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JMT 2017, Vol. 25 Issue (1) :1-11    DOI: 10.1007/s40534-016-0121-7
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Identifying Achilles-heel roads in real-sized networks
Saeed Asadi Bagloee1, Majid Sarvi1, Russell George Thompson2, Abbas Rajabifard3*
1 Smart Cities Transport Group, Department of Infrastructure Engineering, Melbourne School of Engineering, Centre for Disaster Management and Public Safety (CDMPS), The University of Melbourne, Parkville, VIC 3010, Australia;
2 Smart Cities Transport Group, Department of Infrastructure Engineering, Melbourne School of Engineering, The University of Melbourne, Parkville, VIC 3010, Australia;
3 Department of Infrastructure Engineering, Melbourne School of Engineering, Centre for Disaster Management and Public Safety (CDMPS), The University of Melbourne, Parkville, VIC 3010, Australia

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Abstract Ensuring a minimum operational level of road networks in the presence of unexpected incidents is becoming a hot subject in academic circles as well as industry. To this end, it is important to understand the degree to which each single element of the network contributes to the operation and performance of a network. In other words, a road can become an "Achilles-heel" for the entire network if it is closed due to a simple incident. Such insight of the detrimental loss of the closure of the roads would help us to be more vigilant and prepared. In this study, we develop an index dubbed as Achilles-heel index to quantify detrimental loss of the closure of the respective roads. More precisely, the Achilles-heel index indicates how many drivers are affected by the closure of the respective roads (the number of affected drivers is also called travel demand coverage). To this end,roads with maximum travel demand coverage are sorted as the most critical ones, for which a method—known as "link analysis"—is adopted. In an iterative process, first, a road with highest traffic volume is first labeled as "target link", and second, a portion of travel demand which is captured by the target link is excluded from travel demand. For the next iteration, the trimmed travel demand is then assigned to the network where all links including the target links run on the initial travel times. The process carries on until all links are labeled. The proposed methodology is applied to a largesized network of Winnipeg, Canada. The results shed light on also bottleneck points of the network which may warrant provision of additional capacity or parallel roads.
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KeywordsCritical roads   Achilles-heel roads   Sensor location problem   Flow-bundle   Link analysis     
Received 2016-08-26;
Corresponding Authors: Saeed Asadi Bagloee, Majid Sarvi, Russell George Thompson, Abbas Rajabifard     Email: saeed.bagloee@unimelb.edu.au;majid.sarvi@unimelb.edu.au;rgthom@unimelb.edu.au;abbas.r@unimelb.edu.au
Cite this article:   
Saeed Asadi Bagloee, Majid Sarvi, Russell George Thompson, Abbas Rajabifard.Identifying Achilles-heel roads in real-sized networks[J]  JMT, 2017,V25(1): 1-11
URL:  
http://jmt.swjtu.edu.cn/EN/10.1007/s40534-016-0121-7      or     http://jmt.swjtu.edu.cn/EN/Y2017/V25/I1/1
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