Tracking vehicle trajectories and fuel rates in phantom traffic jams: Methodology and data. (February 2019)

• Record Type: Journal Article
• Authors: Wu, Fangyu; Stern, Raphael E.; Cui, Shumo; Delle Monache, Maria Laura; Bhadani, Rahul; Bunting, Matt; Churchill, Miles; Hamilton, Nathaniel; Haulcy, R'mani; Piccoli, Benedetto; Seibold, Benjamin; Sprinkle, Jonathan; Work, Daniel B.
• Is Part Of: Transportation research. Volume 99(2019)
• Page Start: 82
• Page End: 109
• Publication Date: 2019-02
• Subjects: Traffic waves and fuel consumption -- Vehicle trajectories -- Computer vision -- Open data; Transportation -- Periodicals; Transportation -- Technological innovations -- Periodicals; 388.011
• DOI: 10.1016/j.trc.2018.12.012
• Languages: English
• ISSNs: 0968-090X

Abstract

Highlights: Image processing algorithm developed for accurate vehicle trajectory tracking. Development of stop-and-go traffic observed as a result of human driving. Stronger traffic waves lead to higher fuel consumption. Abstract: The traffic experiment conducted by Sugiyama et al. (2007) has been a seminal work in transportation research. In the experiment, a group of vehicles are instructed to drive on a circular track starting with uniform spacing. The isolated experimental environment provides a safe, economic, and controlled environment to study free flow traffic and phantom traffic waves. This article introduces a novel method that automates the data collection process in such an environment. Specifically, the vehicle trajectories are measured using a 360-degree camera, and the fuel rates are recorded via on-board diagnostics (OBD-II) scanners. The video data from the 360-degree camera is then processed by an offline unsupervised computer vision algorithm. To validate the data collection method, the technique is then evaluated on a series of eight experiments. Analysis shows that the collected data are highly accurate, with a mean positional bias of less than 0.002 m and a small standard deviation of 0.11 m. The positional data also yields reliable velocity estimates: the derived velocities are biased by only 0.02 m/s with a small standard deviation of 0.09 m/s. The produced trajectory and fuel rate data can be readily used to study human driving behaviors, to calibrate microsimulation models, to develop fuel consumption models, and to investigate engine emissions. To facilitate future research, the source code and the data are made publicly available online.