A Disaster-Aware Traffic Assignment Model: Comparative Evaluation of Frank-Wolfe and Simulated Annealing Algorithms
DOI:
10.33395/sinkron.v9i4.15316Keywords:
Disaster-aware Traffic Assignment, Emergency Transportation Planning, Frank Wolfe Algorithm, Simulated Annealing, Intelligent Transportation SystemsAbstract
Traffic assignment under disaster-induced disruptions poses unique challenges, as traditional models often overlook sudden capacity loss and unpredictable demand. This study introduces a disaster-aware Traffic Assignment Problem (TAP) model that integrates a modified Bureau of Public Roads (BPR) cost function, explicitly accounting for effective capacity changes during disasters. The Frank-Wolfe (FW) algorithm is applied to solve the model, chosen for its scalability and convergence properties. A comparative analysis with Simulated Annealing (SA) is also performed across various network sizes and disruption scenarios. Results show that FW consistently delivers near-optimal flow distributions with lower travel costs and faster convergence. While SA exhibits higher variability under tight capacity constraints, FW demonstrates robust stability, particularly in medium to large networks under moderate to severe disruptions. Flow patterns from FW highlight adaptive traffic redistribution, effectively bypassing congested or blocked links. This study is the first to systematically compare Frank-Wolfe and Simulated Annealing under disaster-induced TAP conditions with capacity degradation. Contributions include (1) formulating a disaster-aware TAP model, (2) applying FW to disrupted networks, and (3) validating through structured simulations. Findings suggest that FW offers a reliable optimization tool for real-time traffic reallocation, supporting resilient urban mobility in emergencies.
Downloads
References
Ben-Tal, A., Chung, B. Do, Mandala, S. R., & Yao, T. (2011). Robust optimization for emergency logistics planning: Risk mitigation in humanitarian relief supply chains. Transportation Research Part B: Methodological, 45(8), 1177–1189. https://doi.org/10.1016/J.TRB.2010.09.002
Bian, R., Murray-Tuite, P., Edara, P., & Triantis, K. (2022). Modeling the impact of traffic management strategies on households’ stated evacuation decisions. Progress in Disaster Science, 15, 100246. https://doi.org/10.1016/J.PDISAS.2022.100246
Boujemaa, R., Jebali, A., Hammami, S., & Ruiz, A. (2020). Multi-period stochastic programming models for two-tiered emergency medical service system. Computers & Operations Research, 123, 104974. https://doi.org/10.1016/J.COR.2020.104974
Caunhye, A. M., & Alem, D. (2023). Practicable robust stochastic optimization under divergence measures with an application to equitable humanitarian response planning. OR Spectrum, 45(3), 759–806. https://doi.org/10.1007/S00291-023-00724-0/TABLES/7
Erbeyoğlu, G., & Bilge, Ü. (2020). A robust disaster preparedness model for effective and fair disaster response. European Journal of Operational Research, 280(2), 479–494. https://doi.org/10.1016/J.EJOR.2019.07.029
Fukushima, M. (1984). A modified Frank-Wolfe algorithm for solving the traffic assignment problem. Transportation Research Part B: Methodological, 18(2), 169–177. https://doi.org/10.1016/0191-2615(84)90029-8
Gabrel, V., Murat, C., & Thiele, A. (2014). Recent advances in robust optimization: An overview. European Journal of Operational Research, 235(3), 471–483. https://doi.org/10.1016/J.EJOR.2013.09.036
Gao, X., Ci, Y., Kum Fai, Y., Wu, L., & Li, R. (2025). Hybrid traffic flow prediction model for emergency scenarios with scarce historical data. Engineering Applications of Artificial Intelligence, 145, 110219. https://doi.org/10.1016/J.ENGAPPAI.2025.110219
Hu, X., & Xie, C. (2025). Use of graph attention networks for traffic assignment in a large number of network scenarios. Transportation Research Part C: Emerging Technologies, 171, 104997. https://doi.org/10.1016/J.TRC.2025.104997
Jahir, Y., Atiquzzaman, M., Refai, H., Paranjothi, A., & LoPresti, P. G. (2019). Routing protocols and architecture for disaster area network: A survey. Ad Hoc Networks, 82, 1–14. https://doi.org/10.1016/J.ADHOC.2018.08.005
Liu, K., & Bellet, A. (2019). Escaping the curse of dimensionality in similarity learning: Efficient Frank-Wolfe algorithm and generalization bounds. Neurocomputing, 333, 185–199. https://doi.org/10.1016/J.NEUCOM.2018.12.060
Morandi, V. (2024). Bridging the user equilibrium and the system optimum in static traffic assignment: a review. 4OR, 22(1), 89–119. https://doi.org/10.1007/S10288-023-00540-W/TABLES/3
Motaghedi-Larijani, A. (2022). Solving the number of cross-dock open doors optimization problem by combination of NSGA-II and multi-objective simulated annealing. Applied Soft Computing, 128, 109448. https://doi.org/10.1016/J.ASOC.2022.109448
Ngozi, S., & Uche, C. (2025). Mitigating Route Scheduling and Resource Allocation Challenges in the Logistics System: A Survey of the Roles of Transportation and Assignment Problems. IJISET-International Journal of Innovative Science, Engineering & Technology, 12. www.ijiset.com
Nie, Y., Zhang, H. M., & Lee, D. H. (2004). Models and algorithms for the traffic assignment problem with link capacity constraints. Transportation Research Part B: Methodological, 38(4), 285–312. https://doi.org/10.1016/S0191-2615(03)00010-9
Osman, I. H. (1993). Metastrategy simulated annealing and tabu search algorithms for the vehicle routing problem. Annals of Operations Research, 41(4), 421–451. https://doi.org/10.1007/BF02023004/METRICS
Seshadri, R., & Srinivasan, K. K. (2017). Robust traffic assignment model: Formulation, solution algorithms and empirical application. Journal of Intelligent Transportation Systems, 21(6), 507–524. https://doi.org/10.1080/15472450.2017.1358624
Sharifi, M. R., Akbarifard, S., Qaderi, K., & Madadi, M. R. (2021). A new optimization algorithm to solve multi-objective problems. Scientific Reports, 11(1), 1–19. https://doi.org/10.1038/S41598-021-99617-X;SUBJMETA=166,639,705;KWRD=ENGINEERING,MATHEMATICS+AND+COMPUTING
Tian, P., Wang, H., & Zhang, D. (1996). Simulated annealing for the quadratic assignment problem: A further study. Computers & Industrial Engineering, 31(3–4), 925–928. https://doi.org/10.1016/S0360-8352(96)00265-3
Van Vliet, D. (1987). The Frank-Wolfe algorithm for equilibrium traffic assignment viewed as a variational inequality. Transportation Research Part B: Methodological, 21(1), 87–89. https://doi.org/10.1016/0191-2615(87)90024-5
Xiong, H., Xu, Y., Dong, Z. Y., Gan, W., Guo, C., & Yan, M. (2025). A Real-Time Robust Method for Post-Disaster Load Restoration of Coordinated Power-Transportation System With Vehicle-to-Grid Response. IEEE Transactions on Smart Grid. https://doi.org/10.1109/TSG.2025.3568619
XU, M., QU, Y., & GAO, Z. (2008). Implementing Frank-Wolfe Algorithm under Different Flow Update Strategies and Line Search Technologies. Journal of Transportation Systems Engineering and Information Technology, 8(3), 14–22. https://doi.org/10.1016/S1570-6672(08)60022-7
Yang, Y., Liu, B., Chen, D., Xu, X., & Wang, W. (2024). Multiobjective Optimization of Port Collecting and Distributing Network Considering the Balance Among Efficiency, Environmental Performance, and Disruption to Urban Traffic. Journal of Advanced Transportation, 2024(1), 6851139. https://doi.org/10.1155/ATR/6851139
Zhang, S., & Cardin, M. A. (2017). Flexibility and real options analysis in emergency medical services systems using decision rules and multi-stage stochastic programming. Transportation Research Part E: Logistics and Transportation Review, 107, 120–140. https://doi.org/10.1016/J.TRE.2017.09.003
Zhang, Z., Liu, Y., Tong, Q., Guo, S., & Li, D. (2021). Evacuation based on spatio-temporal resilience with variable traffic demand. Journal of Management Science and Engineering, 6(1), 86–98. https://doi.org/10.1016/J.JMSE.2021.02.009
Zuhanda, M. K., Hartono, Hasibuan, S. A. R. S., & Napitupulu, Y. Y. (2024). An exact and metaheuristic optimization framework for solving Vehicle Routing Problems with Shipment Consolidation using population-based and Swarm Intelligence. Decision Analytics Journal, 13. https://doi.org/10.1016/j.dajour.2024.100517
Zuhanda, M. K., Ismail, N., Caraka, R. E., Syah, R., & Gio, P. U. (2023). Hybrid Local Search Algorithm for Optimization Route of Travelling Salesman Problem. International Journal of Advanced Computer Science and Applications, 14(9). https://doi.org/10.14569/IJACSA.2023.0140935
Zuhanda, M. K., Mawengkang, H., Suwilo, S., Mardiningsih, & Sitompul, O. S. (2023). Logistics distribution supply chain optimization model with VRP in the context of E-commerce. AIP Conference Proceedings, 2714. https://doi.org/10.1063/5.0128465
Zuhanda, M. K., Suwilo, S., Sitompul, O. S., & Mardiningsih. (2022). A COMBINATION K-MEANS CLUSTERING AND 2-OPT ALGORITHM FOR SOLVING THE TWO ECHELON E-COMMERCE LOGISTIC DISTRIBUTION. Logforum, 18(2). https://doi.org/10.17270/J.LOG.2022.734
Zuhanda, M. K., Zuhanda, M. K., Hartono, H., Hasibuan, S. A. R. S., Abdullah, D., Gio, P. U., & Caraka, R. E. (2025). Bibliometric analysis of model vehicle routing problem in logistics delivery. Indonesian Journal of Electrical Engineering and Computer Science, 37(1), 590–600. https://doi.org/10.11591/ijeecs.v37.i1.pp590-600
Downloads
|
How to Cite
Issue
Section
License
Copyright (c) 2025 Suranto, Afrizal Rhamadan Siregar
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Google Scholar 
Moraref
PKP Index
Garuda Kemdikbud
Indonesia OneSearch
OCLC Worldcat
Dimensions
Index Copernicus
Scilit
Muhammad Khoiruddin harahap
