Abstract
Many studies have utilized global navigation satellite system (such as global positioning system (GPS)) trajectories in order to successfully infer road networks because such data can reveal the geometry and development of a road network, can be obtained in a timely manner, and updated on a low budget. Unfortunately, existing studies for inferring road networks from vehicle traces suffer from low accuracy, especially in dense urban regions and locations with complex structures, such as roundabouts, overpasses, and complex intersections. This study presents a novel two-stage approach for inferring road networks from trajectory points and capturing road geometry with better accuracy. First, a lane structure-aware filter is proposed to cluster vehicle trajectories influenced by high noise and outliers in order to reveal the continuous structure points of lane curves from massive trajectory points. Second, a road tracing operator is utilized to segment the road network geometry by inserting new vertices and segments to a vigorous vertex in the heading of the structure points that are extracted in the first step. Experimental results demonstrate the increased accuracy of the extracted roads and show that the proposed method exhibits strong robustness to noise and various sampling rates.
Highlights
Road networks are the foundation of location-based services (LBSs), such as autonomous driving, navigation, and real-time route planning [1]
In this work, filtering trajectories based on the road structure-aware method from multiple trajectory points in a local neighbourhood can remove the effects of extreme noise and outliers, which are unrelated to the road of interest because of low positioning accuracy, and sift the structural feature points of road lanes by urging the trajectory points to move along the direction of the local high density of their lanes
The scores are greater on the Chicago dataset compared with the Dongguan dataset because the Chicago data are mostly collected by buses following regular routes; the network geometry is relatively ordinary, with more trajectories covering most extracted road edges
Summary
Road networks are the foundation of location-based services (LBSs), such as autonomous driving, navigation, and real-time route planning [1]. As one of the principal parts of the intelligent transportation system (ITS), road maps are necessary for maintaining highly accurate and timely information [2]. Maintaining real-time information on road networks is challenging due to construction, closures, and accidents, etc. Manually creating and updating maps based on conventional ground-based survey techniques is costly and inefficient for meeting such a demand. Aerial imagery is considered the most common data source used for acquiring road networks rapidly and economically [3]. Frequent occlusions (clouds, trees and buildings), complicated pre-treatments and confusing classifications limit its application
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