Ranking dangerous sections of the road using MCDM model

Dragana Nenadić

doi:10.31181/dmame1901115n

Authors

Dragana Nenadić University of East Sarajevo, Faculty of Transport and Traffic Engineering Doboj, Bosnia and Herzegovina

DOI:

https://doi.org/10.31181/dmame1901115n

Keywords:

Dangerous Sections, Traffic Safety, Multi-criteria Decision-making, FUCOM, WASPAS

Abstract

Traffic accidents are a great concern in traffic safety since they unexpectedly and sometimes unavoidably cause fatal and non-fatal injuries, or material damage. The causes of traffic accidents can vary, but they can always be linked to one of the four basic factors: human, vehicle, road and environment. However, there are some places where traffic accidents happen more frequently than in some other places. The decision-making process about dangerous sections of road using the Multi-criteria decision-making (MCDM) model involves the definition of quantitative and qualitative traffic safety criteria. The model used in the paper consists of five quantitative and two qualitative traffic safety criteria. Based on those criteria the ranking of the prospected sections will be carried out. By analyzing the total number of traffic accidents, by their categories, analyzing the current state of the traffic infrastructure and Annual average daily traffic (AADT), seven traffic safety criteria are defined, which in the first phase of model, will be rated and ranked by their importance. By using the Full Consistency Method (FUCOM), weighted coefficients of the defined criteria will be determined, after what ranking dangerous road sections using Weighted aggregate sum product assessment method (WASPAS) is done. The obtained results show which of the offered alternatives is best ranked, that actually shows which section of the road is the safest one.

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References

Chakraborty, S., & Zavadskas, E. K. (2014). Applications of WASPAS method in manufacturing decision making. Informatica, 25(1), 1-20.

Chakraborty, S., Zavadskas, E. K., & Antuchevičienė, J. (2015). Applications of WASPAS method as a multi-criteria decision-making tool. Economic Computation & Economic Cybernetics Studies & Research, 49(1), 1-17.

Chen, N., Xu, Z., & Xia, M. (2015). The ELECTRE I multi-criteria decision-making method based on hesitant fuzzy sets. International Journal of Information Technology & Decision Making, 14(3), 621-657.

Cook, W. D. (2006). Distance-based and ad hoc consensus models in ordinal preference ranking. European Journal of operational research, 172(2), 369-385.

Dėjus, T., & Antuchevičienė, J. (2013). Assessment of health and safety solutions at a construction site. Journal of Civil Engineering and management, 19(5), 728-737.

Drezner, Z. (1995) Facility Location: A Survey of Applications and Methods. Springer Verlag.

Jantakat, Y., Sarapirome, S., Ongsomwang, S., & Littidej, P. (2014). Risk ranking of road sections on highways using ordered weight averaging (OWA) Decision rule. Research paper, Institute of Science, Suranaree University of Technology, Thailand.

Kahraman, C., Ruan, D., & Doǧan, I. (2003). Fuzzy group decision-making for facility location selection. Information sciences, 157, 135-153.

Köksalan, M. M., Wallenius, J., & Zionts, S. (2011). Multiple criteria decision making: from early history to the 21st century. World Scientific.

Lipovac, K., Tešić, M., Marić, B., & Đerić, M. (2015). Self-reported and observed seat belt use–A case study: Bosnia and Herzegovina. Accident Analysis & Prevention, 84, 74-82.

Lipovac, K. (2008). Bezbednost saobraćaja, Beograd, Službeni list SRJ. (In Serbian).

Nunić, Z. (2018). Evaluation and selection of Manufacturer PVC carpentry using FUCOM-MABAC model. Operational research in engineering sciences: Theory and applications, 1(1), 13-28.

Pamučar, D., Lukovac, V., Božanić, D., & Komazec, N. (2018). Multi-criteria FUCOM-MAIRCA model for the evaluation of level crossings: case study in the Republic of Serbia. Operational Research in Engineering Sciences: Theory and Applications, 1(1), 108-129.

Prentkovskis, O., Erceg, Ž., Stević, Ž., Tanackov, I., Vasiljević, M., & Gavranović, M. (2018). A new methodology for improving service quality measurement: Delphi-FUCOM-SERVQUAL model. Symmetry, 10(12), 757.

Rezaei, J., (2015). Best-worst multi-criteria decision-making method. Omega, 53, 49–57.

Roberts, R., & Goodwin, P. (2002). Weight approximations in multi‐attribute decision models. Journal of Multi‐Criteria Decision Analysis, 11(6), 291-303.

Saaty, T. L. (1980) The Analytic Hierarchy Process. McGraw-Hill, New York

Solymosi, T., & Dombi, J. (1986). A method for determining the weights of criteria: the centralized weights. European journal of operational research, 26(1), 35-41.

Stević, Ž., Pamučar, D., Kazimieras Zavadskas, E., Ćirović, G., & Prentkovskis, O. (2017). The selection of wagons for the internal transport of a logistics company: A novel approach based on rough BWM and rough SAW methods. Symmetry, 9(11), 264.

Stević, Ž., Pamučar, D., Subotić, M., Antuchevičiene, J., & Zavadskas, E. K. (2018). The location selection for roundabout construction using Rough BWM-Rough WASPAS approach based on a new Rough Hamy aggregator. Sustainability, 10(8), 2817.

Tesic, D., Bozanic, D., & Jankovic, D. (2018). The use of the WASPAS method and fuzzy theory for assessing the flood hazard. IV International scientific conference, Safety and crisis management – theory and practise safety for the future – BekMen 2018, 130-138.

Triantaphyllou, E., & Mann, S. H. (1995). Using the analytic hierarchy process for decision making in engineering applications: some challenges. International journal of industrial engineering: applications and practice, 2(1), 35-44.

Turskis, Z. (2008). Multi‐attribute contractors ranking method by applying ordering of feasible alternatives of solutions in terms of preferability technique. Technological and Economic Development of Economy, 14(2), 224-239.

Weber, M., & Borcherding, K. (1993). Behavioral influences on weight judgments in multiattribute decision making. European Journal of Operational Research, 67(1), 1-12.

World Health Organization. (2013). Global status report on road safety 2013: supporting a decade of action: summary (No. WHO. NMH. VIP 13.01). World Health Organization.

Global Health Observatory data repository. (2018). Road traffic deaths, Data by contry. http://apps.who.int/gho/data/node.main.A997 (Accesed 12 January 2019).

Zavadskas, E. K., Nunić, Z., Stjepanović, Ž., & Prentkovskis, O. (2018). A novel rough range of value method (R-ROV) for selecting automatically guided vehicles (AGVs). Studies in Informatics and Control, 27(4), 385-394.

Zavadskas, E. K., Turskis, Z., Antucheviciene, J., & Zakarevicius, A. (2012). Optimization of weighted aggregated sum product assessment. Elektronika ir elektrotechnika, 122(6), 3-6.