A comparative study on measurement of lane-changing trajectory similarities

Hamidreza Hamedi, Rouzbeh Shad and Seyed Ali Ziaee

Physica A: Statistical Mechanics and its Applications, 2022, vol. 604, issue C

Abstract: Lane change (LC) represents an important driving behavior and significantly influences traffic efficiency and safety. Spatial LC behavior should be evaluated for vehicles in a transportation system, identifying vehicle movement patterns through similarities in LC trajectories. Contexts determine the trajectory of an objective. Therefore, it is necessary to deeply understand these contexts so that they could be incorporated into movement investigation. Both internal and external contexts pose direct/indirect impacts on movements and enable movement changes. As a result, contexts should be treated as distinct movement process dimensions, and the recording of data for trajectory evaluation based on spatiotemporal dimensions is inevitable. However, due to complicated inter-dimension associations, trajectory similarity measurement has rarely been studied in the literature. This paper focuses on contextualizing a similarity measure of LC trajectories using internal and external contexts and spatial footprints. A total of three models, including the longest common subsequence (LCSS), edit distance on real sequences (EDR), and dynamic time warping (DTW), are employed to examine multi-dimensional similarities between trajectories. The Next Generation Simulation (NGSIM) data were applied to evaluate these models. Contextual data were observed to be crucial parameters capable of increasing and decreasing movements. The similarities were found to be dependent on the thresholds in the EDR and LCSS models, and a change in the thresholds changed the outcome. To the best of the author’s knowledge, this paper is the first research on the use of several models for the similarity evaluation of LC trajectories.

Keywords: Similarity measurement; LC trajectory data; Dynamic time warping; Longest common subsequence; Edit distance on real sequences (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:604:y:2022:i:c:s037843712200574x

DOI: 10.1016/j.physa.2022.127895

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