Spatial and Temporal Patterns in Large-Scale Traffic Speed Prediction
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Jaillet_Spatiotemporal.pdf
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Author(s)
Asif, Muhammad Tayyab
Dauwels, Justin
Oran, Ali
Fathi, Esmail
Dhanya, Menoth Mohan
Mitrovic, Nikola
Jaillet, Patrick
Goh, Chong Yang
Xu, Muye
Alternative Title
Spatiotemporal Patterns in Large-Scale Traffic Speed Prediction
Date Issued
April 2014
Journal
IEEE Transactions on Intelligent Transportation Systems
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Asif, Muhammad Tayyab, Justin Dauwels, Chong Yang Goh, Ali Oran, Esmail Fathi, Muye Xu, Menoth Mohan Dhanya, Nikola Mitrovic, and Patrick Jaillet. “Spatiotemporal Patterns in Large-Scale Traffic Speed Prediction.” IEEE Transactions on Intelligent Transportation Systems 15, no. 2 (April 2014): 794–804.
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Author's final manuscript
Abstract
The ability to accurately predict traffic speed in a large and heterogeneous road network has many useful applications, such as route guidance and congestion avoidance. In principle, data-driven methods, such as support vector regression (SVR), can predict traffic with high accuracy because traffic tends to exhibit regular patterns over time. However, in practice, the prediction performance can significantly vary across the network and during different time periods. Insight into those spatiotemporal trends can improve the performance of intelligent transportation systems. Traditional prediction error measures, such as the mean absolute percentage error, provide information about the individual links in the network but do not capture global trends. We propose unsupervised learning methods, such as k-means clustering, principal component analysis, and self-organizing maps, to mine spatiotemporal performance trends at the network level and for individual links. We perform prediction for a large interconnected road network and for multiple prediction horizons with an SVR-based algorithm. We show the effectiveness of the proposed performance analysis methods by applying them to the prediction data of the SVR.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Laboratory for Information and Decision Systems
Massachusetts Institute of Technology. Operations Research Center
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DOI of Published Version
https://doi.org/10.1109/tits.2013.2290285
