Automated vehicles (AVs) are widely considered to play a crucial role in future transportation systems because of their speculated capabilities in improving road safety, saving energy consumption, reducing vehicle emissions, increasing road capacity, and stabilizing traffic. To materialize these widely expected potentials of AVs, a sound understanding of AVs’ impacts on traffic flow is essential. Not surprisingly, despite the relatively short history of AVs, there have been numerous studies in the literature focusing on understanding and modeling various aspects of AV-involved traffic flow and significant progresses have already been made. To understand the recent development and ultimately inspire new research ideas on this important topic, this survey systematically and comprehensively reviews the existing AV-involved traffic flow models with different levels of details, and examines the relationship among the design of AV-based driving strategies, the management of transportation systems, and the resulting traffic dynamics. The pros and cons of the existing models and approaches are critically discussed, and future research directions are also provided.

Automated vehicle-involved traffic flow studies: A survey of assumptions, models, speculations, and perspectives

The spatio-temporal imbalance of parking demand and supply results in unwanted on-street cruising-for-parking traffic of conventional vehicles. Autonomous vehicles (AVs) can self-relocate to alleviate the shortage of parking supplies at the trip destinations. The extra floating trips of vacant AVs have adverse impacts on traffic congestion and the parking demand–supply imbalance may still exist when they are not distributed optimally. This paper presents a centralized parking dispatch approach to optimize the distribution of floating AVs and provide regional route guidance. We apply the concept of macroscopic fundamental diagram to represent the evolution of traffic conditions, cruising-for-parking, and dispatched AVs in a congested multi-region network. A model predictive control is suggested to optimize the control inputs. Numerical experiments in a four-region network demonstrate that the proposed parking dispatch and regional route guidance of AVs are effective in reducing intense cruising-for-parking traffic, and the integration of both has the best control performance by regulating the network towards under-saturated conditions. The performance of the proposed schemes is evaluated via simulations with noise in measurement errors and compliance rate prediction. Results show substantial improvements in terms of total time spent, even for low levels of AV market penetration or AV compliance rate to parking dispatch and route guidance.

Macroscopic modeling and dynamic control of on-street cruising-for-parking of autonomous vehicles in a multi-region urban road network

The maximum platoon size is a critical parameter in connected and automated vehicle (CAV) platoon configuration. However, the effect of platoon size on the transportation system has not been well-studied. This paper unveils the effect of maximum CAV platoon size in terms of road capacity and traffic flow stability. Specifically, the analytical formulations of the capacity and flow stability are developed considering the maximum platoon size. Simulations are conducted to verify the developed theoretical models. For capacity analysis, both the analytical and simulation results indicate that a larger maximum platoon size can help increase the capacity. However, the increment becomes smaller with the increase of maximum platoon size. For flow stability analysis, the theoretical analysis and microscopic simulation show that smaller maximum platoon size leads to greater traffic flow stabilization. In addition, analysis shows that improvements in capacity and traffic stability are more profound when CAV penetration and platooning intensity are high.

Analytical analysis of the effect of maximum platoon size of connected and automated vehicles

Fuel consumption and transportation emissions evaluation of mixed traffic flow with connected automated vehicles and human-driven vehicles on expressway

With the development of automated driving technology and communication technology, the emergence of connected automated vehicles (CAVs) will significantly improve traffic efficiency and safety. To study the influence of connected automated vehicles (CAVs) on mixed traffic flow, this study proposes a cellular automata (CA) model of mixed traffic flow considering the driving behavior of the platoon of CAVs. Firstly, three car-following modes, Human-driven vehicle (HDV), Adaptive Cruise Control (ACC), and Cooperative Adaptive Cruise Control (CACC), in mixed traffic flow are analyzed. Secondly, on the bias of the safety distance model and platoon behavior analysis, the acceleration, deceleration, and randomization rules of the cellular automata model of mixed traffic flow are developed. The time step is introduced into the model in the rule design, and the reaction time of the vehicle is considered in the randomization rule. Finally, a numerical simulation is conducted to analyze the fundamental diagram of mixed traffic flow, traffic congestion, and speed volatility under different penetration rates of CAVs. The result shows that (1) the road capacity under the pure CAV environment has increased by 3.24 times compared with the pure HDVs; (2) the maximum congestion reduction percentage can reach 63.36%; and (3) when the penetration rate reaches 80%, the velocity fluctuation decreases significantly. Sensitivity analysis shows that when the penetration rate of CAVs is 100%, the maximum platoon size increase can improve the road capacity. Furthermore, the maximum platoon size has an optimal value in a specific density range.

A cellular automata model for mixed traffic flow considering the driving behavior of connected automated vehicle platoons

Traffic flow fundamental diagram (FD) is viewed as the basis of traffic flow theory and has various applications in transportation. However, the fundamental diagram of mixed human-driven vehicles (HVs) and connected automated vehicles (CAVs) traffic has not been well-studied. This paper derives the FD for mixed HV and CAV traffic considering the stochastic headway. Firstly, the deterministic FD of pure CAV traffic and pure HV traffic are built. Then the FD of mixed HV and CAV traffic is developed with CAV penetration and platooning intensity taken into consideration. A Gaussian mixture model (GMM) is applied to model the stochastic headway, based on which the stochastic FD is derived. Impact of CAV penetration and platooning intensity on the stochasticity of FD is studied. Results from theoretical analysis and case study show that increasing CAV penetration can reduce the scattering of FD, while higher platooning intensity may result in more scattering of FD.

Jiazu Zhou

Papers

Modeling the fundamental diagram of mixed human-driven and connected automated vehicles

Analytical analysis of the effect of maximum platoon size of connected and automated vehicles