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Prediction of railroad user count using number of route searches via bivariate state–space modeling

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Abstract

Conventional demand-prediction methods predominantly rely on past user behaviors to predict regular future transportation demands using acquired user preference data. Nevertheless, predicting unforeseen travel demands arising from bad weather or emergency events remains challenging owing to the absence of data on such future contingencies. This study introduces a method to predict travel demand by leveraging search history data, which potentially signal unforeseen travel requirements. We elucidate the correlation between the search count and integrated circuit (IC) card usage on an aggregate level. Subsequently, we propose a two-stage analytical technique to estimate the number of IC card usages based on route-search counts. Our findings demonstrate that the proposed model has superior accuracy, and the route-search count plays a pivotal role in predicting the number of IC card usages, especially unforeseen shifts in demand.

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Availability of data and materials

The data that support the findings of this study are available from the Takamastu-Kotohira Electric Railroad Co. Ltd. but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of the Takamastu-Kotohira Electric Railroad Co. Ltd.

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Acknowledgements

We express our gratitude to TAKAMATSU-KOTOHIRA ELECTRIC RAILROAD Co., Ltd. for providing the data for this study.

Funding

This work was supported by the Japan Society for the Promotion of Science, JSPS KAKENHI (grant Number JP20H02277).

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Authors and Affiliations

  1. Department of Management of Social Systems and Civil Engineering, Tottori University, 4-101 Koyama-Minami, Tottori, Japan

    Masashi Kuwano, Mio Hosoe & Taku Moriyama

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  1. Masashi Kuwano
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  2. Mio Hosoe
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  3. Taku Moriyama
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Contributions

MK contributed to conceptualization, methodology, software, project administration, writing-review & editing, and supervision. MH helped in data curation, formal analysis, methodology, and software. TM performed methodology, validation, and writing-review & editing.

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Correspondence to Masashi Kuwano.

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Kuwano, M., Hosoe, M. & Moriyama, T. Prediction of railroad user count using number of route searches via bivariate state–space modeling. J Supercomput 80, 4554–4576 (2024). https://doi.org/10.1007/s11227-023-05642-0

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