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.
Location-Based Social Networking Data
Exploration into Use of Doubly Constrained Gravity Model for Origin–Destination Estimation
Transportation Research Record
Transportation Research Record: Journal of the Transportation Research Board ; 2430 , 1 ; 72-82
2014-01-01
Article (Journal)
Electronic Resource
English
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