Weather Impact on Passenger Flow of Rail Transit Lines

Yongqing Guo (a) School of Transportation and Vehicle Engineering, Shandong University of Technology, Zibo 255000, China. b) College of Electromechanical Engineering, Qingdao University of Science & Technology, Qingdao 266000,)
Xiaoyuan Wang (b) College of Electromechanical Engineering, Qingdao University of Science & Technology, Qingdao 266000, China. c) Joint Laboratory for Internet of Vehicles, Ministry of Education - China Mobile Communications Corporation, Tsinghua University, Beijin)
Qing Xu (School of Vehicle and Mobility, Tsinghua University, Beijing 100084,)
Shanliang Liu (College of Electromechanical Engineering, Qingdao University of Science & Technology, Qingdao 266000,)
Shijie Liu (School of Transportation and Vehicle Engineering, Shandong University of Technology, Zibo 255000,)
Junyan Han (School of Transportation and Vehicle Engineering, Shandong University of Technology, Zibo 255000,)

Civil Engineering Journal
Vol 6, No 2 (2020): February

Passenger flow prediction is important for the planning, design and decision-making of urban rail transit lines. Weather is an important factor that affects the passenger flow of rail transit line by changing the travel mode choice of urban residents. A number of previous researches focused on analyzing the effects of   weather (e.g. rain, snow, and temperature) on public transport ridership, but the effects on rail transit line yet remain largely unexplored This study aims to explore the influence of weather on ridership of urban rail transit lines, taking Chengdu rail transit line 1 and line 2 as examples. Linear regression method was used to develop models for estimating the daily passenger flow of different rail transit lines under different weather conditions. The results show that for Chengdu rail transit line 1, the daily ridership rate of rail transit increases with increasing temperature. While, for Chengdu rail transit line 2, the daily ridership rate of rail transit decreases with increasing wind power. The research findings can provide effective strategies to rail transit operators to deal with the fluctuation in daily passenger flow.