@article{ref1,
title="A generic optimization-based enhancement method for trajectory data: two plus one",
journal="Accident analysis and prevention",
year="2024",
author="Zhu, Feng and Chang, Cheng and Li, Zhiheng and Li, Boqi and Li, Li",
volume="200",
number="",
pages="e107532-e107532",
abstract="Trajectory data play a vital role in the field of traffic research such as vehicle safety, traffic flow, and intelligent vehicles. The quality of trajectory data will determine the safety effectiveness of both research and practical applications. Effectively filtering out noise and errors from trajectory data is crucial for improving data quality and further research. However, most enhancement methods only focus on the smoothness of trajectory but overlook abrupt changes. The processed trajectory still exist issues such as incomplete elimination of inconsistency and loss of driving characteristics. In this paper, we propose a generic optimization-based enhancement method to address the issues above. We propose a bilevel optimization method combined with ℓl(1) and ℓl(2) trend filter. First, we design a lℓ(2) trend filter to fuse raw trajectory data and eliminate the inconsistency. Next, we utilize the lℓ(1) trend filter to optimize the data, ensuring physical feasibility and preserving abrupt changes (emergency driving characteristics). Then, we validate the effectiveness of the method through evaluation metrics and prediction models. The generic optimization-based enhancement method proposed in this paper ensures the safety of both research and application by providing high-quality trajectory data.<p /> <p>Language: en</p>",
language="en",
issn="0001-4575",
doi="10.1016/j.aap.2024.107532",
url="http://dx.doi.org/10.1016/j.aap.2024.107532"
}