Urban Travel Demand Analysis for Austin, Texas, Using Location-Based Social Networking Data

Abstract

The location-based social networking (LBSN) is a location-sensitive service interactively carried out by users with mobile devices, such as smart phones, to “check-in” with the “venues” reflecting their daily activities. With its increased popularity and sophistication, the location-based social networking (LBSN) data have emerged as a new data source for studying urban travel demand. Comparing with traditional Origin-Destination (O-D) estimation method such as survey based or traffic count based methods, LBSN data has the potential to provide O-D estimation with much higher temporal resolution at much lower cost. In this paper, the Foursquare LBSN data was used to analyze the O-D demand for the urban area near Austin, Texas, USA. A gravity model with two-regime friction factor functions is proposed to estimate the O-D matrix. The proposed methods are calibrated and evaluated against the ground truth O-D data from CAMPO (Capital Area Metropolitan Planning Organization). The results illustrate the promising potential of using LBSN data for urban travel demand analysis and monitoring.

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