A Novel Evaluation and Decision-Making Approach to Prioritizing the Service Quality Criteria in Road Transportation Systems

Document Type : Research Paper


1 Ph.D. Candidate, Department of Civil Engineering, Transportation Planning, Imam Khomeini International University (IKIU), Qazvin, Iran

2 Faculty Member, Department of Civil Engineering, Transportation Planning, Imam Khomeini International University (IKIU), Qazvin, Iran

3 Full Professor, Department of Transportation Engineering and Planning, School of Civil Engineering, Iran University of Science and Technology (IUST), Tehran, Iran


The passengers’ expectations of road transportation systems' service quality lead transportation policy-makers to determine the technical requirements to meet these expectations. It means that Road trip Designs (RDs) as technical requirements should be translated based on Road users’ Requirements (RRs) as passengers’ expectations. We classified the RRs and RDs to 8 and 10 requirements, respectively. The Quality Function Deployment (QFD) method can translate the RRs to RDs in road transportation systems. On the one hand, due to the inherent uncertainty in decision-makers viewpoints in such systems, the Fuzzy QFD (FQFD) can be applied as a more accurate translation of RRs to RDs. On the other hand, the Evidential Reasoning (ER) approach, as one of the best evidence-based decision analysis methods, deals with the raised ambiguity and chaos by decision makers’ viewpoints. Accordingly, a novel hybrid FQFD-ER approach to prioritize the service quality criteria in road transportation systems has been provided in this paper. Moreover, a novel mathematical lemma is provided to hybridize and link the decision-making approaches of the FQFD and ER. Considering the weights of decision makers and fuzzy trapezoidal numbers to achieve the better results are other innovations of this paper. Totally, this study aims to integrate the FQFD with the ER approach to prioritize the RDs based on RRs in related road transportation systems to the Arba'een ceremony as a real case study. The obtained results by hybrid FQFD-ER revealed that the suitable road lanes in terms of width and number mostly improve the service quality.


-Ahmed, S. M., Sang, L. P., & Torbica, Ž. M. (2003). Use of Quality Function Deployment in Civil Engineering Capital Project Planning. Journal of Construction Engineering and Management, 129(4). 358–368, doi:10.1061/(asce)0733-9364(2003)129:4(358). 
-Afandizadeh Zargari, S., & Safari, F. (2020). Using clustering methods in multinomial logit model for departure time choice. Journal of Advanced Transportation, 2020. doi:10.1155/2020/7382569.
-Agyeman, S., & Cheng, L. (2020). Analysis of barriers to perceived service quality in Ghana: Students’ perspectives on bus mobility attributes. Transport Policy, 99, 63-85. doi:10.1016/j.tranpol.2020.08.015. 
-Alkharabsheh, A., Moslem, S., Oubahman, L., & Duleba, S. (2021). An integrated approach of multi-criteria decision-making and grey theory for evaluating urban public transportation systems. Sustainability, 13(5), 2740. doi: 10.3390/su13052740.
-Asadamraji, M., & Nahavandi, N. (2017). Ranking pattern of safety in rural road using a combination of Accident Severity Index and safety audit. Quarterly Journal of Transportation Engineering, 9(1), 1-15.
-Bappy, M. M., Ali, S. M., Kabir, G., & Paul, S. K. (2019). Supply chain sustainability assessment with Dempster-Shafer evidence theory: Implications in cleaner production. Journal of Cleaner Production, 237, 117771. doi:10.1016/j.jclepro.2019.117771.
-Bolar, A. A., Tesfamariam, S., & Sadiq, R. (2017). Framework for prioritizing infrastructure user expectations using Quality Function Deployment (QFD). International Journal of Sustainable Built Environment, 6(1), 16–29. doi.org/10.1016/j.ijsbe.2017.02.002.
-Chauhan, V., Gupta, A. & Parida, M. (2021). Demystifying service quality of Multimodal Transportation Hub (MMTH) through measuring users’ satisfaction of public transport. Transport Policy, 102, 47-60. doi.org/10.1016/j.tranpol.2021.01.004.
-Chin, K. S., Wang, Y. M., Yang, J. B., & Poon, K. K. G. (2009). An evidential reasoning based approach for quality function deployment under uncertainty. Expert Systems with Applications, 36(3), 5684-5694. doi.org/10.1016/j.eswa.2008.06.104.
-Chin, K. S., Yang, Q., Chan, C. Y., Tsui, K. L., & Li, Y. L. (2019). Identifying passengers' needs in cabin interiors of high-speed rails in China using quality function deployment for improving passenger satisfaction. Transportation Research Part A: Policy and Practice, 119, 326-342.doi.org/10.1016/j.tra.2018.12.004.
-Dabous, S. A., Al-Khayyat, G., & Feroz, S. (2020). Utility-Based Road Maintenance Prioritization Method Using Pavement Overall Condition Rating. The Baltic Journal of Road and Bridge Engineering, 15(1), 126-146 .doi: 10.7250/bjrbe.2020-15.464.
-Epifanov, V., Obshivalкin, M., & Lukonkina, K. (2018). Management of quality and security level of transportation in the system of regular passenger motor transport. Transportation Research Procedia, 36, 141–148. doi.org/10.1016/j.trpro.2018.12.056.
-Ganji, S. S. & Rassafi, A. A. (2018). Measuring the road safety performance of Iranian provinces: a double-frontier DEA model and evidential reasoning approach. International Journal of Injury Control and Safety Promotion, 26(2), 156-169. doi.org/10.1080/17457300.2018.1535510
-Lam, J. S. L. (2015). Designing a sustainable maritime supply chain: A hybrid QFD–ANP approach. Transportation Research Part E: Logistics and Transportation Review, 78, 70-81. doi.org/10.1016/j.tre.2014.10.003.
-Lam, J. S. L., & Bai, X. (2016). A quality function deployment approach to improve maritime supply chain resilience. Transportation Research Part E: Logistics and Transportation Review, 92, 16-27. doi.org/10.1016/j.tre.2016.01.012.
-Lam, J. S. L., & Zhang, X. (2019). Innovative solutions for enhancing customer value in liner shipping. Transport Policy, 82, 88-95. doi.org/10.1016/j.tranpol.2018.09.001.
-Li, Y.-L., Yang, Q., & Chin, K.-S. (2019). A decision support model for risk management of hazardous materials road transportation based on quality function deployment. Transportation Research Part D: Transport and Environment, 74,154–173.
-Liu, J., Yang, J. B., Wang, J., SII, H. S., & Wang, Y. M. (2004). Fuzzy rule-based evidential reasoning approach for safety analysis. International Journal of General Systems,33(2-3),183-204.doi.org/10.1080/03081070310001633536.
-Mavi, R. K., Zarbakhshnia , N. & Khazraei, A. (2018). Bus rapid transit (BRT): A simulation and multi criteria decision making (MCDM) approach. Transport Policy, 72, 187-197.doi.org/10.1016/j.tranpol.2018.03.010.
-Mirzahossein, H., & Zargari, S. A. (2018). A Combined Model of Congestion Toll Pricing Based on System Optimization with Minimum Toll. Tehnički vjesnik, 25(4), 1162-1168.doi.org/10.17559/TV-20160528093317.
-Habibi, H. M., Mirzahossein, H., & Afandizadeh, S. (2021). Hybrid approach of technical requirements prioritizing to meet road high-demand public transportation needs under uncertainty conditions: A case study of Arba'een trips. Case Studies on Transport Policy. In press. doi.org/10.1016/j.cstp.2021.03.006.
-Raiffa, H., & Keeney, R. L. (1975). Decision Analysis with Multiple Conflicting Objectives, Preferences and Value Tradeoffs. IIASA Working Paper. IIASA, Laxenburg, Austria: WP-75-053. http://pure.iiasa.ac.at/375.
-Pakdil, F., & Kurtulmuşoğlu, F. B. (2014). Improving service quality in highway passenger transportation: a case study using quality function deployment. European Journal of Transport and Infrastructure Research, 14(4), 375-393. ISSN: 1567-7141. doi:10.18757/ejtir.2014.14.4.3043.
-Pandey, M. M. (2020). Evaluating the strategic design parameters of airports in Thailand to meet service expectations of Low-Cost Airlines using the Fuzzy-based QFD method. Journal of Air Transport Management, 82, 101738. doi.org/10.1016/j.jairtraman.2019.101738.
-Srivastava, R. P. (2011). An introduction to evidential reasoning for decision making under uncertainty: Bayesian and belief function perspectives. International Journal of Accounting Information Systems, 12(2), 126-135.doi.org/10.1016/j.accinf.2010.12.003.
-Suman, H. K., Bolia, N. B., & Tiwari, G. (2017). Comparing public bus transport service attributes in Delhi and Mumbai: Policy implications for improving bus services in Delhi. Transport Policy, 56, 63-74.doi.org/10.1016/j.tranpol.2017.03.002.
-Wang, R. T. (2007). Improving service quality using quality function deployment: The air cargo sector of China airlines. Journal of Air Transport Management, 13(4), 221-228. doi.org/10.1016/j.jairtraman.2007.03.005.
-Yang, J. B., & Singh, M. G. (1994). An evidential reasoning approach for multiple-attribute decision making with uncertainty. IEEE Transactions on systems, Man, and Cybernetics, 24(1), 1-18.doi: 10.1109/21.259681.
-Yamamoto, C., Kishi, K., Hara, F & Satoh, K. (2005). Using Quality Function Deployment To Evaluate Government Services From The Customer's Perspective. Journal of the Eastern Asia Society for Transportation Studies, Vol. 6, pp. 4160 - 4175, 2005. doi:10.11175/easts.6.4160.
-Zhao, Q., Wang, S., Wang, K., & Huang, B. (2019). Multi-objective optimal allocation of distributed generations under uncertainty based on DS evidence theory and affine arithmetic. International Journal of Electrical Power & Energy Systems, 112, 70-82. doi.org/10.1016/j.ijepes.2019.04.044.