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

Abstract

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.

Road Intersection Extraction Based on Low-Frequency Vehicle Trajectory Data

“…Hu et al rasterized the trajectory data with different resolutions, filtered and refined by mathematical morphology, and fused the information on intersections extracted with different resolutions according to the rules to obtain the best road intersection results [39]. Moreover, [20,22,[40][41][42][43][44][45]: (1) taking the density peak clustering (CFDP) of trajectory data in the vector space and obtaining intersections on the basis of a mathematical morphology in a raster space and (2) establishing a fusion mechanism to finally obtain road intersection information. Multilevel fusion is also performed on the basis of two data sources, vehicle trajectory and remote sensing images, in the extraction of seed intersections, collaborative training, and integrated recognition to extract the vehicle trajectory intersection features and remote sensing images to identify road intersections.…”

Detecting Turning Relationships and Time Restrictions of OSM Road Intersections from Crowdsourced Trajectories

“…In the step of improving trajectory quality, according to the experience from previous studies [25,34], the maximum sampling frequency and sampling distance were set as 120 s and 1.5 km, respectively; the spatial radius and time duration of stop detection were set as 30 m and 60 s, respectively; and since the speed of nonmotor vehicles is usually less than 25 km/h, which is far less than the speed of the free flow of motor vehicles, the speed threshold of reserving motor vehicles was set as 25 km/h. Subsequently, to project the crowdsourced trajectories onto the road segments, we used the improved HMM map matching based on the framework provided by Yang [31] and set dv as 25 m, p f as 0.1, and γ as 0.8.…”

A Guided Deep Learning Approach for Joint Road Extraction and Intersection Detection From RS Images and Taxi Trajectories

“…Yali Li, Longgang Xiang, Caili Zhang, Fengwei Jiao and Chenghao Wu are members of the State Key Laboratory of Information Engineering in Surveying, meet the demand for road map construction and updating. To this end, many scholars have focused on developing data-driven approaches to extract road information automatically, such as GPS trajectory-based methods [3][4][5], remote sensing imagebased methods [6][7][8] and laser point cloud-based methods [9]. Remote sensing (RS) images and vehicle trajectories are two main reliable data sources for road information extraction, with advantages such as low costs, regular updates and wide coverage [10,11].…”