Resilience assessment and enhancement of urban road networks subject to traffic accidents: a network-scale optimization strategy

References

• Behrisch, M., Bieker, L., Erdmann, J., & Krajzewicz, D. (2011). SUMO – Simulation of Urban MObility: An overview. In Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation. ThinkMind.
   Google Scholar
• Bhavathrathan, B. K., & Patil, G. R. (2015). Quantifying resilience using a unique critical cost on road networks subject to recurring capacity disruptions. Transportmetrica A: Transport Science, 11(9), 836–855. https://doi.org/10.1080/23249935.2015.1087230
   Web of Science ®Google Scholar
• Bruneau, M., Chang, S., Eguchi, R., Lee, G., O'Rourke, T., Reinhorn, A., Shinozuka, M., Tierney, K., Wallace, W., & von Winterfeldt, D. (2003). A framework to quantitatively assess and enhance the seismic resilience of communities. Earthquake Spectra, 19(4), 733–752. https://doi.org/10.1193/1.1623497
   Web of Science ®Google Scholar
• Darvishan, A., & Lim, G. J. (2021). Dynamic network flow optimization for real-time evacuation reroute planning under multiple road disruptions. Reliability Engineering & System Safety, 214, 107644. https://doi.org/10.1016/j.ress.2021.107644
   Web of Science ®Google Scholar
• Donovan, B., & Work, D. B. (2017). Empirically quantifying city-scale transportation system resilience to extreme events. Transportation Research Part C: Emerging Technologies, 79, 333–346. https://doi.org/10.1016/j.trc.2017.03.002
   Web of Science ®Google Scholar
• Freckleton, D., Heaslip, K., Louisell, W., & Collura, J. (2012). Evaluation of resiliency of transportation networks after disasters. Transportation Research Record: Journal of the Transportation Research Board, 2284(1), 109–116. https://doi.org/10.3141/2284-13
   Google Scholar
• Gu, Y., Fu, X., Liu, Z., Xu, X., & Chen, A. (2020). Performance of transportation network under perturbations: Reliability, vulnerability, and resilience. Transportation Research Part E: Logistics and Transportation Review, 133, 101809. https://doi.org/10.1016/j.tre.2019.11.003
   Web of Science ®Google Scholar
• Holling, C. S. (1973). Resilience and stability of ecological systems. Annual Review of Ecology and Systematics, 4(1), 1–23. https://doi.org/10.1146/annurev.es.04.110173.000245
   Google Scholar
• Horsuwan, T., & Aswakul, C. (2019, August). Reinforcement learning agent under partial observability for traffic light control in presence of gridlocks. In SUMO (pp. 29–47).
   Google Scholar
• Ip, W. H., & Dingwei, W. (2011). Resilience and friability of transportation networks: Evaluation, analysis and optimization. IEEE Systems Journal, 5(2), 189–198. https://doi.org/10.1109/JSYST.2010.2096670
   Web of Science ®Google Scholar
• Kurth, M., Kozlowski, W., Ganin, A., Mersky, A., Leung, B., Dykes, J., Kitsak, M., & Linkov, I. (2020). Lack of resilience in transportation networks: Economic implications. Transportation Research Part D: Transport and Environment, 86, 102419. https://doi.org/10.1016/j.trd.2020.102419
   Web of Science ®Google Scholar
• Li, C., Fang, Q., Ding, L., Cho, Y. K., & Chen, K. (2020). Time-dependent resilience analysis of a road network in an extreme environment. Transportation Research Part D: Transport and Environment, 85, 102395. https://doi.org/10.1016/j.trd.2020.102395
   Web of Science ®Google Scholar
• Li, Z., Jin, C., Hu, P., & Wang, C. (2019). Resilience-based transportation network recovery strategy during emergency recovery phase under uncertainty. Reliability Engineering & System Safety, 188, 503–514. https://doi.org/10.1016/j.ress.2019.03.052
   Web of Science ®Google Scholar
• Liu, W., & Song, Z. (2020). Review of studies on the resilience of urban critical infrastructure networks. Reliability Engineering & System Safety. 193, 106617. https://doi.org/10.1016/j.ress.2019.106617
   Web of Science ®Google Scholar
• Mendonça, D., & Wallace, W. A. (2015). Factors underlying organizational resilience: The case of electric power restoration in New York City after 11 September 2001. Reliability Engineering & System Safety, 141, 83–91. https://doi.org/10.1016/j.ress.2015.03.017
   Web of Science ®Google Scholar
• Osorio, C., Flötteröd, G., & Bierlaire, M. (2011). Dynamic network loading: A stochastic differentiable model that derives link state distributions. Transportation Research Part B: Methodological, 45(9), 1410–1423. https://doi.org/10.1016/j.trb.2011.05.014
   Web of Science ®Google Scholar
• Ouyang, M., Dueñas-Osorio, L., & Min, X. (2012). A three-stage resilience analysis framework for urban infrastructure systems. Structural Safety, 36–37, 23–31. https://doi.org/10.1016/j.strusafe.2011.12.004
   Web of Science ®Google Scholar
• Parsa, A. B., Movahedi, A., Taghipour, H., Derrible, S., & Mohammadian, A. (2020). Toward safer highways, application of XGBoost and SHAP for real-time accident detection and feature analysis. Accident; Analysis and Prevention, 136, 105405. https://doi.org/10.1016/j.aap.2019.105405
   PubMed Web of Science ®Google Scholar
• Shang, W., Chen, Y., Li, X., & Ochieng, W. Y. (2020). Resilience analysis of urban road networks based on adaptive signal controls: Day-to-day traffic dynamics with deep reinforcement learning. Complexity, 2020, 1–19. https://doi.org/10.1155/2020/8841317
   Web of Science ®Google Scholar
• Sheu, J. B. (2006). A composite traffic flow modeling approach for incident-responsive network traffic assignment. Physica A: Statistical Mechanics and Its Applications, 367, 461–478. https://doi.org/10.1016/j.physa.2005.11.039
   Web of Science ®Google Scholar
• Sun, L., D'Ayala, D., Fayjaloun, R., & Gehl, P. (2021). Agent-based model on resilience-oriented rapid responses of road networks under seismic hazard. Reliability Engineering & System Safety. 216, 108030. https://doi.org/10.1016/j.ress.2021.108030
   Web of Science ®Google Scholar
• Wang, J., & Liu, H. (2019). Snow removal resource location and allocation optimization for urban road network recovery: A resilience perspective. Journal of Ambient Intelligence and Humanized Computing, 10(1), 395–408. https://doi.org/10.1007/s12652-018-0717-3
   Web of Science ®Google Scholar
• Wang, J., He, X., Peeta, S., & Yang, X. (2022). Feedback perimeter control with online estimation of maximum throughput for an incident-affected road network. Journal of Intelligent Transportation Systems, 26(1): 81–99. https://doi.org/10.1080/15472450.2020.1797501
   Web of Science ®Google Scholar
• Yu, H., Ma, R., & Zhang, H. M. (2018). Optimal traffic signal control under dynamic user equilibrium and link constraints in a general network. Transportation Research Part B: Methodological, 110, 302–325. https://doi.org/10.1016/j.trb.2018.02.009
   Web of Science ®Google Scholar
• Zhao, J., Kigen, K. K., & Xia, X. (2022). An alternative design for traffic intersections with work zones by using pre-signals. Journal of Intelligent Transportation Systems, 26(2), 168–182. https://doi.org/10.1080/15472450.2020.1797505
   Web of Science ®Google Scholar
• Zhao, J., Knoop, V., & Wang, M. (2020). Two-dimensional vehicular movement modelling at intersections based on optimal control. Transportation Research Part B: Methodological, 138, 1–22. https://doi.org/10.1016/j.trb.2020.04.001
   Web of Science ®Google Scholar
• Zhang, W., & Wang, N. (2016). Resilience-based risk mitigation for road networks. Structural Safety. 62, 57–65. https://doi.org/10.1016/j.strusafe.2016.06.003
   Web of Science ®Google Scholar
• Zhang, Z., Ji, T., & Wei, H. (2022). Dynamic emergency inspection routing and restoration scheduling to enhance the post-earthquake resilience of a highway–bridge network. Reliability Engineering & System Safety. 220, 108282. https://doi.org/10.1016/j.ress.2021.108282
   Web of Science ®Google Scholar
• Zou, Q., & Chen, S. (2021). Resilience-based recovery scheduling of transportation network in mixed traffic environment: A deep-ensemble-assisted active learning approach. Reliability Engineering & System Safety. 215, 107800. https://doi.org/10.1016/j.ress.2021.107800
   Web of Science ®Google Scholar