## 4 Car-Sharing Application
### Sensitivity Analysis  
#### 4.6.1 Experiment 1  
From Tables 4 and 5, we can find that most BPAs have a similar trend to support the same evaluation level. For example, in Table 5 for the criteria \"Air quality (\(C_{3}\))\", most BPAs from four information sources have the most confidence in supporting the evaluation level \"I\"; for the criteria \"Congestion levels (\(C_{9}\))\", the collected data show that there is strong confidence with the evaluation level \"D\". However, in real life, it is noteworthy that incorrect data is possibly derived from human error, fault in the model, or the subjectivity of experts' opinion. Regardless, conflictive data may occur. Experiments were performed to determine the sensitivity of the final decision with respect to conflictive BPAs, and a comparison between [PERSON] et al. 's method [20] and our proposed method is given. From Table 5, for the criteria \"Number of users\", the model and sensor respectively provide a BPA: m(I) = 0.7, m(D) = 0.1, m(\(\Theta\)) = 0.2; m(I) = 0.8, m(D) = 0.1, m(\(\Theta\)) = 0.1. In this experiment, two new BPAs are substituted: m(I) = 0, m(D) = 0.1, m(\(\Theta\)) = 0.9; m(I) = 0.9, m(D) = 0.1, m(\(\Theta\)) = 0. The other two information sources \"Expert\" and \"Survey\" support the evaluation level \"I\" with a strong confidence \(m_{5}^{1}=0.8\), \(m_{5}^{3}=0.6\), while the new BPA from the model has the most confidence in supporting the set \"G\". Generally, it is called a piece of conflict evidence. Global utilities for nine criteria at the post-test stage with two methods are shown in Tables 8 and 9. From Table 10 and Figure 4, it can be easily seen that before modifying the data, a positive impact on urban transportation can be obtained by testing the transportation solution, while a negative impact \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Evaluation Criteria** & \multicolumn{3}{c}{**[PERSON] et al.’s Method**} & \multicolumn{3}{c}{**Our Proposed Method**} \\ \cline{2-6}  & **I** & **D** & \(\Theta\) & **I** & **D** & \(\Theta\) \\ \hline _cost_ (\(C_{1}\)) & 0.0274 & 0.9589 & 0.0137 & 0.2258 & 0.5053 & 0.2689 \\ _fuel consumption_ (\(C_{2}\)) & 0.0146 & 0.4380 & 0.5474 & 0.2296 & 0.3596 & 0.4108 \\ _air quality_ (\(C_{3}\)) & 0.9910 & 0.0067 & 0.0022 & 0.5893 & 0.2087 & 0.2018 \\ _noise perception_ (\(C_{4}\)) & 0.0020 & 0.0030 & 0.9951 & 0.2026 & 0.2399 & 0.5574 \\ _users numbers_ (\(C_{5}\)) & 0 & 0 & 1.0000 & 0.6913 & 0.1894 & 0.1932 \\ _spatial accessibility_ (\(C_{6}\)) & 0.9956 & 0.0030 & 0.0015 & 0.5946 & 0.2373 & 0.1681 \\ _satisfaction_ (\(C_{7}\)) & 0.9931 & 0.0059 & 0.0010 & 0.5771 & 0.2566 & 0.1661 \\ _security_ (\(C_{8}\)) & 0.0661 & 0.6167 & 0.3172 & 0.2529 & 0.3743 & 0.3727 \\ _congestion level_ (\(C_{9}\)) & 0.0089 & 0.0536 & 0.9375 & 0.2290 & 0.3350 & 0.4359 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: The testing BPA fusion results. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Evaluation Criteria** & **[PERSON] et al.’s Method** & **Our Proposed Method** \\ \hline _cost_ (\(C_{1}\)) & 0.1432 & 0.3565 \\ _fuel consumption_ (\(C_{2}\)) & 0.4630 & 0.8387 \\ _air quality_ (\(C_{3}\)) & 0.9834 & 0.9800 \\ _noise perception_ (\(C_{4}\)) & 0.5722 & 0.9909 \\ _users numbers_ (\(C_{5}\)) & 0 & 0.9846 \\ _spatial accessibility_ (\(C_{6}\)) & 0.9398 & 0.9908 \\ _satisfaction_ (\(C_{7}\)) & 0.9864 & 0.9894 \\ _security_ (\(C_{8}\)) & 0.7449 & 0.3763 \\ _congestion level_ (\(C_{9}\)) & 0.8539 & 0.9615 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Global utilities of testing for nine criteria with two methods. is found after the emergence of conflictive evidence in [PERSON] et al. 's method [20]. However, in our proposed method, we can come to the same conclusion that the influence brought by car-sharing in the city is positive and therefore car-sharing is recommended for adoption, which demonstrates that the proposed method can handle conflictive data well.