|Table of Contents|

Spatial-temporal distribution characteristics of passenger flow in different types of urban rail transit stations(PDF)

长安大学学报（自然科学版）[ISSN:1006-6977/CN:61-1281/TN]

Issue:
2024年1期
Page:
129-140
Research Field:
交通工程
Publishing date:

Info

Title:
Spatial-temporal distribution characteristics of passenger flow in different types of urban rail transit stations
Author(s):
TAN Xiao-wei1;  YANG Xing2;  MA Zhuang-lin3;  GUO Ji4
(1. School of Automobile, Chang'an University, Xi'an 710064, Shaanxi, China; 2. Inspur New Infrastructure Technology Co. Ltd., Jinan 250101, Shandong, China; 3. College of Transportation Engineering,Chang'an University, Xi'an 710064, Shaanxi, China; 4. Yunnan Science Research Institute of Communication Co. Ltd., Kunming 650011, Yunnan, China)
Keywords:
traffic engineering;  urban rail transit;  Gaussian mixed model;  classification of stations;  spatial-temporal distribution of passenger flow
PACS:
U491.17
DOI:
10.19721/j.cnki.1671-8879.2024.01.012
Abstract:
In order to clarify the functional types of rail transit stations, avoid large passenger flow risks and refine urban management, the spatial-temporal distribution characteristics of passenger flow in different types of stations was explored. Gaussian mixture model(GMM)was used to establish the identification method of rail transit station type, and expectation maximization(EM)algorithm was utilized to solve the problem. Nanjing rail transit system was selected to verify the effectiveness of the proposed method. The time distribution regularity of passenger flow at different stations was explored from the perspective of time distribution of checking in and out passenger flow and travel time. By using the passenger flow OD distribution between stations, the spatial distribution regularity of passenger flow at different stations was analyzed. The results show that 128 rail transit stations in Nanjing can be divided into six types, namely residential-oriented station, employment-oriented station, spatial mismatched station, mixed mainly residential-oriented station, mixed mainly employment-oriented station and hub comprehensive station. Significant differences can be found in the time distribution of the passenger flow in different stations. Residential-oriented and employment-oriented stations show a typical single peak shape, with a passenger flow ratio between [0.23,5.59] and a clear passenger flow peak of “early in and late out” or “early out and late in”. Spatial mismatched stations present a typical bimodal pattern, with a balanced passenger flow in the morning and evening peak hours, and a passenger flow ratio of 1.19 and 1.07, respectively. Mixed mainly residential-oriented stations and mixed mainly employment-oriented stations also present a bimodal pattern, but the sizes of the two peaks are different. There is neither significant morning and evening checking in and out passenger flow peak nor obvious regularity in passenger flow fluctuations at hub comprehensive stations. The checking in and out peak hours of different types of stations are inconsistent. Compared with the peak of the arrival time, there is a delay of 15 to 45 minutes about departure time during the morning and evening peak hours. The mean and standard deviation of passenger travel time vary among different types of stations. However, the mean and standard deviation of travel time at residential-oriented and mixed mainly residential-oriented stations are higher than those at other stations, reflecting the characteristics of the long-term commuting. The spatial distribution of stations presents an obvious ring structure. With the main city as the center of the circle, the station type from inside to outside in turn are employment-oriented station, mixed mainly employment-oriented station, spatial mismatched station, mixed mainly residential-oriented station, and residential-oriented station. The passenger flow OD between stations are obviously different. The top 20% OD lines account for 79.01% of the total passenger flow thus conforming to the typical Pareto's law.3 tabs, 10 figs, 23 refs.

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Last Update: 2024-01-10