Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/70421
PIRA download icon_1.1View/Download Full Text
Title: Exploring traffic congestion correlation from multiple data sources
Authors: Wang, YQ 
Cao, JN 
Li, WG 
Gu, T
Shi, WZ 
Issue Date: Oct-2017
Source: Pervasive and mobile computing, Oct. 2017, v. 41, p. 470-483
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 explore 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 through experiments. We found some important patterns that lead to a high/low congestion correlation, and they can facilitate building various transportation applications. In addition, we found that traffic congestion correlation has obvious directionality and transmissibility. The proposed techniques in our framework are general, and can be applied to other pairwise correlation analysis.
Keywords: Traffic congestion
Congestion correlation
Multiple data sources
Classification
Publisher: Elsevier
Journal: Pervasive and mobile computing 
ISSN: 1574-1192
EISSN: 1873-1589
DOI: 10.1016/j.pmcj.2017.03.015
Rights: © 2017 Elsevier B.V. All rights reserved.
© 2017. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/.
The following publication Wang, Y., Cao, J., Li, W., Gu, T., & Shi, W. (2017). Exploring traffic congestion correlation from multiple data sources. Pervasive and Mobile Computing, 41, 470-483 is available at https://doi.org/10.1016/j.pmcj.2017.03.015.
Appears in Collections:Journal/Magazine Article

Files in This Item:
File Description SizeFormat 
Wang_Exploring_Traffic_Congestion.pdfPre-Published version2.22 MBAdobe PDFView/Open
Open Access Information
Status open access
File Version Final Accepted Manuscript
Access
View full-text via PolyU eLinks SFX Query
Show full item record

Page views

69
Last Week
0
Last month
Citations as of Mar 24, 2024

Downloads

56
Citations as of Mar 24, 2024

SCOPUSTM   
Citations

38
Citations as of Mar 29, 2024

WEB OF SCIENCETM
Citations

29
Last Week
0
Last month
Citations as of Mar 28, 2024

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.