# Investigating the Influential Factors of Ride-Hailing Usage Frequency in the Post-Pandemic Era
## 7 Conclusions  
In order to understand the change mechanism of factors that affect the travel frequency of ride-hailing users in the post-pandemic era, this study establishes ordered logit models to analyze and compare the impact of multi-dimensional factors on the usage frequency of ride-hailing users before and after the pandemic (i.e., the pre-pandemic era and the post-pandemic era) based on a survey in Nanjing, China. The model results showed the following: (1) Variables such as occupation and long waiting times are common significant factors that affect the usage frequency of ride-hailing users during these two periods. (2) Increased waiting times have the opposite impact on the usage frequency of ride-hailing users before and after the pandemic. Before the pandemic, increased waiting times increased the usage frequency of ride-hailing users, while in the post-pandemic era, increased waiting times reduce the travel frequency for ride-hailing users. (3) Compared with other modes, ride-hailing services replaced walking less before the pandemic and replace private cars less in the post-pandemic era. (4) Before the pandemic, price increases led to a decrease in ride-hailing users' usage frequency, while it presents a push effect in the post-pandemic era. (5) In the post-pandemic era, worrying about the risk of contact with others reduces the use frequency of ride-hailing users; ride-hailing users are more concerned about their personal safety, avoiding contact and conversation with the driver and touching objects in the car. (6) Before the pandemic, people without private cars used ride-hailing services more frequently, while this effect is opposite and not significant in the post-pandemic era.  
The limitations of this study are as follows: Firstly, this study adopts a questionnaire survey in Nanjing to explore the impact of multi-dimensional factors on the usage frequency of ride-hailing users before and after the COVID-19 pandemic. One limitation is the omission of some dynamic factors (e.g., time, traffic conditions). Therefore, the subsequent research can perform dynamic analysis based on historical ride-hailing order data. Secondly, personal perception factors, such as convenience, the interior environment of the vehicle, and the driver's manner, are quantified in this study using a five-point Likert scale and not quantified by specific values. It would be beneficial for further research to obtain these values on the ride-hailing platform [40]. Thirdly, this study did not consider the impact of built-environment variables [16; 41; 42; 43] (the number of subway stations, commercial facilities, entertainment facilities, etc.) on the usage frequency of ride-hailing users before and after the pandemic. Future research can consider these for further research.  
Data curation, X.L.; resources, M.D., X.L., H.W. and J.Y.; software, X.L.; supervision, M.D.; writing--original draft preparation, M.D. and X.L.; writing--review and editing, M.D., X.L., J.Y., H.W., D.L. and M.-P.K. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (No. 52302392, 52308065), the Humanities and Social Science Fund of the Ministry of Education of China (No. 23Y[CZH042), the Natural Science Research of Jiangsu Higher Education Institutions of China (No. 23 KJB580011), the General Project for Philosophy and Social Science Research in Jiangsu Higher Education Institutions of China (2024S)YBQ135), the China Postdoctoral Science Foundation (No. 2023M731704), and the Scientific Research Startup Fund for Advanced Talents of Nanjing Forestry University (No. 163106079).  
Not applicable.  
Dataset available on request from the corresponding author due to privacy restrictions.  
The authors thank all those who participated in the investigation.  
The authors declare no conflicts of interest.