Force-Driven Traffic Simulation for a Future Connected Autonomous Vehicle-Enabled Smart Transportation System

作者：Zhang Yuming; Zhang Guohui^*; Fierro Rafael; Yang Yin

来源：IEEE Transactions on Intelligent Transportation Systems, 2018, 19(7): 2221-2233.

DOI：10.1109/TITS.2017.2787141

• Smart transportation
• connected vehicle
• autonomous vehicle
• traffic flow model
• visualization
• SPH
• particle system
• numerical solution

Recent technology advances significantly push forward the development and the deployment of the concept of smart, such as smart community and smart city. Smart transportation is one of the core components in modern urbanization processes. Under this context, the connected autonomous vehicle (CAV) system presents a promising solution towards the enhanced traffic safety and mobility through state-of-theart wireless communications and autonomous driving techniques. Being capable of collecting and transmitting real-time vehicle-specific, location-specific, and area-wide traffic information, it is believed that CAV-enabled transportation systems will revolutionize the existing understanding of network-wide traffic operations and reestablish traffic flow theory. This paper develops a new continuum dynamics model for the future CAV-enabled traffic system, realized by encapsulating mutually-coupled vehicle interactions using virtual internal and external forces. Leveraging Newton's second law of motion, our model naturally preserves the traffic volume and automatically handles both the longitudinal and lateral traffic operations due to its 2-D nature, which sets us apart from the existing macroscopic traffic flow models. Our model can also be rolled back to handle the comentional traffic of human drivers, and the experiment shows that the model describes real-world traffic behavior well. Therefore, we consider the proposed model a complement and generalization of the existing traffic theory. We also develop a smoothed particle hydrodynamics-based numerical simulation and an interactive traffic visualization framework. By posing user-specified external constraints, our system allows users to visually understand the impact of different traffic operations interactively.

• 出版日期2018-7