The Effects of Weather on Passenger Flow of Urban Rail Transit
Author(s): |
Xiaoyuan Wang
Yongqing Guo Chenglin Bai Shanliang Liu Shijie Liu Junyan Han |
---|---|
Medium: | journal article |
Language(s): | English |
Published in: | Civil Engineering Journal, 1 January 2020, n. 1, v. 6 |
Page(s): | 11-20 |
DOI: | 10.28991/cej-2020-03091449 |
Abstract: |
Predicting passenger flow on urban rail transit is important for the planning, design and decision-making of rail transit. Weather is an important factor that affects the passenger flow of rail transit by changing the travel mode choice of urban residents. This study aims to explore the influence of weather on urban rail transit ridership, taking four cities in China as examples, Beijing, Shanghai, Guangzhou and Chengdu. To determine the weather effect on daily ridership rate, the three models were proposed with different combinations of the factors of temperature and weather type, using linear regression method. The large quantities of data were applied to validate the developed models. The results show that in Guangzhou, the daily ridership rate of rail transit increases with increasing temperature. In Chengdu, the ridership rate increases in rainy days compared to sunny days. While, in Beijing and Shanghai, the ridership rate increases in light rainfall and heavy rainfall (except moderate rainfall) compared to sunny days. The research findings are important to understand the impact of weather on passenger flow of urban rail transit. The findings can provide effective strategies to rail transit operators to deal with the fluctuation in daily passenger flow. |
Copyright: | © 2020 Xiaoyuan Wang, Yongqing Guo, Chenglin Bai, Shanliang Liu, Shijie Liu, Junyan Han |
License: | This creative work has been published under the Creative Commons Attribution 4.0 International (CC-BY 4.0) license which allows copying, and redistribution as well as adaptation of the original work provided appropriate credit is given to the original author and the conditions of the license are met. |
0.92 MB
- About this
data sheet - Reference-ID
10407994 - Published on:
04/01/2020 - Last updated on:
02/06/2021