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mD-Resilience: A Multi-Dimensional Approach for Resilience-Based Performance Assessment in Urban Transportation

Author

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  • Farnaz Khaghani

    (Department of Civil and Environmental Engineering, Virginia Tech, Blacksburg, VA 24061, USA)

  • Farrokh Jazizadeh

    (Department of Civil and Environmental Engineering, Virginia Tech, Blacksburg, VA 24061, USA)

Abstract

As demonstrated for extreme events, the resilience concept is used to evaluate the ability of a transportation system to resist and recover from disturbances. Motivated by the high cumulative impact of recurrent perturbations on transportation systems, we have investigated resilience quantification as a performance assessment method for high-probability low-impact (HPLI) disturbances such as traffic congestions. Resilience-based metrics are supplementary to conventional travel-time-based indices in literature. However, resilience is commonly quantified as a scalar variable despite its multi-dimensional nature. Accordingly, by hypothesizing increased information gain in performance assessment, we have investigated a multi-dimensional approach (mD-Resilience) for resilience quantification. Examining roadways’ resilience to recurrent congestions as a contributor to sustainable mobility, we proposed to measure resilience with several attributes that characterize the degradation stage, the recovery stage, and possible recovery paths. These attributes were integrated into a performance index by using Data Envelopment Analysis (DEA) as a non-parametric method. We demonstrated the increased information gain by quantifying the performance of major freeways in Los Angeles, California using Performance Measurement System (PeMS) data. The comparison of mD-Resilience approach with the method based on area under resilience curves showed its potential in distinguishing the severity of congestions. Furthermore, we showed that mD-Resilience also characterizes performance from the lens of delay and bottleneck severities.

Suggested Citation

  • Farnaz Khaghani & Farrokh Jazizadeh, 2020. "mD-Resilience: A Multi-Dimensional Approach for Resilience-Based Performance Assessment in Urban Transportation," Sustainability, MDPI, vol. 12(12), pages 1-23, June.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:12:p:4879-:d:371772
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    References listed on IDEAS

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    1. William W. Cooper & Lawrence M. Seiford & Joe Zhu (ed.), 2011. "Handbook on Data Envelopment Analysis," International Series in Operations Research and Management Science, Springer, number 978-1-4419-6151-8, March.
    2. Junqing Tang & Hans Rudolf Heinimann, 2018. "A resilience-oriented approach for quantitatively assessing recurrent spatial-temporal congestion on urban roads," PLOS ONE, Public Library of Science, vol. 13(1), pages 1-22, January.
    3. Tsai-Yun Liao & Ta-Yin Hu & Yi-No Ko, 2018. "A resilience optimization model for transportation networks under disasters," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 93(1), pages 469-489, August.
    4. Tálita Santos & Marcelino Aurélio Silva & Vicente Aprigliano Fernandes & Greg Marsden, 2020. "Resilience and Vulnerability of Public Transportation Fare Systems: The Case of the City of Rio De Janeiro, Brazil," Sustainability, MDPI, vol. 12(2), pages 1-22, January.
    5. Chen, Chao, 2003. "Freeway Performance Measurement System (PeMS)," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt6j93p90t, Institute of Transportation Studies, UC Berkeley.
    6. Kwon, Jaimyoung & Mauch, Michael & Varaiya, Pravin, 2006. "Components of Congestion: Delay from Incidents, Special Events, Lane Closures, Weather, Potential Ramp Metering Gain, and Excess Demand," University of California Transportation Center, Working Papers qt31c9k717, University of California Transportation Center.
    7. Maria do Céu Almeida & Maria João Telhado & Marco Morais & João Barreiro & Ruth Lopes, 2020. "Urban Resilience to Flooding: Triangulation of Methods for Hazard Identification in Urban Areas," Sustainability, MDPI, vol. 12(6), pages 1-18, March.
    8. R. D. Banker & A. Charnes & W. W. Cooper, 1984. "Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis," Management Science, INFORMS, vol. 30(9), pages 1078-1092, September.
    9. Cox, Andrew & Prager, Fynnwin & Rose, Adam, 2011. "Transportation security and the role of resilience: A foundation for operational metrics," Transport Policy, Elsevier, vol. 18(2), pages 307-317, March.
    10. Stephanie E. Chang & Timothy McDaniels & Jana Fox & Rajan Dhariwal & Holly Longstaff, 2014. "Toward Disaster‐Resilient Cities: Characterizing Resilience of Infrastructure Systems with Expert Judgments," Risk Analysis, John Wiley & Sons, vol. 34(3), pages 416-434, March.
    11. Lichun Chen & Elise Miller-Hooks, 2012. "Resilience: An Indicator of Recovery Capability in Intermodal Freight Transport," Transportation Science, INFORMS, vol. 46(1), pages 109-123, February.
    12. Stefano de Luca & Roberta Di Pace & Silvio Memoli & Luigi Pariota, 2020. "Sustainable Traffic Management in an Urban Area: An Integrated Framework for Real-Time Traffic Control and Route Guidance Design," Sustainability, MDPI, vol. 12(2), pages 1-20, January.
    13. Charnes, A. & Cooper, W. W. & Rhodes, E., 1978. "Measuring the efficiency of decision making units," European Journal of Operational Research, Elsevier, vol. 2(6), pages 429-444, November.
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    2. Radosław Łukasik & Tomasz Neumann, 2022. "Economic and Environmental Aspects of Engine Selection in Cargo Transportation," Energies, MDPI, vol. 15(7), pages 1-18, April.

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