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Open AccessArticle

Improving Destination Choice Modeling Using Location-Based Big Data

Institute for Transport Planning and Systems, ETH Zürich, CH-8093 Zurich, Switzerland
Department of Civil, Geo and Environmental Engineering, Technical University of Munich, Modeling Spatial Mobility, 80333 Munich, Germany
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2017, 6(9), 291;
Received: 30 July 2017 / Revised: 15 September 2017 / Accepted: 18 September 2017 / Published: 20 September 2017
(This article belongs to the Special Issue Geospatial Big Data and Transport)
Citizens are increasingly sharing their location and movements through “check-ins” on location based social networks (LBSNs). These services are collecting unprecedented amounts of big data that can be used to study how we travel and interact with our environment. This paper presents the development of a long distance destination choice model for Ontario, Canada, using data from Foursquare to model destination attractiveness. A methodology to collect and process historical check-in counts has been developed, allowing the utility of each destination to be calculated based on the intensity of different activities performed at the destination. Destinations such as national parks and ski areas are very strong attractors of leisure trips, yet do not employ many people and have few residents. Trip counts to such destinations are therefore poorly predicted by models based on population and employment. Traditionally, this has been remedied by extensive manual data collection. The integration of Foursquare data offers an alternative approach to this problem. The Foursquare based destination choice model was evaluated against a traditional model estimated only with population and employment. The results demonstrate that data from LBSNs can be used to improve destination choice models, particularly for leisure travel. View Full-Text
Keywords: destination choice; multinomial logit; MNL; Foursquare; big data; location based social networks destination choice; multinomial logit; MNL; Foursquare; big data; location based social networks
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Molloy, J.; Moeckel, R. Improving Destination Choice Modeling Using Location-Based Big Data. ISPRS Int. J. Geo-Inf. 2017, 6, 291.

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