## 3 The Proposed Method
### The Framework of the Proposed Method
The global utility (\(u_{i}\)) for a criteria \(i\) can be calculated using the individual utility for evaluation levels \(H_{k}\in\{\text{I, D}\}\) and the combined BPA. The formulation is as follows:  
\[u_{i}=\sum_{k=1}^{p}u(H_{k})\times BPA(H_{k}), \tag{9}\]  
where \(p\) is equal to 3. \(H_{k}=\{\text{I, D, [I,D]}\}\), \(u(H_{k})\) represents the individual utility of evaluation levels as shown in Table 3, and \( BPA(H_{k})\) is the basic probability assignment related to each evaluation level \(u(H_{k})\). Step 4: Estimation of city sustainability. The global utilities for nine criteria can be used to estimate city sustainability at any given time t by a Transport Sustainability Index (TSI) [20]. Let us denote the global utilities for the criteria \(C_{1}\), \(C_{2}\), \(C_{3}\), \(\cdots\), \(C_{N}\) at time \(t_{n}\) by \(u_{1}(t_{n})\), \(u_{2}(t_{n})\), \(u_{3}(t_{n})\), \(\cdots\), \(u_{n}(t_{n})\). As a result, the transport sustainability index (TSI) at time \(t_{n}\) is given by:  
\[TSI(t_{n})=u_{1}(t_{n})\times\omega_{1}+u_{2}(t_{n})\times\omega_{2}+u_{3}(t_{ n})\times\omega_{3}+\cdots+u_{n}(t_{n})\times\omega_{n}, \tag{10}\]  
where \(\omega_{i}\) is the weight of criteria obtained from AHP. Step 5: Impact assessment of the transport measure. The impact of the transportation measure on city sustainability is assessed by observing the change in TSI with respect to the pre-test and the post-test stages. Let \(t_{n-1}\) represents a time instant in the pre-test stage and \(t_{n}\) represent a time instant in the post-test stage, then the change in transport sustainability index over time interval \([t_{n-1}\), \(t_{n}]\) is given by:  
\[\triangle TSI[t_{n-1},t_{n}]=TSI(t_{n})-TSI(t_{n-1}), \tag{11}\]  
If \(\triangle\) TSI \(>\) 0, we can conclude that the impact of the transportation measure on city sustainability is positive and thus the measure can be regarded as sustainable transport. If \(\triangle\) TSI \(<\) 0, the measure will be rejected for recommendation due to unsustainability. Step 6: Sensitivity analysis. To check the effectiveness of our proposed method, two experiments of conflictive information and changed weights of criteria will be illustrated in this part to better compare the difference between the method of [PERSON] et al. [20] and ours. The first experiment is about conflictive information to address the question \"How sensitive is the overall decision to conflictive information during the information fusion process?\" conflictive information arises very easily due to man-made error, a fault in the model, or the subjectivity of experts' \begin{table}
\begin{tabular}{c c c} \hline \hline
**Evaluation Criteria** & **Utility Values u(I), u(D), u(D)** \\ \hline Cost (\(C_{1}\)), Fuel consumption (\(C_{2}\)), Noise perception (\(C_{4}\)), Congestion level (\(C_{5}\)) & (0, 1, 0.3) \\ Air quality (\(C_{3}\)), Users numbers (\(C_{5}\)), Spatial accessibility (\(C_{6}\)), Satisfaction (\(C_{7}\)), Security (\(C_{8}\)) & (1, 0, 0.3) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Utility values for criteria [20]. opinions. It is necessary to confirm that a method has redundancy to be more flexible and practical so that it has a more extensive application. The second experiment is about the weight of criteria to address the question \"How sensitive is the overall decision to small changes in the individual weight assigned during the pair-wise comparison process?\" This question can be answered by slightly varying the values of weight and observing the effects on the decision. Moreover, because the weights of criteria are subjectively determined by experts using AHP, it is useful in situations where uncertainty exists in the definition of the importance of different factors.