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Semantic enrichment of secondary activities using smart card data and point of interests: a case study in London / Nilufer Sari Aslam in Annals of GIS, vol 27 n° 1 (January 2021)  

Public [article]  inAnnals of GIS > vol 27 n° 1 (January 2021) . - pp 29 - 41 Titre : Semantic enrichment of secondary activities using smart card data and point of interests: a case study in London Type de document : Article/Communication Auteurs : Nilufer Sari Aslam, Auteur ; Di Zhu, Auteur ; Tao Cheng, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 29 - 41 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] carte à puce [Termes IGN] collecte de données [Termes IGN] données socio-économiques [Termes IGN] données spatiotemporelles [Termes IGN] enrichissement sémantique [Termes IGN] loisir [Termes IGN] Londres [Termes IGN] méthode heuristique [Termes IGN] planification urbaine [Termes IGN] point d'intérêt [Termes IGN] transport urbain Résumé : (auteur) The large volume of data automatically collected by smart card fare systems offers a rich source of information regarding daily human activities with a high resolution of spatial and temporal representation. This provides an opportunity for aiding transport planners and policy-makers to plan transport systems and cities more responsively. However, there are currently limitations when it comes to understanding the secondary activities of individual commuters. Accordingly, in this paper, we propose a framework to detect and infer secondary activities from individuals’ daily travel patterns from the smart card data and reduce the use of conventional surveys. First, we proposed a ‘heuristic secondary activity identification algorithm’, which uses commuters’ primary locations (home & work) and the direction (from & to) information to identify secondary activities for individuals. The algorithm provides a high-level classification of the activity types as before-work, midday and after-work activity patterns of individuals. Second, this classification is semantically enriched using Points of Interests to provide meaningful insights into individuals’ travel purposes and mobility in an urban environment. Lastly, using the transit data of London as a case study, the model is compared with a volunteer survey to demonstrate its effectiveness and offering a cost-effective method to travel demand research. Numéro de notice : A2021-319 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/19475683.2020.1783359 Date de publication en ligne : 01/08/2020 En ligne : https://doi.org/10.1080/19475683.2020.1783359 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97550 [article]