Abstract
The requirements of location-based services have generated an increasing need for up-to-date digital road maps. However, traditional methods are expensive and time-consuming, requiring many skilled operators. The feasibility of using massive GPS trajectory data provides a cheap and quick means for generating and updating road maps. The detection of road intersections, being the critical component of a road map, is a key problem in map generation. Unfortunately, low sampling rates and high disparities are ubiquitous among floating car data (FCD), making road intersection detection from such GPS trajectories very challenging. In this paper, we extend a point clustering-based road intersection detection framework to include a post-classification course, which utilizes the geometric features of road intersections. First, we propose a novel turn-point position compensation algorithm, in order to improve the concentration of selected turn-points under low sampling rates. The initial detection results given by the clustering algorithm are recall-focused. Then, we rule out false detections in an extended classification course based on an image thinning algorithm. The detection results of the proposed method are quantitatively evaluated by matching with intersections from OpenStreetMap using a variety of distance thresholds. Compared with other methods, our approach can achieve a much higher recall rate and better overall performance, thereby better supporting map generation and other similar applications.
Highlights
With the development of the Internet and smart phones, LBSs have become a fundamental part of people’s daily lives, increasing a general need for detailed and up-to-date digital road maps
An indicator to evaluate the quality of a selected turn-point is presented, proving that the concentration of turn-points is significantly improved by our pre-processing and position compensation processes; (3) We extend the point clustering-based road intersection detection framework to include a post-classification course
We evaluate the quality of road intersection detection by our method using both the Chicago and Shenzhen datasets
Summary
With the development of the Internet and smart phones, LBSs (location-based services) have become a fundamental part of people’s daily lives, increasing a general need for detailed and up-to-date digital road maps. Maps generated from high-quality trajectory data collected by experimental cars can only cover very limited routes and, are impractical To address these challenges, methods taking global trajectory information into consideration have been proposed [10,11,12], which are aimed at achieving better detection and extraction of the elements forming a road map. As one can readily construct a road map with its topological structure by connecting known road intersections, road intersection detection from GPS trajectory data is a prerequisite for a category of map generation methods [6] and remains a key problem in map generation Given their geometric and topological features, road intersections can be recognized by algorithms effectively and automatically.
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