Connected and Automated Vehicles (CAV) have been rapidly developed, which, inevitably, renders that human-driven and autonomous vehicles share the road. Thus, trajectory prediction is an important research topic, which helps each CAV to efficiently follow a Human-Driven Vehicle (HV). In a wider scope, trajectory prediction, also, helps to improve the throughput of traffic flow and enhance its stability. To realize the trajectory prediction of Connected and Automated Vehicles to Human-Driven Vehicles, a car-following model, which is based on trajectory data, was established. Adding deep neural networks and an Attention mechanism, this paper established a data-driven car-following model, based on CNN-BiLSTM-Attention for CAV, to predict trajectory, by referring to the modeling idea of the traditional car-following model. The trajectory data in the next-generation-simulation (NGSIM) datasets that match the car-following characteristics were selected. In addition, noise-reduction pre-processing of the trajectory data was performed, to make it match the actual car-following situation. Experiments, for selecting the optimal structure of the model and the method of trajectory prediction, were carried out. The data-driven car-following models, such as LSTM, GRU, and CNN-BiLSTM, were selected for comparative analysis of trajectory prediction. The results show that the CNN-BiLSTM-Attention model has the smallest MAE and MSE as well as the largest R². The CNN-BiLSTM-Attention model has the highest accuracy in vehicle-trajectory prediction. The model can, effectively, realize vehicle-trajectory prediction and provide a theoretical basis for vehicle-trajectory-based velocity guidance of Human-Driven Vehicles. In the future, the model can, also, provide the theoretical basis for Connected and Automated Vehicles, to make car-following decisions in mixed traffic flow.

During signal transitions at road sections and intersections, pedestrians and vehicles often clash and cause traffic accidents due to unclear right-of-way. To solve this problem, a vehicle safety braking distance model considering human–vehicle characteristics is established and applied to the designed crosswalk safety warning system to enable pedestrians to cross the street intelligently. The model developed to consider human–vehicle characteristics improves the parking sight distance and pedestrian crossing safety psychological distance models by adding consideration of the effect of vehicle size and type on pedestrian psychology. The established model considering human–vehicle characteristics was improved for the stopping sight distance and pedestrian crossing safety psychological distance models. The effects of vehicle size and type on pedestrian psychology were taken into account. The designed warning system can be divided into a detection module, control module, warning module, and wireless communication module. The system detects the position and speed of pedestrians and vehicles and discriminates the conflict situation, executing the corresponding warning plan for three different types of situations. The system provides warning to pedestrians and vehicles through the different color displays of the intelligent crosswalk. The results show that the proposed model, which synergistically couples vehicle speed, driver reaction time, road characteristic correlation coefficients, and the psychological impact of vehicle size and type on pedestrians, is safe and effective. The designed system solves the problem of pedestrian crossing safety from both theoretical and technical aspects.

As the main road type in an urban traffic system, the increasingly severe congestion of the expressway restricts social and economic development. It is essential to explore the evolution law of congestion and dissipation to ensure the efficient operation of the expressway. In order to accurately grasp the evolution law of the expressway traffic flow state, this paper selects the expressway weaving section, which is a traffic flow frequency-changing area, to study the traffic operation state, change process, and evolution law, and determine the traffic state discrimination standard. The simulation analysis was carried out using the traffic simulation platform, Vissim software. The simulation results showed an apparent catastrophe phenomenon in the mutual transformation between free and congestion flow. The spectral clustering analysis algorithm was used to accurately extract the boundary of traffic state mutation, combined with the cusp catastrophe theory, to study and analyze the traffic flow state at different times and positions, thus completely displaying the evolution characteristics of traffic flow state. The research results provide an essential theoretical basis for formulating control measures of expressway traffic flow and strategies for traffic congestion dissipation.