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.

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.

Based on the optimal velocity model, with the consideration of the effect of drivers heterogeneity of the disturbance risk appetite, an extended model is proposed. The linear stable judging condition is obtained for the extended model by its stability analysis, which shows the smaller the ratio of the coefficient of the disturbance risk appetite for the former car driver to that for the following-car driver, the larger the stability area of the extended model is. The kink-antikink density wave is attained in an unstable area of the model by nonlinear analysis. Simulation results also show that the smaller the ratio of the coefficient of the disturbance risk appetite for the former car driver to that for the following-car driver, the better it is for the initial traffic flow to inhibit the disturbance propagation, hinder the formation of traffic jams and restore homeostasis. This paper provides a viable idea to reduce traffic congestion, for example, by forming convoys traveling in order from small to big according to the relative size of drivers coefficients of the disturbance risk appetite.

This paper reviews the traffic flow lattice model for road vehicle traffic flow, and describes its advantages and disadvantages; based on the research carried out by the authors, discusses the reality conformity of some model assumptions and puts forward some new insights and views.