Heterogeneous data collected by smartphone sensors offer new opportunities to study a person’s mobility behavior. The mobility patterns extracted from the travel histories found in these data enable agents residing in mobile devices to model transitions between visited locations, so that upcoming trips can be predicted after observing a set of events, and assistance can be planned in advance. When several agents cooperate, the forecasted trips made by multiple users can provide a potential supply for shared mobility systems such as dynamic ridesharing. These trips must be sufficiently regular and frequent to be reliably announced as shareable trips. This paper describes a methodology to identify a predictable aggregate supply for ridesharing via mobility patterns discovered in users’ travel histories. The methodology empirically quantifies measures like the regularity and frequency of these patterns on a dataset consisting of 967 users scattered across different geographical areas. The sample exhibits high heterogeneity with respect to these measures (hence, of predictability, regardless of the prediction method). This paper shows how frequency of trip patterns decreases, while regularity increases, when additional dimensions such as departure times are added to the analysis. It was concluded that the traveler flexibility with regard to accepting less regular trips is vital to discover a larger supply. These results provide insights to develop future applications able to take advantage of this approach, to increase ridesharing rates, allowing a critical mass to be more easily attained.


    Access

    Download

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Discovering Regularity in Mobility Patterns to Identify Predictable Aggregate Supply for Ridesharing


    Additional title:

    Transportation Research Record: Journal of the Transportation Research Board


    Contributors:


    Publication date :

    2018-09-09




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    Predictive Ridesharing Based on Personal Mobility Patterns

    Roor, Roman / Karg, Michael / Liao, Andy et al. | British Library Conference Proceedings | 2017


    Predictive ridesharing based on personal mobility patterns

    Roor, Roman / Karg, Michael / Liao, Andy et al. | IEEE | 2017


    RIDESHARING SUPPORT SYSTEM, RIDESHARING SUPPORT METHOD, AND RIDESHARING SUPPORT DEVICE

    MORIOKA WATARU | European Patent Office | 2019

    Free access

    Ridesharing management device, ridesharing management method, and program

    FUJIMOTO NAOTOSHI / ITO YO / IWAMOTO SUSUMU et al. | European Patent Office | 2024

    Free access

    Ridesharing 1981

    D. W. Wiersig / F. W. Davis / D. A. Burkhalter et al. | NTIS | 1982