Origin-Destination Estimation for Non-Commuting Trips Using Location-Based Social Networking Data

Abstract

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.

Keywords:

• gravity model
• location-based social networking
• origin-destination estimation

Acknowledgments

The authors would like to thank Foursquare for allowing the research team to obtain data through their developer API and the Chicago Metropolitan Agency for Planning (CMAP) for providing the OD data for this study.