Abstract
Evaluation of service quality (SQ) based on user preferences has become a primary concern for the transportation authorities. The most significant attributes of public transportation systems are revealed through service quality analysis. This information serves as valuable input in constantly updating the quality of public transportation services. An appropriate tool is therefore mandatory in this regard. This paper represents a comparative study on the bus SQ prediction capabilities of three effective Artificial Neural Network (ANN) approaches, namely: Generalized Regression Neural Network (GRNN), Probabilistic Neural Network (PNN) and Pattern Recognition Neural Network (PRNN). To calibrate the parameters of the developed ANN models, data on users’ perception toward bus services of Dhaka city, Bangladesh, have been used. Taking the public opinion as baseline, GRNN and PNN have proven to be better prediction models since both have achieved higher accuracy compared to PRNN. Among 22 selected SQ attributes, the most significant attributes have been ranked according to their influence on the users’ decision making process. According to the GRNN and PNN models, punctuality and reliability, service frequency, seat availability and commuting experience are found to be the most significant attributes, which support the user-stated preferences.
Similar content being viewed by others
References
Aksoy S, Atilgan E, Akinci S (2003) Airline services marketing by domestic and foreign firms: differences from the customers’ viewpoint. J Air Trans Manage 9(6):343–351. doi:10.1016/S0969-6997(03)00034-6
Baidoo IK, Nyarko E (2015) A discrete choice modeling of service Quality attributes in public transport. Res J Math Stat 7(1):6–10
Costa Á, Markellos RN (1997) Evaluating public transport efficiency with neural network models. Trans Res Part C Emerg Technol 5(5):301–312. doi:10.1016/S0968-090X(97)00017-X
Davies F, Goode M, Mazanec J, Moutinho L (1999) LISREL and neural network modelling: two comparison studies. J Retail Consumer Serv 6(4):249–261. doi:10.1016/S0969-6989(98)00009-5
de Oña R, de Oña J (2015) Analysis of transit quality of service through segmentation and classification tree techniques. Transportmet A Trans Sci. doi:10.1080/23249935.2014.1003111
de Oña J, Garrido C (2014) Extracting the contribution of independent variables in neural network models: a new approach to handle instability. Neural Comput Appl 25(3–4):859–869. doi:10.1007/s00521-014-1573-5
de Ona J, de Oña R, Eboli L, Mazzulla G (2015) Heterogeneity in perceptions of service quality among groups of railway passengers. International Journal of Sustainable Transportation 9(8):612–626. doi:10.1080/15568318.2013.849318
de Oña J, de Oña R, Calvo FJ (2012) A classification tree approach to identify key factors of transit service quality. Expert Syst Appl 39(12):11164–11171. doi:10.1016/j.eswa.2012.03.037
de Oña J, de Oña R, Eboli L, Mazzulla G (2013) Perceived service quality in bus transit service: a structural equation approach. Transp Policy 29:219–226. doi:10.1016/j.tranpol.2013.07.001
de Oña R, López G, de los Rios FJD, de Oña J (2014) Cluster analysis for diminishing heterogeneous opinions of service quality public transport passengers. Procedia Soc Behav Sci 162:459–466. doi:10.1016/j.sbspro.2014.12.227
Dell’Olio L, Ibeas A, Cecin P (2010) Modeling user perception of bus transit quality. Transp Policy 17(6):388–397. doi:10.1016/j.tranpol.2010.04.006
Dell’Olio L, Ibeas A, Cecin P (2011) The quality of service desired by public transport users. Transp Policy 18(1):217–227. doi:10.1016/j.tranpol.2010.08.005
Eboli L, Mazzulla G (2008) A stated preference experiment for measuring service quality in public transport. Transport Plan Technol 31(5):509–523. doi:10.1080/03081060802364471
Eboli L, Mazzulla G (2009) A new customer satisfaction index for evaluating transit service quality. J Public Transport 12(3):21–37. doi:10.5038/2375-0901.12.3.2
Eboli L, Mazzulla G (2010) How to capture the passengers’ point of view on a transit service through rating and choice options. Transp Rev 30(4):435–450. doi:10.1080/01441640903068441
Funahashi KI (1989) On the approximate realization of continuous mappings by neural networks. Neural Networks 2(3):183–192. doi:10.1016/0893-6080(89)90003-8
Gan C, Limsombunchai V, Clemes M, Weng A (2005) Consumer choice prediction: artificial neural networks versus logistic models. Lincoln University. Commerce Division, Chicago
Garrido C, De Oña R, De Oña J (2014) Neural networks for analyzing service quality in public transportation. Expert Syst Appl 41(15):6830–6838. doi:10.1016/j.eswa.2014.04.045
Hagan MT, Demuth HB, Beale MH (1996) Neural network design. Campus Publishing Service, Colorado University Bookstore, Colorado. ISBN 0-9717321-0-8
Huang HC, Chang AY, Ho CC (2013) Using Artificial Neural Networks to Establish a Customer-cancellation Prediction Model. Przeglad Elektrotechniczny 89(1b):178–180
Kadiyali LR (2008) Traffic engineering and transport planning, 7th edn. Second Reprint Khanna Publishers, NaiSarak
Kasabov NK (1996) Foundations of neural networks, fuzzy systems, and knowledge engineering. MIT Press, Marcel Alencar
Lai WT, Chen CF (2011) Behavioral intentions of public transit passengers—The roles of service quality, perceived value, satisfaction and involvement. Transp Policy 18(2):318–325. doi:10.1016/j.tranpol.2010.09.003
Mahmoud MM, Hine J, Kashyap A (2012) Segmentation Analysis of Users’ Preference towards Bus Service Quality. Proceedings of the ITRN 2012. 29–30th August 2012. University of Ulster
Mikhaylov AS, Gumenuk IS, Mikhaylova AA (2016) Russian public transport system: the customers’ feedback on the service provision. Public Transport 8:1–17. doi:10.1007/s12469-015-0111-x
Mouwen A, Rietveld P (2013) Does competitive tendering improve customer satisfaction with public transport? A case study for the Netherlands. Transport Res Part A: Policy Pract 51:29–45. doi:10.1016/j.tra.2013.03.002
NCHRP-National Cooperative highway Research Program (2008) Multimodal Level of Service Analysis for Urban Streets. Report No. 616, pp 72–81
Olden JD, Jackson DA (2002) Illuminating the “black-box”: a randomization approach for understanding variable contributions in artificial neural networks. Ecol Model 154(1–2):135–150. doi:10.1016/S0304-3800(02)00064-9
Olsson LE, Friman M, Pareigis J, Edvardsson B (2012) Measuring service experience: applying the satisfaction with travel scale in public transport. J Retail Consumer Serv 19(4):413–418. doi:10.1016/j.jretconser.2012.04.002
Pandit D, Das S (2013) A framework for determining commuter preference along a proposed bus rapid transit corridor. Procedia-Social Behav Sci 104:894–903. doi:10.1016/j.sbspro.2013.11.184
Semeida AM (2015) Derivation of level of service by artificial neural networks at horizontal curves: a case study in Egypt. Euro Trans Res Rev 7(1):1–12. doi:10.1007/s12544-014-0152-2
Stuart KR, Mednick M, Bockman J (2000) Structural equation model of customer satisfaction for the New York City subway system. Transp Res Record J Transp Res Board 1735(1):133–137. doi:10.3141/1735-16
Trajkovic S (2005) Temperature-based approaches for estimating reference evapotranspiration. J Irrigat Drain Eng (ASCE) 131(4):316–323. doi:10.1061/(ASCE)0733-9437(2005)131:4(316)
Trajkovic S, Todorovic B, Stankovic M (2003) Forecasting reference evapotranspiration by artificial neural networks. J Irrigat Drain Eng (ASCE) 129(6):454–457. doi:10.1061/(ASCE)0733-9437(2003)129:6(454)
Transport Research Board (TRB) (2010) Highway Capacity manual (HCM 2010). Transport Research Board, National Research Council, Washington
Tyrinopoulos Y, Antoniou C (2008) Public transit user satisfaction: variability and policy implications. Transp Policy 15(4):260–272. doi:10.1016/j.tranpol.2008.06.002
Weinstein A (2000) Customer satisfaction among transit riders. How customer rank the relative importance of various service attributes. Transp Res Rec 1735:123–132
Yaya LHP, Fortià MF, Canals CS, Marimon F (2015) Service quality assessment of public transport and the implication role of demographic characteristics. Public Transport 7(3):409–428. doi:10.1007/s12469-014-0099-7
Zeithaml VA, Parasuraman A, Berry LL (1988) Delivering quality service: balancing customer perceptions and expectations. Simon and Schuster
Acknowledgments
The authors would like to express thanks to the Committee for Advanced Studies and Research (CASR) of Bangladesh University of Engineering and Technology (BUET) for the financial support. Thanks also go to the faculty and graduate students of department of Civil Engineering, BUET for their assistance with data collection to perform this study.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Islam, M.R., Hadiuzzaman, M., Banik, R. et al. Bus service quality prediction and attribute ranking: a neural network approach. Public Transp 8, 295–313 (2016). https://doi.org/10.1007/s12469-016-0124-0
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12469-016-0124-0