Abstract
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.
Highlights
Destination Choice modeling using multinomial logit models allows for more sophisticated models than the aggregate approaches that have persisted in the field since the 1950s [1]
This paper presents an alternative approach, using aggregated data from the location based social network (LBSN) Foursquare to represent destination attractiveness in the utility function of a multinomial logit model
This paper confirmed the hypothesis that aggregated geotagged big data can improve the modeling of destination choice when combined with traditional data sources
Summary
Destination Choice modeling using multinomial logit models allows for more sophisticated models than the aggregate approaches that have persisted in the field since the 1950s [1]. An example demonstrates where such traditional metrics fall short; national parks have no population and little employment but are large attractors of leisure trips. Ski areas are another example of this effect. It is desirable to better represent the zonal utility, destination choice modeling is often characterized by a large set of alternatives [2]. This paper presents an alternative approach, using aggregated data from the location based social network (LBSN) Foursquare to represent destination attractiveness in the utility function of a multinomial logit model
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