# Bike-Sharing Travel Demand Forecasting via Travel Environment-Based Modeling
## 4 Selection and Quantification of the Independent Variable
### Evaluation of Non-Motorized Transport Facilities
#### 4.1.2 Evaluation of Intersection Facilities  
This index was used to investigate the effect of constructing non-motorized transport facilities at road intersections on the riding demand in the study region. Because the data are not a complete sample, it only measures the construction of intersection facilities on high-grade roads in Shijingshan District, with a total of 19 roads. The setting rate of bicycle lane signal lights at intersections and the degree of damaged signs in bicycle lanes commonly reflect the quality of those infrastructures. As for colored pavement, it is not necessary to use it on a large scale. Instead, it can have the greatest impact near intersections with roads [28]. Figure 3 represents the pavement color settings and signage damage in the bike lanes.  
The entropy weight method was employed to evaluate the placement of bicycle lane facilities, with the pavement setting rate, the signs qualified rate, and bicycle lane signal lights serving as positive indicators, whereas the moderate and severe damage rates of bicycle lane signs were the negative indicators. The entropy method is an objective weighting approach. Its principle lies in reflecting the magnitude of information based on the degree of variation among indicator values during the evaluation process, thereby determining the corresponding weights. Given the distinct differentiation between positive  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Evaluation** & **Eigenvector** & **Weight Value (\%)** &
\begin{tabular}{c} **The Maximum** \\ **Eigenvalue** \\ \end{tabular} & **CI Value** & **RI Value** & **CR Value** \\ \hline Lane width & 2.166 & 54.159 & & & & \\ Isolation belt & 0.822 & 20.553 & & & & \\ Roadside parking spaces & 0.822 & 20.553 & 4.033 & 0.011 & 0.882 & 0.012 \\ Shade cover & 0.189 & 4.736 & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: AHP hierarchical analysis results and consistency check.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Lane Width** & **Score** & **Isolation Belt** & **Score** &
\begin{tabular}{c} **Roadside Parking** \\ **Spaces** \\ \end{tabular} & **Score** & **Shade Cover** & **Score** \\ \hline \(<\)1.5 m & 0 & Mixed traffic & 0 & Not set & 0 & No shade cover & 0 \\
1.5-2 m & 1 & Scribing or guardrail & 1 & Settings & 1 & Single-sided cover & 1 \\
2-2.5 m & 2 & Greenery or footpaths & 2 & - & - & Both & 2 \\ \(>\)2.5 m & 3 & - & - & - & - & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 2: Roadway facility assignment score evaluation.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Lane Width** & **Isolation Belt** & **Roadside Parking Spaces** & **Shade Cover** \\ \hline Lane width & 1 & 3 & 3 & 9 \\ Isolation belt & 0.333 & 1 & 1 & 5 \\ Roadside parking spaces & 0.333 & 1 & 1 & 5 \\ Shade cover & 0.111 & 0.2 & 0.2 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Weight matrix table among different facilities.  
and negative indicators evident from the outset of data collection for this particular set of indicators, the adoption of the entropy method was deemed appropriate. The calculation results are provided in Table 5.  
Finally, the scores of intersection non-motorized transport facilities for 19 roads were obtained and matched with spatial data. Since intersections have a substantial impact on surrounding roads, this study established a 200 m buffer zone around each intersection. Then, the facility evaluation scores were applied to the unit areas within this range, which were affected by the evaluation results. The resulting score serves as the independent variable for intersection facilities, denoted by the symbol X2.