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Bus Arrival Time Prediction Using Support Vector Machines
Abstract
Effective prediction of bus arrival time is central to many advanced traveler information systems. This article presents support vector machines (SVM), a new neural network algorithm, to predict bus arrival time. The objective of this paper is to examine the feasibility and applicability of SVM in vehicle travel time forecasting area. Segment, the travel time of current segment, and the latest travel time of next segment are taken as three input features. Bus arrival time predicted by the SVM is assessed with the data of transit route number 4 in Dalian economic and technological development zone in China and conclusions are drawn.
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Headway-based bus bunching prediction using transit smart card data
Yu, H., Chen, D., Wu, Z., Ma, X., & Wang, Y. (2016) · Transportation Research Part C: Emerging Technologies · doi:10.1016/j.trc.2016.09.007 · Cited by 80
An online learning approach to eliminate Bus Bunching in real-time
Moreira-Matias, L., Cats, O., Gama, J., Mendes-Moreira, J., & de Sousa, J. F. (2016) · Applied Soft Computing · doi:10.1016/j.asoc.2016.06.031 · Cited by 75
Bus Arrival Time Prediction: A Spatial Kalman Filter Approach
Achar, A., Bharathi, D., Kumar, B. A., & Vanajakshi, L. (2020) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2019.2909314 · Cited by 72
A two‐stage method for bus passenger load prediction using automatic passenger counting data
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Metadata
- Authors
- Bin, Y., Zhongzhen, Y., & Baozhen, Y.
- Venue
- Journal of Intelligent Transportation Systems
- Publisher
- Informa UK Limited
- Published
- 2006-12
- Volume
- 10
- Issue
- 4
- Pages
- 151-158
- ISSN
- 1024-8072 (ISSN-L)
- 1547-2450
- 1547-2442
- OpenAlex
- W2039559615
- Cited by
- 268
- References
- 2
- FWCI
- 13.16
- Type
- journal article
- Language
- en
- Open access
- closed
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- Bin, Y., Zhongzhen, Y., & Baozhen, Y. (2006). Bus Arrival Time Prediction Using Support Vector Machines. Journal of Intelligent Transportation Systems. https://doi.org/10.1080/15472450600981009
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