Link-level vulnerability indicators for real-world networks

It is computationally expensive to find out where vulnerable parts in a network are. In literature a variety of methods were introduced that use relatively simple selection criteria (measured in real-life or calculated in a traffic simulator) to pre-determine the seriousness of the delays caused by the blocking of that link and thereafter perform a more detailed analysis. This paper reviews the selection criteria proposed in the literature and assesses the quality of these criteria. Furthermore, a multi linear fit of the criteria is made to find a better, combined, criterion to rank the links according to their vulnerability. The paper shows that different criteria indicate different links to be vulnerable. Also combined they cannot well predict the vulnerability of a link. Therefore, it is concluded that to find vulnerable links, one has to look further than link-based indicators.

[1]  Patrice Marcotte,et al.  Bilevel Programming , 2009, Encyclopedia of Optimization.

[2]  P. I. Richards.  Shock Waves on the Highway , 1956 .

[3]  H. J. Van Zuylen,et al.  Rerouting behaviour of travellers under exceptional traffic conditions: An empirical analysis of route choice , 2010 .

[4]  M J Lighthill,et al.  On kinematic waves II. A theory of traffic flow on long crowded roads , 1955, Proceedings of the Royal Society of London. Series A. Mathematical and Physical Sciences.

[5]  Michael G.H. Bell,et al.  A game theory approach to measuring the performance reliability of transport networks , 2000 .

[6]  Lambertus H. Immers,et al.  Marginal Incident Computation , 2009 .

[7]  Yusen Chen,et al.  Generating Optimal Controlled Prescriptive Route Guidance in Realistic Traffic Networks; A Generic Approach , 2006 .

[8]  Serge P. Hoogendoorn,et al.  Approach to Critical Link Analysis of Robustness for Dynamical Road Networks , 2007 .

[9]  C. Spearman.  The proof and measurement of association between two things. , 2015, International journal of epidemiology.

[10]  Erik Jenelius,et al.  INCORPORATING DYNAMICS AND INFORMATION IN A CONSEQUENCE MODEL FOR ROAD NETWORK VULNERABILITY ANALYSIS , 2007 .

[11]  Serge P. Hoogendoorn,et al.  Joint Modeling of Advanced Travel Information Service, Habit, and Learning Impacts on Route Choice by Laboratory Simulator Experiments , 2005 .

[12]  Ben Immers,et al.  Methodology for Identifying Vulnerable Sections in a National Road Network , 2006 .

[13]  Michiel C.J. Bliemer,et al.  Dynamic Queuing and Spillback in Analytical Multiclass Dynamic Network Loading Model , 2007 .

[14]  Henk J. van Zuylen.  COMBINING DTA APPROACHES FOR STUDYING ROAD NETWORK ROBUSTNESS , 2006 .

[15]  H. J. Van Zuylen,et al.  The Influence of Spillback Modelling when Assessing Consequences of Blockings in a Road Network , 2008, European Journal of Transport and Infrastructure Research.

[16]  Pamela Murray-Tuite,et al.  Methodology for Determining Vulnerable Links in a Transportation Network , 2004 .

[17]  Max F. Meyer,et al.  The Proof and Measurement of Association between Two Things. , 1904 .

[18]  D. Brillinger,et al.  Handbook of methods of applied statistics , 1967 .

[19]  B. P. Murphy,et al.  Handbook of Methods of Applied Statistics , 1968 .