## 2 Data Processing and Track Information Extraction of Floating Vehicle
### Information Extraction of Trajectory Data  
(1) Track extraction  
Step1: cluster tracks with similar trips together to observe the tracks of one group.  
Step2: select the track data set covering the target path from the above group, then each track \(i\) observation includes a series of interval observations (\(i_{1},\,\dots,\,i_{n}\)) composed of GPS data points of floating cars, and the total track length is \(L_{i}\).  
(2) The trajectory of the floating car is scaled to the coverage path  
Convert the trajectory of the floating car with the covered path as shown in Figure 3, and the conversion factor is \(\phi_{i}=\frac{\sum_{k}\beta_{ik}t_{ik}^{0}}{\sum_{k}\rho_{ik}t_{ik}^{0}}\) (\(t_{ik}^{0}\) is the pre-calculated travel time of the road section \(k\)).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Category** & **Mean Velocity** \\ \hline green & 72.556 \\ carmine & 77.182 \\ red & 77.616 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Average velocity value of cluster center.  
Map the travel time in the free flow state to the travel time when the floating car passes in equal proportion, including:  
\[\tau_{i}^{\prime}=\phi_{i}\tau_{i} \tag{3}\]  
where:  
\(\tau^{\prime}_{i}\) is the travel time of the covered path.  
\(\tau_{i}\) is the trajectory travel time of the floating car.  
For the \(i\)-th observation track:  
\[\phi_{i}=\frac{1}{2}\left(\frac{L_{o}}{L_{i}}+\frac{\sum_{k}\beta_{ik}t_{ik}^{0 }}{\sum_{k}\rho_{ik}t_{ik}^{0}}\right)=\frac{1}{2}\left(\frac{\sum_{k}\beta_{ik }l_{k}}{\sum_{k}r_{ik}}+\frac{\sum_{k}\beta_{ik}t_{ik}^{0}}{\sum_{k}\rho_{ik}t_{ ik}^{0}}\right) \tag{4}\]  
(3) Zoom the overlay path to the target path  
The travel time of the target can be expressed as:  
\[T_{i}=\frac{1}{\eta_{i}}\tau_{i}^{\prime} \tag{5}\]  
where  
\(\eta_{i}\) is the scaling factor.  
Figure 3: Distribution of traffic capacity \(c\) after earthquake.  
Figure 2: Floating cars trace, network route and target route: (**a**) Definition of parameters \(r\) and \(a\); (**b**) The relationship among floating vehicle trajectory, road network path, target path and coverage path.  
\(\eta_{i}\) is taken as:  
\[\eta_{i}=\frac{1}{2}\Bigg{(}\frac{L_{0}}{L_{i}}+\frac{\sum_{k}\beta_{ik}t_{ik}^{0} }{\sum_{k}\alpha_{ik}t_{ik}^{0}}\Bigg{)}=\frac{1}{2}\Bigg{(}\frac{\sum_{k}\beta_ {ik}l_{k}}{\sum_{k}r_{ik}}+\frac{\sum_{k}\beta_{ik}t_{ik}^{0}}{\sum_{k}\alpha_{ ik}t_{ik}^{0}}\Bigg{)} \tag{6}\]  
(4) Time stamp estimation of access path  
Keep the travel time between the first detection point (time stamp \(s_{i}\)) of the floating car GPS track and the first road network node inside the target path, then:  
\[\tau_{i}^{(1)}=\frac{1}{2}\Bigg{(}\frac{\sum_{k\in p^{\prime}}\rho_{ik}l_{k}}{ \sum_{k}\rho_{ik}l_{k}}+\frac{\sum_{k\in p^{\prime}}\rho_{ik}t_{ik}^{0}}{\sum_ {k}\rho_{ik}t_{ik}^{0}}\Bigg{)}\tau_{i} \tag{7}\]  
where:  
\(p^{\prime}\) is the path of the floating car to the first road network node in the target path interval.  
The estimated travel time when the starting point of the whole path reaches the first node is:  
\[\tau_{i}^{(2)}=\frac{1}{2}\Bigg{(}\frac{\sum_{k\in\pi t}\alpha_{ik}l_{k}}{\sum _{k}\alpha_{ik}l_{k}}+\frac{\sum_{k\in\pi t}\alpha_{ik}t_{ik}^{0}}{\sum_{k} \alpha_{ik}t_{ik}^{0}}\Bigg{)}\tau_{i} \tag{8}\]  
where:  
\(\pi^{\prime}\) is the path from the starting point of the path to the first road network node in the target path interval. The path entry timestamp \({s^{\prime}}_{i}\) can be estimated as:  
\[s^{\prime}_{i}=s_{i}+\tau_{i}^{(1)}-\tau_{i}^{(2)} \tag{9}\]