## 4 Results
Second, mixed land use (X3), bus line density (X6), and bus stop density (X7) had a significant inhibitory effect on online car-hailing commuting trips, and the variable coefficients were all negative, which was consistent with most previous studies. Third, the road network density (X4) and the proportion of non-motorized lanes (X5) had significant negative impacts on the ED. The reason may have been that increasing road network density and the proportion of non-motorized lanes in residential areas promoted non-motorized travel to a certain extent, thereby inhibiting the demand for online car-hailing travel. For the workplace, the impact of built environment variables on the EO and MD of DiDi commuting trips was basically the same as that of urban villages, while the road network density and the proportion of non-motorized lanes did \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Index** & **MO** & **ED** & **MD** & **EO** \\ \hline Lagrange Multiplier (lag) & 0.563 ** & 3.016 ** & 5.211 ** & 0.258 ** \\ Lagrange Multiplier (error) & 3.254 ** & 0.491 ** & 7.629 ** & 4.363 ** \\ \hline \hline \end{tabular}  
* Significant \(\alpha=0.05\); *** Significant \(\alpha=0.01\).  
\end{table} 
Table 4: Application test for the SDEM model. \begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Variable** & \multirow{2}{*}{**Variables Name**} & \multicolumn{2}{c}{**Urban Villages**} & \multicolumn{2}{c}{**Workplace**} \\ \cline{3-6}
**Index** & & **MO** & **ED** & **MD** & **EO** \\ \hline  & Constant & \(-1.562\) ** & 0.638 ** & 0.913 & \(-2.336\) ** \\ X1 & Population density & 0.391 ** & 0.007 ** & 2.197 ** & 0.955 * \\ X2 & Building density & 0.517 ** & 0.366 ** & 0.793 ** & 0.955 ** \\ X3 & Mixed land use & \(-1.327\) ** & \(-1.809\) ** & \(-2.286\) * & \(-2.674\) ** \\ X4 & Road network density & 0.537 & 0.183 * & 0.097 & 0.247 \\ X5 & \% of non-motorized lane & \(-0.989\) & \(-0.721\) * & \(-0.235\) & \(-0.699\) \\ X6 & Bus line density & \(-0.042\) * & \(-0.448\) ** & \(-0.485\) ** & \(-0.461\) * \\ X7 & Bus stop density & \(-0.038\) ** & \(-0.091\) ** & \(-0.908\) ** & \(-0.209\) ** \\ W\({}_{\rm X1}\) & W\(\cdot\)Population density & \(-0.708\) ** & \(-0.832\) ** & \(-0.448\) ** & \(-0.341\) ** \\ W\({}_{\rm X2}\) & W\(\cdot\)Building density & \(-0.903\) & \(-0.606\) & \(-0.809\) & \(-0.396\) \\ W\({}_{\rm X3}\) & W\(\cdot\)Mixed land use & 0.593 ** & 0.257 ** & 0.666 ** & 0.299 ** \\ W\({}_{\rm X4}\) & W\(\cdot\)Road network & \(-0.797\) & \(-0.753\) & \(-0.996\) & \(-0.905\) \\ W\({}_{\rm X5}\) & W\(\cdot\)\% of non-motorized lane & \(-0.699\) & \(-0.081\) & 0.708 & \(-0.255\) \\ W\({}_{\rm X6}\) & W\(\cdot\)Bus line density & 0.642 & 0.057 & \(-0.213\) & 0.598 \\ W\({}_{\rm X7}\) & W\(\cdot\)Bus stop density & \(-0.557\) * & \(-0.812\) ** & \(-0.766\) * & \(-0.488\) * \\ / & \(\rho\) & 0.351 ** & 0.296 ** & 0.262 ** & 0.414 ** \\ / & R\({}^{2}\) & 0.796 & 0.638 & 0.621 & 0.665 \\ / & Log likelihood & 563.8 & 601.3 & 589.1 & 543.2 \\ / & LR test & 463.4 & 511.5 & 488.6 & 426.7 \\ \hline \hline \end{tabular}  
* Significant \(\alpha=0.1\); ** Significant \(\alpha=0.05\); *** Significant \(\alpha=0.01\).  
\end{table} 
Table 5: Estimation results of the SDEM model. not show a significant impact; therefore, the number of impact factors in the workplace was even smaller. It is worth noting that the absolute value of the correlation coefficient of a significant influencing factor of the workplace was higher than that of the urban village areas, so the influence of the workplace was greater. The interaction items of the weight matrix and the built environment variables (WX1-WX7) were spatially lagging variables, and the corresponding parameter estimation results indicate that the DiDi travel demand of a certain TAZ was affected by the space lag effect of the built environment of urban villages.