Identifying Road Links and Variables Influencing the Applicability of Variable Speed Limits Using Supervised Machine Learning and Travel Time Data

Authors

  • Sarvani Duvvuri
  • Sonu Mathew
  • Srinivas Pulugurtha The University of North Carolina at Charlotte
  • Raghuveer Gouribhatla

DOI:

https://doi.org/10.13021/jmms.2020.2931

Keywords:

Intelligent transportation systems, Variable speed limit, Supervised machine learning.

Abstract

With increasing congestion and associated challenges to manage the transportation network, intelligent transportation systems (ITS) have gained popularity due to their data-driven approach and application of advanced technologies. A variable speed limit (VSL) is a popular ITS-based solution which uses dynamic speed limit to promote harmonization along a corridor. However, not much was done in identifying road links and influencing variables for their applicability. Therefore, this paper focuses on examining road link-level data to identify road links and variables influencing the applicability of VSL signs. A multivariate cluster analysis was first used to identify potential road links susceptible to speed variation for the implementation of VSL. A supervised machine learning algorithm, forest-based classification and regression, was then used to model and examine the influence of average annual daily traffic (AADT), historical speed of the road link, and the speeds of upstream and downstream road links on the average speed of the corresponding road link. Modelling and validation were performed using data for Mecklenburg County, North Carolina, USA, for all the road links as well as for road links with low- and high-speed variation.

References

D. Schrank, B. Ersele, and T. Lomax, “2019 Urban mobili-ty report,” Texas Transportation Institute, Texas A&M Uni-versity, College Station, TX, 2019, Retrieved from https://static.tti.tamu.edu/tti.tamu.edu/documents/mobility-report-2019.pdf.

M. Papageorgiou, M. Ben-Akiva, J. Bottom, P. H. Bovy, S. P. Hoogendoorn, N. B. Hounsell, , ... and M. McDonald, “ITS and traffic management,” Handbooks in Operations Research and Management Science, vol. 14, pp. 715-774.

Cambridge Systematics, “Traffic congestion and reliability: Trends and advanced strategies for congestion mitiga-tion,” Report No. FHWA-HOP-05-064, Federal Highway Administration, 2005, Retrieved from https://ops.fhwa.dot.gov/congestion_report/.

K. Fitzpatrick, B. Shamburger and D. Fambro, “Design speed, operating speed, and posted speed sur-vey. Transportation Research Record, vol. 1523, no. 1, pp. 55-60, 1996.

M. Robinson, “Examples of variable speed limit applica-tions,” Speed Management Workshop at 79th Annual Meet-ing of Transportation Research Board, Washington, D.C., 2000.

M. Hadiuzzaman, T. Z. Qiu, and X. Y. Lu, “Variable speed limit control design for relieving congestion caused by active bottlenecks,” Journal of Transportation Engineer-ing, vol. 139, no. 4, pp. 358-370, 2013.

M. Abdel-Aty, J. Dilmore, and A. Dhindsa, “Evaluation of variable speed limits for real-time freeway safety improve-ment,” Accident Analysis & Prevention, vol. 38, no. 2, pp. 335-345, 2006.

M. Hadiuzzaman, and T. Z. Qiu, “Cell transmission model based variable speed limit control for freeways,” Canadian Journal of Civil Engineering, vol. 40, no. 1, pp. 46-56, 2013.

R. Yu and M. Abdel-Aty, “An optimal variable speed lim-its system to ameliorate traffic safety risk,” Transportation Research Part C: Emerging Technologies, vol. 46, pp. 235-246, 2014.

A. H. Ghods, A. R. Kian, and M. Tabibi, “A genetic-fuzzy control application to ramp metering and variable speed limit control,” In 2007 IEEE International Conf. on Sys-tems, Man and Cybernetics, pp. 1723-1728, Montreal, Que., Canada, 2008.

H. Chen, H. A. Rakha, and C. C. McGhee, “Dynamic trav-el time prediction using pattern recognition,” In 20th World Congress on Intelligent Transportation Systems, TU Delft, 2013.

A. Hegyi, B. De Schutter, and J. Hellendoorn, “Optimal Coordination of Variable Speed Limits to Suppress Shock Waves”. IEEE Transactions on Intelligent Transportation Systems, Vol. 6(1), pp. 102-112, 2005.

B. Katz, J. Ma, H. Rigdon, K. Sykes, Z. Huang, K. Raboy, and J. Chu, “Synthesis of Variable Speed Limit Signs,” Report No. FHWA-HOP-17-003, Federal Highway Admin-istration, 2017, Retrieved from https://ops.fhwa.dot.gov/publications/fhwahop17003/fhwahop17003.pdf.

ArcGIS Pro, Esri. Multivariate Clustering Analysis, Re-trieved from https://pro.arcgis.com/en/pro-app/tool-reference/spatial-statistics/how-multivariate-clustering-works.htm.

ArcGIS Pro, Esri, Forest-based Classification and Regres-sion, Spatial Statistics, Retrieved from https://pro.arcgis.com/en/pro-app/tool-reference/spatial-statistics/forestbasedclassificationregression.htm.

Downloads

Published

2020-12-29

Issue

Section

Articles