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Who you are is how you travel: A framework for transportation mode detection using individual and environmental characteristics

Haworth, J; Bantis, A; (2017) Who you are is how you travel: A framework for transportation mode detection using individual and environmental characteristics. Transportation Research Part C: Emerging Technologies , 80 pp. 286-309. 10.1016/j.trc.2017.05.003. Green open access

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Abstract

With the increasing prevalence of geo-enabled mobile phone applications, researchers can collect mobility data at a relatively high spatial and temporal resolution. Such data, however, lack semantic information such as the interaction of individuals with the transportation modes available. On the other hand, traditional mobility surveys provide detailed snapshots of the relation between socio-demographic characteristics and choice of transportation modes. Transportation mode detection is currently approached using features such as speed, acceleration and direction either on their own or in combination with GIS data. Combining such information with socio-demographic characteristics of travellers has the potential of offering a richer modelling framework that could facilitate better transportation mode detection using variables such as age and disability. In this paper, we explore the possibility to include both elements of the environment and individual characteristics of travellers in the task of transportation mode detection. Using dynamic Bayesian Networks, we model the transition matrix to account for such auxiliary data by using an informative Dirichlet prior constructed using data from traditional mobility surveys. Results have shown that it is possible to achieve comparable accuracy with the most widely used classification algorithms while having a rich modelling framework, even in the case of sparse mobility data.

Type: Article
Title: Who you are is how you travel: A framework for transportation mode detection using individual and environmental characteristics
Open access status: An open access version is available from UCL Discovery
DOI: 10.1016/j.trc.2017.05.003
Publisher version: http://dx.doi.org/10.1016/j.trc.2017.05.003
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Transportation mode detection, Dynamic Bayesian networks, Mobility, Disabilities, Smartphones
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Civil, Environ and Geomatic Eng
URI: https://discovery.ucl.ac.uk/id/eprint/1555609
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