Predicting traffic speed in urban transportation subnetworks for multiple horizons

Justin Dauwels, Aamer Aslam, Muhammad Tayyab Asif, Xinyue Zhao, Nikola Mitro Vie, Andrzej Cichocki, Patrick Jaillet

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

17 Citations (Scopus)

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.

Original languageEnglish
Title of host publication2014 13th International Conference on Control Automation Robotics and Vision, ICARCV 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages547-552
Number of pages6
ISBN (Electronic)9781479951994
DOIs
Publication statusPublished - 2014
Externally publishedYes
Event2014 13th International Conference on Control Automation Robotics and Vision, ICARCV 2014 - Singapore, Singapore
Duration: 10 Dec 201412 Dec 2014

Publication series

Name2014 13th International Conference on Control Automation Robotics and Vision, ICARCV 2014

Conference

Conference2014 13th International Conference on Control Automation Robotics and Vision, ICARCV 2014
Country/TerritorySingapore
CitySingapore
Period10/12/1412/12/14

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