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 360degree 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.

Impact of Stochasticity on Traffic Flow Dynamics in Macroscopic Continuum Models

“…The data used in the calibration were collected from a field car-following experiment that was conducted in the U.S.A. on a circular track of 260 m circumference. For more details, see Wu et al ( 19 ). To calibrate the macroscopic models, the 260 m circuit is divided into 13 cells of equal length of 20 m, and the speed of each cell is extracted every 0.1 s. Data from the time intervals is used, as shown in Table 1.…”

“…It was found that traffic instability is caused by the competition between speed adaptation and the cumulative effect of stochastic factors. Other experimental studies also found that each driver's own different responses to similar stimuli might be the source of oscillations in the absence of other interference factors (16)(17)(18)(19). Inspired by the experimental findings, 2D intelligent driver model (IDM) (12,20), 2D optimal velocity model (OVM) (12,21), 2D full velocity difference model (FVDM) (12,22), and desired time gap-brake light model (BLM) (6,23) were developed through considering a 2D stochastic speed-spacing relationship in the original model.…”

Integrated Framework of Vehicle Dynamics, Instabilities, Energy Models, and Sparse Flow Smoothing Controllers

“…To illustrate the importance of human driving behavior on traffic flow, the seminal work [20] showed experimentally that humancontrolled traffic can exhibit "phantom jams" in which a traffic jam emerges, not from an outside influence such as an accident or lanereduction, but from the collective behavior of drivers. Phantom jams are an important area of traffic control research because they represent a true inefficiency in the flow in which both system throughput is degraded and average fuel efficiency declines [19,26], and frequently arise in non-automated (human) traffic flows [20]. It was subsequently shown [19] that these jams could be effectively "smoothed out" with a single automated vehicle on a ring of 20+ other human drivers.…”

Feedback Control Algorithms for the Dissipation of Traffic Waves with Autonomous Vehicles

“…The video footage recorded during the experiment was processed using image processing algorithms. More details on the image processing algorithms used can be found in the article by Wu, et al [59]. Additionally, vehicle performance data such as fuel consumption was recorded during the experiment using OBDLink MX onboard diagnostics (OBD-II) data loggers.…”