Abstract
Evaluating road networks’ performance during and after a disruption and/or malfunction is of great importance. The performance of the road networks includes four concepts: reliability, vulnerability, robustness, and resilience. Among these concepts, the concept of resilience, which evaluates the road network’s performance after a disruption/malfunction, is very significant. On the other hand, given that the road network is one of the primary sources of air pollution and plays a crucial role in urban sustainability, the amount of polluted emission should be considered in road network performance (resilience) analysis. The literature presents several measures such as travel time, queue length, recovery time, network’s total cost, etc., to study road network resiliency. A review of previous studies demonstrates that the number of studies that considered environmental aspects in road network resiliency evaluation is scarce. Therefore, in this study, new network resilience measures that consider environmental factors are presented. These new measures show how the amount of polluted emission will change when a disruption occurs in the road network. After introducing and defining these new environmental resiliency measures, the Sioux Falls road network is simulated as the case study in Aimsun. The Sioux Falls road network (based on new resiliency measures) is evaluated when the speed of links (sections) is reduced randomly. The London Emission Model (LEM) is used for estimating the amount of polluted emission.
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References
Aghababaei, M. T., Costello, S. B., & Ranjitkar, P. (2020). Assessing operational performance of New Zealand’s South Island road network after the 2016 Kaikoura Earthquake. International Journal of Disaster Risk Reduction, 101553.
AIMSUN Version 8.4 User's Manual, (2018). TSS-Transport Simulation Systems.
Balal, E., Valdez, G., Miramontes, J., & Cheu, R. L. (2019). Comparative evaluation of measures for urban highway network resilience due to traffic incidents. International Journal of Transportation Science and Technology, 8(3), 304–317.
Mehrabani, B. B., Sgambi, L., Garavaglia, E., & Madani, N. (2021). Modeling methods for the assessment of the ecological impacts of road maintenance sites. In Environmental Sustainability and Economy, (pp. 171–193). Elsevier.
Bruneau, M., Chang, S. E., Eguchi, R. T., Lee, G. C., O’Rourke, T. D., Reinhorn, A. M., Shinozuka, M., Tierney, K., Wallace, W. A., & Von Winterfeldt, D. (2003). A framework to quantitatively assess and enhance the seismic resilience of communities. Earthquake Spectra, 19(4), 733–752.
Calvert, S. C., & Snelder, M. (2018). A methodology for road traffic resilience analysis and review of related concepts. Transportmetrica a: Transport Science, 14(1–2), 130–154.
El Rashidy, R. A. H. (2014). The resilience of road transport networks redundancy, vulnerability and mobility characteristics (Doctoral dissertation, University of Leeds).
Ganin, A. A., Kitsak, M., Marchese, D., Keisler, J. M., Seager, T., & Linkov, I. (2017). Resilience and efficiency in transportation networks. Science Advances, 3(12), e1701079.
Gauthier, P., Furno, A., & El Faouzi, N. E. (2018). Road network resilience: How to identify critical links subject to day-to-day disruptions. Transportation Research Record, 2672(1), 54–65.
Kamga, C. N., Mouskos, K. C., & Paaswell, R. E. (2011). A methodology to estimate travel time using dynamic traffic assignment (DTA) under incident conditions. Transportation Research Part c: Emerging Technologies, 19(6), 1215–1224.
Kaviani, A., Thompson, R. G., & Rajabifard, A. (2017). Improving regional road network resilience by optimised traffic guidance. Transportmetrica a: Transport Science, 13(9), 794–828.
LeBlanc, L. J., Morlok, E. K., & Pierskalla, W. P. (1975). An efficient approach to solving the road network equilibrium traffic assignment problem. Transportation Research, 9(5), 309–318.
Lhomme, S., Serre, D., Diab, Y., & Laganier, R. (2013). Analyzing resilience of urban networks: A preliminary step towards more flood resilient cities. Natural Hazards and Earth System Sciences, 13(2), 221.
Liu, W., & Song, Z. (2020). Review of studies on the resilience of urban critical infrastructure networks. Reliability Engineering & System Safety, 193, 106617.
MacArthur, R. (1955). Fluctuations of animal populations and a measure of community stability. Ecology, 36(3), 533–536.
McDaniels, T., Chang, S., Cole, D., Mikawoz, J., & Longstaff, H. (2008). Fostering resilience to extreme events within infrastructure systems: Characterizing decision contexts for mitigation and adaptation. Global Environmental Change, 18(2), 310–318.
Murray-Tuite, P. M. (2006). A comparison of transportation network resilience under simulated system optimum and user equilibrium conditions. In Proceedings of the 2006 winter simulation conference, (pp. 1398–1405). IEEE.
National Academies of Sciences, Engineering, and Medicine (NCHRP). (2004). Traffic data collection, analysis, and forecasting for mechanistic pavement design. Washington, DC: The National Academies Press. https://doi.org/10.17226/13781.
Omer, M., Mostashari, A., & Nilchiani, R. (2013). Assessing resilience in a regional road-based transportation network. International Journal of Industrial and Systems Engineering, 13(4), 389–408.
Patil, G. R., & Bhavathrathan, B. K. (2016). Effect of traffic demand variation on road network resilience. Advances in Complex Systems 19(01n02):1650003
Rose, A., & Krausmann, E. (2013). An economic framework for the development of a resilience index for business recovery. International Journal of Disaster Risk Reduction, 5, 73–83.
Scope, D. (2015). Transportation system resilience to extreme weather and climate change. Federal Highway Administration (FHWA).
Sgambi, L., Jacquin, T., Basso, N., & Garavaglia, E., (2020). The robustness of infrastructure network assessed through a probabilistic flow model and a static traffic assignment algorithm—the case of the Belgian road network. In Proceedings of the 10th international conference on bridge maintenance, safety and management (IABMAS 2020) Hokkaido, Japan.
Shang, W. (2016). Robustness and resilience analysis of urban road networks, Imperial College London.
Sun, W., Bocchini, P., & Davison, B. D. (2018). Resilience metrics and measurement methods for transportation infrastructure: The state of the art. Sustainable and Resilient Infrastructure, 5(3), 168–199.
Twumasi-Boakye, R., & Sobanjo, J. (2019). Civil infrastructure resilience: State-of-the-art on transportation network systems. Transportmetrica a: Transport Science, 15(2), 455–484.
Tympakianaki, A., Koutsopoulos, H. N., Jenelius, E., & Cebecauer, M. (2018). Impact analysis of transport network disruptions using multimodal data: A case study for tunnel closures in Stockholm. Case Studies on Transport Policy, 6(2), 179–189.
Zhang, X., Miller-Hooks, E., & Denny, K. (2015). Assessing the role of network topology in transportation network resilience. Journal of Transport Geography, 46, 35–45.
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Mehrabani, B.B., Sgambi, L., Madani, N. (2022). Assessing the Environmental Aspects of Road Network Resiliency. In: Altan, H., et al. Advances in Architecture, Engineering and Technology . Advances in Science, Technology & Innovation. Springer, Cham. https://doi.org/10.1007/978-3-031-11232-4_7
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DOI: https://doi.org/10.1007/978-3-031-11232-4_7
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