Electric vehicles (EVs) have the advantages of low pollution, low energy consumption, and high energy efficiency, so they are highly valued by governments, enterprises, and consumers. However, the promotion and use of electric vehicles is restricted to a certain extent because of their limited range. This paper selects electric vehicle intercity medium- and long-distance travel as the research object, and takes the classical flow-capturing location problem as the theoretical basis for the expressway network or national highway network. This paper also considers the driver’s charging choice behavior and range anxiety, studies the electric vehicle charging station location problem, establishes the charging station location model, and uses the Tabu search algorithm to solve the problem. Finally, the effectiveness of the model and algorithm is verified by empirical analysis. The results show that the charging station location model considering the driver’s charging choice behavior and range anxiety performs better.

This paper proposes a bus line capacity optimization design model considering the scale of multiple vehicles, which is achieved by minimizing system operating costs and user costs. The proposed model takes into account the difference of passenger demand in different periods, and can get the optimal headway and delivery and reserve plan. In order to prove that the method can effectively minimize the cost, we solved a numerical example and compared the cost of the method in multi-transit model planning. Furthermore, the optimization results show that the total costs (TC) were reduced by 14.48%. Among them, the user costs (UC) decreased by 30.38% and the operator costs (OC) increased by 4.18%. Sensitivity analyses are presented to verify the validity of the model. The analysis results show that multi size bus optimization can reduce the total cost, especially the user cost in a certain cost weight interval. Besides this, the cost weight which reflects the passenger volume and waiting time value, optional bus size and cross-section passenger volume all affect vehicle scheme and system cost.

Student commuting is an important part of urban travel demand and private car commuting plays an important role in urban traffic, especially in areas near schools. Since parents, especially the parents of elementary and junior high school students, prefer to drive rather than take public transport, there will be a negative effect on traffic management. To address the challenge, a simulation model is established based on schools’ surrounding regions to analyze traffic status. Specifically, the model focuses on urban construction and transportation near the entrance of schools and neighborhoods. In addition, four variable parameters consisting of the directional hourly volume, the parking demand of delivery vehicles, the distance between the school and intersection, and the average parking time for pick-up vehicles are set as influence factors, while traffic efficiency, energy consumption, and pollutant emissions are considered as the evaluation criteria of our model. Extensive simulated experiments show that comparing different scenarios, the traffic state of schools’ surrounding areas can achieve much better performance when the distance between entrances and intersections is 400 m under the 1000 pcu/h condition. This research can provide a scientific basis for school regional traffic management and organization optimization.

This study aims to identify key factors and sensitive intervals affect the school road traffic characteristics. We collect traffic data from the parking area and the school road (400-700 meters). The simulation is calibrated to ensure the error of outputs are within 1.5%. A sensitivity analysis method is proposed, it makes the multifactor comparable. The sensitivity factors of vehicle delay, queue length, and average speed are 1.44, 2.03, and 0.28 in school road, and the bottleneck road are 3.07, 4.44, and 0.65. The traffic indicators change more concentrated in bottleneck road but greater in school road. 6 scenarios are created to analyze school road traffic characteristics. Traffic flow (TF), number of parking spaces (NPS), and stopping time (ST) are selected as variables. Scenarios 1-3 are univariate, and scenarios 4-6 are bivariate. TF is the key factor with a sensitivity interval of [1300,1700].