Towards Universal Freeway Incident Detection Algorithms

This paper reports the intensive test of the new transport systems centre (TSC) algorithm applied to incident detection on freeways. The TSC algorithm is designed to fulfill the universality expectations of automated incident detection. The algorithm consists of two modules: data processing module and incident detection module. The data processing module is designed to handle specific features of different sites. The Bayesian network based incident detection module is used to store and manage general expert traffic knowledge, and to perform coherent reasoning to detect incidents. The TSC algorithm is tested using 100 field incident data sets obtained from Tullamarine Freeway and South Eastern Freeway in Melbourne, Australia. The performance of the algorithm demonstrates its competitiveness with the best performing neural network algorithm which was developed and tested using the same incident data sets in an early research. Most importantly, both the detection rate and false alarm rate of the TSC algorithm are not sensitive to the incident decision threshold, which greatly improves the stability of incident detection. In addition, a very consistent algorithm performance is achieved when the TSC algorithm is transferred from Southern Expressway of Adelaide to both Tullamarine Freeway and South Eastern Freeway of Melbourne. No substantial algorithm retraining is required. A significant step towards algorithm universality is possible from this research.

• Record URL:
  • http://www.sciencedirect.com/science/article/B6VGJ-4KH47PH-2/1/9df45d352cb3d0a60f1dba3019939e45
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/0968090X
• Supplemental Notes:
  • Abstract reprinted with permission from Elsevier
• Authors:
  • Zhang, Kun
  • Taylor, Michael A P
• Publication Date: 2006-4

Language

• English

Media Info

• Media Type: Print
• Features: Figures; References; Tables;
• Pagination: pp 68-80
• Serial:
  • Transportation Research Part C: Emerging Technologies
  • Volume: 14
  • Issue Number: 2
  • Publisher: Elsevier
  • ISSN: 0968-090X
  • Serial URL: http://www.sciencedirect.com/science/journal/0968090X

Subject/Index Terms

• TRT Terms: Advanced traffic management systems; Algorithms; Alternatives analysis; Automatic incident detection; Bayes' theorem; Field tests; Freeways; Incident detection; Information processing; Neural networks
• Uncontrolled Terms: False alarms (Incident detection)
• Geographic Terms: Australia
• Subject Areas: Highways; Operations and Traffic Management; Planning and Forecasting; I21: Planning of Transport Infrastructure;

Filing Info

• Accession Number: 01036013
• Record Type: Publication
• Files: TRIS, ATRI
• Created Date: Oct 27 2006 8:09AM