# Bike-Sharing Travel Demand Forecasting via Travel Environment-Based Modeling
## 6 Results and Analysis
### Analysis of the Factors Influencing Demand  
Table 9 presents the fitting results of both the OLS and MGWR models. It can be seen that adjacent road distance, company, tourist attractions, leisure sports, health care, life services, and financial units are not significant in the OLS-based regression results, but they are remarkable in the MGWR-based model. The results show that these variables are not irrelevant to the demand for riding, but there is a large spatial variation in the degree of impact, so the demand forecast needs to be refined to a smaller spatial area. Table 10 presents the statistical description of the regression coefficients for each variable in the MGWR model, while Figure 5 illustrates the spatial distribution of these coefficients. In Figure 5, the darker areas represent the regression coefficients of the influential factors with significant influence, whereas the gray squares denote the regression coefficients of the influential factors with insignificant influence. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Variable Name** & **GWR-Based Bandwidth** & **MGWR-Based Bandwidth** & **Variable Name** & **GWR-Based Bandwidth** & **MGWR-Based Bandwidth** \\ \hline Riding environment & 474 & 1321 & Tourist attractions & 474 & 1321 \\ Intersection facilities & 474 & 59 & Commercial areas & 474 & 284 \\ Quantity of intersections & 474 & 57 & Leisure sports & 474 & 1190 \\ Adjacent road distance & 474 & 1321 & Health care & 474 & 940 \\ Company & 474 & 416 & Public restrooms & 474 & 159 \\ Bus and subway station & 474 & 268 & Government & 474 & 193 \\ Residential area & 474 & 450 & Life services & 474 & 1321 \\ Research and education & 474 & 1314 & Financial sector & 474 & 746 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Bandwidth results of the GWR and MGWR models. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Variable Type**} & \multirow{2}{*}{**Variable Name**} & \multicolumn{2}{c}{**OLS**} & \multicolumn{2}{c}{**MGWR**} \\ \cline{3-6}  & & **Regression Coefficient** & \(p\)**-Value** & **Significance** & \(p\)**-Value** & **Significance** \\ \hline \multirow{4}{*}{Traveling environment} & Roadway facilities & 0.061 & 0 & *** & 0.035 & * \\  & Intersection facilities & 0.027 & 0.001 & ** & 0.001 & ** \\  & Quantity of intersections & 0.044 & 0 & *** & 0.001 & ** \\  & Adjacent road distance & 0.005 & 0.583 & N/A & 0.033 & * \\ \hline \multirow{4}{*}{Functional area} & Company & 0.011 & 0.75 & N/A & 0.012 & * \\  & Bus and subway station & 0.087 & 0 & *** & 0.005 & ** \\  & Residential area & 0.094 & 0 & *** & 0.011 & * \\  & Research and education & 0.083 & 0.015 & * & 0.042 & * \\  & Tourist attractions & 0.06 & 0.303 & N/A & 0.037 & * \\  & Commercial areas & 0.243 & 0 & *** & 0.008 & ** \\  & Leisure sports & \(-\)0.005 & 0.86 & N/A & 0.041 & * \\  & Health care & 0.036 & 0.116 & N/A & 0.027 & * \\ \hline \multirow{4}{*}{Functional area} & Public restrooms & \(-\)0.034 & 0.029 & * & 0.003 & ** \\  & Government & 0.072 & 0.012 & * & 0.005 & ** \\  & Life services & \(-\)0.045 & 0.267 & N/A & 0.043 & * \\ \cline{1-1}  & Financial sector & 0.007 & 0.832 & N/A & 0.023 & * \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Regression results of the OLS and MGWR models. Table 10 shows that roadway facilities significantly affect the demand for bike-sharing and have a positive effect, meaning that better roadway facilities lead to greater demand for bike-sharing in the same region. Figure 5(a) demonstrates that the roadway facilities coefficient is in the range of 0.081-0.107, with a gradual decrease in the influence range from west to east in terms of spatial distribution, with the strongest impact intensity in the west. There are two main reasons for this. First, the northwest area of Shijingshan District is adjacent to the West Mountain, which has a total area of 5970 hectares and is 300-400 m high.