Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/67009
Title: Mining traffic congestion correlation between road segments on GPS trajectories
Authors: Wang, YQ
Cao, JN 
Li, WG
Gu, T
Keywords: Traffic congestion
Congestion correlation
GPS trajectories
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Issue Date: 2016
Publisher: Institute of Electrical and Electronics Engineers
Source: 2016 2nd IEEE International Conference on Smart Computing (SMARTCOMP), May 18-20, 2016, St Louis, MO, p. 131-138 How to cite?
Abstract: Traffic congestion is a major concern in many cities around the world. Previous work mainly focuses on the prediction of congestion and analysis of traffic flows, while the congestion correlation between road segments has not been studied yet. In this paper, we propose a three-phase framework to study the congestion correlation between road segments from multiple real world data. In the first phase, we extract congestion information on each road segment from GPS trajectories of over 10,000 taxis, define congestion correlation and propose a corresponding mining algorithm to find out all the existing correlations. In the second phase, we extract various features on each pair of road segments from road network and POI data. In the last phase, the results of the first two phases are input into several classifiers to predict congestion correlation. We further analyze the important features and evaluate the results of the trained classifiers. We found some important patterns that lead to a high/low congestion correlation, and they can facilitate building various transportation applications. The proposed techniques in our framework are general, and can be applied to other pairwise correlation analysis.
URI: http://hdl.handle.net/10397/67009
ISBN: 978-1-5090-0898-8
DOI: 10.1109/SMARTCOMP.2016.7501704
Rights: © 2016 IEEE
The following publication Wang, Y., Cao, J., Li, W., & Gu, T. (2016, May). Mining traffic congestion correlation between road segments on GPS trajectories. In Smart Computing (SMARTCOMP), 2016 IEEE International Conference on (pp. 1-8). IEEE is available at https://doi.org/10.1109/SMARTCOMP.2016.7501704
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