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http://dx.doi.org/10.5626/KTCP.2015.21.1.19

An Algorithm for Identifying the Change of the Current Traffic Congestion Using Historical Traffic Congestion Patterns  

Lee, Kyungmin (Pusan Univ.)
Hong, Bonghee (Pusan Univ.)
Jeong, Doseong (Pusan Univ.)
Lee, Jiwan (Pusan Univ.)
Publication Information
KIISE Transactions on Computing Practices / v.21, no.1, 2015 , pp. 19-28 More about this Journal
Abstract
In this paper, we proposed an algorithm for the identification of relieving or worsening current traffic congestion using historic traffic congestion patterns. Historical congestion patterns were placed in an adjacency list. The patterns were constructed to represent spatial and temporal length for status of a congested road. Then, we found information about historical traffic congestions that were similar to today's traffic congestion and will use that information to show how to change traffic congestion in the future. The most similar pattern to current traffic status among the historical patterns corresponded to starting section of current traffic congestion. One of our experiment results had average error when we compared identified changes of the congestion for one of the sections in the congestion road by using our proposal and real traffic status. The average error was 15 minutes. Another result was for the long congestion road consisting of several sections. The average error for this result was within 10 minutes.
Keywords
traffic big data; congestion patterns; pattern matching; adjacency list;
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  • Reference
1 P. Pongpaibool, P. Tangamchit and K. Noodwong, "Evaluation of Road Traffic Congestion Using Fuzzy Techniques," Proc. TENCON 2007-2007 IEEE Region 10 Conference, pp. 1-4, 2007.
2 J. Lu and L. Cao, "Congestion evaluation from traffic flow information based on fuzzy logic," Proc. Intelligent Transportation Systems, 2003. Proc. 2003 IEEE, Vol. 1, pp. 50-33, 2003.
3 Seoul transport operation & information service center, "Seoul TOPIS," http://topis.seoul.go.kr/
4 Korea Expressway Coporation, "Open OASIS," http://data.ex.co.kr/
5 N. Theerawat, K. Hiriotappa and S. Thajchayapong, "Traffic Congestion Prediction System using Artificial Pattern-based Dynamic Time Warping," ATRANS 2012, SCS12-012, 2012.
6 F. Maier, R. Braun, F. Busch and P. Mathias, "Pattern-based short-term prediction of urban congestion propagation and automatic response," Traffic Engineering & Control, pp. 227-231, June. 2008.
7 Wikipedia, "Adjacency list," [Online]. Available: http://en.wikipedia.org/wiki/Adjacency list/
8 ITS center of Busan metropolitan city, [Online]. Available: http://its.busan.go.kr/
9 California department of transportation, "Caltrans PeMs," [Online]. Available: http://pems.dot.ca.gov/
10 J. Zhong and S. Ling, "Key Factors of K-nearest Neighbors Nonparametric Regression in Short-time Traffic Flow Forecasting," The 21st International Conference on Industrial Engineering and Engineering Management 2014 (IEEM 2014), 2014.
11 G. Gopi, J. Dauwels, M. T. Asif, S. Ashwin, N. Mitrovic, U. Rasheed and P. Jaillet, "Bayesian Support Vector Regression for Traffic Speed Prediction with Error Bars," the 16th International IEEE Annual Conference on Intelligent Transportation Systems (ITSC 2013), 2013.
12 J. M. McNew, "Predicting Crusiing Speed through Data-driven Driver Modeling," the 15th International IEEE Annual Conference on Intelligent Transportation Systems (ITSC 2012), 2012.