In this work, to clarify the impact of electric bicycle drivers’ risky driving behavior on driving safety, we used multiple regression analysis methods combined with a questionnaire survey of residents of the city of Guilin, China. We studied the impact of the two dimensions of safety knowledge and safety attitude on risky driving behavior, and identified the differences in the impact of these two dimensions from the perspective of personal characteristics. Through modeling analysis, we found that “responsible attitude” and “group behavior attitude” explain 62.4% of the variation in aggressive behavior; 48.5% of the variation in negligent behavior is caused by “age”, “safety knowledge” and “responsible attitude”; and 52% of the variation in violations is caused by “age”, “violation attitude” and “group behavior attitude”. The results show that “group behavior attitude” affects the occurrence of aggression; that safety knowledge has a significant negative impact on unintentional negligence but has no significant effect on deliberate violations and aggression; and that the difference in risky driving behavior is mainly manifested in “age”, “gender”, “violation” and “accident experience”.

The outbreak of multiple disaster sites during the coronavirus disease 2019 (COVID-19) pandemic has presented challenges due to varying access time intensity, population density, and medical resources at each site. To address these issues, this study focuses on 13 districts and counties in Wuhan, China. The importance of each research area is analyzed using the improved PageRank and TOPSIS algorithms to determine the optimal site selection plan. Additionally, a particle swarm algorithm is used to construct an emergency material dispatching model that targets both distribution and site selection costs to solve the multi-distribution center dispatching problem. The results suggest that constructing 10 distribution centers can satisfy the demand for epidemic prevention and control in Wuhan city while saving costs associated with site selection and material distribution. Compared to the previous optimal solution, the distribution and site selection costs under the optimal solution decreased by 27.9% and 17.82%, respectively. This approach can serve as a basis for dispatching emergency materials during public health emergencies.

Based on the complex network theory, we established a topological network of the Beijing subway under Space L, Space P, and Space C. Then, we analyzed the complex characteristics of the subway network under each topological network, proposed the global impact indexes (including aggregation impact coefficient, path length impact coefficient, network efficiency impact coefficient, and connectivity impact coefficient), and interline impact indexes (including degree centrality impact coefficient, near-centrality impact coefficient and intermediate centrality impact coefficient, the higher the value, the more obvious the effect on other lines; degree centrality sensitivity coefficient, near-centrality sensitivity coefficient and intermediate centrality sensitivity coefficient, the higher the value, the more vulnerable to the impact of other lines). At the global and local levels, it is possible to analyze the effect of different lines on the global situation and other lines. The concept of the “line importance index” is proposed to identify the key lines in the Beijing subway network. The network is characterized by scale-free and small-world characteristics under Space P, and scale-free network characteristics but no small-world characteristics under Space L and Space C. Subway Line 10, Line 9, Line 1, Line 2, and Line 5 are the five subway lines with the highest importance. Subway Line S1, Changping Line, Xijiao Line, Capital Airport Line, and Daxing International Airport Line are the five subway lines with the lowest importance.