Pre-crash traffic flow trend analysis on motorways

Hamzehei, Asso, Chung, Edward, & Miska, Marc (2013) Pre-crash traffic flow trend analysis on motorways. In OPTIMUM 2013 : International Symposium on Recent Advances in Transport Modelling, 21-23 April 2013, Mantra on Salt Beach Resort, Kingscliffe, NSW.

Abstract

Crashes on motorway contribute to a significant proportion (40-50%) of non-recurrent motorway congestions. Hence reduce crashes will help address congestion issues (Meyer, 2008). Crash likelihood estimation studies commonly focus on traffic conditions in a Short time window around the time of crash while longer-term pre-crash traffic flow trends are neglected. In this paper we will show, through data mining techniques, that a relationship between pre-crash traffic flow patterns and crash occurrence on motorways exists, and that this knowledge has the potential to improve the accuracy of existing models and opens the path for new development approaches. The data for the analysis was extracted from records collected between 2007 and 2009 on the Shibuya and Shinjuku lines of the Tokyo Metropolitan Expressway in Japan. The dataset includes a total of 824 rear-end and sideswipe crashes that have been matched with traffic flow data of one hour prior to the crash using an incident detection algorithm. Traffic flow trends (traffic speed/occupancy time series) revealed that crashes could be clustered with regards of the dominant traffic flow pattern prior to the crash. Using the k-means clustering method allowed the crashes to be clustered based on their flow trends rather than their distance. Four major trends have been found in the clustering results. Based on these findings, crash likelihood estimation algorithms can be fine-tuned based on the monitored traffic flow conditions with a sliding window of 60 minutes to increase accuracy of the results and minimize false alarms.

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ID Code: 63216
Item Type: Conference Paper
Refereed: No
Additional URLs:
Keywords: Traffic Flow Fluctuations, Traffic Flow Trends, Motorway Crashes, Clustering
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Pattern Recognition and Data Mining (080109)
Australian and New Zealand Standard Research Classification > ENGINEERING (090000) > CIVIL ENGINEERING (090500) > Transport Engineering (090507)
Divisions: Current > Schools > School of Civil Engineering & Built Environment
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Current > Research Centres > Smart Transport Research Centre
Copyright Owner: Copyright 2013 [please consult the author]
Deposited On: 10 Oct 2013 00:45
Last Modified: 11 Oct 2013 06:54

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