Volume List  / Volume 11 (3)



DOI: 10.7708/ijtte.2021.11(3).09

11 / 3 / 465 - 474 Pages


Hassan Shuaibu Abdulrahman - The Graduate School of Natural and Applied Sciences, Dokuz Eylül University, Izmir, Turkey -

Mustafa Ozuysal - Department of Civil Engineering, Dokuz Eylül University, Izmir, Turkey -


This paper contributes to an emerging literature on the application of smart card fare payment data in public transportation planning. While this data is not mainly aimed at planning purposes, it can provide very useful evidences on travel behavior variability over space and time. Consequently, the analysis of various groups’ behaviors in using a multi modal transit system a case Izmir city is evaluated. It argues a case for the potential of smartcard data in revealing a daily pattern especially using boarding information. In this study, 1,967,955 boarding data of 7 November 2018 in the smart card system of İzmir Metropolitan Municipality were analyzed. Analysis based on fare categories, type of boarding, number of daily boarding, and daily cost inquired by the various groups were estimated. The distribution of most users across all public transportation modes fits well with generalized extreme value distribution. These groups (adults, teachers and students) seem to be commuters and mostly active during the peak periods while the non-commuters tend to have a uniform boarding across time. The application of boarding information data can reveal important patterns, which is relevant to planners and policy makers.

Download Article

Number of downloads: 91


The authors are grateful to ESHOT for the provision on their database.


Ali, A.; Kim, J.; Lee, S. 2016. Travel behavior analysis using smart card data, KSCE Journal of Civil Engineering 20(4): 1532-1539.


Bagchi, M.; White, P. R. 2005. The potential of public transport smart card data, Transport Policy 12(5): 464-474.


Boyle, D.K.; Foote, P.J.; Karash, K.H. 2000. Public transportation marketing and fare policy, Transportation in the New Millennium. 6p.


Dieleman, F. M.; Dijst, M.; Burghouwt, G. 2002. Urban form and travel behaviour: micro-level household attributes and residential context, Urban Studies 39(3): 507-527.


ESHOT. 2020. Eshot Genel Müdürlüğü Resmi Web Sitesi. Available from Internet: https://www.eshot.gov.tr.


Ma, X.; Wu, Y. J.; Wang, Y.; Chen, F.; Liu, J. 2013. Mining smart card data for transit riders’ travel patterns, Transportation Research Part C: Emerging Technologies 36: 1-12.


Pelletier, M. P.; Trepanier, M.; Morency, C. 2011. Smart card data use in public transit: A literature review, Transportation Research Part C: Emerging Technologies 19(4): 557-568.


Trepanier, M.; Tranchant, N.; Chapleau, R. 2007. Individual trip destination estimation in a transit smart card automated fare collection system, Journal of Intelligent Transportation Systems 11(1): 1-14.


Turk Stat. 2013. Population of provinces by years, 2007–2014. Turkish Statistical Institute. Available from Internet: http://www.tuik.gov.tr/UstMenu.do?metod=temelist.


Utsunomiya, M.; Attanucci, J.; Wilson, N. 2006. Potential uses of transit smart card registration and transaction data to improve transit planning, Transportation Research Record 1971(1): 118-126.


Zhao, J.; Qu, Q.; Zhang, F.; Xu, C.; Liu, S. 2017. Spatio-temporal analysis of passenger travel patterns in massive smart card data, IEEE Transactions on Intelligent Transportation Systems 18(11): 3135-3146.


Zhou, J.; Murphy, E. 2019. Day-to-day variation in excess commuting: An exploratory study of Brisbane, Australia, Journal of Transport Geography 74: 223-232.

Quoted IJTTE Works

Related Keywords