Real time transit demand prediction capturing station interactions and impact of special events
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Short-term forecasting of public transit ridership can improve with ML.
“Also, short-term forecasting of public transit ridership can improve with ML; see for example [158,586].”
From Tackling climate change with machine learning (2023) · cites this paper as [586]
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- Authors
- Noursalehi, P., Koutsopoulos, H. N., & Zhao, J.
- Affiliations
- Northeastern University (US)
- Massachusetts Institute of Technology (US)
- Corresponding author
- Noursalehi, Peyman
- Venue
- Transportation Research Part C: Emerging Technologies
- Publisher
- Elsevier BV
- Published
- 2018-12
- Volume
- 97
- Pages
- 277-300
- ISSN
- 0968-090X (ISSN-L)
- OpenAlex
- W2899828597
- Cited by
- 71
- References
- 58
- Citations by year
- 2026: 9 · 2025: 10 · 2024: 7 · 2023: 7 · 2022: 13 · 2021: 10 · 2020: 11 · 2019: 3 · 2017: 1
- FWCI
- 4.81
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Transportation Research Part C Emerging Technologies (publishedVersion)
- Topics
- Physical Sciences › Engineering › Building and Construction › Traffic Prediction and Management Techniques
- Social Sciences › Transportation › Transportation Planning and Optimization
- Social Sciences › Transportation › Human Mobility and Location-Based Analysis
- Keywords
- state-space model, dynamic factor models, hierarchical clustering, ensemble forecasting, special events, football match, London Underground, public transport demand, service quality
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