The impacts of transportation infrastructure on sustainable economic development are multifaceted. The existing literature works on the assumption that transportation infrastructure influences sustainable economic development by influencing production behavior. By observing the increasingly common phenomenon of family tourism, this paper finds that transportation infrastructure not only affects production behavior, but also directly impacts residents’ consumption behavior. Based on this, this paper constructs a growth model in which the traffic infrastructure affects both production and consumption in order to reveal the mechanism by which railway infrastructure changes residents’ consumption structures and ultimately promotes sustainable economic growth by promoting private tourism. In order to verify this theoretical proposition, this paper also constructs panel data from the provinces of China from 2008 to 2018 and conducts an empirical study. It was found that railway and railway infrastructure investments can not only directly promote sustainable economic development through the multiplier effect, but can also indirectly promote economic growth by increasing the amounts of private tourism and their proportions.

The safety of dangerous goods transport by air is directly related to human health and environmental pollution. This paper investigates a model to evaluate the safety performance of the transport of dangerous goods by air carriers. Based on a literature review, international regulations related to dangerous goods air transportation, and expert opinions, this paper identifies an assessment factor system with five drivers: organization/regulations, equipment/facilities, operations, emergency, and training. A hybrid evaluation method of a joint analytical hierarchy process and entropy weight is used to determine the importance of each factor and driver. The results suggest that the regulation of dangerous goods acceptance, sufficient equipment/facilities, and the condition of the equipment/facilities are the most important factors affecting the safety performance of dangerous goods transportation by air. An empirical study reveals that the proposed model is stable and reliable; thus, the model can guide resource allocation for air carriers to improve safety management of dangerous goods transportation.

Safe traffic is an important part of sustainable transportation. Road traffic accidents lead to a large number of casualties and property losses every year. Current research mainly studies some types of traffic accidents and ignores other types of traffic accidents; therefore, taking various types of road traffic accidents as a whole, an overall study of their influencing factors is urgently needed. To improve road traffic safety, taking various types of road traffic accidents as a whole, this paper analyzes the influencing factors and finds out the causative factors of road traffic accidents. A new index system of road traffic accident influencing factors is constructed based on the existing literature and real traffic data, and their subjective weights and objective weights are obtained by the analytic hierarchy process based on the subjective data and the normalization of the actual traffic data for Yizheng City, Yangzhou, China from January 2020 to December 2020, where the subjective weights are the main weights, and comprehensive weights are obtained by the minimum discrimination information principle correcting the subjective weights with the objective weights. Finally, the global weights, their ranks, and their weight differences are obtained. The main findings are as follows: (1) compared with the real traffic data, experts generally overestimate the impact of road factors on traffic accidents and underestimate the impact of human factors on traffic accidents; (2) in the first-level, human factors and road factors are the causative factors; (3) in the second-level, “motor vehicle drivers’ misconduct”, “road condition”, and “road section” are the causative factors; and (4) in the third-level, “slippery road”, “rain and snow weather”, “intersection”, and “untimely braking” are the causative factors. The research results can provide some scientific basis for improving road traffic safety.

Topological analyses of multi-airport regional air transport networks are the basis for the sustainable development of multi-airport systems. In this study, we modeled the Yangtze River Delta (YRD) region’s airport infrastructure as a network and presented a weighted approach by which to analyze the network structure and robustness from the perspective of complex network theory. The analysis of the Yangtze River Delta Airport Network (YRDAN) indicates that it is a small-world network, and its cumulative degree has a power-law distribution, suggesting that it has scale-free properties. As its weighted clustering coefficient was found to be much smaller than the non-weighted counterpart, this demonstrates that most of the network traffic is focused on a hub-and-spoke pattern. Furthermore, the over-centrality of the YRDAN suggests weak accessibility of small cities and high dependence of air transport on the hub-and-spoke pattern. The assessment of the robustness of the YRDAN in the face of intentional attacks found that domestic networks are more robust than foreign aviation networks. However, the isolation of a small fraction of selected nodes can cause serious problems in the functioning of the YRDAN.

Investigation shows that train travel has a lower pollution impact on the environment than flight travel or car travel. A stated preferences (SP) survey can effectively obtain the data of the commuter’s response to the hypothetical train price changes beyond the scope of previous observations. To this end, based on SP survey, we estimate the price elasticity of train travel demand and analyze its variation rules. It is shown that: (1) the own-price elasticities of demand are −1.049028 during peak period and −1.090438 during off-peak period, respectively; (2) the cross-price elasticities of demand are 0.001280 for train and air and 0.001156 for train and car during peak period; and 0.001350 for train and air and 0.001230 for train and car during off-peak period; (3) the own and cross-price elasticities of demand during off-peak period are bigger than the ones during peak period; (4) when the influence factors’ influence degree is 3 or 5, the own and cross-price elasticities of demand are largest; when the influence degree is 1, the own and cross-price elasticities of demand are smallest. A result application example shows that the elasticities obtained from this paper could be used to reduce energy used and CO₂ emissions effectively.