ACP-Based Energy-Efficient Schemes for Sustainable Intelligent Transportation Systems

Reducing vehicle exhaust pollution and energy consumption is of great significance for improving the sustainability of social development. Currently, most energy-efficient and regenerative energy recovery methods are from a vehicle control perspective, ignoring the impact on the overall traffic environment. An important reason is that the transportation system's large scale, complexity and social nature restrict its energy-efficient development. Hence, this letter proposes energy-efficient and regenerative energy recovery schemes for sustainable intelligent transportation system using the Artificial societies, Computational experiments, Parallel execution (ACP) framework. The framework includes three parts: energy-efficient oriented intelligent road infrastructure design, transportation traffic flow and vehicle velocity profile planning co-design, and cloud-based vehicle engine parameter calibration. This letter is the second part of Distributed/Decentralized Hybrid Workshop on Sustainability for Transportation and Logistics (DHW-STL) and aims to enhance the sustainability of transportation system from the energy-efficient perspective.