Generating Road Networks for Old Downtown Areas Based on Crowd-Sourced Vehicle Trajectories

With the popularity of portable positioning devices, crowd-sourced trajectory data have attracted widespread attention, and led to many research breakthroughs in the field of road network extraction. However, it is still a challenging task to detect the road networks of old downtown areas with complex network layouts from high noise, low frequency, and uneven distribution trajectories. Therefore, this paper focuses on the old downtown area and provides a novel intersection-first approach to generate road networks based on low quality, crowd-sourced vehicle trajectories. For intersection detection, virtual representative points with distance constraints are detected, and the clustering by fast search and find of density peaks (CFDP) algorithm is introduced to overcome low frequency features of trajectories, and improve the positioning accuracy of intersections. For link extraction, an identification strategy based on the Delaunay triangulation network is developed to quickly filter out false links between large-scale intersections. In order to alleviate the curse of sparse and uneven data distribution, an adaptive link-fitting scheme, considering feature differences, is further designed to derive link centerlines. The experiment results show that the method proposed in this paper preforms remarkably better in both intersection detection and road network generation for old downtown areas.

Automated summary

This paper presents a novel intersection-first approach for generating road networks in old downtown areas based on low quality crowd-sourced vehicle trajectories. The method utilizes virtual representative points and the CFDP algorithm to improve intersection detection accuracy, and employs Delaunay triangulation network and adaptive link-fitting scheme to generate road networks, demonstrating remarkable performance in road network generation for old downtown areas.