Lane-level trajectory reconstruction based on data-fusion

Abstract

While lane-changing movements are performed on the entire motorway network for various reasons, such as overtaking, their intensity is substantially greater near complex segments, such as weaving areas. Aside from mandatory lane changes, some drivers also conduct lateral maneuvers for cooperation or anticipation near network nodes. Unlike longitudinal driver behavior (car-following models), lateral driver behavior (lane-changing movements) has received fewer research efforts. The scarcity of suitable data resources to analyze these behaviors and movements might be a crucial cause for this research gap. This paper presents a four-step approach for reconstructing and correcting lateral bias in trajectories collected by a commercial traffic information application running on everyday smartphones. The resulting lateral position is accurate enough to allow for identification of the driving lane, and thus, the lane changes. The algorithm's core is built on a data fusion method using trajectory and loop detector data. The evaluation and validation of the proposed algorithm using drones and closed-circuit television (CCTV) data demonstrate that the core of the algorithm can correctly match more than 94% of trajectory and loop detector data. Between each pair of successive detector stations, the lateral position error has been significantly corrected and reduced to less than half the width of a standard lane of motorway networks. As a result, more than 90% of processed trajectory sample points are in the correct lane. The algorithm requires just two calibration parameters, so it is relatively simple to apply to other test networks.