A two‐stage method for bus passenger load prediction using automatic passenger counting data
Open accessAbstract
In high‐frequency transit, providing real‐time crowding information (RTCI) is a potential way to promote passenger satisfaction and reduce negative crowding externalities, by assisting passengers in choosing less crowded vehicles. To make RTCI convincing and reliable, it is necessary to provide predictive RTCI, in which bus passenger load (BPL) prediction is the primary problem. This paper proposes a novel two‐stage BPL prediction method using automatic passenger counting (APC) data. The first stage is to predict short‐term passenger flows at stops based on an adaptive Kalman filter approach. Using the outputs from the first stage as well as other variables directly from APC data, the second stage is to predict BPL based on a support vector regression algorithm. Several methods from the existing literature are used as benchmarks to test the relative performance of the proposed method. An empirical study on bus line 1 in Suzhou, China shows that the proposed method outperforms all the benchmarks, and shows significant superiority over other methods for stops with sharp increases in BPL and for multi‐step ahead prediction. This study contributes to the limited literature on BPL prediction and lays the foundation for providing accurate and reliable predictive RTCI in the future.
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Addressing challenges through preprocessing techniques, comprehensive training datasets, adherence to data privacy regulations, and strategic system design will be crucial for successful deployment and operation of these systems in public transport.
“Addressing these challenges through preprocessing techniques [102], comprehensive training datasets [103], adherence to data privacy regulations [104], and strategic system design will be crucial for the successful deployment and operation of these systems in public transport.”
From A Review of Passenger Counting in Public Transport Concepts with Solution Proposal Based on Image Processing and Machine Learning (2024) · cites this paper as [102]
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- Authors
- Wang, P., Chen, X., Chen, J., Hua, M., & Pu, Z.
- Affiliations
- Southeast University (CN)
- Monash University Malaysia (MY)
- Corresponding author
- Chen, Xuewu
- Venue
- IET Intelligent Transport Systems
- Published
- 2021-01-07
- Volume
- 15
- Issue
- 2
- Pages
- 248-260
- ISSN
- 1751-956X (ISSN-L)
- 1751-9578
- OpenAlex
- W3119724063
- Cited by
- 28
- References
- 28
- Citations by year
- 2026: 5 · 2025: 3 · 2024: 11 · 2023: 4 · 2022: 4 · 2021: 1
- FWCI
- 2.29
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- IET Intelligent Transport Systems (publishedVersion, cc-by)
- Monash University Research Portal (Monash University) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Basic Research Program of China (2018YFB1601300)
- National Natural Science Foundation of China (71901059)
- Topics
- Physical Sciences › Engineering › Building and Construction › Traffic Prediction and Management Techniques
- Social Sciences › Transportation › Transportation Planning and Optimization
- Physical Sciences › Engineering › Civil and Structural Engineering › Urban Transport Systems Analysis
- Keywords
- automatic passenger counting, adaptive Kalman filter, support vector regression, two-stage prediction, multi-step forecasting, passenger demand forecasting, bus stops
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