Technical Papers
May 28, 2020

Utilizing Low-Ping Frequency Vehicle Trajectory Data to Characterize Delay at Traffic Signals

Publication: Journal of Transportation Engineering, Part A: Systems
Volume 146, Issue 8

Abstract

Probe vehicle data is changing the landscape of transportation engineering. The availability of vehicle trajectory data, or GPS waypoint data, has expanded the utility of probe data. However, the low penetration rate of vehicles prevents signal-performance assessment during short-term or low-volume periods, such as special events, seasonal traffic patterns, and overnight timing plans. Current research has used high-ping frequency data or the temporal distributions of waypoints of less than 2 s. This paper evaluates different approaches for using low-ping frequency data to measure delays at signalized intersections. The results of statistical testing show that 30- and 60-s ping data provide delay values that are not significantly different from 1-s ping data. These sampling frequencies increase the number of observable trajectories by 700%. This data allows for scalable approaches to immediately measure delays at signalized intersections nationwide in the US, thereby reducing costly infrastructure needed for signalized performance measures.

Get full access to this article

View all available purchase options and get full access to this article.

Data Availability Statement

Some or all data, models, or code used during the study were provided by a third party. Direct requests for these materials may be made to the provider as indicated in the section “Acknowledgments.”

Acknowledgments

The vehicle trajectory data provided in this paper was provided by INRIX. The signal-performance measure data for this paper was provided by the Michigan Department of Transportation. The contents of this paper reflect the views of the authors, who are responsible for the facts and the accuracy of the data presented in this study, and do not necessarily reflect the official views or policies of the sponsoring organizations. These contents do not constitute a standard, specification, or regulation.

References

Argote-Cabanero, J., E. Christofa, and A. Skabardonis. 2015. “Connected vehicle penetration rate for estimation of arterial measures of effectiveness.” Transp. Res. C. 60 (Nov): 298–312. https://doi.org/10.1016/j.trc.2015.08.013.
Bucknell, C., and J. C. Herrera. 2014. “A trade-off analysis between penetration rate and sampling frequency of mobile sensors in traffic state estimation.” Transp. Res. C. 46 (Sep): 132–150. https://doi.org/10.1016/j.trc.2014.05.007.
Day, C. M., and D. M. Bullock. 2016. “Detector-free signal offset optimization with limited connected vehicle market penetration: A proof-of-concept study.” Transp. Res. Rec. 2558 (1): 54–65. https://doi.org/10.3141/2558-06.
Day, C. M., H. Li, L. Richardson, J. Howard, T. Platte, and D. M. Bullock. 2017. “Detector-free optimization of traffic signal offset with connected vehicle data.” Transp. Res. Rec. 2620 (1): 54–68. https://doi.org/10.3141/2620-06.
Federal Highway Administration. 2019. “Automated traffic signal performance measures (ATSPMs).” Accessed May 12, 2019. https://www.fhwa.dot.gov/innovation/everydaycounts/edc_4/atspm.cfmñ.
Feng, Y., K. L. Head, S. Khoshmagham, and M. Zamanipour. 2015. “A real-time adaptive signal control in a connected vehicle environment.” Transp. Res. C. 55 (Jun): 460–473. https://doi.org/10.1016/j.trc.2015.01.007.
Feng, Y., J. Zheng, and H. X. Liu. 2018. “Real-time detector-free adaptive signal control with low penetration of connected vehicles.” Transp. Res. Rec. 2672 (18): 35–44. https://doi.org/10.1177/0361198118790860.
Goodall, N. J., B. L. Smith, and B. Park. 2013. “Traffic signal control with connected vehicles.” Transp. Res. Rec. 2381 (1): 65–72. https://doi.org/10.3141/2381-08.
Gordon, R. 2012. Methodologies to measure and quantify transportation management center benefits: Final synthesis report. McLean, VA: Federal Highway Administration.
He, Q., K. L. Head, and J. Ding. 2012. “PAMSCOD: Platoon-based arterial multi-modal signal control with online data.” Transp. Res. C. 20 (1): 164–184. https://doi.org/10.1016/j.trc.2011.05.007.
Herrera, J. C., D. B. Work, R. Herring, X. Ban, Q. Jacobson, and A. M. Bayen. 2010. “Evaluation of traffic data obtained via GPS-enabled mobile phones: The Mobile Century field experiment.” Transp. Res. C. 18 (4): 568–583. https://doi.org/10.1016/j.trc.2009.10.006.
Jenelius, E., and H. N. Koutsopoulos. 2013. “Travel time estimation for urban road networks using low frequency probe vehicle data.” Transp. Res. B. 53 (Jul): 64–81. https://doi.org/10.1016/j.trb.2013.03.008.
Lee, J., B. Park, and I. Yun. 2013. “Cumulative travel-time responsive real-time intersection control algorithm in the connected vehicle environment.” J. Transp. Eng. 139 (10): 1020–1029. https://doi.org/10.1061/(ASCE)TE.1943-5436.0000587.
National Transportation Operations Coalition. 2012. National traffic signal report card. Washington, DC: Institute of Transportation Engineers.
Patire, A. D., M. Wright, B. Prodhomme, and A. M. Bayen. 2015. “How much GPS data do we need?” Transp. Res. C. 58 (1): 325–342. https://doi.org/10.1016/j.trc.2015.02.011.
Piccoli, B., K. Han, T. L. Friesz, T. Yao, and J. Tang. 2015. “Second-order models and traffic data from mobile sensors.” Transp. Res. C. 52 (Mar): 32–56. https://doi.org/10.1016/j.trc.2014.12.013.
Rahman, M. M., and S. C. Wirasinghe. 2016. “The effect of time interval of bus location data on real-time bus arrival estimations.” Transportmetrica A. 12 (8): 700–720. https://doi.org/10.1080/23249935.2016.1166159.
Remias, S. M., C. M. Day, J. M. Waddell, J. N. Kirsch, and T. Trepanier. 2018. “Evaluating the performance of coordinated signal timing: A comparison of common data types with automated vehicle location data.” Transp. Res. Rec. 2672 (18): 128–142. https://doi.org/10.1177/0361198118794546.
Texas Transportation Institute. 2011. 2011 Urban mobility report. College Station, Texas: Texas A&M Univ.
Wada, K., T. Ohata, K. Kobayashi, and M. Kuwahara. 2015. “Traffic measurements on signalized arterials from vehicle trajectories.” Interdiscip. Info. Sci. 21 (1): 77–85. https://doi.org/10.4036/iis.2015.77.
Zheng, J., and H. X. Liu. 2017. “Estimating traffic volumes for signalized intersections using connected vehicle data.” Transp. Res. C. 79 (Jun): 347–362. https://doi.org/10.1016/j.trc.2017.03.007.
Zheng, J., W. Sun, S. Huang, S. Shen, C. Yu, J. Zhu, B. Liu, and H. X. Liu. 2018. Traffic signal optimization using crowdsourced vehicle trajectory data. Washington, DC: Transporation Research Board.

Information & Authors

Information

Published In

Go to Journal of Transportation Engineering, Part A: Systems
Journal of Transportation Engineering, Part A: Systems
Volume 146Issue 8August 2020

History

Received: Jun 19, 2019
Accepted: Jan 31, 2020
Published online: May 28, 2020
Published in print: Aug 1, 2020
Discussion open until: Oct 28, 2020

Permissions

Request permissions for this article.

Authors

Affiliations

Jonathan M. Waddell [email protected]
Graduate Research Assistant, Dept. of Civil and Environmental Engineering, Wayne State Univ., 5050 Anthony Wayne Dr., Detroit, MI 48202. Email: [email protected]
Assistant Professor, Dept. of Civil and Environmental Engineering, Wayne State Univ., 5050 Anthony Wayne Dr., Detroit, MI 48202 (corresponding author). ORCID: https://orcid.org/0000-0002-1622-5170. Email: [email protected]
Jenna N. Kirsch [email protected]
Graduate Research Assistant, Dept. of Civil and Environmental Engineering, Wayne State Univ., 5050 Anthony Wayne Dr., Detroit, MI 48202. Email: [email protected]
Ted Trepanier [email protected]
Senior Director, Public Sector Services, INRIX, 10210 NE Points Dr., Suite 400 Kirkland, WA 98033. Email: [email protected]

Metrics & Citations

Metrics

Citations

Download citation

If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.

Cited by

View Options

Get Access

Access content

Please select your options to get access

Log in/Register Log in via your institution (Shibboleth)
ASCE Members: Please log in to see member pricing

Purchase

Save for later Information on ASCE Library Cards
ASCE Library Cards let you download journal articles, proceedings papers, and available book chapters across the entire ASCE Library platform. ASCE Library Cards remain active for 24 months or until all downloads are used. Note: This content will be debited as one download at time of checkout.

Terms of Use: ASCE Library Cards are for individual, personal use only. Reselling, republishing, or forwarding the materials to libraries or reading rooms is prohibited.
ASCE Library Card (5 downloads)
$105.00
Add to cart
ASCE Library Card (20 downloads)
$280.00
Add to cart
Buy Single Article
$35.00
Add to cart

Get Access

Access content

Please select your options to get access

Log in/Register Log in via your institution (Shibboleth)
ASCE Members: Please log in to see member pricing

Purchase

Save for later Information on ASCE Library Cards
ASCE Library Cards let you download journal articles, proceedings papers, and available book chapters across the entire ASCE Library platform. ASCE Library Cards remain active for 24 months or until all downloads are used. Note: This content will be debited as one download at time of checkout.

Terms of Use: ASCE Library Cards are for individual, personal use only. Reselling, republishing, or forwarding the materials to libraries or reading rooms is prohibited.
ASCE Library Card (5 downloads)
$105.00
Add to cart
ASCE Library Card (20 downloads)
$280.00
Add to cart
Buy Single Article
$35.00
Add to cart

Media

Figures

Other

Tables

Share

Share

Copy the content Link

Share with email

Email a colleague

Share