## 3 The Proposed Method  
### The Framework of the Proposed Method  
The motivation behind the development of the proposed method is to propose a generalized method based on belief functions to evaluate the sustainable transport solutions under consideration. The proposed method addresses both objectives. One is that the flexibility is improved since the method can deal with uncertainty and conflictive information due to D-S theory. The other is that the modified method has good performance in sensitivity analysis. The framework of our proposed method is shown in Figure 1.  
Step 1: Selection of criteria. The elements of sustainability refer to the [PERSON] report of 1987 [64], and social, economic, and environmental indicators were concluded by transport researchers. The more detailed elements  
Figure 1: The framework of our proposed method. BPA: basic probability assignment. on three indicators are based on specific situations. In [20], nine evaluation criteria for sustainability assessment are introduced--namely, cost (\(C_{1}\)), fuel consumption (\(C_{2}\)), air quality (\(C_{3}\)), noise perception (\(C_{4}\)), users numbers (\(C_{5}\)), spatial accessibility (\(C_{6}\)), satisfaction (\(C_{7}\)), security (\(C_{8}\)), and congestion level (\(C_{9}\)), which are shown in Figure 2. AHP is used to determine the weight of different criteria, and detailed steps are represented in Section 2.2. After identifying evaluation criteria, data sets can be collected from various channels during the testing stage of the transportation measure. [PERSON] et al. [20] mention four information sources: \"Human experts\", \"Sensors\", \"Models\" and \"Survey\". For example, experts usually have professional related experience or knowledge on city transportation to guarantee the reliability of information. Surveys are conducted with city residents and the responses are further aggregated to obtain BPAs. Models can be determined by the type of given data. For example, input data can be the number of votes that support a certain transport solution on different criteria, and thus we can apply a probability model to construct BPAs. Sensors use a measurement technique to allocate BPA values to different criteria for the transportation measure under study. With respect to each criteria, BPAs from different information sources are aggregated using [PERSON]'s combination rule to generate a single belief function. The following will introduce the construction and representation of BPAs for collected data. Experts allocate confidence to evaluation levels for each criteria. A new framework of evaluation level is proposed in this article: two evaluation levels, \(I\) and \(D\). \(I\) means \"increase\" and \(D\) means \"decrease\". The vector of utility related to evaluation levels is given by u: u = {u(I), u(D), u(I,D)}. In other words, there exists a frame of discernment represented as \(\Theta\) = {I, D}, 2\({}^{\Theta}\) = {{I}, {D}, {I, D}}, the belief distribution on evaluation levels is represented with a form of mass function (i.e., BPA). For example, in terms of the criteria \"Cost\" for car-sharing, a BPA from expert is shown as follows:  
\[m\{I\}=0.25;m\{D\}=0.25;m\{I,D\}=0.5.\]  
This means that:  
1. \"car-sharing solution will increase utility in terms of cost with the belief degree 0.25\". 2. \"car-sharing solution will decrease utility in terms of cost with the belief degree 0.25\". 3. \"the expert does not know whether the utility will increase or decrease with the belief degree 0.5\". Figure 2: Evaluation criteria for sustainability assessment. Step 3: Utilities estimation. For a certain transport solution, each criteria is either positively or negatively oriented with the utilities. For instance, a higher cost gets a lower utility while a higher air quality gets a higher utility. Complete utility values for nine criteria are shown in Table 3. Note that the number 1 represents the highest utility value, 0 represents the lowest utility value, and 0.3 represents an intermediate value chosen between 0 and 1.