This study proposes a framework to impute travel mode for trips identified from cellphone traces by developing a deep neural network model. In our framework, we use the trips from a home interview survey and transit smartcard data, for which the travel mode is known, to create a set of artificial pseudo-cellphone traces. The generated artificial pseudo-cellphone traces with known mode are then used to train a deep neural network classifier. We further apply the trained model to infer travel modes for the cellphone traces from cellular network data. The empirical case study region is Montevideo, Uruguay, where high-quality data are available for all three types of data used in the analysis: a large dataset of cellphone traces, a large dataset of public transit smartcard transactions, and a small household travel survey. The results can be used to create an enhanced representation of origin-destination trip-making in the region by time of day and travel mode.


    Access

    Download


    Export, share and cite



    Title :

    Modelling cellphone trace travel mode with neural networks using transit smartcard and home interview survey data


    Contributors:


    Publication date :

    2020




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    Unknown




    Validating travel behavior estimated from smartcard data

    Munizaga, Marcela | Online Contents | 2014


    Validating travel behavior estimated from smartcard data

    Munizaga, Marcela / Devillaine, Flavio / Navarrete, Claudio et al. | Elsevier | 2014


    Exploring Potential Travel Demand of Customized Bus Using Smartcard Data*

    Guo, Rongge / Guan, Wei / Huang, Ailing et al. | IEEE | 2019


    Use of public transport smartcard data for understanding travel behaviour

    Bagchi, M. / White, P. / Association for European Transport | British Library Conference Proceedings | 2003


    Multidimensional visualization of transit smartcard data using space–time plots and data cubes

    Song, Ying / Fan, Yingling / Li, Xin et al. | Online Contents | 2017