IDENTIFYING KEY FACTORS OF COMMUTER TRAIN SERVICE QUALITY:AN EMPIRICAL ANALYSIS FOR DHAKA CITY

Authors

  • Anika Nowshin Mowrin Department of Civil Engineering, Stamford University, Dhaka-1217, Bangladesh
  • Md. Hadiuzzaman Department of Civil Engineering, Bangladesh University of Engineering and Technology, Dhaka-1000, Bangladesh
  • Saurav Barua Department of Civil Engineering, Daffodil International University, Dhaka-1207, Bangladesh
  • Md. Mizanur Rahman Department of Civil Engineering, Bangladesh University of Engineering and Technology, Dhaka-1000, Bangladesh

DOI:

https://doi.org/10.11113/mjce.v31.16118

Keywords:

Commuter train, ANFIS, Service Quality, Passenger perception, Questionnaire survey

Abstract

Commuter train is a viable alternative to road transport to ease the traffic congestion which requires appropriate planning by concerned authorities. The research is aimed to assess passengers’ perception about commuter train service running in areas near Dhaka city. An Adaptive Neuro Fuzzy Inference System (ANFIS) model has been developed to evaluate service quality (SQ) of commuter train. Field survey data has been conducted among 802 respondents who were the regular user of commuter train and 12 attributes have been selected for model development. ANFIS was developed by the training and then tested by 80% and 20% of the total sample respectively. After that, model performance has been evaluated by (i) Confusion Matrix (ii) Root Mean Square Error (RMSE) and attributes are ranked based on their relative importance. The proposed ANFIS model has 61.50% accuracy in training and 47.80% accuracy in testing.  From the results, it is found that 'Bogie condition', 'Cleanliness', ‘Female harassment’, 'Behavior of staff' and 'Toilet facility' are the most significant attributes. This indicates that some necessary measures should be taken immediately to recover the effects of these attributes to improve the SQ of commuter train.

 

Author Biographies

  • Anika Nowshin Mowrin, Department of Civil Engineering, Stamford University, Dhaka-1217, Bangladesh

    Assistant Professor, Department of Civil Engineering
    Stamford University, Dhaka-1217, Bangladesh

  • Md. Hadiuzzaman, Department of Civil Engineering, Bangladesh University of Engineering and Technology, Dhaka-1000, Bangladesh

    Associate Professor, Department of Civil Engineering
    Bangladesh University of Engineering and Technology, Dhaka-1000, Bangladesh

  • Saurav Barua, Department of Civil Engineering, Daffodil International University, Dhaka-1207, Bangladesh
    Assistant Professor, Department of Civil Engineering, Daffodil International University, Dhaka-1207, Bangladesh
  • Md. Mizanur Rahman, Department of Civil Engineering, Bangladesh University of Engineering and Technology, Dhaka-1000, Bangladesh

    Professor, Department of Civil Engineering
    Bangladesh University of Engineering and Technology, Dhaka-1000, Bangladesh

     

References

Agunloye, O. O., & Oduwaye, L. 2011. Factors influencing the quality of rail transport services in metropolitan Lagos. Journal of Geography and Regional Planning, 4(2): 98-103.

Ahmed, I.U., Banik, R., Hasnat, M., Hadiuzzaman, M., Qiu, T.Z. and Rahman, F. 2017. Probabilistic Neural Network and Adaptive Neuro Fuzzy Inference System Based Paratransit Service Quality Prediction and Attribute Ranking, TRB 96th Annual Meeting. (No. 17-00259).

Andrade, K., Uchida, K., & Kagaya, S. 2006. Development of transport mode choice model by using adaptive neuro-fuzzy inference system. Transportation Research Record: Journal of the Transportation Research Board, 1977: 8-16.

Aydin, N., Celik, E., & Gumus, A. T. 2015. A hierarchical customer satisfaction framework for evaluating rail transit systems of Istanbul. Transportation Research Part A: Policy and Practice, 77: 61-81.

Balakrishnan, K. P. 2012. A study on service quality perception of railway passengers of southern railway. International Journal of Management Research, 2(2): 105-110.

Basu, D., & Hunt, J. D. 2012. Valuing of attributes influencing the attractiveness of suburban train service in Mumbai city: A stated preference approach. Transportation Research Part A: Policy and Practice, 46(9): 1465-1476.

Chakour, V., & Eluru, N. 2014. Analyzing commuter train user behavior: a decision framework for access mode and station choice. Transportation, 41(1): 211-228.

Chan, K. and Farber, S. 2019. Factors underlying the connections between active transportation and public transit at commuter rail in the Greater Toronto and Hamilton Area. Transportation: 1-22. https://doi.org/10.1007/s11116-019-10006-w

Chou, P. F., Lu, C. S., & Chang, Y. H. 2014. Effects of service quality and customer satisfaction on customer loyalty in high-speed rail services in Taiwan. Transportmetrica A: Transport Science, 10(10): 917-945.

Cui, Y., Martin, U. and Zhao, W. 2016. Calibration of disturbance parameters in railway operational simulation based on reinforcement learning. Journal of Rail Transport Planning & Management, 6(1): 1-12.

De Oña, R., Eboli, L., & Mazzulla, G. 2014. Key factors affecting rail service quality in the Northern Italy: a decision tree approach. Transport, 29(1): 75-83.

Eboli, L., & Mazzulla, G. 2012. Structural equation modelling for analysing passengers’ perceptions about railway services. Procedia-Social and Behavioral Sciences, 54: 96-106.

Farajpour, A., Bazeghi Kisomi, P. and Bagheri, M. 2017. Identifying the Factors Affecting on Service Quality & Passenger Satisfaction in Commuter Train Services. International Journal of Railway Research, 4(2): 57-66.

Ghius Malik, D. M. 2017. Development of passenger train service quality model for special occasion through neural networks and fuzzy inference system. M.Sc. Thesis, Bangladesh University of Engineering & Technology (BUET), Dhaka. http://lib.buet.ac.bd:8080/xmlui/handle/123456789/4878

Hadiuzzaman, M., Siam, M. R. K., Haque, N., Shimu, T. H., & Rahman, F. 2018. Adaptive neuro-fuzzy approach for modeling equilibrium speed–density relationship. Transportmetrica A: Transport Science, 14(9): 784-808.

Halse, A.H., Østli, V. and Killi, M. 2019. Revealed and stated preferences for reliable commuter rail in Norway. Transportation Letters: 1-5. https://doi.org/10.1080/19427867.2019.1586088

Harrison, J. 2012. Gender segregation on public transport in South Asia: A critical evaluation of approaches for addressing harassment against women (Doctoral dissertation, School of Oriental and African Studies, University of London).

Heyns, G.J. and Luke, R. 2018. Rail commuter service quality in South Africa: results from a longitudinal study. 37th Annual Southern African Transport Conference (SATC 2018), Proceedings ISBN Number: 978-1-920017-89-7

Hsu, H. P. 2011. How Does Fear of Sexual Harassment on Transit Affect Women’s Use of Transit? In Women’s issues in transportation: Summary of the 4th international conference, 2(46): 85-94.

Ibrahim, A.N.H., Borhan, M.N., Zakaria, N.A. and Zainal, S.K. 2019. Effectiveness of Commuter Rail Service toward Passenger’s Satisfaction: a Case Study from Kuala Lumpur, Malaysia. International Journal of Engineering & Technology, 8(1.2): 50-55.

Irfan, S. M., Kee, D. M. H., & Shahbaz, S. 2012. Service quality and rail transport in Pakistan: A passenger perspective. World Applied Sciences Journal, 18(3): 361-369.

Islam, M.R., Barua, S., Anwari, N., Hoque, M. S. 2018. Factors Attributing to the Service Quality of Railway Station in Bangladesh, DUET Journal, 4 (1): 11-20. https://www.researchgate.net/publication/330716119_Factors_Attributing_to_the_Service_Quality_of_Railway_Station_in_Bangladesh

Islam, M. R., Hadiuzzaman, M., Banik, R., Hasnat, M. M., Musabbir, S. R., & Hossain, S. 2016. Bus service quality prediction and attribute ranking: a neural network approach. Public Transport, 8(2): 295-313.

Jang, J. S. 1993. ANFIS: adaptive-network-based fuzzy inference system. IEEE transactions on systems, man, and cybernetics, 23(3): 665-685.

Mikhaylov, A. S., Gumenuk, I. S., & Mikhaylova, A. A. 2015. The SERVQUAL model in measuring service quality of public transportation: evidence from Russia. Calitatea, 16(144): 78.

Mucsi K, Khan AM, Ahmadi, M. 2011. An Adaptive Neuro-Fuzzy Inference System for estimating the number of vehicles for queue management at signalized intersections. Transportation Research Part C: Emerging Technologies, 19(6): 1033-1047.

Nathanail, E. 2008. Measuring the quality of service for passengers on the Hellenic railways. Transportation Research Part A: Policy and Practice, 42(1): 48-66.

Nandan, S. 2010. Determinants of customer satisfaction on service quality: A study of railway platforms in India. Journal of public transportation, 13(1): 6.

Nelson, D., & O’Neil, K. 2000. Commuter rail service reliability: On-time performance and causes for delays. Transportation research record, 1704(1): 42-50.

Nugraha, R.S. 2019. Commuter Train Mode Choice Modelling Using Binary Logit Model. WIDYAKALA JOURNAL, 6(1): 1-8.

Park, B. 2002. Hybrid neuro-fuzzy application in short-term freeway traffic volume forecasting. Transportation Research Record: Journal of the Transportation Research Board, 1802: 190-196.

Prˇibyl, O., & Goulias, K. G. 2003. Application of adaptive neuro-fuzzy inference system to analysis of travel behavior. Transportation research record, 1854(1): 180-188.

Rahaman, K. R., & Rahaman, M. A. 2009. Service quality attributes affecting the satisfaction of railway passengers of selective route in southwestern part of Bangladesh. Theoretical and Empirical Researches in Urban Management, 4.3 (12): 115-125.

Rahman, M., Yasmin, S. and Eluru, N. 2019. Evaluating the impact of a newly added commuter rail system on bus ridership: a grouped ordered logit model approach. Transportmetrica A: Transport Science: 1-21. https://doi.org/10.1080/23249935.2018.1564800

Redman, L., Friman, M., Gärling, T., & Hartig, T. 2013. Quality attributes of public transport that attract car users: A research review. Transport policy, 25: 119-127.

St-Louis, E., Manaugh, K., van Lierop, D., & El-Geneidy, A. 2014. The happy commuter: A comparison of commuter satisfaction across modes. Transportation research part F: traffic psychology and behaviour, 26: 160-170.

Transit Development Corporation, Morpace International, Transit Cooperative Research Program and Cambridge Systematics. 1999a. A Handbook for Measuring Customer Satisfaction and Service Quality, Transportation Research Board, 47, Chapter 7: Quantitative Research Design: 29-32.

Transit Development Corporation, Morpace International, Transit Cooperative Research Program and Cambridge Systematics. 1999b. A Handbook for Measuring Customer Satisfaction and Service Quality, Transportation Research Board, 47, Chapter 3. Identifying Determinants of Service Quality: 11-14

Wang R., Kudrot-E-Khuda M., Nakamura F. and Tanaka S. 2014a. A Cost-benefit Analysis of Commuter Train Improvement in the Dhaka Metropolitan Area, Bangladesh. The 9th International Conference on Traffic and Transportation Studies (ICTTS 2014).

Wang, R., Kudrot-E-Khuda, M., Nakamura, F. and Tanaka, S. 2014b. A Cost-Benefit Analysis of Commuter Train Improvement in the Dhaka Metropolitan Area, Bangladesh. Procedia-Social and Behavioral Sciences, 138: 819-829.

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Published

2019-07-16

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How to Cite

IDENTIFYING KEY FACTORS OF COMMUTER TRAIN SERVICE QUALITY:AN EMPIRICAL ANALYSIS FOR DHAKA CITY. (2019). Malaysian Journal of Civil Engineering, 31(2). https://doi.org/10.11113/mjce.v31.16118