Pure electric buses have become an important component of urban public transportation due to their environmental benefits. However, their widespread adoption is constrained by limited driving range, placing high demands on the planning of charging infrastructure and the formulation of vehicle schedules. Existing research often treats charging station siting and vehicle scheduling as independent problems, overlooking their interdependence. Moreover, most studies focus on single-depot or small-scale scenarios, which cannot adequately address the requirements for coordinated, cross-regional dispatching in large-scale and complex networks. To address these issues, this study constructs an integrated optimization model for electric bus charging station siting and vehicle scheduling. The model is built upon a spatio-temporal network framework designed for a multi-depot electric bus system. The objective is to minimize the total system cost, subject to various constraints including charging station construction, trip connection, state-of-charge(SOC) maintenance, vehicle scheduling, and charger matching.In order to accurately describe the operating cost, the model introduces the time-of-use electricity pricing and accounts for the parallel charging capacity of stations. To effectively solve this high-dimensional, discrete combinatorial optimization problem, an enhanced cultural memetic algorithm is designed. The algorithm incorporates improved genetic operators, introduces local search strategies such as trip-chain relocation and merging, and integrates a hierarchical constraint repair mechanism to ensure solution feasibility. The model and algorithm are validated using a case study based on a partial bus network in Chancheng District, Foshan City. The results demonstrate their effectiveness in handling problems of varying scales. Compared to traditional genetic algorithm and simulated annealing algorithm, the proposed algorithm can achieve better cost reduction in both small and large-scale instances. Sensitivity analysis further reveals that increasing battery capacity and reducing unit energy consumption can significantly reduce the total cost of the system, while the electricity pricing policy, especially off-peak rates, has a decisive influence on the operating cost. The study also confirms that charging station siting indirectly affects total cost by influencing scheduling efficiency, highlighting the necessity of joint optimization. This research enriches the theoretical framework for electric bus charging station siting and vehicle scheduling. The findings provide valuable, simultaneous insights for both the strategic planning and day-to-day operational decision-making of electric bus systems.

To provide a deep insight into the significant factors that affect the severity of freeway crashes, this study took the crash data from the Dongguan section of the Guang-Shen Yanjiang Freeway in China from 2014 to 2019 as the research object. Crash severity levels were divided into three categories (i. e., no injury crash, minor injury crash, severe injury or fatality crash). Accounting for spatial correlation among adjacent crashes via conditional autoregressive priors, spatial generalized ordered Probit models with different correlation distance thresholds were developed, where the crash severity was used as the dependent variable and 13 potential influencing factors were used as independent variables. The research results show that there is significant spatial correlation among crashes; the spatial generalized ordered Probit models outperform the generalized ordered Probit model and multinomial Logit model; and the spatial model with 250-meters correlation distance threshold achieves the best performance. The results of model parameter estimation reveal that the type and registered province of vehicles, the time of crash occurrence, curvature of crash location, bridge section, and crash type have significant effects on freeway crash severity. The marginal effects of these factors indicate that: as compared with crashes with cars involved only, the involvement of bus, truck and other type vehicles will increase the probability of severe injury or fatality by 3.27%, 1.53%, and 4.11%, respectively; the involvement of vehicles from other provinces will increase the probability of severe injury or fatality by 1.02%; as compared with those occurring on weekend, spring, and bridge, crashes occurring on weekdays, summer, and non-bridge sections would increase the probability of severe injury or fatality by 0.87%, 2.38%, and 0.08%, respectively; the probability of heavy casualties caused by bicycle accidents is 1.64%lower than that of multi-vehicle accidents; the probability of severe injury or fatality will decrease by 1.54% for per 1 km^-1 increase in horizontal curvature of crash location.

Private car commuters were divided into two groups: group Ⅰ who feel anxious about road congestion and group Ⅱ who are more concerned about whether they can arrive punctually. The trip sensitivity coefficient (congestion anxiety coefficient, punctual anxiety coefficient) and the bottleneck tolerance coefficient were introduced to characterize the travel anxiety level of the two types of commuters. A new trip cost function was developed based on the standard bottleneck model and was applied to analyze indicators such as departure-rate, peak-start-end time and commuter-composition in single and shared routes. The results show that when there is only a certain type of commuters on the road, the increase of the congestion anxiety coefficient, bottleneck tolerance coefficient and punctual anxiety coefficient will reduce the total perceived travel cost of the system for commuters on a single line. The number of group Ⅱ increases, the total perceived travel cost of system decreases, and the peak period shifts forward as the punctual anxiety coefficient of group Ⅱ in mixed situation 1 rises; the total perceived travel cost of system falls as the bottleneck tolerance coefficient increases, while the peak period moves back. In the mixed situation 2, the congestion anxiety coefficient and the bottleneck tolerance coefficient of group Ⅰ have dual effects on the number of this group, and under the combinations of different bottleneck tolerance coefficient and congestion anxiety coefficient, the attractiveness of this group exhibits three different trends: monotonically decreasing, first decreasing and then increasing, and monotonically increasing.

Accident data collected from 2014 to 2019 for the Dongguan section of the Guangshen Yanjiang Expressway in China were utilized to investigate the key factors influencing the severity of road property damage in highway traffic accidents. The spatial correlation among adjacent accidents was addressed using a spatial generalized ordered Probit model, which employed varying association distance thresholds. An XGBoost machine learning algorithm was developed to estimate the model parameters, and the SHAP (SHapley Additive exPlanations) method was employed to elucidate the model outputs. The results show that significant spatial correlations are present within the accident data. The spatial generalized ordered Probit model demonstrated superior performance compared to the conventional generalized ordered Probit model, with the model based on a 200 m association distance threshold yielding the best results. The SHAP method significantly enhanced the interpretability of the XGBoost machine learning model. Parameter estimation revealed that variables such as single-vehicle accidents, passenger cars, lorries, heavy tractors, nighttime occurrences, early morning periods, cloudy conditions, rainy conditions, and bridge locations were significantly associated with the severity of road property damage resulting from traffic accidents.