With the ongoing development of integrated vehicle-road-cloud systems, mixed traffic flow composed of human-driven vehicles (HDVs) and connected and autonomous vehicles (CAVs) is expected to become the dominant form of future transportation. To explore the influence mechanism of CAV human-like driving strategy and sensing information capability on human-machine mixed driving traffic flow under occasional accident, this paper improved the cellular automata rules under the framework of the KKW (Kerner-Klenov-Wolf) model, introduced the synchronization factor to consider the CAV human-like driving strategy, and constructed the HDV and CAV car-following rules for different following modes. Considering the lane-changing demand in accident scenarios, a multi-lane discretionary lane-changing strategy incorporating the lane preference of HDVs and CAVs was constructed, along with a mandatory lane-changing rule for CAVs based on lane-changing pressure. Sensitivity analysis was conducted on different lane-changing pressure parameters. Through numerical simulations, the effects of varying traffic volume, CAV penetration rate, CAV perception range of accident information, and CAV human-like driving strategies on mixed traffic flow were analyzed. The results show that the increase of CAVs can effectively alleviate the congestion of traffic flow after occasional accident and limit the spatial and temporal scope of congestion, and the average speed and average traffic volume of the low traffic volume are increased by 11.74% and 6.32%, respectively, when CAV penetration rate is increased from 0 to 1. The enhancement is lower than that of medium and high traffic volume. In the case of medium and high traffic volume with CAV penetration rate greater than 0.4, with the increase of CAV accident information sensing range, the congestion space in the merging area is gradually dispersed, and traffic efficiency is improved. With the transition of the CAV human-like driving strategy from aggressive to conservative, the flow of the human-machine mixed driving traffic flow is gradually reduced, and the range of slow queues expands, traffic congestion gradually worsens, and the trend of speed fluctuations in each lane gradually converges over time.

In order to improve the electricity exchange efficiency of urban pure electric bus and reduce the construction and operation costs of charging stations, this paper studied the optimization of urban battery exchange pure electric bus charging station siting and capacity selection. Firstly, considering the operation guarantee capacity of pure electric bus exchange stations, the study established a model of the number of exchange facilities and battery reserve capacity and obtained the number of exchange stations and optimal battery configuration. Then, based on the pure electric public exchange demand, the charging station operation conditions were modeled by using queuing theory, and penalty factors were set to ensure the service quality of charging stations. A site selection and capacity model with service radius, service intensity and supply and demand balance as constraints and minimum annual total cost as the object was established, and GA, PSO and PSO-GA algorithms were applied to solve it. Finally, a sensitivity analysis of the siting and capacity model was performed to obtain the effects of parameters such as charging rate and rated driving range on the siting results. The results of the case application show that the PSO-GA algorithm is better than the GA and PSO algorithms in terms of objective function and convergence speed, and the optimal number of charging stations is 30, the number of charging piles is 943, and the lowest total cost is 12151429000 RMB. The rated driving range of pure electric buses is negatively correlated with the number of stations, transportation cost and construction cost; the charging rate is negatively correlated with the number of stations and construction cost and positively correlated with transportation cost, and increasing the charging rate will accelerate battery aging and reduce battery life. The research results can provide theoretical basis for the reasonable planning and operation of urban pure electric bus charging stations.

In order to obtain the spatial distributing characteristics of hazardous bus driving status, this paper identified the spatial clustering through spatial autocorrelation analysis, determined the hot spots, and analyzed the significant influencing factors. Firstly, the study collected position system data samples of the urban buses for one week in each of the four quarters and modified the duplicate, abnormal and missing data. Bus stops were used as nodes to divide spatial spots, and every spot was numbered. Over speed, urgent acceleration, urgent deceleration and sharp turn were identified as hazardous driving status. The four conditions thresholds were obtained according to the kinematic characteristics of vehicles. The study calculated statistical indicators and global Moran’s I_g of four conditions. The results show that hazardous driving status are spatially clustered (probability of a spatial random distribution p ＜ 0.01, standard deviation score Z ＞ 2.58). Over speed has most significant characteristic of spatial clustering (I_g = 0.731). The study performed local spatial autocorrelation analysis for the four conditions. According to the analysis, local Moran’s I scatter plots and LISA clustering plots are plotted at 90%, 95% and 99% confidence levels. The hazardous hot spots of urban buses were obtained combining with city maps. Finally, the study selected 9 factors such as road length, number of lanes and straightness to formulate models. The compare and analysis were performed to get the fitting goodness of OLS, SLE, SEM and SDM model. The SDM model was used to obtain the significant influencing factors for 4 dangerous driving states. The results can provide a theoretical basis for supervising the safety operation and identifying the hazardous driving status of urban buses in spatial perspective.