The development of rural e-commerce has rapidly driven the development of rural logistics in China. Improving the service quality of the last mile delivery is an important measure to promote the sustainable development of rural e-commerce logistics. However, such work is challenging because the current rural last mile delivery is inefficient and unsustainable and is influenced by a set of interacting factors. It is necessary to explore the relationships among the sustainability influencing factors of rural last mile delivery. A total of 15 sustainability influencing factors are selected. The improved fuzzy analytic hierarchy process (FAHP) is used to assign the weights of the factors and then the interpretative structural model (ISM) is used to determine the hierarchical structure of each factor. The driving force-dependency quadrant graph is constructed by cross-impact matrix multiplication (MICMAC). The research results show that four factors, including “convenience of returning goods”, “integrity of goods”, “advance reservation of goods pickup”, and “delivery costs”, are the most basic factors affecting the sustainability of rural last mile delivery and are the deepest and most indispensable factors. This research provides valuable information for decision makers to develop proactive strategies and reinforcement policies to improve the service quality of rural last mile delivery, which could promote the sustainable development of rural logistics.

Carbon emissions from the logistics industry have been rising year after year. Correct handling of the relationship between economic development and environmental protection is of great significance to the implementation of green logistics, which is an important component of China’s strategy for strong transportation. This paper focuses on the evaluation of the carbon emissions efficiency of logistics industry from a new strong transportation strategy perspective. A super-efficiency slack-based measurement (Super-SBM) model and Malmquist index are combined to evaluate the static and dynamic carbon emissions efficiency of the logistics industry. The results indicate that compared with the SBM model, the Super-SBM model can more effectively measure the carbon emissions efficiency of the logistics industry. Pilot regions for the strong transportation strategy were divided into two categories, namely regions with slow carbon emission growth rates but high efficiency, and regions with high carbon emission growth rates but low efficiency. Some policy recommendations from the strong transportation strategy perspective were proposed to improve the carbon emissions efficiency of the logistics industry, especially for the second category of pilot regions. This study is expected to provide a basis for decision-making for efficient emissions reduction measures and policies, and to encourage the pilot regions to take the lead in achieving the goal of China’s strategy for transportation.

The primary objective of this study is to identify the characteristics of vehicle headway on multi-lane freeway under lane management in China considering car–truck interaction. More specifically, the study focused on answering the following two questions: (1) whether the car–truck interaction has impact on headway, that is, headway varies by different leading and following vehicle types and (2) what is the best-fitted distribution model for particular headway type under lane management. The team collected traffic data, including traffic flow rates, percentage of trucks, speeds, headways, and so on from four segments of Shanghai-Nanjing freeway, Jiangsu Province in China. Then, some statistical methods were used to analyze the vehicle headway. It was found that car–car, car/truck, and truck–truck headways are significantly different from each other. Also, the traffic flow rate, percentage of trucks, and lane position were found to have an influence on the vehicle headway through the tests. Using the maximum-likelihood estimation, Kolmogorov–Smirnov test, and chi-square test techniques, the distribution models and parameter functions for each headway type were built and validated. The results showed that lognormal model is suitable for car–car and truck–truck headway types, and inverse Gaussian model fits the car/truck headway type well.

As an important part of urban public transportation systems, the feeder bus fills a service gap left by rail transit, effectively extending the range of rail transit’s service and solving the problem of short‐distance travel and interchanges. By defining the potential demand of feeder bus services and considering its relationship with the traffic demands of corresponding staging areas, the distance between road and rail transit, and the repetition factor of road bus lines, this paper established a potential demand model of roads by opening feeder bus services and applying a logit model for passenger flow distribution. Based on a circular route model, a route starting and ending at urban rail transit stations was generated, and a genetic algorithm was then applied to solve it. The Wei‐Fang community of Shanghai was selected as the test area. Per the model and algorithm, the feeder route length was conformed to a functional orientation of short‐distance travel and the feeder service of a feeder bus; the route mostly covered where conventional bus lines were fewer, which is a finding that is in agreement with the actual situation; the feasibility of the model and algorithm was verified.

This paper aims to analyze the impact of trucks on traffic flow and propose an improved cellular automaton model, which considers both the performance difference between passenger cars and trucks and the behaviour change of passenger cars under the impact of trucks. A questionnaire survey has been conducted to find out whether the impact of trucks exists and how the behaviour of passenger car drivers changes under the impact of trucks. The survey results confirm that the impact of trucks exists and indicate that passenger car drivers will enlarge the space gap, decelerate, and change lanes in advance when they are affected. Simulation results show that traffic volume is still affected by percentages of trucks in the congestion phase in the proposed model compared with traditional heterogeneous cellular automaton models. Traffic volume and speed decrease with the impact of trucks in the congestion phase. The impact of trucks can increase traffic congestion as it increases. However, it has different influences on the speed variance of passenger cars in different occupancies. In the proposed model, the relative relationship of the space gap between car‐following‐truck and car‐following‐car is changeable at a certain value of occupancy, which is related to the impact of trucks.