## 4 Results  
Before performing spatial regression, a multi-collinearity analysis was conducted on independent built environment variables. Variables with a variance expansion factor greater than 5 were removed using a stepwise method. Finally, seven built environment variables were included in the model. [PERSON]'s I test of spatial correlation can reflect the similarity of the unit attribute values in the neighborhoods of the space. If the dependent variable is the observed value of the area, then the [PERSON]'s I value of the variable is expressed as Equation (7) as follows:  
\[\text{Moran}^{\prime}\text{s I}=\frac{\sum\limits_{i=1}^{n}\sum\limits_{j=1}^{n} \omega_{ij}(Y_{i}-\overline{Y})(\overline{Y}-Y_{j})}{S^{2}\sum\limits_{i=1}^{n }\sum\limits_{j=1}^{n}\omega_{ij}} \tag{7}\]  
The value of [PERSON]'s I statistic is generally between \(-1\) and \(1\). When the value is smaller than 0, it indicates a negative correlation. When it is equal to 0, it indicates incoherence. When it is greater than 0, it indicates a positive correlation. Based on the spatial relationship of 490 TAZs in Shenzhen, the software GeoDa 1.20 and Stata 15 were applied to generate a first-order adjacency matrix of the traffic district, reflecting the spatial adjacency relationship of the traffic district, and then the value of [PERSON]'s I statistic was obtained, as shown in Figures 6-9. From Figures 6-9, it can be seen that the [PERSON]'s I statistics of the DiDi travel demand for the MO, MD, EO, and ED within a TAZ were all greater than 0 and passed the 1% significance level test. The results indicate that the spatial distribution of DiDi travel activities was not completely random. In general, its positive spatial correlation characteristics indicate that the characteristics of DiDi travel were similar in space. The applicability of the SDEM model established in this section was tested, and it was concluded that the test statistics of the Lagrange multiplier (error) and Lagrange multiplier (lag) were very significant (Table 4), from which it can be determined that the spatial Dubin error model of this section was applicable. This means that the dependent variables of the DiDi travel demand based on the geographic unit of the TAZ, the independent variables of the built environment, and the unobserved error variables all had a spatial correlation. Therefore, the explanatory power of the SDEM model in this section was better than that of SAR, which only considered spatial lag. Additionally, it was better than the SDM and SEM models, which only considered spatial error. Figure 8: Moran’s I statistic for the EO. Figure 9: Moran’s I statistic for the ED. In terms of parameter estimation methods, the generalized method of moments (GMM) was applied because it did not require knowledge of the accurate distribution information of the random error term and allowed for the random error term to have heteroscedasticity and sequence correlation. It was more effective than other parameter estimation methods such as the least-square, maximum likelihood, and instrumental variable methods. The parameter estimation results of the SDEM model are shown in Table 5.  
The parameter estimation results corresponding to X1-X7 in Table 5 represent the relationship between the built environment variables of a certain TAZ and the travel demand of the TAZ. For urban village areas, built environment variables basically had the same influence on the MO and ED of online car-hailing commuters. First, population density (X1) and building density (X2) played a significant positive role in promoting the demand for online car-hailing travel, and the variable coefficients were both positive, indicating that the higher the population and building density, the greater the demand for online car-hailing travel. The possible reason was that high density meant more commuting activities and travel demand.