# Short-Term Passenger Flow Prediction of Urban Rail Transit Based on a Combined Deep Learning Model
## 4 Analysis of Passenger Flow Characteristics and Influencing Factors of URT
### Analysis of Passenger Flow Distribution Characteristics  
The spatial distribution characteristics of passenger flow are related to the location of stations. Based on the above daily average passenger flow data of some stations of Chongqing Rail Transit Line 3, the K-means clustering algorithm [38] is used to cluster the passenger flow of stations and determine the classification standards of passenger flow of different stations, to provide a guidance for subsequent passenger flow prediction. The K-means algorithm is an iterative clustering analysis algorithm. By updating the values of each clustering center, the samples can be clustered through this algorithm, and the samples with similar characteristics can be clustered into a class. The process is shown in Figure 6 and algorithm steps are as follows:  
Step 1: For the passenger flow data set of a station, the number of categories k to be clustered is selected, and k center points are selected;  
Step 2: For each sample point, the center point closest to it (find the organization) is found, and the point closest to the same center point is a class to complete a clustering;  
Step 3: Determine whether the categories of the sample points before and after clustering are the same. If so, the algorithm terminates; otherwise, go to Step 4;  
Figure 5: Structure diagram of TCN-LSTM model. Step 4: For the sample points in each category, the center points of these sample points are calculated as the new center points of the class and continue Step 2. After executing the K-means algorithm on the data set, the clustering results are shown in Figure 7. It can be seen that the 15 stations are divided into three clusters, and the centroid of each cluster can better represent the characteristics of passenger flow data. The three clusters in Figure 7 are divided into three categories: high, medium, and low passenger flow, and the results are shown in Table 1. It can be seen that there is only one station with high passenger flow, which is Guanyin Bridge, with an average daily passenger flow of more than 120,000 person-times. Guanyinqiao is located in the urban tourist area of the Guanyinqiao business district in Chongqing. It is the economic center and commercial core area of Jiangbei District and is known as \"the most competitive business district in China\". There are a large number of people traveling, shopping, and going to work every day. There are 5 stations with medium passenger flow, which are densely populated residential areas or business districts, or important transportation interchange hubs. The low passenger flow stations are mostly located in relatively sparsely populated areas. The temporal distribution characteristics of passenger flow have regular changes in the morning and evening peak due to the impact of residents' commuting and school hours. For different stations, the morning and evening peak hours may be different. Taking the daily average station passenger flow of April 18 (Sunday) and April 19 (Monday) in 2021 as an example, the temporal distribution characteristics of high, medium (select 2 stations), and low (select 3 stations) passenger flow stations are analyzed, as shown in Figure 8.  
Figure 6: K-means algorithm execution process. It can be seen from Figure 8 that the passenger flow of high and medium passenger flow stations at weekends is at a high level from 8:00 to 20:00. There is a short peak around 18:00. For low passenger flow stations, there is no apparent peak. During working days, all stations have obvious morning and evening peak periods. The passenger flow has an obvious bimodal distribution, and the peak time of each station is the same. It can be seen from the analysis that the peak hours of passenger flow at different stations are 7:00-9:00 and 17:00-19:00 on weekdays, and that at the stations with high and medium passenger flow are 14:00-19:00 at weekends.