## 3 Methodology  
### Overview  
To solve the aforementioned problem, we propose a method that can analyze the associations between passengers in the public transportation system and interactively explore the travel information of associated passengers. To analyze the associations between passengers, we need to consider the spatial similarity, temporal similarity, spatial-temporal similarity, and similarity of passengers' choice of vehicles. Firstly, we need to analyze the time distribution of passenger travel and observe whether there is an overlap in the travel time of passengers. Next, analyze the travel trajectory of the passenger to observe whether the passenger has an intersecting or similar travel trajectory. We also need to know the areas visited by passengers and how often they are visited. Then, the vehicle selection and frequency of selecting the vehicle when the passenger is riding needs to be analyzed to see if they choose the same vehicle and whether they have the same driving frequency. Finally, it is also necessary to analyze the passenger's entry and exit stations to see if the passengers have similar departure and destination points. Based on the results of the above analysis, we can determine whether the passengers are related and deduce the relationship between them. Therefore, our method needs to meet the following requirements:  
R1: It should support exploring the distribution of origin-destination for passenger travel.  
R2: It should support exploring the trajectory of passenger travel.  
R3: It should support exploring areas visited by passengers.  
R4: It should support exploring the frequency of areas visited by passengers.  
R5: It should support exploring the time distribution of passenger travel.  
R6: It should support exploring the distribution of vehicles used by passengers for travel.  
Our method mainly consists of two modules: Data pre-processing and analysis module and visual analytic module, as shown in Figure 1.  
In the data processing module, we first process the raw data. Then we extract the travel features from the raw data. After that, based on the extracted features, we cluster the passengers' travel to divide them into different travel categories. Finally, we analyzed the associations between passengers.  
In the visual analytics module, we designed five views to explore the associations between passengers:  
1. Passenger 3D travel trajectory view This view shows the 3D trajectory of passenger travel, it can explore the trajectory (R2) and travel time (R5) of passenger travel.
2. Passenger travel time pixel matrix view This view is used to explore the time distribution of passenger travel (R5). It uses a pixel matrix to show the time of passengers entering, exiting and the time spent in the public transportation system.
3. Passenger origin-destination chord view This view is used to explore the entry station and exit station of specific passengers (R1). It uses a chord view to show the distribution of entry and exit stations, and also shows the ratio of entry and exit for each station.  
* Passenger travel vehicle bubble chart view This view is used to explore the distribution of vehicles used by passengers for travel (R6). It shows the routes of the passenger's vehicle and the frequency of passengers on each route.
* Passenger 2D travel trajectory view This view shows the 2D trajectory of passenger travel. It can be used to explore the trajectory of passenger travel (R2), the travel area of passengers (R3) and the travel frequency of each area (R4).  
We use the above five views to form a visual analytics system that explores the associations between passengers. Due to the importance of human-computer interaction, these five views are cooperative and interactive.