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Dynamic Origin-Destination Travel Demand Estimation Using Location Based Social Networking Data

The Location-based Social Networking (LBSN) data have emerged as new data sources for studying travel demand. This paper investigates the feasibility of using LBSN data to estimate dynamic Origin-Destination (OD) travel demand for general trips. A combined non-parametric cluster and regression model is used to establish the relationship between LBSN data and the trip production and attraction. A modified gravity model based trip distribution method with three friction function variations is proposed to estimate the OD matrix. The proposed methods are calibrated and evaluated against the ground truth OD data from CMAP (Chicago Metropolitan Agency for Planning). The results demonstrate the promising potential of using LBSN data for dynamic OD estimation.