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Impact analysis of reductions in tram services in rural areas in Japan using smart card data

Author

Listed:
  • Hiroaki Nishiuchi

    (Kochi University of Technology)

  • Yasuyuki Kobayashi

    (Nihon University)

  • Tomoyuki Todoroki

    (Nihon University)

  • Tomoya Kawasaki

    (Tokyo Institute of Technology)

Abstract

Rural cities in Japan need to maintain public transport services because the proportion of elderly people in the population is increasing. However, measures to reduce the frequency of public transport services are under consideration in such cities because the number of passengers and the income necessary to keep services in operation are both decreasing. This study empirically analyzes the change in the number of tram passengers after the frequency of service was reduced in the study area. Especially, an analysis is applied to a survival time model using smart card data to evaluate what origin–destination pairs between tram stops (tram OD) can maintain a suitable number of passengers. The parameters estimated in the model show that a reduction in the number of trams does not directly lead to a change in the number of tram OD passengers. However, the average number of tram OD passengers and its variance are significant factors in explaining the decrease in the number of tram OD passengers. Sensitivity analysis by using the estimated model during the period of study shows that a tram OD pair that originates in a suburban area and terminates in a city center tends to have a higher probability of survival, but a tram OD pair originating from a city center tends to have a lower probability of survival. The results of this study are fundamental materials for a discussion on which tram OD pairs should be considered by public transport authorities to maintain or increase the number of passengers.

Suggested Citation

  • Hiroaki Nishiuchi & Yasuyuki Kobayashi & Tomoyuki Todoroki & Tomoya Kawasaki, 2018. "Impact analysis of reductions in tram services in rural areas in Japan using smart card data," Public Transport, Springer, vol. 10(2), pages 291-309, August.
    • Handle: RePEc:spr:pubtra:v:10:y:2018:i:2:d:10.1007_s12469-018-0185-3
    DOI: 10.1007/s12469-018-0185-3
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    References listed on IDEAS

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    Cited by:

    1. Filip Covic & Stefan Voß, 2019. "Interoperable smart card data management in public mass transit," Public Transport, Springer, vol. 11(3), pages 523-548, October.
    2. Prasanta K. Sahu & Babak Mehran & Surya P. Mahapatra & Satish Sharma, 2021. "Spatial data analysis approach for network-wide consolidation of bus stop locations," Public Transport, Springer, vol. 13(2), pages 375-394, June.
    3. Andrzej Czerepicki & Tomasz Krukowicz & Anna Górka & Jarosław Szustek, 2021. "Traffic Light Priority for Trams in Warsaw as a Tool for Transport Policy and Reduction of Energy Consumption," Sustainability, MDPI, vol. 13(8), pages 1-22, April.
    4. Li He & Martin Trépanier & Bruno Agard, 2021. "Space–time classification of public transit smart card users’ activity locations from smart card data," Public Transport, Springer, vol. 13(3), pages 579-595, October.
    5. Hiroaki Nishiuchi & Kohei Nishimura & An Minh Ngoc & Charitha Dias, 2024. "Identifying the relationship between intention to use flat-rate public transport and trip frequency by a discrete-continuous model," Public Transport, Springer, vol. 16(2), pages 485-504, June.

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    More about this item

    Keywords

    Public transport; Smart card data; Cox hazard model; Number of OD passengers;
    All these keywords.

    JEL classification:

    • R4 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics

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