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Fine-Grained Trajectory Reconstruction by Microscopic Traffic Simulation With Dynamic Data-Driven Evolutionary Optimization
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AI
DOI：10.1109/TITS.2024.3502213.png)
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摘要
En 中文
Vehicle trajectory data are essential in smart mobility applications, yet often incomplete, necessitating systematic reconstruction for effective use. Existing methods often overlook traffic rules and vehicle interactions in their reconstruction process, a research gap that becomes critical for fine-grained reconstruction of incomplete and irregular microscopic traffic data. To address this limitation, this paper introduces a novel fine-grained trajectory reconstruction (FTR) framework, particularly for urban signalized intersections, considering both traffic rules and vehicle interactions through a microscopic traffic simulation (MTS) model. This is motivated by challenging missing patterns in real-world data from Alibaba City Brain Lab and limitations in existing reconstruction approaches. To this end, the FTR problem is first formulated as an MTS-based optimization problem. Then, to solve this problem effectively under a limited computing budget, an advanced dynamic data-driven evolutionary optimization technique, D3GA++, is proposed. Through the validation involving two real-world datasets, D3GA++ has demonstrated superior performance under various missing data scenarios consistently surpassing baselines such as brute-force random search and standard evolutionary algorithm in terms of reconstruction accuracy. Our work can have crucial implications for traffic management, urban planning, and autonomous vehicle technology development.
Keyword:
Trajectory reconstruction
microscopic traffic simulation
data-driven evolutionary optimization
data-driven evolutionary optimization
surrogate-assisted evolutionary optimization
surrogate-assisted evolutionary optimization
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IF:
8.4
论文数:
9.7K
被引数:
6.3W
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