Based on the passenger flow statistics all around the country in China, the travel distance data, and the multi-modal passenger flow data, this paper uses statistical analysis methods to analyze the changes in passenger travel patterns and the choice of modes before and after the COVID-19 pandemic. The study finds that after the end of the COVID-19, in terms of the characteristics of the flow volume, passenger travel has fully recovered on weekdays and even increased during holidays. Passenger car travel has continued to grow, while the average travel distance has decreased. On the contrary, the total volume of commercial passenger travel has declined. Judging from the specific quantitative relationship, there is a strong correlation and an incomplete substitution relationship between self-driving car travel and commercial passenger transport. On the whole, the COVID-19 epidemic has changed citizens’ travel habits and increased the attractiveness of car travel, which will have a long-term impact on the transportation system. Based on the analysis of the above-mentioned characteristics and quantitative relationship, the future demand development trend is studied and judged. Suggestions for future development are proposed in the “post-covid” era, including the following: enhance long-term travel service support capability, deepen supply-side structural reform of travel services, and strengthen key period travel service support. This study can provide a reference for reasonable guidance and scientific service of passenger travel in the “post-covid” era.
Research on the Changes in Passenger Travel Patterns in the Post-Covid Era
24th COTA International Conference of Transportation Professionals ; 2024 ; Shenzhen, China
CICTP 2024 ; 3413-3423
11.12.2024
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
Post-COVID-19 Travel Patterns: UT Austin Survey
TIBKAT | 2023
|British Library Online Contents | 1997
Short-distance passenger travel
British Library Conference Proceedings | 1994
|Engineering Index Backfile | 1898
Passenger Travel Demand Forecasting
NTIS | 1976