THE EFFECT OF PUBLIC TRANSPORT PASSENGERS' SOCIO-DEMOGRAPHIC CHARACTERISTICS ON ACCESSIBILITY, WAITING AND TRAVEL TIME PERCEPTION

Abstract

The most important factors that determine the quality of public transportation service are travel time, waiting time and access time. In this study, the public transportation system of Isparta is examined and it is aimed to determine the service quality that based on the perceptions of the passengers. For this reason, the five most used lines were selected. In-vehicle surveys were carried out on the lines. The methodology involves determining the weights of three parameters according to socio-demographic characteristics with statistical tools: access time of passengers to stops, waiting time at the stops and travel time. This study was investigated to improve service quality and attract more passengers to public transport to the interaction of the weights of the parameters with the multi nomial logit model. Significance levels with each other were determined using the Pearson model. While working status with more than two values (employed, student, retired, unemployed, student and employed) and educational status (secondary school, high school, university) is considered as the dependent variable, the independent variables are access time, waiting time and travel time. In addition to these, age, purpose of travel, gender, city card usage and private vehicle ownership of the passengers is found as explanatory variables. As a result, it is seen that the change in working status and educational status affected the perceived service quality. As a result, when working status is chosen as the dependent variable in the multinomial logit model, the independent variable coefficients are obtained for accessibility time (βaccessibility=0.0808), waiting time (βwait=-0.0709) and travel time (βtravel=0.1246). When education status is chosen as the dependent variable, the independent variable coefficients are obtained for accessibility (βaccessibility=0.0518), waiting time (βwait=-0.1963) and travel time (βtravel=0.1711).

Keywords

• Service Factors
• Public Transportation
• Accessibility
• Urban Transportation
• Multinomial Logit Regression
• Multinomial Logit Regression

TOPLU TAŞIMA YOLCULARININ SOSYO-DEMOGRAFİK ÖZELLİKLERİNİN ERİŞİLEBİLİRLİK, BEKLEME VE SEYAHAT SÜRESİ ALGISINA ETKİSİ

Abstract

Toplu taşıma hizmetinin kalitesini belirleyen faktörlerden en önemlileri seyahat süresi, bekleme süresi ve erişilebilirlik süresidir. Bu çalışmada, Isparta ili toplu taşıma sistemi incelenmiş ve yolcuların algılarına dayalı hizmet kalitesinin belirlenmesi amaçlanmıştır. Bu nedenle en çok kullanılan beş hat seçilmiştir. Hatlarda araç içi anket çalışmaları gerçekleştirilmiştir. Metodoloji, yolcuların duraklara erişim süresi, duraklarda bekleme süresi ve seyahat süresi olmak üzere üç parametrenin ağırlıklarının yolcuların sosyo-demografik özelliklerine göre istatistiksel araçlarla belirlenmesini içermektedir. Bu çalışmada, hizmet kalitesini iyileştirmek ve toplu taşımaya daha fazla yolcu çekmek için parametrelerin ağırlıklarının çok terimli logit modeli ile etkileşimi araştırılmıştır. Pearson modeli kullanılarak birbirleri ile anlamlılık dereceleri tespit edilmiştir. İkiden fazla değeri olan çalışma durumu (çalışıyor, öğrenci, emekli, çalışmıyor, öğrenci ve çalışıyor), eğitim durumu (ilköğretim, lise, üniversite) bağımlı değişken olarak ele alınırken, bağımsız değişkenler erişilebilirlik süresi, bekleme süresi ve seyahat süresidir. Bunlara ek olarak yolcuların yaşı, seyahat amacı, cinsiyeti, kent kart kullanımı ve özel araç sahipliği açıklayıcı değişkenler olarak yorumlanmıştır. Sonuç olarak çok terimli logit modelinde çalışma durumu bağımlı değişken seçildiğinde erişilebilirlik süresi (βerişilebilirlik=0.0808), bekleme süresi (βbekleme=-0.0709) ve seyahat süresi (βseyahat=0.1246) bağımsız değişken katsayıları elde edilmiştir. Eğitim durumu bağımlı değişken seçildiğinde erişilebilirlik süresi (βerişilebilirlik=0.0518), bekleme süresi (βbekleme=-0.1963) ve seyahat süresi (βseyahat=0.1711) bağımsız değişken katsayıları elde edilmiştir.

Keywords

• Hizmet Faktörleri
• Toplu Taşıma
• Erişebilirlik
• Kent İçi Ulaşım
• Çok Terimli Logit Regresyon

References

1. Bates, J., Polak, J., Jones, P., Cook, A., 2001. The valuation of reliability for personal travel. Transportation Research Part E: Logistics and Transportation Review, 37(2–3), 191–229. https://doi.org/10.1016/S1366-5545(00)00011-9
2. Beimborn, E. A., Greenwald, M. J., Jin, X., 2003. Accessibility, connectivity, and captivity: impacts on transit choice. Transportation Research Record. 1835(1), 1–9.
3. Beirão, G., Sarsfield Cabral, J. A., 2007. Understanding attitudes towards public transport and private car: A qualitative study. Transport Policy, 14(6), 478–489. https://doi.org/10.1016/j.tranpol.2007.04.009
4. Bohte, W., Maat, K., van Wee, B., 2009. Measuring attitudes in research on residential self-selection and travel behaviour: A review of theories and empirical research. In Transport Reviews (Vol. 29, Issue 3, pp. 325–357). https://doi.org/10.1080/01441640902808441
5. Brownstone, D., Small, K., Brownstone, D., Small, K., 2005. Valuing time and reliability: assessing the evidence from road pricing demonstrations. Transportation Research Part A: Policy and Practice, 39(4), 279–293. https://EconPapers.repec.org/RePEc:eee:transa:v:39:y:2005:i:4:p:279-293
6. Carrel, A., Halvorsen, A., Walker, J., 2013. Passengers’ perception of and behavioral adaptation to unreliability in public transportation. Transportation Research Record, 2351, 153–162. https://doi.org/10.3141/2351-17
7. Carrus G., Passafaro P., Bonnes M., 2008. Emotions, habits and rational choices in ecological behaviours: The case of recycling and use of public transportation. Journal of Environmental Psychology, 28, 51–62.
8. 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. https://doi.org/10.1016/J.TRANPOL.2021.01.004

9. Cheng, Y. H., 2010. Exploring passenger anxiety associated with train travel. Transportation, 37(6), 875–896. https://doi.org/10.1007/s11116-010-9267-z
10. Cox, T., Houdmont, J., Griffiths, A., 2006. Rail passenger crowding, stress, health and safety in Britain. Transportation Research Part A: Policy and Practice, 40(3), 244–258. https://doi.org/10.1016/j.tra.2005.07.001
11. de Oña, J., de Oña, R., Calvo, F. J., 2012. A classification tree approach to identify key factors of transit service quality. Expert Systems with Applications, 39(12), 11164–11171. https://doi.org/10.1016/j.eswa.2012.03.037
12. de Vos, J., Waygood, E. O. D., Letarte, L., 2020. Modeling the desire for using public transport. Travel Behaviour and Society, 19, 90–98. https://doi.org/10.1016/J.TBS.2019.12.005
13. Dell’Olio, L., Ibeas, A., Cecin, P., 2011. The quality of service desired by public transport users. Transport Policy, 18(1), 217–227. https://doi.org/10.1016/j.tranpol.2010.08.005
14. DESA., 2018. 68% of the World Population Projected to Live in Urban Areas by 2050. Says UN United Nations Department of Economic and Social Affairs (2018).
15. Eboli, L., Mazzulla, G., Eboli, L., 2015. Relationships between rail passengers’ satisfaction and service quality: a framework for identifying key service factors. 7, 185–201. https://doi.org/10.1007/s12469-014-0096-x
16. Fellesson, M., Friman, M., 2008. Perceived Satisfaction with Public Transport Service in Nine European Cities Transportation Research Forum Perceived Satisfaction with Public Transport Service in Nine European Cities. In Source: Journal of the Transportation Research Forum (Vol. 47, Issue 3).
17. Fu, L., 2007. A New Performance Index for Evaluating Transit Quality of Service. In Journal of Public Transportation (Vol. 10, Issue 3, pp. 47–69).
18. Golob, t. F., Canty, E. T., Gustafson, R. L., Vitt, J. E., 1972. Analysıs of consumer preferences for a publıc transportatıon system. 6(1), 81–102.
19. Habib, K. M. N., Kattan, L., Islam, T., 2011. Model of personal attitudes towards transit service quality. Journal of Advanced Transportation, 45(4), 271–285. https://doi.org/10.1002/atr.106
20. Hensher, D. A., Stopher, P., Bullock, P., 2003. Service quality––developing a service quality index in the provision of commercial bus contracts. Transportation Research Part A: Policy and Practice, 37(6), 499–517. https://doi.org/10.1016/S0965-8564(02)00075-7
21. Ikhrata, H., Michell, P., 1997. Technical Report of Southern California Association of Governments’ Transportation Performance Indicators.
22. Islam, N., 2021. A Review of Methodological Approaches and Modeling Techniques in Service Quality Evaluation of Surface Transportation during the Last Decade. Journal of Engineering Advancements, 197–202. https://doi.org/10.38032/jea.2021.04.005
23. Javid, M. A., Ali, N., Hussain Shah, S. A., Abdullah, M., 2021. Travelers’ Attitudes Toward Mobile Application–Based Public Transport Services in Lahore. SAGE Open, 11(1). https://doi.org/10.1177/2158244020988709
24. Joewono, T. B. ve Kubota H., 2007. User Perceptions of Private Paratransit Operation in Indonesia. Journal of PublicTransportation, 10(4), 99–118.
25. Johnson, R. A. ve Wichern, D. W., 2002. Applied Multivariate Statistical Analysis. Prentice Hall, Upper Saddle River, NJ No. 8.
26. Katz, D. ve Rahman, M. M., 2010. Levels of overcrowding in bus system of Dhaka, Bangladesh. Transportation Research Record, 2143, 85–91. https://doi.org/10.3141/2143-11
27. Kutlu Gündoğdu, F., Duleba, S., Moslem, S., Aydın, S., 2021. Evaluating public transport service quality using picture fuzzy analytic hierarchy process and linear assignment model. Applied Soft Computing, 100, 106920. https://doi.org/10.1016/J.ASOC.2020.106920
28. Lin, C.-Y., Ephen Kennedy, S., Chen, L.-J., Chen, Y.-Y., Lee, W.-C., 2010. A Comfort Measuring System for Public Transportation Systems Using Participatory Phone Sensing.
29. Malekzadeh, A. ve Chung, E. (2019). International Journal of Sustainable Transportation A review of transit accessibility models: Challenges in developing transit accessibility models A review of transit accessibility models: Challenges in developing transit accessibility models. https://doi.org/10.1080/15568318.2019.1625087
30. Mohd Mahudin, N. D., Cox, T., Griffiths, A., 2011. Modelling the spillover effects of rail passenger crowding on individual well being and organisational behaviour. Transactions on The Built Environment, 116, 1743–3509. https://doi.org/10.2495/UT110
31. Mokhtarian, P. L., Salomon, I., Singer, M. E., 2015. What Moves Us? An Interdisciplinary Exploration of Reasons for Traveling. Transport Reviews, 35(3), 250–274. https://doi.org/10.1080/01441647.2015.1013076
32. Nassereddine, M. ve Eskandari, H., 2017. An integrated MCDM approach to evaluate public transportation systems in Tehran. Transportation Research Part A: Policy and Practice, 106, 427–439. https://doi.org/10.1016/J.TRA.2017.10.013
33. Pan, L., Waygood, E. O. D., Patterson, Z., 2022. Would You Wait? Bus Choice Behavior Analysis Considering Various Incentives. Transportation Research Record: Journal of the Transportation Research Board, 036119812210768. https://doi.org/10.1177/03611981221076843
34. Prashker, J. N., 1979. Direct Analysis of The Perceived Importance of Attributes of Reliability of Travel Modes in Urban Travel. Transportation (Netherlands), 8(4), 329–346. https://trid.trb.org/view/159718
35. Proffitt, D. G., Bartholomew, K., Ewing, R., Miller, H. J., 2019. Accessibility planning in American metropolitan areas: Are we there yet? Urban Studies, 56(1), 167–192. https://doi.org/10.1177/0042098017710122
36. Sarkar, P. P. ve Mallikarjuna, C., 2018. Effect of perception and attitudinal variables on mode choice behavior: A case study of Indian city, Agartala. Travel Behaviour and Society, 12, 108–114. https://doi.org/10.1016/J.TBS.2017.04.003
37. TÜİK. (2021a). Adrese dayalı nüfus kayıt sistemi . Türkiye İstatistik Kurumu. https://data.tuik.gov.tr/Bulten/Index?p=Adrese-Dayali-Nufus-Kayit-Sistemi-Sonuclari-2020-37210
38. TÜİK. (2021b). Motorlu Kara Taşıtları. Türkiye İstatistik Kurumu. https://data.tuik.gov.tr/Bulten/Index?p=Motorlu-Kara-Tasitlari-Ekim-2021-37432
39. Wang, D., Brown, G., Mateo-Babiano, I., 2013. Beyond Proxımıty: an Integrated Model of Accessıbılıty for Public Parks. www.ajssh.leena-luna.co.jp
40. Wardman, M., 2004. Public transport values of time. Transport Policy, 11(4), 363–377. https://doi.org/10.1016/J.TRANPOL.2004.05.001