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Abstract
Modeling and analyzing metro station ridership is of great importance to passenger flow management and transportation planning operations. In practice, ridership can be affected by multiple factors, including spatial factors (distance and network topology), temporal factors (e.g., period and trend), and external factors (e.g., land use and socioeconomics). However, existing studies mainly focus on external factors but are rarely concerned with investigating temporal influencing factors on metro station ridership.
In this article, we propose a novel data-driven method for estimating metro ridership and identifying influencing factors at a refined granular level based on general estimating equation (GEE) models. Different from prior research, this study looks at longitudinal station-level metro ridership at varied time resolutions. The longitudinal ridership data of the Taipei Metro and its potential influencing factors data in an urban environment in the year 2015 are used to validate the effectiveness of our proposed method.
The results demonstrate that the proposed method performs well in estimating longitudinal metro ridership. It implies that the land use for shopping, feeder bus systems, days since stations were opened, and transportation hub are significant factors influencing ridership at any time resolution. Temporal factors as categorical parameters are also found to be crucial for determining metro ridership, which could facilitate the implementation of flexible transportation planning strategies adapted with temporal changes in real practice.
In this article, we propose a novel data-driven method for estimating metro ridership and identifying influencing factors at a refined granular level based on general estimating equation (GEE) models. Different from prior research, this study looks at longitudinal station-level metro ridership at varied time resolutions. The longitudinal ridership data of the Taipei Metro and its potential influencing factors data in an urban environment in the year 2015 are used to validate the effectiveness of our proposed method.
The results demonstrate that the proposed method performs well in estimating longitudinal metro ridership. It implies that the land use for shopping, feeder bus systems, days since stations were opened, and transportation hub are significant factors influencing ridership at any time resolution. Temporal factors as categorical parameters are also found to be crucial for determining metro ridership, which could facilitate the implementation of flexible transportation planning strategies adapted with temporal changes in real practice.
| Original language | English |
|---|---|
| Pages (from-to) | 195-207 |
| Journal | IEEE Intelligent Transportation Systems Magazine |
| Volume | 12 |
| Issue number | 4 |
| Online published | 1 Sept 2020 |
| DOIs | |
| Publication status | Published - 2020 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 11 Sustainable Cities and Communities
Research Keywords
- Analytical models
- Data models
- Sociology
- Statistics
- Transportation
- Urban areas
Fingerprint
Dive into the research topics of 'Modeling and Analyzing Impact Factors of Metro Station Ridership: An Approach Based on a General Estimating Equation'. Together they form a unique fingerprint.Projects
- 1 Finished
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TBRS: Safety, Reliability, and Disruption Management of High Speed Rail and Metro Systems
XIE, M. (Principal Investigator / Project Coordinator), BENSOUSSAN, A. (Co-Principal Investigator), LO, S. M. (Co-Principal Investigator), SHOU, B. (Co-Principal Investigator), SINGPURWALLA, N. D. (Co-Principal Investigator), TSE, W. T. P. (Co-Principal Investigator), TSUI, K. L. (Co-Principal Investigator), YU, Y. (Co-Principal Investigator), YUEN, K. K. R. (Co-Principal Investigator), CHAN, A. B. (Co-Investigator), CHAN, N.-H. (Co-Investigator), CHIN, K. S. (Co-Investigator), CHOW, H. A. (Co-Investigator), Chow, W. K. (Co-Investigator), EDESESS, M. (Co-Investigator), GOLDSMAN, D. M. (Co-Investigator), Huang, J. (Co-Investigator), LEE, W. M. (Co-Investigator), LI, L. (Co-Investigator), LI, C. L. (Co-Investigator), LING, M. H. A. (Co-Investigator), LIU, S. (Co-Investigator), MURAKAMI, J. (Co-Investigator), NG, S. Y. S. (Co-Investigator), NI, M. C. (Co-Investigator), TAN, M.H.-Y. (Co-Investigator), Wang, W. (Co-Investigator), Wang, J. (Co-Investigator), WONG, C. K. (Co-Investigator), WONG, S. Y. Z. (Co-Investigator), WONG, S. C. (Co-Investigator), Xu, Z. (Co-Investigator), ZHANG, Z. (Co-Investigator), Zhang, D. (Co-Investigator), ZHAO, J. L. (Co-Investigator) & Zhou, Q. (Co-Investigator)
1/01/16 → 31/12/21
Project: Research
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