@article{ref1,
title="Generating road networks for old downtown areas based on crowd-sourced vehicle trajectories",
journal="Sensors (Basel)",
year="2021",
author="Li, Yali and Zhang, Caili and Xiang, Longgang and Li, Siyu and Wu, Chenhao and Jiao, Fengwei",
volume="21",
number="1",
pages="e235-e235",
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.<p /> <p>Language: en</p>",
language="en",
issn="1424-8220",
doi="10.3390/s21010235",
url="http://dx.doi.org/10.3390/s21010235"
}