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Predicting arrival times of buses using real-time GPS measurements
Abstract
Predicting arrival times of buses is a key challenge in the context of building intelligent public transportation systems. In this paper, we describe an efficient non-parametric algorithm which provides highly accurate predictions based on real-time GPS measurements. The key idea is to use a Kernel Regression model to represent the dependencies between position updates and the arrival times at bus stops. The performance of the proposed algorithm is evaluated on real data from the public bus transportation system in Dublin, Ireland. For a time horizon of 50 minutes, the prediction error of the algorithm is less than 10 percent on average. It clearly outperforms parametric methods which use a Linear Regression model, predictions based on the K-Nearest Neighbor algorithm, and a system which computes predictions of arrival times based on the current delay of buses. A study investigating the selection of interpolation points to reduce the size of the training set concludes the paper.
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In the citing paper
“We pick bus line 46A (outbound, from Phoenix Park to Dun Laoghaire, also used in the literature [3]) to study as it is the most frequent bus line in our dataset and it is a long bus trip (i.e.nearly 20km) that gives us enough points to study.”
From BusTime: Which is the Right Prediction Model for My Bus Arrival Time? (2020) · cites this paper as [3]
In the citing paper
“A year later, the kernel regression (KR) [3] model is applied to improve k-NN by automatically selecting the proper number of most similar past trajectories for predictions, which have resulted in much stable arrival time estimations.”
From BusTime: Which is the Right Prediction Model for My Bus Arrival Time? (2020) · cites this paper as [3]
In the citing paper
“due to the fact that the raw GPS data is often noisy and have irregular update frequency”
From BusTime: Which is the Right Prediction Model for My Bus Arrival Time? (2020) · cites this paper as [3]
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No references could be resolved yet.
Research works printed but not linked: 6
- gibbens, 2006, Road Traffic Analysis Using MIDAS Data Journey Time Prediction
- 10.1145/2068984.2068985 · DOI · Paper page
- 10.1109/ITSC.2011.6082962 · doi:10.1109/itsc.2011.6082962
- 10.1016/j.trc.2011.01.003 · DOI · Paper page
- 10.1145/1463434.1463452 · DOI
- 10.1080/15472450.2011.570109 · DOI
Cited by
A predictive-control framework to address bus bunching
Andres, M., & Nair, R. (2017) · Transportation Research Part B: Methodological · doi:10.1016/j.trb.2017.06.013 · Cited by 95
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
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
BusTime: Which is the Right Prediction Model for My Bus Arrival Time?
Liu, D., Sun, J., & Wang, S. (2020) · 2020 5th IEEE International Conference on Big Data Analytics (ICBDA) · doi:10.1109/icbda49040.2020.9101265 · Cited by 11
Metadata
- Authors
- Sinn, M., Yoon, J. W., Calabrese, F., & Bouillet, E.
- Venue
- 2012 15th International IEEE Conference on Intelligent Transportation Systems
- Publisher
- IEEE
- Published
- 2012-09
- Pages
- 1227-1232
- Cited by
- 44
- References
- 0
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- CoChat. (2026). Citation check: Predicting arrival times of buses using real-time GPS measurements. https://app.cochat.ai/papers/10.1109/itsc.2012.6338767
- The paper
- Sinn, M., Yoon, J. W., Calabrese, F., & Bouillet, E. (2012). Predicting arrival times of buses using real-time GPS measurements. 2012 15th International IEEE Conference on Intelligent Transportation Systems. https://doi.org/10.1109/itsc.2012.6338767
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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