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

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-the-art 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 conventional 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.

I. Introduction

The concept of smart transportation has drawn more and more attention while addressing important challenges and concerns like traffic congestion, fuel consumption, air pollution and so on. Emerging Connected Vehicle (CV) and Autonomous Vehicle (AV) technologies can improve network-wide traffic safety, mobility, and operation efficiency through real-time Dedicated Short Range Communications (DSRC) based Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communications [1]–[3]. The research team at the University of Toronto including Alberto Leon-Garcia, Hans-Arno Jacobsen, Baher Adbulhai, etc. conducted pioneering work to establish the Connected Vehicles and Smart Transportation (CVST) portal to share live integrated traffic information in CV environments [4]–[7]. Many other researchers in the university investigated driving challenges and opportunities in AV-enabled traffic systems [8], [9]. According to the Research and Innovative Technology Administration (RITA) of the U.S. Department of Transportation (USDOT), 81% of all vehicle-involved crashes can be avoided or significantly mitigated based on CV techniques annually. Meanwhile, AV is capable of sensing its environment and self-piloting based on navigation hardware such as cameras, radar, Lidar, laser rangefinders, and GPS. AVs can much more accurately judge distances and velocities, attentively monitor their surroundings, and react instantly to emergent situations. By combining CV and AV technologies seamlessly, it is believed that Connected Autonomous Vehicle or CAV enabled traffic systems can revolutionize the existing understanding of vehicle-infrastructure interactions and network-wide traffic system operations. However, the existing traffic theory becomes awkward when comes to the context of CAV. Existing traffic flow models [10]–[18] were developed for Human Driven Vehicle (HDV)-based traffic flow operations based on one-way coupled vehicle interactions adopted in classic car-following models (a following vehicle adjusts its operation conditions, such as acceleration/deceleration only based on its leading vehicle’s position, relative speed difference, etc.). To incorporate the lateral traffic flow operations, additional lane-changing models must be involved. Enabled by CAVs, the two-way communication and collaborative linkages among CAVs can greatly facilitate us to formulate the mutually-coupled vehicle interactions (not only a following vehicle will be impacted by its leading vehicle, but also the leading vehicle will be impacted by its following vehicle and its surrounding vehicles too) for CAV-enabled traffic flow. This feature implies that CAVs are expected to move freely along both the longitudinal and lateral direction and a two-dimensional traffic flow model could be more reasonable. Currently, an aggregated macroscopic model for CAV-based traffic is still an under-investigated problem.