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Inferring Social-Demographics of Travellers based on Smart Card Data

Zhang, Y; Cheng, T; (2018) Inferring Social-Demographics of Travellers based on Smart Card Data. In: Proceedings of the 2nd International Conference on Advanced Research Methods and Analytics (CARMA2018). (pp. pp. 55-62). Universitat Politècnica València: Valencia, Spain. Green open access

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Abstract

With the wide application of the smart card technology in public transit system, traveller’s daily travel behaviours can be possibly obtained. This study devotes to investigating the pattern of individual mobility patterns and its relationship with social-demographics. We first extract travel features from the raw smart card data, including spatial, temporal and travel mode features, which capture the travel variability of travellers. Then, travel features are fed to various supervised machine learning models to predict individual’s demographic attributes, such as age group, gender, income level and car ownership. Finally, a case study based on London’s Oyster Card data is presented and results show it is a promising opportunity for demographic study based on people’s mobility behaviour.

Type: Proceedings paper
Title: Inferring Social-Demographics of Travellers based on Smart Card Data
Event: 2nd International Conference on Advanced Research Methods and Analytics (CARMA2018)
Dates: 12 July 2018 - 13 July 2018
ISBN-13: 9788490486894
Open access status: An open access version is available from UCL Discovery
DOI: 10.4995/carma2018.2018.8310
Publisher version: http://www.carmaconf.org/wp-content/uploads/pdfs/8...
Language: English
Additional information: This work is licensed under a Creative Commons License CC BY-NC-ND 4.0 (https://creativecommons.org/licenses/by-nc-nd/4.0/).
Keywords: social-demographics; smart card data; travel variability.
UCL classification: UCL
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/10076815
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