CMMTSE: Complex Road Network Map Matching Based on Trajectory Structure Extraction

Abstract

Trajectory mapping onto a road network is a complex yet important task. This is because, in the presence of circular sections, Y-shaped intersections, and sections with elevated overlaps with the ground, the conditions of road networks become complicated. Consequently, trajectory mapping becomes challenging owing to the complexities of road networks and the noise generated by high positioning errors. In this study, in response to the difficulty in handling redundant noisy trajectory data in complex road network environments, a complex road network map-matching method based on trajectory structure extraction is proposed. The features of the structure are extracted from the original trajectory data to reduce the effects of redundancy and noise on matching. An adaptive screening candidate method is proposed using driver behavior to estimate the road density and reduce the matching time by selecting effective candidates. A spatiotemporal analysis function is redefined using speed and distance features, and a directional analysis function is proposed for use in combination with directional features to improve the matching accuracy of complex road networks. An experimental evaluation based on real-ground trajectory data collected using in-vehicle sensing devices is conducted to verify the effectiveness of the algorithm. Moreover, extensive experiments are performed on challenging real datasets to evaluate the proposed method, and its accuracy and efficiency are compared with those of two state-of-the-art map-matching algorithms. The experimental results confirm the effectiveness of the proposed algorithm.