# Investigating the Influential Factors of Ride-Hailing Usage Frequency in the Post-Pandemic Era
## 4 Survey Results
### Results and Discussions
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Variables**} & \multicolumn{2}{c}{**Before the Pandemic**} & \multicolumn{2}{c}{**After the Pandemic**} \\ \cline{2-5}  & **B** & \(p\)**-Value** & **B** & \(p\)**-Value** \\ \hline Travel characteristics & & & & \\ Original travel mode = Walking & \(-\)1.311 & 0.154 & \(-\)0.213 & 0.824 \\ Original travel mode = Bicycle & \(-\)1.015 & 0.271 & \(-\)0.362 & 0.702 \\ Original travel mode = Public transportation & \(-\)1.231 & 0.164 & \(-\)1.308 & 0.142 \\ Original travel mode = Private car & \(-\)1.31 & 0.199 & \(-\)1.911 * & 0.064 \\ Original travel mode = Taxi & 0 \({}^{\text{a}}\) & - & 0 \({}^{\text{a}}\) & - \\ Trip purpose = Commutuer travel & 0.132 & 0.933 & 19.112 *** & 0 \\ Trip purpose = Shopping & \(-\)1.898 & 0.309 & 18.896 *** & 0 \\ Trip purpose = Entertainment & 0.846 & 0.585 & 19.467 *** & 0 \\ Trip purpose = Official business & 0.997 & 0.552 & 20.035 & - \\ Trip purpose = Healthcare & 0 \({}^{\text{a}}\) & - & 0 \({}^{\text{a}}\) & - \\ Usage duration = 5 min & \(-\)1.459 & 0.127 & \(-\)0.771 & 0.474 \\ Usage duration = 5-10 min & 0.696 & 0.294 & \(-\)0.021 & 0.976 \\ Usage duration = 10–20 min & \(-\)0.563 & 0.399 & \(-\)0.004 & 0.995 \\ Usage duration = 20–30 min & 1.227 & 0.116 & 2.106 *** & 0.006 \\ Usage duration \(\Rightarrow\) 30 min & 0 \({}^{\text{a}}\) & - & 0 \({}^{\text{a}}\) & - \\ \hline Attitude perception attributes & & & & \\ Poor weather conditions & 0.554 ** & 0.029 & 0.212 & 0.429 \\ Long travel time & 0.025 & 0.932 & 0.302 & 0.374 \\ Early departure time of travel & \(-\)0.059 & 0.848 & \(-\)0.006 & 0.987 \\ High travel efficiency & 0.102 & 0.75 & 0.399 & 0.332 \\ Convenient and efficient & \(-\)0.086 & 0.806 & \(-\)0.61 & 0.158 \\ High fares & \(-\)0.237 & 0.321 & 0.057 * & 0.085 \\ Environment is clean in-car & \(-\)0.168 & 0.505 & 0.09 & 0.763 \\ Ride-hailing driver has good manner & 0.459 & 0.13 & 0.801 & 0.467 \\ Good driving skills & 0.169 & 0.513 & 0.09 & 0.787 \\ Long waiting time & 0.449 * & 0.076 & \(-\)0.741 *** & 0.005 \\ Worried about personal safety & 0.683 ** & 0.035 & \(-\)0.056 & 0.876 \\ Worried about personal information security & \(-\)0.289 & 0.512 & \(-\)0.708 *** & 0.005 \\ Worried about the risk of contact with others & 0.086 & 0.81 & \(-\)0.327 & 0.165 \\ Avoid touching objects in the car & \(-\)0.127 & 0.71 & 0.402 & 0.181 \\ Avoid contact and conversation with the driver & \(-\)0.262 & 0.324 & 0.048 & 0.878 \\ \hline Personal attributes & & & & \\ Male & 0.485 & 0.203 & 0.15 & 0.718 \\ Female & 0 \({}^{\text{a}}\) & - & 0 \({}^{\text{a}}\) & - \\ Age & 0.012 & 0.982 & 0.723 & 0.176 \\ Educational level = Middle school or below & \(-\)1.583 & 0.348 & 3.318 & 0.113 \\ Educational level = Senior high school & 2.91 ** & 0.047 & 5.032 *** & 0.01 \\ Educational level = Associate degree & 1.262 & 0.287 & 3.298 ** & 0.043 \\ Educational level = Undergraduate & 1.145 & 0.877 & 2.105 & 0.158 \\ Educational level = Graduate or above & 0 \({}^{\text{a}}\) & - & 0 \({}^{\text{a}}\) & - \\ Student & \(-\)0.608 & 0.476 & \(-\)2.886 *** & 0.002 \\ Government officer & \(-\)3.296 ** & 0.012 & \(-\)2.387 ** & 0.05 \\ Enterprise employee & \(-\)0.369 & 0.693 & \(-\)0.771 & 0.427 \\ Reitree & 0.377 & 0.82 & \(-\)1.491 & 0.442 \\ Others & 0 \({}^{\text{a}}\) & - & 0 \({}^{\text{a}}\) & - \\ Monthly income (CNY) & \(-\)0.131 & 0.514 & 0.034 & 0.883 \\ Whether you own a car = no & 0.102 & 0.812 & \(-\)0.039 & 0.935 \\ Whether you own a car = yes & 0 \({}^{\text{a}}\) & - & 0 \({}^{\text{a}}\) & - \\ \hline \hline \end{tabular} Note: *** \(p<0.01\): the results are significant at the 1% significance level. * \(p<0.05\): the results are significant at the 5% significance level. * \(p<0.1\): the results are significant at the 10% significance level. \({}^{\text{a}}\) denotes that this category of the variable is redundant and therefore it is set to zero.  
\end{table} 
Table 3: Estimation results of ordered logit models.