Electric Vehicles.
Published in Chapter:
Cloud Solutions for Smart Parking and Traffic Control in Smart Cities
Maganti Syamala (Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, India),
J. Malathi (Department of Computer Science and Business Systems, Sri Sai Ram Engineering College, Chennai, India),
Vikash Singh (Department of Civil Engineering, Institute of Engineering and Technology, Lucknow, India),
Hari Priya G. S. (Department of Computer Science, M.S. Ramaiah College of Arts Science and Commerce, Bengaluru, India),
B. Uma Maheswari (Department of Computer Science and Engineering, St. Joseph's College of Engineering, Chennai, India), and Murugan S. (Sona College of Technology, India)
Copyright: © 2024
|Pages: 26
DOI: 10.4018/978-1-6684-9999-3.ch008
Abstract
Urban mobility trends include 5G connectivity, autonomous vehicles, electric and sustainable modes, AI and machine learning, drones, and air mobility. These technologies enable real-time data exchange, reduce congestion, enhance safety, optimize road capacity, and optimize infrastructure planning. AI and machine learning algorithms provide accurate predictive analytics, adaptive traffic control, and personalized services. Cloud computing, IoT, and data analytics enable predictive modeling for mobility planning, traffic flow forecasting, demand forecasting, and behavioral analysis. MaaS platforms facilitate seamless integration of modes, while shared mobility services like car-sharing and ride-hailing grow, reducing private vehicle ownership and promoting efficient resource use. Mobility data transforms urban planning, infrastructure optimization, mixed-use development, and smart city integration, guiding transportation layouts, traffic signal placements, parking facilities, and neighborhood design.