Tracking vehicle trajectories and fuel rates in phantom traffic jams: Methodology and data

Abstract : High-fidelity vehicle trajectory data is becoming increasingly important in traffic modeling, especially to capture dynamic features such as stop-and-go waves. This article presents data collected in a series of eight experiments on a circular track with human drivers. The data contains smooth flowing and stop-and-go traffic conditions. The vehicle trajectories presented in this article are collected using a panoramic 360-degree camera, and fuel rate data is recorded via an on-board diagnostics scanner installed in each vehicle. The video data from the 360-degree camera is processed with an offline unsupervised algorithm to extract vehicle trajectories from experimental data. The trajectories are highly accurate, with a mean positional bias of less than 0.01 m and a standard deviation of 0.11 m. The velocities are also validated to be highly accurate with a bias of 0.02 m/s and standard deviation of 0.09 m/s. The source code and data used in this article are published with this work.
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Fangyu Wu, Raphael Stern, Shumo Cui, Maria Laura Delle Monache, Rahul Bhadani, et al.. Tracking vehicle trajectories and fuel rates in phantom traffic jams: Methodology and data. Transportation research. Part C, Emerging technologies, Elsevier, 2019, 99, pp.82-109. ⟨10.1016/j.trc.2018.12.012⟩. ⟨hal-01614665⟩

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