Location-Based Social Networking Data

Abstract

Trip distribution is an invaluable portion of the transportation planning process; this distribution leads to the creation of origin–destination (O-D) matrices. Location-based social networking (LBSN) has increased in popularity and sophistication and has emerged as a new travel demand data source. Users of LBSN provide location-sensitive data interactively with mobile devices, including smartphones and tablets. These data can provide O-D estimates with significantly higher temporal resolution at a much lower cost in comparison with traditional methods. An LBSN O-D estimation model based on the doubly constrained gravity model was proposed to improve a previously proposed model based on the singly constrained gravity model. The proposed methodology was calibrated and comparatively evaluated against the O-D matrix generated by the method based on the singly constrained gravity model as well as a reference matrix from the local metropolitan planning organization. The results of this method illustrate significant improvement in reducing the O-D estimation errors caused by the sampling bias from the method based on the singly constrained gravity model.