Vehicle Trajectory Reconstruction from not working Sparse Data Using a Hybrid Approach
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A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
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en
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12
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Journal of Transportation Engineering, Part A: Systems, Volume 151, issue 2
Abstract
Vehicle trajectories deliver precious information, supporting traffic state estimation and congested traffic mitigation. However, collecting fully sampled vehicle trajectories is difficult due to unaffordable data-collection costs and maintenance costs of data collection equipment. This study aims to accurately reconstruct missing vehicle trajectories by proposing a novel approach based on sparse data collected from different types of urban roads. First, an improved map-matching algorithm combining a hidden Markov model (HMM) and a bidirectional Dijkstra algorithm is proposed to ensure the high quality of the input data for trajectory reconstruction. The matched trajectory points are then converted into a two-dimensional time-space map. Subsequently, a piecewise cubic Hermite interpolating polynomial (PCHIP) algorithm is developed to reconstruct vehicle trajectories based on a total of 371 taxi trajectories on three types of urban roads. The results demonstrate that the speed-based mean relative error (MRE) value is less than 9%, and the speed-based root mean square error (RMSE_v) value is less than 6 km=h. Furthermore, the location-based MAE is found to be less than 5.86 m, and the location-based RMSE_x value is less than 7 m. Additionally, a model comparison is conducted, and the outcomes evidence that the combined method performs better than state-of-the-art approaches.Description
Publisher Copyright: © 2024 American Society of Civil Engineers.
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Citation
Ma, J, Roncoli, C, Ren, G, Yang, Y, Cao, Q, Deng, Y & Li, J 2025, 'Vehicle Trajectory Reconstruction from not working Sparse Data Using a Hybrid Approach', Journal of Transportation Engineering, Part A: Systems, vol. 151, no. 2, 04024108. https://doi.org/10.1061/JTEPBS.TEENG-8569