Characterization of the Coronavirus Pandemic on Signalized Intersections Using Probe Vehicle Data
DOI:
https://doi.org/10.13021/jmms.2020.2736Keywords:
Roads & Highways, Transport Management, Infrastructure PlanningAbstract
Probe vehicle speed data are being used to evaluate traffic congestion characteristics, resiliency, and network response at local, corridor, and regional levels. A better understanding of changes in traffic characteristics, 24-hours a day, 7 days a week, can be realized through the analysis of spatially located, temporal speed data. This paper explores the possibility to use probe vehicle data sets to quantify the impact of Coronavirus (COVID-19), which closed New Jersey State schools and buildings starting around March 18, 2020. The preliminary research analyzed about 500,000 speed records over a 21 week period at two intersections in Northern New Jersey to numerically and visually characterize the speed patterns. A simplistic comparison of average speeds binned in 15, 10, and 1-minute increments was conducted to quantify the change in vehicle travel speeds. Although further research and statistical analysis is necessary to evaluate the data as it relates to the New Jersey State pandemic policies, the preliminary results indicate that school closures and stay-at-home orders effectively increased speeds at the two study sites. Based on these results a statewide analysis, with evolving performance metrics, will be conducted.
References
2. Dadashova, B., P. Lasley, P. Koeneman, and S. Turner (2018), Approaches to Presenting External Factors with Operations Performance Measures, Report # FHWA-HOP-18-002
3. Brennan, T.M., Gurriell, R.A., Bechtel, A.J. and Venigalla, M.M., 2019. Visualizing and Evaluating Interdependent Regional Traffic Congestion and System Resiliency, a Case Study Using Big Data from Probe Vehicles. Journal of Big Data Analytics in Transportation, 1(1), pp.25-36. https://doi.org/10.1007/s42421-019-00002-y
4. Brennan Jr, T.M., Gurriell, R.A., Bechtel, A., & Venigalla, M.M. Performance Metrics for Visualizing Interdependent Regional Traffic Congestion Using Aggregated Probe Vehicle Data. Compendium of Papers, Transportation Research Board 97th Annual Meeting, January 2019.
5. Brennan Jr, T. M., Venigalla, M. M., Hyde, A., and LaRegina, A. (2018). Performance Measures for Characterizing Regional Congestion Using Aggregated Multi-Year Probe Vehicle Data Transportation Research Record: Journal of the Transportation Research Board (https://doi.org/10.1177/0361198118797190).
6. Brennan, T.M. and Venigalla, M., “Incorporating Speed Data to Analyze Evacuation Route Resiliency”, ITS World Congress; October 29-November 2, Montreal, Canada (2017) Paper AM-SP0892
7. Brennan Jr, T. M., Day, C. M., & Volovski, M. (2018b). Ranking Statistically Similar Highway Travel Corridors Using Speed Data Sets. Infrastructure Asset Management, 1-28.
8. California Department of Transportation, “Mobility Performance Report 2009”, February 2011.
9. Haghani, A., M. Hamdi, K.F. Sadabadi, “I-95 Corridor coalition vehicle probe project: validation of Inrix data July-September 2008-Final Report,” I-95 Corridor Coalition. January 2009.
10. Lomax, T., D. Shrank, B. Eiselee. 2011 Urban Mobility Report Powered by INRIX Traffic Data. College Station, Texas; The Texas A&M University System, Texas A& M Transportation Institute, 2011.
11. Lomax, T., D. Shrank, B. Eiselee. 2012 Annual Urban Mobility Report. College Station, Texas; The Texas A&M University System, Texas A& M Transportation Institute, 2012.
12. Maryland Department of Transportation, “2012 Maryland State Highway Mobility Report,” July 2012.
13. Remias S, Brennan T, Day C, Summers H, Cox E, Horton D, Bullock D. 2012 Indiana Mobility Report - Full Version. West Lafayette, Indiana: Purdue University, Indiana Mobility Reports; 2013. Report No.: ISBN: 978-1-62260-257-5.(2014)
14. Remias S, Brennan T, Grimmer G, Cox E, Horton D, Bullock D. 2011 Indiana Interstate Mobility Report - Full Version. West Lafayette, Indiana: Purdue University, Indiana Mobility Reports; 2012. Report No.: ISBN: 978-1-62260-209-4.
15. Remias, Stephen, M., T.M. Brennan, C.M. Day, H.T. Summers, D.K. Horton, E.D. Cox and D.M. Bullock, “Spatially Referenced Probe Data Performance Measures for Infrastructure Investment Decision Makers,” Submitted to Transportation Research Board, August 1, 2013, Paper No. 14-1062.
16. Schrank, D., Eisele, B., Lomax, T., & Bak, J. (2015). 2015 urban mobility scorecard.
17. Washington State Department of Transportation, “The 2012 Congestion Report,” August 2012.
18. Brennan, T. M., Remias, S. M., and Mariani, L. J. (2015). “Performance measures to characterize corridor travel time delay based on probe vehicle data.” Transportation Research Board 2526: 39-50)
19. Racioppi, Dustin. “Murphy Orders All NJ Schools to Close Starting Wednesday amid Coronavirus Outbreak.” North Jersey, 16 Mar. 2020, www.northjersey.com/story/news/new-jersey/2020/03/16/nj-coronavirus-schools-closed-across-state-announces-gov-murphy/5057646002/.
20. Schuman, Melanie. “New Jersey Governor Issues ‘Stay at Home’ Order.” CNN, 21 Mar. 2020, www.cnn.com/world/live-news/coronavirus-outbreak-03-21-20-intl-hnk/h_5749efe403dae8d2b7a9916b34db3e6e.
21. Schuman, Rick. “INRIX U.S. National Traffic Volume Synopsis Issue #11 (May 23 – May 29, 2020).” INRIX, 1 June 2020, inrix.com/blog/2020/06/covid19-us-traffic-volume-synopsis-11/.
22. www.ritis.org; lased accessed June 1, 2020
23. https://data.covid.umd.edu/; lased accessed June 1, 2020
