A Decision Tree Approach for Achieving High Customer Satisfaction at Urban Interchanges

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Abstract

This paper introduces a decision tree approach, which can be used for the assessment of the design, operation and services provided at urban transport interchanges. Realizing a customer satisfaction survey, feedback was received from 239 users of the Riga International Coach Terminal on crucial attributes, including: travel information, wayfinding information, time and movement, access, comfort and convenience, station attractiveness, safety and security, emergency situation handling and overall satisfaction. Findings revealed the most significant parameters that need to be addressed in order to increase users’ satisfaction, which can gradually improve the overall attractiveness of the terminal and the efficient provision of its services.

1. ADB (2015) Improving interchanges: Introducing best practices on multimodal interchange hub development in the People’s Republic of China. ASIAN DEVELOPMENT BANK.

2. Acharia, T., Yang, I., Lee, D. (2015) Application of J48 Decision Tree for the Identification of Water Bodies Using Landsat 8 OLI Imagery. In: 2nd International Electronic Conference on Sensors and Applications. Online.

3. Ahishakiye, E., Omulo, E., Taremwa, D., Niyonzima, I. (2017) Crime Prediction Using Decision Tree (J48) Classification Algorithm, International Journal of Computer and Information Technology, 6(3): 188-195.

4. Allmuali, H., Kaneda, S., Akiba, Y. (2002) Development and Applications of Decision Trees. In: Expert Systems, Vol.1, Academic Press.

5. Breiman, L., Friedman, J H., Olshen, R.A., and Stone, C.J. (1984) Classification and regression trees. Monterey, CA: Wadsworth.

6. de Ona, J., de Ona, R., Calvo, F. (2011) A Classification Tree Approach to Identify Key Factors of Transit Service Quality. Expert Systems with Applications, 39: 11164-11171.

7. de Ona, J., de Ona, R., Lopez, G. (2016) Transit Service Quality Analysis and Decision Trees: A Step Forward to Personalized Marketing in Public Transportation. Transportation, 43(5): 725-747.

8. Dota, M., Cugnasca, C., Barbosa, D. (2015) Comparative Analysis of Decision Tree Algorithms on Quality of Water Contaminated with Soil. Ciencia Rural, 45(2): 267-273.

9. European Commission (2001) White Paper “European transport policy for 2010: Time to decide”. CEC.

10. Ewing, R., Schmid, T., Killingsworth, R., Zlot, A. and Raudenbush, S. (2008) Relationship between urban sprawl and physical activity, obesity and morbidity. In: J. M. Marzluff, E. Shulenberger, W. Endlicher, M. Alberti, G. Bradley, C. Ryan, U. Simon and C. ZumBrunnen (Eds). New York: Urban Ecology, 567–582.

11. Gao, W., Tang, W., Wang, X. (2013) Application of an Improved C4.5 Algorithm in Performance Analysis. Applied Mechanics and Materials, 380-384: 1681-1684.

12. Kapoor, P., Rani, R. (2015) Efficient Decision Tree Algorithm Using J48 and Reduced Error Pruning. International Journal of Engineering Research and General Science, 3(3): 1613-1621.

13. Kaur, G., Chhabra, A. (2014) Improved J48 Classification Algorithm for the Prediction of Diabetes. International Journal of Computer Applications, 98(22): 13-17.

14. Kumar, A. (2014) Design and Applications of Decision Trees. International Journal of Computer Science Trends and Technology, 2(4): 94-98.

15. Maleki-Entezari, R., Rezaei, A., Minaei-Bidgoli, B. (2009) Comparison of Classification Methods Based on the Type of Attributes and Sample Size. Journal of Convergence Information Technology, 4(3): 94-102.

16. Nathanail, E., Adamos, G., Tsami, M. (2016) Why Interchanges? Understanding intermodality, In: A.Monzon and F.d.Ciommo (eds), CITY-HUBs: Sustainable and Efficient Urban Transport Interchanges, 13-36.

17. Patil, T., Sherekar, S. (2013) Performance Analysis of Naive Bayes and J48 Classification Algorithm for Data Classification. International Journal of Computer Science and Applications, 6(2): 256-261.

18. Quinlan, (1993) C4.5: Programs for machine learning. San Francisco: Morgan Kaufmann.

19. RD PAD, (2017) The economic profile of Riga 2017. Riga: Riga city council Development department.

20. Song, Y., Lu, Y. (2015) Decision Tree Methods: Applications for Classification and Prediction. Shanghai Archives of Psychiatry, 27(2): 130-135.

21. Tsami, M., Nathanail, E. (2016) Guidance Provision for Increasing Quality of Service of Public Transport. In: 16th Conference on Reliability and Statistics in Transportation and Telecommunication. Riga, Latvia.

22. Tsami M., Budilovich (Budiloviča) E., Magginas V., Adamos G., Yatskiv (Jackiva) I. (2018) Assessing the Design and Operation of Riga’s International Coach Terminal. In: Kabashkin I., Yatskiv I., Prentkovskis O. (eds) Reliability and Statistics in Transportation and Communication. RelStat 2017. Lecture Notes in Networks and Systems, vol.36. Springer, 497-506

23. Vaadaala, V., Rao, R., Rao, V. (2013) Classification of Web Services Using JForty Eight. International Journal of Electronics Communication and Computer Engineering, 4(6): 181-184.

24. Vuchic, V. R. (2005) Urban Transit: Operations, Planning, and Economics. New York: John Wiley.

25. Yan, N., Ju, W., Fang, H., Sato, R. (2015) Application of J48 Decision Tree Classifier in Emotion Recognition Based on Chaos Characteristics. In: International Conference on Automation, Mechanical Control and Computational Engineering. Changsha, China.

Transport and Telecommunication Journal

The Journal of Transport and Telecommunication Institute

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Cite Score 2017: 1.21

SCImago Journal Rank (SJR) 2017: 0.294
Source Normalized Impact per Paper (SNIP) 2017: 1.539

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