Predicting traffic speed in urban transportation subnetworks for multiple horizons
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Jaillet_Predicting traffic.pdf
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292.5 KB
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Author(s)
Dauwels, Justin
Aslam, Aamer
Asif, Muhammad Tayyab
Zhao, Xinyue
Vie, Nikola Mitro
Cichocki, Andrzej
Jaillet, Patrick
Date Issued
December 2014
Journal
Proceedings of the 2014 13th International Conference on Control Automation Robotics & Vision (ICARCV)
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Dauwels, Justin, Aamer Aslam, Muhammad Tayyab Asif, Xinyue Zhao, Nikola Mitro Vie, Andrzej Cichocki, and Patrick Jaillet. “Predicting Traffic Speed in Urban Transportation Subnetworks for Multiple Horizons.” 2014 13th International Conference on Control Automation Robotics & Vision (ICARCV) (December 2014).
Version
Author's final manuscript
Abstract
Traffic forecasting is increasingly taking on an important role in many intelligent transportation systems (ITS) applications. However, prediction is typically performed for individual road segments and prediction horizons. In this study, we focus on the problem of collective prediction for multiple road segments and prediction-horizons. To this end, we develop various matrix and tensor based models by applying partial least squares (PLS), higher order partial least squares (HO-PLS) and N-way partial least squares (N-PLS). These models can simultaneously forecast traffic conditions for multiple road segments and prediction-horizons. Moreover, they can also perform the task of feature selection efficiently. We analyze the performance of these models by performing multi-horizon prediction for an urban subnetwork in Singapore.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Laboratory for Information and Decision Systems
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1109/ICARCV.2014.7064363
