Inertia Mutation Energy Model to Extract Roads by Crowdsourcing Trajectories

Abstract

Obtaining trajectory-related data through crowdsourcing is a useful approach for acquiring and updating information on road networks because these data are easily accessible and up to date. However, it is challenging to ensure the accuracy of shape- and connectivity-related information on road networks when this is extracted from massive amounts of data. To address this challenge, this paper proposes an inertia mutation energy model (IMEM) to extract and mine information on road networks based on crowdsourced trajectories by using features of images of vehicular trajectories to mine the connectivity between roads. The central assumption of the model is that a road segment may have inertial energy to extend to another road segment that decreases in case of the change in direction. Three critical steps are involved in extracting information concerning roads. First, images of the trajectory and corresponding feature images are constructed, and high-quality trajectories are filtered to split the road into several segments. Second, the IMEM is proposed to measure the potential extension of each road segment with the aim of mining connectivity-related information between the given trajectories. Finally, the centerline of the road is obtained using mathematical morphology and a thinning algorithm. The proposed algorithm was tested by using Global Position System trajectories of the Didi Taxi in Wuhan, China, and the results show that it reduced time cost by over 99% compared with vector algorithms proposed in the literature. Moreover, it enhanced the precision of the results of extraction by 10%-20% compared with traditional kernel density evaluation algorithms.

Keywords

• Crowdsourced trajectory data
• connectivity
• data mining
• road reconstruction