The connectivity of the urban public transport network and the convenience of transfers between modes of transit are important factors that affect whether passengers choose public transport. Identifying the key nodes that affect network connectivity, direct access, transfers, and clarifying the main factors that restrict the network efficiency play important roles in improving the efficiency of the public transport network and establishing a “green city”. On this premise, this paper constructs two single-layer networks and a composite network that can reflect the transfer relationship between ‘bus–bus’, ‘metro–metro’, and ‘metro–bus’ based on the method of Space-P. The composite network realizes the integration study of homogeneous and heterogeneous stops, lines, and transfer relationships in the public transport network. At the same time, five kinds of centrality indexes are applied to the transport transfer network, and the significance of these indexes in the network is explained. Through the comprehensive analysis of these five types of indexes, the key nodes affecting the network connectivity, direct access and transfer efficiency, can be identified more accurately. Taking the public transport network of Xi’an as an example, the structural characteristics of the networks, including scale-free and small-world characteristics, were empirically analyzed. The main stops that play important roles in networks were identified based on the integrated centrality, degrees, and weight degrees. The research results showed the following: (1) Xi’an’s metro network, bus network, and metro–bus composite network all have scale-free and small-world characteristics. (2) The influence of the key stops of the metro network is concentrated, while the influence of the key stops of the bus network is scattered. (3) The public transport network in the first ring road area of Xi’an has the highest degree of direct access, and the core areas of the south, west, and north of Xi’an also have high direct access. However, the direct access in the area east of Xi’an is slightly lower. (4) Xi’an’s bus transport network covers a large area, showing the characteristics of a dual-core “central + southern” network. (5) The metro–bus composite network demonstrates a closer connection between stops and a more balanced network. (6) Finally, the degree of direct access to stops in the bus transport network and metro transport network shows the characteristics of “the single core is dominant, and the circle diffusion weakens step by step”.

This study examines the application of large language models (LLMs) in analyzing subjective driver perceptions during tunnel driving simulations, comparing the effectiveness of questionnaires and interviews. Building on previous research involving driver simulations, we recruited 29 new participants, collected their perceptions via questionnaires, and conducted follow-up interviews. The interview data were analyzed using three LLMs: GPT-3.5, GPT-4, and Google-Gemini. The results revealed that while GPT-4 provides more in-depth and accurate analysis, it is significantly slower than GPT-3.5. Conversely, Google-Gemini demonstrated a balance between analysis quality and speed, outperforming the other models overall. Despite the challenge of occasional misunderstandings, LLMs still have the potential to enhance the efficiency and accuracy of subjective data analysis in transportation research.

This paper defines the weighted super adjacency matrix based on the existing super adjacency matrix. This paper, for the first time, combines the trade network, competitive network, and complementary network to construct the trade multilayer network, and innovatively defines the interlayer connections. Based on this, we build trade multilayer networks for three major agricultural products in the western region along the “Belt and Road”. The paper then proposes analytical methods, including a classification algorithm for local network relations and a comparative analysis of trade development priorities based on the local network relations. The former is used to identify and categorize key trading countries and potential trading countries for western agricultural products along the “Belt and Road”. The latter is used to examine the western regions’ order of priority in developing trade relations with the classified countries. According to the findings, category I agricultural trade has 37 key trading countries among the “Belt and Road” countries. Their local network relationships are classified into six groups, and their trade development priorities are classified into four levels. There are 49 key trading countries in category II agricultural trade. Their local network relations can be classified into three groups, and their trade development priorities can be classified into two levels. There are 62 key trading countries in category IV agricultural trade. Their local network relationships are classified into six groups, and their trade development priorities are classified into four levels. Furthermore, only Chile is a potential trading partner in category I agricultural trade. Finally, this paper offers policy recommendations for the development of agricultural trade along the “Belt and Road” in the western region.

Reducing urban transportation carbon emissions is a fashionable task around the globe. Electric cars, fuel cell cars, hydrogen cars, restriction policies, etc. are all aimed at this goal. But they are all expensive to make them into reality. Transportation management can also provide some contribution to this, like optimizing operation rules such as Left-turn Waiting Area Twice Startup. From the stop line to the Left-turn Waiting Area (LWA), one vehicle will start twice with more emissions due to the engine’s inefficient operating range. We proposed an Accurate Left-turn One-time Startup Model (ALOSM) based on the LWA rule, which includes the intersection geometry parameters, traffic light timing schemes, vehicle start speeds, vehicle operation speeds, and fuel vehicle ratios. Make the Left-turn Vehicle (LV) waiting behind the stop line during the through vehicle light green. Calculate the left-turn green light adjustment duration and make the LV and the last through vehicle across at one moment. With real collected data in a symbolic intersection in Xi’an, China, the simulation result shows vehicle emission would be reduced by 13% and traffic efficiency would increase by 5% in this single intersection. The whole city of 5 million vehicles would reduce 2,087.88kg CO emission in one day due to estimation.

Fragmentation in road networks and hierarchical imbalances among nodes increase circulation impedance in China’s county-level delivery networks, hindering efficient county–township–village circulation. This study integrates spatial syntax, complex network analysis, hierarchical connection-cost analysis, and a coupling–coordination degree model to develop a network evaluation framework for identifying structural bottlenecks and analyzing circulation efficiency. Using eight counties in Shaanxi Province as empirical cases, we quantify road-network accessibility using a spatial syntax axial model and examine hierarchical node organization across county, township, and village tiers using complex network indicators. Network-level circulation efficiency is analyzed using road accessibility, hierarchical connection costs, county–village connection impedance, and average path length. Correlation and regression analyses are further used to examine the relationship between morphology–structure coordination and network impedance. The results show that higher road-network integration is generally associated with lower hierarchical connection costs, and higher morphology–structure coordination is significantly associated with lower county–village connection impedance and shorter average path length. The proposed network-level evaluation framework provides a structural basis for identifying accessibility deficiencies, hierarchical connectivity constraints, and morphology–structure mismatches, thereby informing efforts to reduce circulation impedance in county–township–village delivery networks.