# Resilience Analysis of Traffic Network under Emergencies: A Case Study of Bus Transit Network
## 4 Case Analysis  
### Description of Usage Data  
(1) Data for filtering key indicators  
The data used in the experiment comes from the statistical data provided by the Beijing Traffic Operation Monitoring and Dispatching Center, an authoritative institution in Beijing. The data quality is relatively high. There are 12 datasets used in the case analysis: actual departure interval, actual arrival interval, passenger volume, card swipe volume of stations on the route, occupancy rate, OD passenger flow, on-time departure rate, online operating vehicles, station punctuality rate, one-way travel time, instances of bus bunching, instances of long interval, which form the indicator system shown in Table 2. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Indicator Criteria Layer** & **Indicator Name** & **Indicator Define** \\ \hline \multirow{3}{*}{Vehicle (A)} & Actual departure interval (A1) & The time interval between two adjacent buses on the same route departing from the starting station \\  & Actual arrival interval (A2) & The average actual arrival time of two adjacent bus trips on the same route \\ \hline \multirow{3}{*}{Passenger flow (B)} & Card swiping passenger volume (B1) & Number of passengers using the bus card to swipe their cards \\  & Landing volume (B2) & The number of boarding and alighting passengers of bus \\  & Load factor (B3) & The ratio of actual passenger capacity to rated \\  & OD passenger flow (B4) & Average traffic volume between the starting and \\ \hline \multirow{3}{*}{Schedule (C)} & On schedule departure rate (C1) & The percentage of scheduled on-time departures in the total number of departures during the statistical period \\  & Number of online operating vehicles (C2) & Number of real-time vehicles used for \\ \cline{1-1}  & & operational business \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Bus operation monitoring index system. To facilitate an accurate evaluation based on the patterns contained in each indicator during the cycle, the selected time range is from 10 to 16 August 2020, a total of one week. The data are collected at an hourly granularity, and there was heavy rain on August 12. Before using the data, it is necessary to check and process redundant data, missing values, and outliers. The necessary data filtering conditions are shown in Table 3.  
The trend comparison of some data is shown in Figure 11:  
(2) Data for bus route resilience analysis \begin{table}
\begin{tabular}{c c c} \hline \hline
**Indicator Criteria Layer** & **Indicator Name** & **Indicator Define** \\ \hline \multirow{2}{*}{Service (D)} & \multirow{2}{*}{Punctuality rate (D1)} & The probability of public transportation vehicles arriving at a certain station on schedule during the \\  & & statistical period \\ \hline \multirow{4}{*}{Others (E)} & Bus single-trip time (E1) & The actual time from the initial station to the final station \\  & & for public transportation vehicles \\ \cline{1-1}  & \multirow{2}{*}{Bunching (E2)} & The number of occurrences where the interval between \\ \cline{1-1}  & & adjacent bus arrivals at each station is less than 1 minute \\ \cline{1-1}  & \multirow{2}{*}{Big interval (E3)} & The number of times the arrival interval of adjacent buses \\ \cline{1-1}  & & at each station is greater than 15 min \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._  
Figure 11: Trend of indicators (\(y\)-axis: indicator value). \begin{table}
\begin{tabular}{c c} \hline \hline
**Condition** & **Value** \\ \hline Date & 10–16 August 2020 \\ Bus line & line 1 (East-west), line 139 (north-south) \\ Operation time & 00:00-23:00 \\ Time granularity & 1 h \\ Running direction & 0/1 \\ Ascending/descending & Ascending, descending, ascending and descending \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Data filtering condition-filtering key indicators.