The development of sustainable mobility solutions calls for significant advances in travel demand data collection beyond the long-term static planning data usually available at planning agencies. This paper proposes a combined clustering, regression, and gravity model to estimate an origin-destination (OD) matrix for non-commuting trips based on Foursquare user check-in data in the Chicago urban area. The estimated OD matrix is found to be similar to the ground-truth OD matrix obtained from CMAP (Chicago Metropolitan Agency for Planning). The potential applications for generating day-of-the-week and dynamic bihourly OD patterns from Foursquare data are also illustrated.
Origin-Destination Estimation for Non-Commuting Trips Using Location-Based Social Networking Data
International Journal of Sustainable Transportation ; 9 , 8 ; 551-564
2015-11-17
14 pages
Article (Journal)
Electronic Resource
English
Origin-Destination Estimation for Non-Commuting Trips Using Location-Based Social Networking Data
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