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Forecasting passenger load in a transit network using data driven models

Résumé : Passenger load forecasting can be valuable in transportation planning, operation management and for enriching the information available to passengers, particularly in high-density megacities. This paper investigates the long and short term forecasting of passenger loads in a transit network by using multiple sources of data (on-board headcount data and train timetables). With each passenger load being treated as a time series, one of the main challenges of this study is related to the dependence of the temporal dynamics of the time series to be predicted on the railway timetable. Machine learning models are proposed to predict the passenger load on each train passing each station. We will compare different models, including a random forest, and a gradient boosting tree. Different types of features (calendar, hour, last passenger load, train delay, and train route) will be considered to measure their contributions to the prediction task. The experiments are conducted on a real historical dataset covering the period from 2015 to 2016. The dataset was collected on a railway transit network line operated by SNCF in suburban Paris.
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Submitted on : Wednesday, April 14, 2021 - 6:44:55 PM
Last modification on : Tuesday, October 18, 2022 - 8:34:05 AM


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  • HAL Id : hal-02278238, version 2



Kevin Pasini, Mostepha Khouadjia, Fabrice Ganansia, Latifa Oukhellou. Forecasting passenger load in a transit network using data driven models. WCRR 2019, 12th World Congress on Railway Research, Oct 2019, Tokyo, Japan. ⟨hal-02278238v2⟩



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