Artificial Intelligence and Machine Learning in Intelligent Transportation Systems
Article Sidebar
Main Article Content
Abstract
Rapid urbanization and increasing vehicle populations have created significant challenges related to traffic congestion, road safety, transportation efficiency, and environmental sustainability. Intelligent Transportation Systems (ITS) increasingly rely on Artificial Intelligence (AI), Machine Learning (ML), and Internet of Things (IoT) technologies to provide intelligent and automated transportation services. This review examines the application of AI and ML in intelligent transportation, including traffic flow prediction, congestion management, route optimization, accident detection, autonomous driving, parking management, and public transportation optimization. The study reviews conventional machine learning algorithms as well as advanced deep learning, reinforcement learning, computer vision, and time-series forecasting approaches used in transportation systems. The role of IoT-enabled vehicles, roadside sensors, cameras, GPS devices, and connected infrastructure in generating real-time transportation data is also examined. Particular attention is given to challenges involving data privacy, cybersecurity, communication latency, model reliability, infrastructure requirements, and the safety of AI-based transportation decisions. Emerging technologies such as edge computing, vehicle-to-everything communication, 5G, digital twins, and autonomous vehicles are discussed as important future directions. The review provides a consolidated perspective on how AI-enabled ITS can contribute to safer, more efficient, sustainable, and intelligent transportation networks.
Article Details
References
Sandra, K. (2024). The regulated banking AI lakehouse. Indo-Continental Academic Publishers.
Aversano, L., Bernardi, M. L., Cimitile, M., & Pecori, R. (2021). A systematic review on deep learning approaches for IoT security. Computer Science Review, 40, 100389. https://doi.org/10.1016/j.cosrev.2021.100389
Paruchuri, J. K. (2024). Privacy-preserving machine learning on financial data: Federated learning, differential privacy, and practical deployment challenges in banking. International Journal for Multidisciplinary Research, 6(4), 1–9.
Sandra, K. (2024). Large language models for data catalog enrichment: A survey with operational evidence from enterprise deployments. International Journal of Computer Science Engineering Techniques, 12(3), 1–8.
Latif, S., Driss, M., Boulila, W., Huma, Z., Jamal, S. S., Idrees, Z., & Ahmad, J. (2021). Deep learning for the Industrial Internet of Things (IIoT): A comprehensive survey of techniques, implementation frameworks, potential applications, and future directions. Sensors, 21(22), 7518. https://doi.org/10.3390/s21227518
Paruchuri, J. K. (2023). Data mesh adoption in financial enterprises: A survey of six organizations and a production case study. International Journal for Multidisciplinary Research, 5(4), 1–9.
Sandra, K. (2022). Intelligent data workbench design for multi-language code compatibility. International Journal of Computer Science Engineering Techniques, 12(1), 1–17.
Ullah, Z., Al-Turjman, F., Mostarda, L., & Gagliardi, R. (2020). Applications of artificial intelligence and machine learning in smart cities. Computer Communications, 154, 313–323.
Paruchuri, J. K. (2023). Credit risk modeling with real-time streaming features: PD/LGD pipeline design. International Journal for Multidisciplinary Research, 5(2), 1–14.
Mishra, N., & Pandya, S. (2021). Internet of Things applications, security challenges, attacks, intrusion detection, and future visions: A systematic review. IEEE Access, 9, 59353–59377. https://doi.org/10.1109/ACCESS.2021.3073408
Kuzlu, M., Fair, C., & Guler, O. (2021). Role of artificial intelligence in the Internet of Things (IoT) cybersecurity. Discover Internet of Things, 1, Article 7. https://doi.org/10.1007/s43926-020-00001-4
Ben Atitallah, S., Driss, M., Boulila, W., & Ben Ghézala, H. (2020). Leveraging deep learning and IoT big data analytics to support the smart cities development: Review and future directions. Computer Science Review, 38, 100303. https://doi.org/10.1016/j.cosrev.2020.100303
Li, W., Chai, Y., Khan, F., Ullah Jan, S. R., & others. (2021). A comprehensive survey on machine learning-based big data analytics for IoT-enabled smart healthcare systems. Mobile Networks and Applications, 26, 234–252. https://doi.org/10.1007/s11036-020-01700-6
Alshamrani, M. (2021). IoT and artificial intelligence implementations for remote healthcare monitoring systems: A survey. Journal of King Saud University—Computer and Information Sciences. https://doi.org/10.1016/j.jksuci.2021.04.005
Ghazal, T. M., Hasan, M. K., Alshurideh, M. T., Alzoubi, H. M., Ahmad, M., Akbar, S. S., Al Kurdi, B., & Akour, I. A. (2021). IoT for smart cities: Machine learning approaches in smart healthcare—A review. Future Internet, 13(8), 218. https://doi.org/10.3390/fi13080218
Al-Fuqaha, A., Guizani, M., Mohammadi, M., Aledhari, M., & Ayyash, M. (2015). Internet of Things: A survey on enabling technologies, protocols, and applications. IEEE Communications Surveys & Tutorials, 17(4), 2347–2376. https://doi.org/10.1109/COMST.2015.2444095