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Advancing Transportation with Intelligent Solutions
Abstract
This paper explores the advancement of transportation through intelligent solutions enabled by AI-driven engineering. Through case studies and research insights, it investigates how artificial intelligence is reshaping various aspects of transportation, including traffic management, vehicle autonomy, and logistics optimization. The study highlights the application of AI techniques such as traffic prediction, route optimization, and autonomous navigation in improving safety, efficiency, and sustainability in transportation systems. Additionally, it discusses the integration of AI with smart infrastructure, connected vehicles, and mobility-as-a-service platforms to enable seamless mobility, reduce congestion, and enhance user experience. The paper also addresses challenges such as cybersecurity, regulatory compliance, and ethical considerations in the adoption of AI-driven engineering solutions in transportation. It emphasizes the importance of interoperability, public-private partnerships, and user-centric design in harnessing AI's potential to create more accessible, equitable, and resilient transportation systems for the future.
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References
- Ban, X., Shao, C., Wang, Z., & Gu, L. (2019). A review of intelligent transportation systems based on big data and artificial intelligence. Journal of Advanced Transportation, 2019, 1-15.
- Cai, H., & Guo, Y. (2018). Autonomous vehicle perception systems: A review. Journal of Zhejiang University-SCIENCE C (Computers & Electronics), 19(10), 739-753.
- Chen, C., & Wan, J. (2019). A review of the application of artificial intelligence in transportation. IEEE Access, 7, 116365-116375.
- Dresner, K., & Stone, P. (2008). A multiagent approach to autonomous intersection management. Journal of Artificial Intelligence Research, 31, 591-656.
- Ma, M., Chen, C., & Wan, J. (2019). A survey on internet of vehicles: Architecture, enabling technologies, security and privacy, and applications. IEEE Internet of Things Journal, 6(3), 5217-5252.
- Raykin, L., Bahram, L. K., & Ahuja, V. (2017). Real-time data-driven predictive analytics for intelligent transportation systems: Concepts and principles. IEEE Transactions on Intelligent Transportation Systems, 18(4), 857-867.
- Wang, C., Wang, H., Zhu, Q., & Liu, L. (2018). Deep reinforcement learning for traffic light control in vehicular networks. IEEE Transactions on Vehicular Technology, 67(10), 10096-10107.