Microscopic traffic simulation can provide scientific support for traffic design, traffic planning, traffic monitoring, and traffic demand management, and how to construct accurate and efficient microscopic traffic simulation is an important research direction. Current research on microscopic traffic simulation mainly focuses on the basic theory, such as the car following model and lane changing model. However, there is a lack of research on the practice and application of microscopic traffic simulation, especially for large-scale microscopic traffic simulation. In this study, we proposed a simple and efficient method for large-scale microscopic traffic simulation, and built a city-level microscopic traffic simulation system of Xiaoshan District, Hangzhou, China as an example. OpenStreetMap (OSM) data and license plate recognition (LPR) data were firstly fused, and then the road network, traffic infrastructure and travel information of vehicles were obtained based on the fused data. Next, the travel demand was obtained using the dynamic traffic assignment method and route choice algorithm. On this basis, the Simulation of Urban MObility (SUMO) platform was used for city-level microscopic traffic simulation. Finally, a calibration method was proposed to calibrate the microscopic traffic simulation system. The results show that the proposed method can simulate the traffic operation dynamics well.

To improve the adaptability of Connected and Automated Vehicles (CAVs) in mixed traffic, this study proposes a prediction model training indicator that comprehensively considers drivers' Social Value Orientation (SVO) and planning goals. Active Influence Factor (AIF) is used as the goal to predict the future safety loss and consistency loss of CAVs. Second, an objective function based on SVO is constructed to understand the driver’s characteristics to evaluate the safety, comfort, efficiency, and consistency of candidate trajectories. The results showed that integrating SVO and consistency functions can help ensure that CAVs drive under a more stable risk potential energy field. The prediction planning model that considers SVO can improve the reliability of the CAV output trajectory to a certain extent. The prediction planning under the AIF has better accuracy and stability of the output trajectory; however, it still has strong adaptability and superiority under different sensitivity parameters. The minimum and maximum standard deviations of our model are 0.78 and 0.78 m, respectively, whereas the minimum and maximum standard deviations of the comparative model reach 2.07 and 4.56 m, respectively. The minimum standard deviation of the other comparative model reaches 1.35 m, and the maximum standard deviation reaches 4.45 m.

The outbreak and spreading of the COVID-19 pandemic have had a significant impact on transportation system. By analyzing the impact of the pandemic on the transportation system, the impact of the pandemic on the social economy can be reflected to a certain extent, and the effect of anti-pandemic policy implementation can also be evaluated. In addition, the analysis results are expected to provide support for policy optimization. Currently, most of the relevant studies analyze the impact of the pandemic on the overall transportation system from the macro perspective, while few studies quantitatively analyze the impact of the pandemic on individual spatiotemporal travel behavior. Based on the license plate recognition (LPR) data, this paper analyzes the spatiotemporal travel patterns of travelers in each stage of the pandemic progress, quantifies the change of travelers' spatiotemporal behaviors, and analyzes the adjustment of travelers' behaviors under the influence of the pandemic. There are three different behavior adjustment strategies under the influence of the pandemic, and the behavior adjustment is related to the individual's past travel habits. The paper quantitatively assesses the impact of the COVID-19 pandemic on individual travel behavior. And the method proposed in this paper can be used to quantitatively assess the impact of any long-term emergency on individual micro travel behavior.