. https://journalofcloudcomputing.springeropen.com/articles/10.1186/s13677-023-00569-6
