This paper proposes a new dynamic driving risk potential field model under the connected and automated vehicles environment that fully considers the dynamic effect of the vehicle's acceleration and steering angle. The statistical analysis of the model's parameter reveals that acceleration and steering angle will directly affect the distribution of the driving risk potential field and that this strong correlation should not be ignored if one is interested in the vehicle's microscopic motion behavior. We further develop a driving risk potential field-based car-following model (DRPFM) to remedy the failure of acceleration consideration under the conventional environment, whose parameters are calibrated by filtered I-80 NGSIM data with frequent traf?c oscillations. Simulation results indicate that our proposed DRPFM model is proved to be a good description of car-following behavior and outperforms two classical car-following models (Optimal Velocity Model and Intelligent Driver Model) in frequent oscillation phases due to our consideration of potential acceleration data acquisition in real-time under the CAVs environment. In addition, this DRPFM model is applied to deduce the safety conditions for vehicle lane-changing. The analysis results prove that this model can reasonably explain the influencing factors between driver types and lane-changing safety conditions in practice.

Traffic safety has always been an important issue in sustainable transportation development, and the prediction of traffic accident severity remains a crucial challenging issue in the domain of traffic safety. A huge variety of forecasting models have been proposed to meet this challenge. These models gradually evolved from linear to nonlinear forms and from traditional statistical regression models to current popular machine learning models. Recently, a machine learning algorithm called Deep Forests based on the decision tree ensemble has aroused widespread concern, which was proposed for the first time by a research team of Nanjing University. This algorithm was proved to be more accurate and robust in comparison with other machine learning algorithms. Motivated by this benefit, this study employs the UK road safety dataset to propose a novel method for predicting the severity of traffic accidents based on the Deep Forests algorithm. To verify the superiority of our proposed method, several other machine learning algorithm-based perdition models were implemented to predict traffic accident severity with the same dataset, and the prediction results show that the Deep Forests algorithm present good stability, fewer hyper-parameters, and the highest accuracy under different level of training data volume. It is expected that the findings from this study would be helpful for the establishment or improvement of effective traffic safety system within a sustainable transportation system, which is of great significance for helping government managers to establish timely proactive strategies in traffic accident prevention and effectively improve road traffic safety.

Safety and efficiency have always been significant challenges to the development of road traffic. Detailed vehicle motion information is the prerequisite for achieving optimal control of the platoon and improving traffic safety and efficiency. The connected and automated vehicles (CAVs) system has offered unprecedented opportunities for the real-time collection and processing of these detailed vehicle motion data. Based on the model predictive control (MPC) framework and safety potential field (SPF) model, we developed an alternative CAVs platoon dynamic control method. The SPF model was applied to describe the road risk distribution under the complex driving environment and was embedded in the MPC framework to optimize the vehicle dynamics from the perspective of capacity, safety, and energy-saving. Also, some experiments were performed to verify the validity of our platoon control strategy. Compared with the fixed time-headway strategy, our proposed strategy can increase the traffic capacity by about 24.4%, while ensuring safety and improving fuel economy. The results indicate that the novel CAVs platoon control methodology proposed in this paper can be potentially applied to alleviate various traffic problems (e.g., traffic congestion, traffic accidents, and high emissions).

The macroscopic traffic flow model is the cornerstone of traffic flow theory and can be applied to investigate manytraffic planning and management issues. However, the macroscopic model of the heterogeneous traffic flow has not been profoundly studied. This paper developed a novel macroscopic model of the heterogeneous traffic flow under a partly connected and automated environment based on the safety potential field (SPF) theory. The SPF model was applied to describe the microscopic driving behaviour of traditional human-driven vehicles (HVs) and connected and automated vehicles (CAVs). A fundamental diagram (FD) model was derived according to the correlation with the microscopic car-following model under a steady-state condition. To verify the correlation between the FD and the microscopic simulation results, a Monte-Carlo-simulation was also performed. Finally, the macroscopic model of the heterogeneous traffic flow was validated through a simulation on a road section with a bottleneck under different CAVs market penetration rates ranging from 0.0 to 1.0. The results indicate that introducing the CAVs into the pure traffic flow of HVs may slightly have a negligible impact on road capacity when the CAVs penetration rate at a lower level, but the impact increases significantly as the CAVs penetration rate increased.

Platooning is considered to be a very effective method for improving traffic efficiency, traffic safety and fuel economy under the connected and automated environment. The prerequisite for realizing these advantages is how to form a platoon without any collisions and how to maintain and optimize the car-following behavior after platoon formation. However, most of the existing studies focus on the platoon configuration and information transmission method, while only a few attempt to address the issue of platoon formation and optimization methods. To this end, this study proposes a novel platoon formation and optimization model combining graph theory and safety potential field (G-SPF) theory for connected and automated vehicles (CAVs) under different vehicle distributions. Compared to previous studies, we innovatively incorporate the concept of the safety potential field to better describe the actual driving risk of vehicles and ensure their absolute safety. A four-step platoon formation and optimization strategy is developed to achieve platoon preliminary formation and platoon driving optimization control. Three traffic scenarios with different CAVs distributions are designed to verify the effectiveness of our proposed platoon formation method based on G-SPF theory, and the simulation results indicate that a collision-free platoon can be formed in a short time. Additionally, the G-SPF-based platoon driving optimization control method is demonstrated by comparing it with two typical control strategies. Compared with the constant spacing and constant time headway control strategies, the simulation results show that our proposed method can improve the traffic capacity by approximately 48.8% and 26.6%, respectively.