# Bike-Sharing Travel Demand Forecasting via Travel Environment-Based Modeling
## 6 Results and Analysis
### Analysis of the Factors Influencing Demand
This makes the construction of non-motorized transport facilities in this area more challenging than in the east and southeast, which are flat and have better road facilities. Second, the southeast region is located within Beijing City and is close to Haidian District, which has a relatively complete non-motorized transport facility. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Variable Type** & **Variable Name** & **Mean** & **Min** & **Max** & **Median** \\ \hline \multirow{4}{*}{\
\begin{tabular}{c} Traveling \\ environment \\ \end{tabular} } & Roadway facilities & 0.095 & 0.081 & 0.107 & 0.097 \\  & Intersection facilities & 0.119 & \(-\)0.313 & 0.967 & 0.064 \\  & Quantity of intersections & 0.088 & \(-\)0.279 & 1.219 & 0.032 \\  & Adjacent road distance & 0.006 & 0.002 & 0.013 & 0.005 \\ \hline \multirow{4}{*}{
\begin{tabular}{c} Functional area \\ \end{tabular} } & Company & 0.048 & \(-\)0.088 & 0.233 & 0.022 \\  & Bus and subway station & 0.093 & \(-\)0.035 & 0.362 & 0.026 \\  & Residential area & 0.085 & 0.019 & 0.184 & 0.066 \\  & Research and education & 0.047 & 0.04 & 0.058 & 0.046 \\  & Tourist attractions & 0.047 & 0.016 & 0.093 & 0.05 \\  & Commercial areas & 0.255 & 0.119 & 0.428 & 0.251 \\  & Leisure sports & 0.037 & 0.013 & 0.058 & 0.037 \\  & Health care & 0.053 & 0.009 & 0.083 & 0.063 \\  & Public restrooms & \(-\)0.031 & \(-\)0.286 & 0.313 & \(-\)0.014 \\  & Government & 0.031 & \(-\)0.236 & 0.405 & 0.003 \\  & Life services & \(-\)0.019 & \(-\)0.021 & \(-\)0.015 & \(-\)0.02 \\  & Financial units & 0.068 & \(-\)0.024 & 0.134 & 0.071 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Summary of the MGWR-based regression coefficients. Figure 5: _Cont_. Intersection facilities have a significant positive effect on the riding demand, as presented in Figure 5b, with a coefficient effect in the interval of [\(-\)0.313, \(-\)0.967]. This influence is mostly concentrated in the eastern and central areas of Shijingshan District. The reason for this phenomenon could be attributed to both geographical factors and urban development plans. The concentration of intersection facilities in the eastern and central areas of Shijingshan District leads to a more pronounced impact of these facilities in these areas. As shown in Figure 5c, the quantity of intersections also has a positive effect on the riding demand, with a coefficient effect ranging in the interval of [\(-\)0.279, 1.219]. This impact is more noticeable in the middle and southeast of the region and less in the northwest. A possible explanation for this phenomenon is that because of the influence of the mountains, the overall development of the eastern region is better than that of the western region. According to land use, commercial areas, bus and subway stations, and residential areas exhibit a significant positive impact on shared-bike demand with coefficients ranging in the intervals of [0.119-0.428], [\(-\)0.035-0.362], and [0.019-0.184], respectively. Specifically, bus and subway stations essentially affect the riding demand in the central parts, commercial areas have more influence on the eastern and central parts, and residential areas mainly affect the riding demand in the central parts of Shijingshan District. This is probably due to the geographical distribution of these functional areas including research and education, tourist attractions, leisure sports, health care, public restrooms, government, life services, and financial units. Actually, the coefficients of these factors and their spatial distribution do not follow a clear rule. The reason for this phenomenon may be that some small-scale geographic point-of-interest data were not filtered during the data screening process, resulting in errors that affect the results. Figure 5: Coefficient distribution of various influencing factors.