Chapter Four – Serviceability Methods

This chapter provides a comprehensive review of the definition, development, and application of a number of approaches to vulnerability analysis, with a focus on the serviceability of a transportation network and the transport consequences of network failure and degradation under different conditions, including traffic congestion and incidents, severe weather, and natural disasters. The methods cover a range of different intensities and extensions of disruptive incidents, from link-specific incidents and incident durations of a few hours, to area-wide disruptions and consequences lasting for days or weeks. Most of these methods require consideration of adverse changes in travel costs, notably as increases in travel times and delays as a consequence of network failure, blockages, or service disruption. The performance indices and metrics invoked in these methods often bear directly on the performance measures commonly employed by transportation system planners and managers, and relate directly to system performance and diagnostics for improved performance.

[1]  Kash Barker,et al.  Flow-based vulnerability measures for network component importance: Experimentation with preparedness planning , 2016, Reliab. Eng. Syst. Saf..

[2]  Darren M. Scott,et al.  Network Robustness Index : a new method for identifying critical links and evaluating the performance of transportation networks , 2006 .

[3]  W. Y. Szeto,et al.  Identification of critical combination of vulnerable links in transportation networks – a global optimisation approach , 2016 .

[4]  Zhong-Ren Peng,et al.  Analysis of Transportation Network Vulnerability under Flooding Disasters , 2015 .

[5]  E. Jenelius Network structure and travel patterns: explaining the geographical disparities of road network vulnerability , 2009 .

[6]  Katja Berdica,et al.  AN INTRODUCTION TO ROAD VULNERABILITY: WHAT HAS BEEN DONE, IS DONE AND SHOULD BE DONE , 2002 .

[7]  David Watling,et al.  Assessing the Demand Vulnerability of Equilibrium Traffic Networks via Network Aggregation , 2015 .

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

[9]  Gerardo W Flintsch,et al.  Impact of Road Conditions and Disruption Uncertainties on Network Vulnerability , 2014 .

[10]  Jeremy Woolley,et al.  Integration of the global positioning system and geographical information systems for traffic congestion studies , 2000 .

[11]  Hwasoo Yeo,et al.  A Flow-based Vulnerability Measure for the Resilience of Urban Road Network , 2015 .

[12]  Federico Rupi,et al.  The Evaluation of Road Network Vulnerability in Mountainous Areas: A Case Study , 2015 .

[13]  Qiang Qiang,et al.  A network efficiency measure with application to critical infrastructure networks , 2008, J. Glob. Optim..

[14]  Michael A. P. Taylor,et al.  Critical Transport Infrastructure in Urban Areas: Impacts of Traffic Incidents Assessed Using Accessibility-Based Network Vulnerability Analysis , 2008 .

[15]  Michael A. P. Taylor,et al.  Investigating the impact of maintenance regimes on the design life of road pavements in a changing climate and the implications for transport policy , 2015 .

[16]  Gopal R. Patil,et al.  Quantifying resilience using a unique critical cost on road networks subject to recurring capacity disruptions , 2015 .

[17]  Kay W. Axhausen,et al.  Graph-Theoretical Analysis of the Swiss Road and Railway Networks Over Time , 2009 .

[18]  Rawia Ahmed El-Rashidy,et al.  An Assessment Method for Highway Network Vulnerability , 2014 .

[19]  Chandra Balijepalli,et al.  Measuring vulnerability of road network considering the extent of serviceability of critical road links in urban areas , 2014 .

[20]  Kun Zhang,et al.  Effective arterial road incident detection: A Bayesian network based algorithm , 2006 .

[21]  Richard L. Church,et al.  A Framework for Modeling Rail Transport Vulnerability , 2008 .

[22]  Erik Jenelius User inequity implications of road network vulnerability , 2010 .