This chapter is about how AI is transforming road transportation by improving efficiency, safety, and comfort. By emphasising the importance of AI integration and moving on to main AI technologies such as machine learning (ML) for applications like traffic prediction and autonomous driving. Deep learning techniques, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), are essential for vehicle detection and traffic analysis. Also, natural language processing (NLP) enhances traffic planning and customer support by providing real-time information and virtual assistants. We can also know from the chapter that AI applications like self-driving cars that use AI for vision and control, intelligent traffic management systems that optimise signal timings, and predictive maintenance to avoid vehicle problems. It also discusses data privacy, technology, and ethics questions, as well as demonstrates effective real-world AI deployments and future trends. Overall, the chapter emphasises AI’s disruptive impact on road transport and its potential for ongoing innovation and improvement in the pursuit of a smarter, more efficient transportation network.
Revolutionizing Road Transportation: The Role of Artificial Intelligence in Smart and Efficient Systems
Springer Tracts on Transportation, Traffic
Applications of Computational Learning and IoT in Smart Road Transportation System ; Chapter : 6 ; 87-107
2025-05-09
21 pages
Article/Chapter (Book)
Electronic Resource
English
Smart cities , Artificial intelligence , Machine learning , Autonomous vehicles , Traffic management , Predictive maintenance , Road transport , Smart transportation systems , AI-based traffic management systems , Intelligent Traffic Management Systems (ITMS) Engineering , Transportation Technology and Traffic Engineering , Computational Intelligence , Cyber-physical systems, IoT , Professional Computing
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