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

Fangyu Wu, Raphael E. Stern, Shumo Cui, Maria Laura Delle Monache, Rahul Bhadani, Matt Bunting, Miles Churchill, Nathaniel Hamilton, R'mani Haulcy, Benedetto Piccoli, Benjamin Seibold, Jonathan Sprinkle, Daniel B. Work

Research output: Contribution to journalArticle

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.

Original languageEnglish (US)
Pages (from-to)82-109
Number of pages28
JournalTransportation Research Part C: Emerging Technologies
Volume99
DOIs
StatePublished - Feb 1 2019

Fingerprint

Telecommunication traffic
Trajectories
traffic
methodology
Cameras
Experiments
experiment
Fuel consumption
Computer vision
data collection method
traffic behavior
Engines
Economics
diagnostic
video
trend
economics
Group

Keywords

  • Computer vision
  • Open data
  • Traffic waves and fuel consumption
  • Vehicle trajectories

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Automotive Engineering
  • Transportation
  • Computer Science Applications

Cite this

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

In: Transportation Research Part C: Emerging Technologies, Vol. 99, 01.02.2019, p. 82-109.

Research output: Contribution to journalArticle

Wu, F, Stern, RE, Cui, S, Delle Monache, ML, Bhadani, R, Bunting, M, Churchill, M, Hamilton, N, Haulcy, R, Piccoli, B, Seibold, B, Sprinkle, J & Work, DB 2019, 'Tracking vehicle trajectories and fuel rates in phantom traffic jams: Methodology and data' Transportation Research Part C: Emerging Technologies, vol. 99, pp. 82-109. https://doi.org/10.1016/j.trc.2018.12.012
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. / Tracking vehicle trajectories and fuel rates in phantom traffic jams : Methodology and data. In: Transportation Research Part C: Emerging Technologies. 2019 ; Vol. 99. pp. 82-109.
@article{ec8c1dd9a02c43fb8c7aeeb73f77136d,
title = "Tracking vehicle trajectories and fuel rates in phantom traffic jams: Methodology and data",
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.",
keywords = "Computer vision, Open data, Traffic waves and fuel consumption, Vehicle trajectories",
author = "Fangyu Wu and Stern, {Raphael E.} and Shumo Cui and {Delle Monache}, {Maria Laura} and Rahul Bhadani and Matt Bunting and Miles Churchill and Nathaniel Hamilton and R'mani Haulcy and Benedetto Piccoli and Benjamin Seibold and Jonathan Sprinkle and Work, {Daniel B.}",
year = "2019",
month = "2",
day = "1",
doi = "10.1016/j.trc.2018.12.012",
language = "English (US)",
volume = "99",
pages = "82--109",
journal = "Transportation Research Part C: Emerging Technologies",
issn = "0968-090X",
publisher = "Elsevier Limited",

}

TY - JOUR

T1 - Tracking vehicle trajectories and fuel rates in phantom traffic jams

T2 - Methodology and data

AU - Wu, Fangyu

AU - Stern, Raphael E.

AU - Cui, Shumo

AU - Delle Monache, Maria Laura

AU - Bhadani, Rahul

AU - Bunting, Matt

AU - Churchill, Miles

AU - Hamilton, Nathaniel

AU - Haulcy, R'mani

AU - Piccoli, Benedetto

AU - Seibold, Benjamin

AU - Sprinkle, Jonathan

AU - Work, Daniel B.

PY - 2019/2/1

Y1 - 2019/2/1

N2 - 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.

AB - 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.

KW - Computer vision

KW - Open data

KW - Traffic waves and fuel consumption

KW - Vehicle trajectories

UR - http://www.scopus.com/inward/record.url?scp=85059951964&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85059951964&partnerID=8YFLogxK

U2 - 10.1016/j.trc.2018.12.012

DO - 10.1016/j.trc.2018.12.012

M3 - Article

VL - 99

SP - 82

EP - 109

JO - Transportation Research Part C: Emerging Technologies

JF - Transportation Research Part C: Emerging Technologies

SN - 0968-090X

ER -