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Criticality analysis in road networks with graph-theoretic measures, traffic assignment, and simulation

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  • Mylonas, Chrysostomos
  • Mitsakis, Evangelos
  • Kepaptsoglou, Konstantinos

Abstract

Criticality serves as a foundational concept in the evaluation of the impact of disruptive events on the operation of road networks supporting the identification of road links whose unavailability affects to the greatest extent the mobility of people and goods. Several methods and techniques have been suggested so far for the quantification of criticality in road networks drawing from complex network theory, strategic transportation modeling, and traffic simulation. The current article provides a concise yet comprehensive overview of the relevant literature. Subsequently, it formulates a series of hybrid graph-theoretic measures that aim to evaluate, in a computationally efficient manner, the criticality of a road network’s links by respecting their topological attributes and incorporating the inputs/outputs of user equilibrium-based traffic assignment models, assuming that all links are available and operational. Moreover, it formulates a flow reduction-based criticality metric, quantified through microscopic traffic simulation based on the well-established theory on the Macroscopic Fundamental Diagram. The validity and application potential of both approaches are experimentally scrutinized in two tailor-made case studies. The results of the first case study reveal that traffic volume centrality and two variations of a hybrid measure estimating the demand weighted decrease in network efficiency or the increase in total travel time complement each other and sufficiently explain road link criticality, as assessed through two different metrics of increased computational intensity and accuracy. Building upon this finding, an algorithmic procedure for combining the previously mentioned measures is proposed, enabling the targeted sampling of critical road link candidates. The results of the second case study reveal that the macroscopic flow-density relationship may not provide valid insights into road link criticality due to the weak correlation of average network flow reduction with average travel time increase as well as the occasional and paradoxical increase in average network flow after certain road infrastructure disruptions. It is experimentally proven that this is attributed to the shifted route choice behavior of road users. It is also shown that the macroscopic flow-density relationship can adequately assess the impact of disruptive events when route choices are kept fixed.

Suggested Citation

  • Mylonas, Chrysostomos & Mitsakis, Evangelos & Kepaptsoglou, Konstantinos, 2023. "Criticality analysis in road networks with graph-theoretic measures, traffic assignment, and simulation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 629(C).
    • Handle: RePEc:eee:phsmap:v:629:y:2023:i:c:s0378437123007525
    DOI: 10.1016/j.physa.2023.129197
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    1. Yao, Wenbin & Chen, Nuo & Su, Hongyang & Hu, Youwei & Jin, Sheng & Rong, Donglei, 2023. "A novel self-adaption macroscopic fundamental diagram considering network heterogeneity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 613(C).
    2. Jayakrishnan, R. & Tsai, Wei T. & Prashker, Joseph N. & Rajadhyaksha, Subodh, 1994. "A Faster Path-Based Algorithm for Traffic Assignment," University of California Transportation Center, Working Papers qt2hf4541x, University of California Transportation Center.
    3. Almotahari, Amirmasoud & Yazici, M. Anil, 2019. "A link criticality index embedded in the convex combinations solution of user equilibrium traffic assignment," Transportation Research Part A: Policy and Practice, Elsevier, vol. 126(C), pages 67-82.
    4. Bramka Arga Jafino & Jan Kwakkel & Alexander Verbraeck, 2020. "Transport network criticality metrics: a comparative analysis and a guideline for selection," Transport Reviews, Taylor & Francis Journals, vol. 40(2), pages 241-264, March.
    5. Jordi Casas & Jaime L. Ferrer & David Garcia & Josep Perarnau & Alex Torday, 2010. "Traffic Simulation with Aimsun," International Series in Operations Research & Management Science, in: Jaume Barceló (ed.), Fundamentals of Traffic Simulation, chapter 0, pages 173-232, Springer.
    6. Geroliminis, Nikolas & Sun, Jie, 2011. "Properties of a well-defined macroscopic fundamental diagram for urban traffic," Transportation Research Part B: Methodological, Elsevier, vol. 45(3), pages 605-617, March.
    7. Berdica, Katja, 2002. "An introduction to road vulnerability: what has been done, is done and should be done," Transport Policy, Elsevier, vol. 9(2), pages 117-127, April.
    8. Almotahari, Amirmasoud & Yazici, Anil, 2021. "A computationally efficient metric for identification of critical links in large transportation networks," Reliability Engineering and System Safety, Elsevier, vol. 209(C).
    9. Oded Cats & Erik Jenelius, 2014. "Dynamic Vulnerability Analysis of Public Transport Networks: Mitigation Effects of Real-Time Information," Networks and Spatial Economics, Springer, vol. 14(3), pages 435-463, December.
    10. Geroliminis, Nikolas & Daganzo, Carlos F., 2008. "Existence of urban-scale macroscopic fundamental diagrams: Some experimental findings," Transportation Research Part B: Methodological, Elsevier, vol. 42(9), pages 759-770, November.
    11. Nicholson, Alan & Du, Zhen-Ping, 1997. "Degradable transportation systems: An integrated equilibrium model," Transportation Research Part B: Methodological, Elsevier, vol. 31(3), pages 209-223, June.
    12. Snelder, M. & van Zuylen, H.J. & Immers, L.H., 2012. "A framework for robustness analysis of road networks for short term variations in supply," Transportation Research Part A: Policy and Practice, Elsevier, vol. 46(5), pages 828-842.
    13. Knoop, Victor L. & Snelder, Maaike & van Zuylen, Henk J. & Hoogendoorn, Serge P., 2012. "Link-level vulnerability indicators for real-world networks," Transportation Research Part A: Policy and Practice, Elsevier, vol. 46(5), pages 843-854.
    14. Chen, Bi Yu & Lam, William H.K. & Sumalee, Agachai & Li, Qingquan & Li, Zhi-Chun, 2012. "Vulnerability analysis for large-scale and congested road networks with demand uncertainty," Transportation Research Part A: Policy and Practice, Elsevier, vol. 46(3), pages 501-516.
    15. Jenelius, Erik & Petersen, Tom & Mattsson, Lars-Göran, 2006. "Importance and exposure in road network vulnerability analysis," Transportation Research Part A: Policy and Practice, Elsevier, vol. 40(7), pages 537-560, August.
    16. Gu, Yu & Fu, Xiao & Liu, Zhiyuan & Xu, Xiangdong & Chen, Anthony, 2020. "Performance of transportation network under perturbations: Reliability, vulnerability, and resilience," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 133(C).
    17. Sullivan, J.L. & Novak, D.C. & Aultman-Hall, L. & Scott, D.M., 2010. "Identifying critical road segments and measuring system-wide robustness in transportation networks with isolating links: A link-based capacity-reduction approach," Transportation Research Part A: Policy and Practice, Elsevier, vol. 44(5), pages 323-336, June.
    18. Bittihn, Stefan & Schadschneider, Andreas, 2021. "The effect of modern traffic information on Braess’ paradox," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 571(C).
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