Algorithm fusion method to enhance automatic incident detection on Melbourne freeways

Chin Long Mak and Henry S.L. Fan

Transportation Planning and Technology, 2014, vol. 37, issue 2, 169-185

Abstract: This paper addresses the transferability issue faced by many practitioners in developing an effective and efficient automatic incident detection algorithm for different freeways. An algorithm fusion procedure developed for the Central Expressway in Singapore is evaluated to demonstrate its transferability potential in detecting lane-blocking incidents along freeways in Melbourne, Australia. This study observes that the flow-based algorithm fusion options that use a set of different detection threshold values for various pre-incident traffic flow conditions possess promising transferability potential. They give a reasonably high detection rate of above 80% with false alarm rate levels below 0.2% with mean-time-to-detect values less than 150 seconds. These flow-based algorithm fusion options significantly outperform a model specifically developed for traffic conditions on freeways in Melbourne. In conclusion, this method is capable of providing an alternative to the commonly practiced methods in detecting incidents along different sites.

Date: 2014
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/03081060.2013.870790 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:transp:v:37:y:2014:i:2:p:169-185

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/GTPT20

DOI: 10.1080/03081060.2013.870790

Access Statistics for this article

Transportation Planning and Technology is currently edited by Dr. David Gillingwater

More articles in Transportation Planning and Technology from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().