Quantitative analysis and modeling of ride sharing behavior based on internet of vehicles - Cai Liang (Nonlinear Engineering)
As an important measure of traffic demand management, ride sharing can effectively increase the number of passengers from private car and bicycle, reduce vehicle travel rates, and thus alleviate traffic congestion and air pollution. With the rapid development of the internet of vehicles technology, the systematic study of private car sharing behavior and key technologies of sharing systems, and the construction of a road network spatiotemporal resource dynamic optimization theory and method guided by sharing priority in the internet of vehicles environment is a research work with great theoretical and practical values. This study investigates the ride sharing behavior and the influencing factors, such as education background, occupation, time and cost, and so on, based on internet of vehicles. Meanwhile, a Game-theory-based model was proposed and the local stability at equilibrium point was quantitatively studied. Private car sharing travel is influenced by various factors such as policies, technology, and system efficiency. Only by coordinating the operation of multiple factors can its effectiveness be fully utilized, and traffic congestion can be truly alleviated.