We present a framework for estimating pedestrian demand within a train station. It takes into account ridership data, and various direct and indirect indicators of demand. Such indicators may include link flow counts, density measurements, survey data, historical, or other information. The problem is considered in discrete time and at the aggregate level, i.e., for groups of pedestrians associated with the same origin-destination pair and departure time interval. The formulation is probabilistic, allowing to consider the stochasticity of demand. A key element is the use of the train timetable, and in particular of train arrival times, to capture demand peaks. A case study analysis of a Swiss train station underlines the practical applicability of the proposed framework. Compared to a classical estimator that ignores the notion of a train timetable, the gain in accuracy in terms of root-mean-square error is between 20% and 50%. More importantly, the incorporation of the train schedule allows for prediction when little or no data besides the timetable and ridership information is available. The online appendix is available at https://doi.org/10.1287/trsc.2016.0723 .


    Access

    Access via TIB

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Estimation of Pedestrian Origin-Destination Demand in Train Stations


    Published in:

    Publication date :

    2017




    Type of media :

    Article (Journal)


    Type of material :

    Print


    Language :

    English



    Classification :

    Local classification TIB:    770/1935/7000
    BKL:    55.80 / 55.80 Verkehrswesen, Transportwesen: Allgemeines



    Estimation of Pedestrian Origin-Destination Demand in Train Stations

    Hänseler, Flurin S. / Molyneaux, Nicholas A. / Bierlaire, Michel | British Library Online Contents | 2017


    Time-Dependent Origin–Destination Demand Estimation

    Verbas, I. Ömer / Mahmassani, Hani S. / Zhang, Kuilin | Transportation Research Record | 2011


    Distributionally robust origin–destination demand estimation

    Wang, Jingxing / Song, Jun / Zhao, Chaoyue et al. | Elsevier | 2024


    First-Train Timetable Synchronization in Metro Networks under Origin-Destination Demand Conditions

    Hetian Chai / Xiaopeng Tian / Huimin Niu | DOAJ | 2022

    Free access

    Dynamic Origin-Destination Trip Demand Estimation for Subarea Analysis

    Zhou, Xuesong / Erdoğan, Sevgi / Mahmassani, Hani S. | Transportation Research Record | 2006