MCDA-Based Decision Support Model for Advanced Warning Distance in Highway Maintenance
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This study develops a data-driven, risk-sensitive decision-support model. It determines optimal Advanced Warning Distances (AWD) for highway work zones, replacing fixed-value guidelines that ignore site-specific risks. Research integrates microscopic traffic simulation with a Hybrid Multi-Criteria Decision Analysis (MCDA) framework. Empirical data were collected from four and six - lane national highways in Thailand. The Surrogate Safety Assessment Model (SSAM) quantified near-miss conflicts (TTC = 1.3s). The Analytic Hierarchy Process (AHP) weighted key metrics: travel time (0.29), delay (0.35), and conflict frequency (0.36). Six MCDA techniques ranked the AWD configurations. Findings show that on six-lane highways, closing two right lanes requires an AWD over 1,075 meters; closing two left lanes works best below 575 meters. Four-lane highways with single-lane closures require a distance of 750 meters or less. All six MCDA algorithms showed high alignment. Sensitivity analysis confirmed conflict frequency as the most critical safety factor. This framework bridges a safety gap by replacing static guidelines with adaptive, context-specific metrics. It offers highway authorities a robust, evidence-based tool for designing customized traffic control strategies, thereby reducing crash risk and delays.
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[1] Wang, B., Chen, T., Zhang, C., Wong, Y. D., Zhang, H., & Zhou, Y. (2024). Toward safer highway work zones: An empirical analysis of crash risks using improved safety potential field and machine learning techniques. Accident Analysis & Prevention, 194, 107361. doi:10.1016/j.aap.2023.107361.
[2] Chen, E., & Tarko, A. P. (2014). Modeling safety of highway work zones with random parameters and random effects models. Analytic Methods in Accident Research, 1, 86–95. doi:10.1016/j.amar.2013.10.003.
[3] Chien, S., & Schonfeld, P. (2001). Optimal work zone lengths for four-lane highways. Journal of Transportation Engineering, 127(2), 124–131. doi:10.1061/(ASCE)0733-947X(2001)127:2(124).
[4] Pal, R., & Sinha, K. C. (1996). Evaluation of crossover and partial lane closure strategies for Interstate work zones in Indiana. Transportation Research Record, 1529(1529), 10–18. doi:10.3141/1529-02.
[5] Younesi Heravi, M., Demeke, A. Y., Dola, I. S., Jang, Y., Jeong, I., & Le, C. (2025). Vehicle intrusion detection in highway work zones using inertial sensors and lightweight deep learning. Automation in Construction, 176, 106291. doi:10.1016/j.autcon.2025.106291.
[6] Shahin, F., Elias, W., & Toledo, T. (2023). Drivers’ perception of highway work zone risks. Transportation Engineering, 14, 100213. doi:10.1016/j.treng.2023.100213.
[7] Shahin, F., Elias, W., Rosenfeld, Y., & Toledo, T. (2022). An Optimization Model for Highway Work Zones Considering Safety, Mobility, and Project Cost. Sustainability (Switzerland), 14(3). doi:10.3390/su14031442.
[8] Department of Highway (DOH). (2025). Travelled Vehicle-kilometers on Highways Report 2025. Department of Highways, Ministry of Transport, Bureau of Highway Safety, Washington, United States. Available online: https://data.doh.go.th/dataset/vk/resource/d318a913-89ca-41a3-9828-e5f7671e6def (accessed on May 2026).
[9] Abbasi, M., Bilal, M. T., Consilvio, A., Giglio, D., Sacco, N., & Arevalo, P. L. (2025). On minimizing the impact on traffic of maintenance work zones in a highway network. Transportation Engineering, 21. doi:10.1016/j.treng.2025.100381.
[10] Lyu, P., Lin, Y., Wang, L., & Yang, X. (2017). Variable Speed Limit Control for Delay and Crash Reductions at Freeway Work Zone Area. Journal of Transportation Engineering, Part A: Systems, 143(12), 4017062. doi:10.1061/jtepbs.0000099.
[11] Wang, W., & Cheng, Z. (2017). Variable speed limit signs: Control and setting locations in freeway work zones. Journal of Advanced Transportation, 2017(1), 4390630. doi:10.1155/2017/4390630.
[12] Schonfeld, P., & Chien, S. (1999). Optimal work zone lengths for two-lane highways. Journal of Transportation Engineering, 125(1), 21–29. doi:10.1061/(ASCE)0733-947X(1999)125:1(21).
[13] Chien, S., Tang, Y., & Schonfeld, P. (2002). Optimizing work zones for two-lane highway maintenance projects. Journal of Transportation Engineering, 128(2), 145–155. doi:10.1061/(ASCE)0733-947X(2002)128:2(145).
[14] Yang, X., Lu, Y. (Carl), & Lin, Y. (2017). Optimal Variable Speed Limit Control System for Freeway Work Zone Operations. Journal of Computing in Civil Engineering, 31(1), 4016044. doi:10.1061/(asce)cp.1943-5487.0000610.
[15] Chen, C. H., & Schonfeld, P. (2004). Work zone optimization for two-lane highway maintenance projects. Transportation Research Record, 1877, 95–105. doi:10.3141/1877-11.
[16] Du, S., & Razavi, S. (2019). Variable Speed Limit for Freeway Work Zone with Capacity Drop Using Discrete-Time Sliding Mode Control. Journal of Computing in Civil Engineering, 33(2), 4019001. doi:10.1061/(asce)cp.1943-5487.0000815.
[17] Kang, K.-P., Chang, G.-L., & Zou, N. (2003). An Optimal Variable Speed Limit Control Model for Freeway Work-Zone Operations. IFAC Proceedings Volumes, 36(14), 221–226. doi:10.1016/S1474-6670(17)32423-0.
[18] Singh, M. K., Pandey, G., & Ramachandra Rao, K. (2019). Optimization of work zone segments on urban roads using cellular automata model in mixed traffic. Periodica Polytechnica Transportation Engineering, 47(1), 29–35. doi:10.3311/PPtr.9992.
[19] Gladson, J., Sivagnanasundaram, K., Kasi, K., & Karuppanan, G. (2020). Examining the characteristics of heterogeneous traffic at various lane closures. Periodica Polytechnica Transportation Engineering, 48(4), 384–392. doi:10.3311/PPTR.15232.
[20] Phumma, T., Athigakunagorn, N., & Limsawasd, C. (2022). Optimal Lane Closure for Rail Line Construction to Mitigate Urban Traffic Impacts. Civil Engineering and Architecture, 10(7), 2921–2932. doi:10.13189/cea.2022.100711.
[21] Huang, Q., & Shi, J. (2008). Optimizing work zones for two-lane urban road maintenance projects. Tsinghua Science and Technology, 13(5), 644–650. doi:10.1016/s1007-0214(08)70106-8.
[22] Ge, H., & Yang, Y. (2020). Research on calculation of warning zone length of freeway based on micro-simulation model. IEEE Access, 8, 76532–76540. doi:10.1109/ACCESS.2020.2989471.
[23] Weng, J., & Meng, Q. (2014). Rear-end crash potential estimation in the work zone merging areas. Journal of Advanced Transportation, 48(3), 238–249. doi:10.1002/atr.211.
[24] Hu, A., Huang, R., Yang, Y., El-Dimeery, I., & Easa, S. M. (2025). Length Requirements for Urban Expressway Work Zones’ Warning and Transition Areas Based on Driving Safety and Comfort. Systems, 13(7), 525. doi:10.3390/systems13070525.
[25] Dowling, R., Skabardonis, A., & Alexiadis, V. (2004). Traffic analysis toolbox, volume III: Guidelines for applying traffic microsimulation modeling software. FHWA-HRT-04-040, Bureau of Transportation Statistics, U.S. Department of Transportation, Washington, United States.
[26] Burghout, W., Koutsopoulos, H. N., & Andréasson, I. (2005). Hybrid mesoscopic-microscopic traffic simulation. Transportation Research Record, 1934, 218–225. doi:10.3141/1934-23.
[27] Institution of Highways & Transportation. (2006). Traffic Micro-Simulation Modelling. The Institution of Highways & Transportation, London, United Kingdom.
[28] Hardy, M., & Wunderlich, K. (2009). Traffic analysis tools volume IX: work zone modeling and simulation: a guide for analysts. Federal Highway Administration (FHWA), Washington, United States.
[29] Raju, N., & Farah, H. (2021). Evolution of Traffic Microsimulation and Its Use for Modeling Connected and Automated Vehicles. Journal of Advanced Transportation, 2021, 1–29. doi:10.1155/2021/2444363.
[30] PTV. (2015). VISSIM User Manual-Version 7.0. PTV Planug Transport Verkehr AG, Karlsruhe, Germany.
[31] Bureau of Highway Safety. (2019). Executive summary: Development of traffic information management system phase 2 (TIMS2). Department of Highways, Ministry of Transport, Bangkok, Thailand. Available online: https://bhs.doh.go.th/files/Project/Tims2/TIMS2_Exsum.pdf (accessed on May 2026).
[32] WSDOT. (2014). Protocol for VISSIM Simulation. Washington State Department of Transportation (WSDOT), Washington, United States.
[33] WSDOT. (2014). Protocol for VISSIM Simulation: Technical Report. Washington State Department of Transportation (WSDOT), Washington, United States.
[34] Tanwanichkul, L., Chaipanha, W., & Pitaksringkarn, J. (2018). Evaluation of Effect of Median U-Turns on Multilane Primary Highway Capacity in Thailand through Traffic Micro-Simulation Models. Engineering Journal, 22(5), 227–241. doi:10.4186/ej.2018.22.5.227.
[35] Florida Department of Transportation. (2021). Traffic Analysis Handbook. Florida Department of Transportatione, Tallahassee, United States.
[36] Lu, X. Y., Lee, J., Chen, D., Bared, J., Dailey, D., & Shladover, S. E. (2014). Freeway micro-simulation calibration: Case study using aimsun and VISSIM with detailed field data. 93rd Annual Meeting of the Transportation Research Board, 12-16 January, 2014, Washington, United States.
[37] Department of Highways. (2018). Manual of traffic control devices in construction, restoration, and maintenance works on national highways (Volume 3), Ministry of Transport, Bangkok, Thailand. (In Thai).
[38] Huang, F., Liu, P., Yu, H., & Wang, W. (2013). Identifying if VISSIM simulation model and SSAM provide reasonable estimates for field measured traffic conflicts at signalized intersections. Accident Analysis and Prevention, 50, 1014–1024. doi:10.1016/j.aap.2012.08.018.
[39] Gettman, D., Pu, L., Sayed, T., & Shelby, S. G. (2008). Surrogate safety assessment model and validation (No. FHWA-HRT-08-051). Turner-Fairbank Highway Research Center, McLean, United States.
[40] Kronprasert, N., Sutheerakul, C., Satiennam, T., & Luathep, P. (2021). Intersection safety assessment using video-based traffic conflict analysis: The case study of Thailand. Sustainability (Switzerland), 13(22), 12722. doi:10.3390/su132212722.
[41] Wątróbski, J., Jankowski, J., Ziemba, P., Karczmarczyk, A., & Zioło, M. (2019). Generalised framework for multi-criteria method selection. Omega (United Kingdom), 86, 107–124. doi:10.1016/j.omega.2018.07.004.
[42] Saaty, T. L. (2004). Decision making — the Analytic Hierarchy and Network Processes (AHP/ANP). Journal of Systems Science and Systems Engineering, 13(1), 1–35. doi:10.1007/s11518-006-0151-5.
[43] Kahraman, C. (2008). Fuzzy multi-criteria decision making: theory and applications with recent developments. Springer Science & Business Media, New York, United States. doi:10.1007/978-0-387-76813-7.
[44] Thakkar, J. J. (2021). Multi-criteria decision making. Springer, Singapore. doi:10.1007/978-981-33-4745-8.
[45] Aljaghoub, H., Alasad, S., Alashkar, A., AlMallahi, M., Hasan, R., Obaideen, K., & Alami, A. H. (2023). Comparative analysis of various oxygen production techniques using multi-criteria decision-making methods. International Journal of Thermofluids, 17, 100261. doi:10.1016/j.ijft.2022.100261.
[46] Pal, P., Gopal, P. R. C., & Ramkumar, M. (2023). Impact of transportation on climate change: An ecological modernization theoretical perspective. Transport Policy, 130, 167–183. doi:10.1016/j.tranpol.2022.11.008.
[47] Görçün, Ö. F., Pamucar, D., & Küçükönder, H. (2024). Selection of tramcars for sustainable urban transportation by using the modified WASPAS approach based on Heronian operators. Applied Soft Computing, 151, 111127. doi:10.1016/j.asoc.2023.111127.
[48] Arslan, Ö., & Cebi, S. (2024). A novel approach for multi-criteria decision making: Extending the WASPAS method using decomposed fuzzy sets. Computers and Industrial Engineering, 196, 110461. doi:10.1016/j.cie.2024.110461.
[49] Bayhun, S., & Demirel, N. Ç. (2026). Evaluating sustainable disaster management in shipyard operations using an integrated fuzzy LMAW and spherical fuzzy WASPAS approach: A case study in Istanbul. Applied Soft Computing, 190. doi:10.1016/j.asoc.2025.114539.
[50] Yoon, K., & Hwang, C.-L. (1995). Multiple Attribute Decision Making. In Multiple Attribute Decision Making. Sage Publications, Thousand Oaks, United States. doi:10.4135/9781412985161.
[51] Hoveidafard, A., Moradinia, S. F., Golchin, B., & Ghaffari, A. (2025). Identification of required stations for autonomous vehicles using AHP and TOPSIS method with GIS approach. Sustainable Futures, 10, 100755. doi:10.1016/j.sftr.2025.100755.
[52] Theilig, K., Vollmer, M., Lang, W., & Albus, J. (2025). Multi-criteria decision-making for energy building renovation: Comparing exterior wall structures with the AHP, ANP, utility analysis, and TOPSIS. Building and Environment, 280, 113075. doi:10.1016/j.buildenv.2025.113075.
[53] Trivedi, P., Shah, J., Moslem, S., & Pilla, F. (2023). An application of the hybrid AHP-PROMETHEE approach to evaluate the severity of the factors influencing road accidents. Heliyon, 9(11), 21187– 21187. doi:10.1016/j.heliyon.2023.e21187.
[54] Tanwar, R., & Kumari, S. (2025). Developing strategies for improving travel time performance in multimodal public transport using ANP, PROMETHEE, network DEA, and optimization algorithms. Transportation Engineering, 22, 100408. doi:10.1016/j.treng.2025.100408.
[55] Sennaroglu, B., & Varlik Celebi, G. (2018). A military airport location selection by AHP integrated PROMETHEE and VIKOR methods. Transportation Research Part D: Transport and Environment, 59, 160–173. doi:10.1016/j.trd.2017.12.022.
[56] Cevikbas, M., Okudan, O., & Işık, Z. (2023). Determination of the Most Appropriate Alternative Dispute Resolution Methods for Airport Projects: A Fuzzy VIKOR Approach. KSCE Journal of Civil Engineering, 27(8), 3192–3203. doi:10.1007/s12205-023-1864-4.
[57] Javaherikhah, A. (2026). Prioritizing criteria for selecting advanced technologies for Martian smart buildings: A fuzzy Delphi study using the VIKOR method. Space Habitation, 2(1), 100035. doi:10.1016/j.spaceh.2025.100035.
[58] Alioğulları, E., Türkan, Y. S., Çakmak, E., & Tirkolaee, E. B. (2024). Evaluation of risk strategies for supply chain sustainability with interval-valued neutrosophic fuzzy EDAS. Heliyon, 10(19), 38607– 38607. doi:10.1016/j.heliyon.2024.e38607.
[59] Dhumras, H., Kumar, M., & Bajaj, R. K. (2025). Effectiveness of debris flow mitigation measures through T-spherical fuzzy Soft Dombi aggregation operators with EDAS-based multi-criteria decision making in mountainous regions. Journal of Industrial Information Integration, 48, 100968. doi:10.1016/j.jii.2025.100968.
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