eprintid: 1555609
rev_number: 23
eprint_status: archive
userid: 608
dir: disk0/01/55/56/09
datestamp: 2017-09-12 14:43:29
lastmod: 2021-09-20 22:29:18
status_changed: 2017-09-12 14:43:29
type: article
metadata_visibility: show
creators_name: Haworth, J
creators_name: Bantis, A
title: Who you are is how you travel: A framework for transportation mode detection using individual and environmental characteristics
ispublished: pub
divisions: UCL
divisions: B04
divisions: C05
divisions: F44
keywords: Transportation mode detection, Dynamic Bayesian networks, Mobility, Disabilities, Smartphones
note: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
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.
date: 2017-07
publisher: Elsevier
official_url: http://dx.doi.org/10.1016/j.trc.2017.05.003
oa_status: green
full_text_type: other
language: eng
primo: open
primo_central: open_green
verified: verified_manual
elements_id: 1293787
doi: 10.1016/j.trc.2017.05.003
lyricists_name: Haworth, James
lyricists_id: JHAWO13
actors_name: Haworth, James
actors_id: JHAWO13
actors_role: owner
full_text_status: public
publication: Transportation Research Part C: Emerging Technologies
volume: 80
pagerange: 286-309
issn: 0968-090X
citation:        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 <https://doi.org/10.1016/j.trc.2017.05.003>.       Green open access   
 
document_url: https://discovery.ucl.ac.uk/id/eprint/1555609/1/TRC_1784_inline.pdf