Tracking vehicle trajectories and fuel rates in oscillatory traffic

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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Pré-publication, Document de travail
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Contributeur : Maria Laura Delle Monache <>
Soumis le : mercredi 11 octobre 2017 - 11:18:18
Dernière modification le : mardi 4 septembre 2018 - 01:13:37
Document(s) archivé(s) le : vendredi 12 janvier 2018 - 13:38:11


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  • HAL Id : hal-01614665, version 1



Fangyu Wu, Raphael Stern, Shumo Cui, Maria Laura Delle Monache, Rahul Bhadani, et al.. Tracking vehicle trajectories and fuel rates in oscillatory traffic. 2017. 〈hal-01614665〉



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