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 formulates a dynamic approach for real-time bus control in uncertain demand. This dynamic approach aims to save the total cost for passengers and operators, while improving transit service reliability. An unfixed rolling horizon was implemented to choose the best dynamic approach. Real-time control predicts two discrete variables (arrival time and bus position) and determines the space-time point of buses. Furthermore, controlled actions include stop skipping and bus holding. The holding time starts when a bus serves a station and depends on previous intervals of passenger boarding and alighting at the station. The stop skipping action allows a bus to skip not only stations with a short-turning exception, but also stations with low demand for boarding that have been alighted in the short-turning segment. Stop skipping and bus holding actions for short-turning service both decrease the travel time of served passengers and the running time of buses, thus improving transit service reliability. A genetic algorithm was applied to solve the problem and the validity of the proposed dynamic approach was tested with four different scenarios. The result of these tests shows that a dynamic short-term bus control can significantly reduce total cost and improve transit service reliability.

The urban public transportation system is an important part of urban transportation, and the rationality of public transportation routes layout plays a vital role in the transportation of the city. Improving the efficiency of public transportation can have a positive impact on the operation of the public transportation system. This paper uses complex network theory and the symmetry of the up and down bus routes and stations to establish an urban public transit network model and calculates the probability of passengers choosing different routes in the public transit network according to passenger travel impedance. Based on passenger travel impedance, travel path probability and passenger travel demand, the links are weighed, and the network efficiency calculation method is improved. Finally, the public transit network optimization model was established with network efficiency as the objective function and solved by the ant colony algorithm. In order to verify the effectiveness of the model and the solution method, this paper selects areas in Nanguan District of Changchun City for example analysis. The result shows that the efficiency of the optimized network is 8.5% higher than that of the original network, which proves the feasibility of the optimized model and solution method.

Within the scope of sustainability, the development of park-and-ride (P&R) facilities can reduce the exhaust emissions of motor vehicles and help build an environmentally friendly society. The location of P&R facilities is the primary consideration in planning, and it plays a decisive role in their transportation, environmental and economic benefits. Given the difficulty of obtaining full data information of traffic network attributes and travel demand characteristics, in this study the P&R facilities in the outer suburbs and cities as were taken as the research object, with a simplified transportation network, improving the principle of nearby selection and 0–1 demand coverage in existing research. By analyzing the P&R facility coverage and demand intensity characteristics, a P&R facility location model considering the coverage demand characteristics was constructed, and a solution algorithm is proposed. The validity of the established model was verified by comparing the existing location model and conducting sensitivity analysis.

In small, underdeveloped Chinese cities, residents have few options for transportation and travel service problems have not received enough attention from the authorities. This study examines residents’ preferred mode of travel in Lhasa and Yushu, China, two small and underdeveloped cities in which travelers tend to be economically disadvantaged. Travel data from different regions was analyzed to explore their commonalities. A structural equation model with latent variables is proposed to capture the heterogeneity not observed in the selection process. Results indicate that four of the six latent variables—preference for comfort, preference for reliability, preference for convenience and safety consciousness—are more helpful than preferences for flexibility and environmental awareness in explaining transportation utility, which could reflect residents’ travel behavior. Based on the results, respondents were divided into five groups with similar travel preferences through the k-means clustering method. The findings show that ensuring high comfort and convenience and moderate safety and reliability is conducive to increasing residents’ use of public transport. Furthermore, an examination of residents’ sociodemographic differences reflects that, in the future, active transport demand management should focus on trying to satisfy the preferences of female, low-income and elderly travelers.

With the evolution of people's consumption habits and the rapid growth of urban logistics, the number of trucks and delivery frequency has increased significantly, exacerbating urban traffic congestion and environmental pollution. Consequently, there is an urgent need to improve the current inefficient and highly polluted distribution mode. Currently, in most cities in China, the metro passenger flow is insufficient, and the capacity is excessive during off-peak hours, resulting in underutilized carriage capacity. The integration of surface and underground transport resources can effectively address these issues and facilitate complementary advantages and win–win cooperation between express companies and metro enterprises. This study proposes an innovative problem of split demand route planning in the cooperative distribution system involving express companies and subways. A cooperative distribution model is developed to minimize the total cost, and solved by the Cuckoo Search algorithm to obtain the optimized solution. The model is applied to the urban rail transit network in Changchun, and the results demonstrate that it is effective in reducing truck mileage and distribution costs. Compared to the single delivery pattern by trucks, the cooperative distribution approach proves to be more cost-effective and environmentally friendly.