Improvement of an optimal bus scheduling model based on transit smart card data in Seoul

    Kihwan Nam Affiliation
    ; Myungkeun Park Affiliation


This study was initiated with a goal of improving the bus scheduling model using the past data of “smart card”. Traffic congestion level of Seoul is keep aggravating and it also has negative influence on air pollution and our health. Additionally, this heavy traffic causes high congestion costs. The continuous quantitative growth of the public transportation system brings the necessity of its efficient operation system for its future qualitative growth. The improvement of operation system is necessary also to improve public transportation operation cost efficiency of Seoul. In other words, the systematic planning is necessary for maximizing passengers’ satisfaction level and the public transportation operation cost efficiency of Seoul. The current allocation interval of Seoul bus system is designed based on the empirical data of the past, which is incapable of immediate response to rapidly changing passenger demands. This research analyses passengers’ behaviour and makes a proposal for the traffic network operation by analysing the “traffic card (smart card) big data”, which comes from over 90% of the passengers so as to be flexible in dealing with rapid changes in demand.

Keyword : optimal public transport scheduling, smart card data, passenger time analysis, waiting time analytical model, moving time analysis model

How to Cite
Nam, K., & Park, M. (2018). Improvement of an optimal bus scheduling model based on transit smart card data in Seoul. Transport, 33(4), 981-992.
Published in Issue
Dec 5, 2018
Abstract Views
PDF Downloads
Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.


Ally, J.; Pryor, T. 2007. Life-cycle assessment of diesel, natural gas and hydrogen fuel cell bus transportation systems, Journal of Power Sources 170(2): 401–411.

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

Ben-Akiva, M.; Lerman, S. R. 1985. Discrete Choice Analysis: Theory and Application to Travel Demand. The MIT Press. 384 p.

Bowman, L. A.; Turnquist, M. A. 1981. Service frequency, schedule reliability and passenger wait times at transit stops, Transportation Research Part A: General 15(6): 465–471.

Ceder, A. 2002. Urban transit scheduling: framework, review and examples, Journal of Urban Planning and Development 128(4): 225–244.

Cesaroni, G.; Badaloni, C.; Porta, D.; Forastiere, F.; Perucci, C. A. 2008. Comparison between various indices of exposure to traffic-related air pollution and their impact on respiratory health in adults, Occupational and Environmental Medicine 65(10): 683–690.

Chester, M. V.; Horvath, A. 2009. Environmental assessment of passenger transportation should include infrastructure and supply chains, Environmental Research Letters 4(2): 1–8.

Chester, M.; Pincetl, S.; Elizabeth, Z.; Eisenstein, W.; Matute, J. 2013. Infrastructure and automobile shifts: positioning transit to reduce life-cycle environmental impacts for urban sustainability goals, Environmental Research Letters 8(1): 1–10.

Constantin, I.; Florian, M. 1995. Optimizing frequencies in a transit network: a nonlinear bi-level programming approach, International Transactions in Operational Research 2(2): 149–164.

Cooney, G.; Hawkins, T. R.; Marriott, J. 2013. Life cycle assessment of diesel and electric public transportation buses, Journal of Industrial Ecology 17(5): 689–699.

Forbes, M. A.; Holt, J. N.; Watts, A. M. 1994. An exact algorithm for multiple depot bus scheduling, European Journal of Operational Research 72(1): 115–124.

Herndon, S. C.; Shorter, J. H.; Zahniser, M. S.; Wormhoudt, J.; Nelson, D. D.; Demerjian, K. L.; Kolb, C. E. 2005. Real-time measurements of SO2, H2CO, and CH4 emissions from in-use curbside passenger buses in New York City using a chase vehicle, Environmental Science & Technology 39(20): 7984–7990.

Jansson, J. O. 1980. A simple bus line model for optimisation of service frequency and bus size, Journal of Transport Economics and Policy 14(1): 53–80.

Ko, J. H. 2009. Strategies to Promote Green Car Supply in Seoul. Korean Policy Report No 52. 22 p. (in Korean).

Manyika, J.; Chui, M.; Brown, B.; Bughin, J.; Dobbs, R.; Roxburgh, C.; Byers, A. H. 2011. Big data: The Next Frontier for Innovation, Competition, and Productivity. McKinsey Global Institute. 156 p.

Messa, C. 2006. Comparison of emissions from light rail transit, electric commuter rail, and diesel multiple units, Transportation Research Record: Journal of the Transportation Research Board 1955: 26–33.

Ministry of Land, Infrastructure and Transport. 2011. The 1st Sustainable National Transportation and Logistics Development Basic Plan. Republic of Korea. Available from Internet: (in Korean).

Mohring, H. 1972. Optimization and scale economies in urban bus transportation, The American Economic Review 62(4): 591–604.

Munizaga, M. A.; Palma, C. 2012. Estimation of a disaggregate multimodal public transport origin–destination matrix from passive smartcard data from Santiago, Chile, Transportation Research Part C: Emerging Technologies 24: 9–18.

Oldfield, R. H.; Bly, P. H. 1988. An analytic investigation of optimal bus size, Transportation Research Part B: Methodological 22(5): 319–337.

Park, J. S.; Lee, K. S. 2007. Analysis of traffic pattern exploration and traffic behavior in large traffic card transaction database, Journal of Economic Geography 10(1): 44–63. (in Korean).

Park, J.-S.; Oh, D.-K.; Kho, S.-Y. 2008. Estimation of operating cost and efficiency of the introduction of urban subway, Journal of Korean Society of Transportation 26(6): 113–122. (in Korean).

Puchalsky, C. 2005. Comparison of emissions from light rail transit and bus rapid transit, Transportation Research Record: Journal of the Transportation Research Board 1927: 31–37.

Salzborn, F. J. M. 1972. Optimum bus scheduling, Transportation Science 6(2): 137–148.

Seoul Metro. 2017. Website of the Seoul Metro. Available from Internet:

Tom, V. M.; Mohan, S. 2003. Transit route network design using frequency coded genetic algorithm, Journal of Transportation Engineering 129(2): 186–195.

Trépanier, M.; Morency, C.; Agard, B. 2009. Calculation of transit performance measures using smartcard data, Journal of Public Transportation 12(1): 79–96.

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

Utsunomiya, M.; Attanucci, J.; Wilson, N. 2006. Potential uses of transit smart card registration and transaction data to improve transit planning, Transportation Research Record: Journal of the Transportation Research Board 1971: 119–126.

Yu, B.; Yang, Z.; Yao, J. 2010. Genetic algorithm for bus frequency optimization, Journal of Transportation Engineering 136(6): 576–583.