## 4 Pre-Evaluation Based on the Spatio-Temporal Model
### Passenger Travel Demand Analysis Based on Card (Code) Data and Biometric Recognition Data  
In the adjustment of public transportation networks, passengers' travel demand is manifested as the origin and destination (OD) stops, travel volume, and travel time distribution. With the traditional 'four-stage method', passengers' travel needs are obtained based on manual surveys or statistical data sets. This article comprehensively uses passenger card (code) data, biometric-based payment data, and bus operation data (operating time, vehicle location, and operating route) to analyze the travel information of each passenger in the current bus network. The main steps for doing this include matching boarding stations, inferring alighting stations, and identifying the true origin and destination stops of transfers.Process of passenger travel demand analysis is shown below in Figure 3. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Data Type** & **Content of Entry** \\ \hline Line & Line truncation or cancellation & The ID of all deleted stops \\ Parameter & Extension and & Line ID, direction, stop order, distance from the previous stop, line name, ID \\  & Detour or New & (unique), stop name, stop type (terminal/midway stop), longitude and \\  & & latitude, distance from the next stop \\  & & Passenger ID, passenger type (elderly, students, working), trading time, \\ Passenger Travel Demand & & boarding time, boarding stop, alighting time, alighting stop, travel route, \\  & & direction, travel distance, etc. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Input parameters. At present, public transportation generally adopts a 'one ticket' payment method--passengers only need to swipe or scan when getting on the bus, without doing so when alighting. This makes it relatively straightforward to identify the boarding stop by associating and matching payment information with bus operation data based on time. To infer the alighting stop, the general principle is as follows: first, define the'set of possible alighting stops for this trip', \(D\)--the set of all downstream stations on the route they are traveling on. Second, define the'set of surrounding stations the passenger could next board', \(O\)--the set of next boarding stations and their surrounding stations (within a radius of 0.5-1 km). Third, if \(O\cap D=Q\
eq\varnothing\), the nearest station to the next boarding station at a medium distance \(Q\) will be used as the station of alighting for this trip. After obtaining the destination stops, the transfer behavior can be determined based on the time difference between the two trips (the next alighting time--the current boarding time \(\leq\) 30 min), and the true origin and destination stops of the passenger's trip can be obtained. In addition to card and code data, face and palm print recognition plays a significant role in modern public transportation. At present, this type of recognition technology is being promoted and applied in public transportation payment scenarios in various cities. In this study, a system is proposed to collect real-time facial and palm print data (with passenger consent), which is then cross-referenced with a historical biometric database to identify passengers. Combined with passengers' electronic wallets, this facilitates biometric-based payments. Multimodal convolutional neural network biometric fusion was adopted to improve recognition efficiency and accuracy [25]. The public transportation demand analysis mainly looks at passenger identity information, travel payment time, payment terminal information, and payment amount. Passenger identity information, payment times, terminal data, and payment amounts are analyzed to provide a comprehensive picture of travel demand. This approach integrates biometric data with card/code payment data, offering advantages for more detailed analyses, including multimodal travel patterns. By utilizing this method, the origin and destination stops, travel times, and travel volumes of each trip within the current bus network can be inferred.