@article{ref1,
title="Video-based trajectory extraction with deep learning for High-Granularity Highway Simulation (HIGH-SIM)",
journal="Communications in transportation research",
year="2021",
author="Shi, Xiaowei and Zhao, Dongfang and Yao, Handong and Li, Xiaopeng and Hale, David K. and Ghiasi, Amir",
volume="1",
number="",
pages="e100014-e100014",
abstract="High-granularity vehicle trajectory data can help researchers develop traffic simulation models, understand traffic flow characteristics, and thus propose insightful strategies for road traffic management. This paper proposes a novel vehicle trajectory extraction method that can extract high-granularity vehicle trajectories from aerial videos. The proposed method includes video calibration, vehicle detection and tracking, lane marking identification, and vehicle motion characteristics calculation. In particular, the authors propose a Monte-Carlo-based lane marking identification approach to identify each vehicle's lane. This is a challenging problem for vehicle trajectory extraction, especially when the aerial videos are taken from a high altitude. The authors applied the proposed method to extract vehicle trajectories from several high-resolution aerial videos recorded from helicopters. The extracted dataset is named by the High-Granularity Highway Simulation (HIGH-SIM) vehicle trajectory dataset. To demonstrate the effectiveness of the proposed method and understand the quality of the HIGH-SIM dataset, we compared the HIGH-SIM dataset with a well-known dataset, the NGSIM US-101 dataset, regarding the accuracy and consistency aspects. The comparison results showed that the HIGH-SIM dataset has more reasonable speed and acceleration distributions than the NGSIM US-101 dataset. Also, the internal and platoon consistencies of the HIGH-SIM dataset give lower errors compared to the NGSIM US-101 dataset. To benefit future research, the authors have published the HIGH-SIM dataset online for public use.<p /> <p>Language: en</p>",
language="en",
issn="2772-4247",
doi="10.1016/j.commtr.2021.100014",
url="http://dx.doi.org/10.1016/j.commtr.2021.100014"
}