Analysis of the Relationship between Zhengzhou Subway Passenger Flow and Weather

Authors

  • Tiantian Zhang

DOI:

https://doi.org/10.61173/4fpwmr41

Keywords:

Urban rail transit, Weather factors, Zhengzhou

Abstract

Nowadays, subways have become the primary mode of transportation due to their various advantages, such as large carrying capacity, convenience, speed, and low cost. Zhengzhou, a new first-tier city known as a transportation hub, is primarily populated by young working professionals. The young group has a fast-paced commute and high demand for transportation efficiency, so they tend to prefer the subway as their daily commuting mode. Different weather conditions also affect people’s choice of transportation mode. Weather, as an important factor of daily change, also influences people’s choice of transportation mode. Based on this social reality, this paper employs a combination of correlation analysis, multiple linear regression analysis, and stepwise regression analysis to study the impact of weather on subway ridership. Through data analysis from April to September 2025, it was found that there is a negative correlation between subway ridership on the same day and both minimum temperature and total precipitation. When the minimum temperature or total precipitation increases, subway ridership on the same day decreases accordingly. The study confirms that minimum temperature and total precipitation have a tangible impact on daily ridership. This research can provide reference suggestions for subway dispatch management, ridership prediction, and emergency management.

References

[1] Lyu Y. Short-term passenger flow forecast of urban subway transportation based on deep learning methods. Journal of Physics: Conference Series, 2021, 1915(2): 022064.

[2] Xiao L, Lo S, Zhou J, Liu J, Yang L. Predicting vibrancy of metro station areas considering spatial relationships through graph convolutional neural networks: The case of Shenzhen, China. Urban Analytics and City Science, 2020, 0(0): 1-22.

[3] Sun Yingbao. Research on passenger flow forecasting of urban rail transit under snowfall. Shijiazhuang Railway University, 2024.

[4] Xu Bo, Zhang Nan, Wei Juntao, Du Mengmeng, Zhang Gaojian. Research on the impact of rainfall on the fluctuation of rail transit passenger flow. Shaanxi Meteorology, 2023, (06): 59- 63.

[5] Jiang Y, Gao Y, Yuan Q, et al. The combined influence of extreme weather and sea-level rise on water damage around the entrance of rail transit in coastal areas. Journal of Ocean Engineering and Marine Energy, 2025, 11: 327-338.

[6] Ngo N S, Bashar S. The impacts of extreme weather events on U.S. public transit ridership. Transportation Research Part D: Transport and Environment, 2024, 113: 104185.

[7] Adduōğlu I, Ergün M. Evaluating multimodal travel choices through MGWR: Insights from the Ankara-Istanbul corridor. Travel Behaviour and Society, 2025, 32: 100789.

[8] Fondzenyuy S K, Dahdah S, Neki K, Usami D S, Burlacu A F, Feudjio S L T, Persia L. Developing operating speed prediction models in mixed traffic environments using multiple linear regression and multilevel modeling approaches: Evidence from five low- and middle-income countries. Results in Engineering, 2025, 27: 107799.

[9] Brumercikova E, Bukova B. The regression and correlation analysis of carried persons by means of public passenger transport of the Slovak Republic. Transportation Research Procedia, 2020, 44: 61-68.

[10] Yang Xiaohong. Correlation analysis between urban rail transit and urban tourism. Urban Rail Transit Research, 2024, 27(1): 21-22.

[11] Metro Passenger Flow Data. Daily passenger flow data of Zhengzhou metro. 2025. Retrieved from https://www. metrolinehub.com/zh

[12] Soil and Water Conservation Archive. Historical weather data of Zhongyuan District in October 2025. 2025. Retrieved from https://www.yanshou100.com/

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Published

2026-02-28