Investigating Road Link-Level Data during Peak Hours to Identify Potential Areas for Implementing Variable Speed Limit Signs
Publication: International Conference on Transportation and Development 2021
ABSTRACT
Increasing congestion challenges call for better utilization of existing infrastructure by opting for data-driven and cost-effective solutions. Intelligent transportation systems (ITS)-based solution like the variable speed limit (VSL) is a popular advanced data-driven solution to enhance mobility and safety by harmonizing speeds along a road. Prior to allocating resources to a road, a thorough analysis of travel and speed patterns is needed. This paper focuses on investigating road link-level data to identify the threshold of speeds for different peak hours and, thereby, investigating potential parameters influencing the speed of road links. Multivariate clustering 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 the average annual daily traffic (AADT), the historical speed of the road link, and the average speeds of upstream and downstream road links on the average speed of corresponding road link. Modeling and validation were performed using morning and evening peak hour data for Mecklenburg County, North Carolina, USA, for all the road links as well as for road links with low- and high-speed variation.
Get full access to this article
View all available purchase options and get full access to this chapter.
REFERENCES
Aarts, L., van Nes, N., Wegman, F. C. M., Van Schagen, I., and Louwerse, R. (2009). “Safe Speeds and Credible Speed Limits (Sacred speed): A New Vision for Decision Making on Speed Management.” In Compendium of papers of the 88th TRB Annual Meeting, 11-15.
Abdel-Aty, M., Dilmore, J., and Dhindsa, A. (2006). “Evaluation of Variable Speed Limits for Real-Time Freeway Safety Improvement.” Accident Analysis & Prevention, 38(2), 335-345.
Burfeind, M. (2020). “INRIX: Press Release, “Congestion Costs Each American Nearly 100 hours, $1,400 A Year.” INRIX. <https://inrix.com/press-releases/2019-traffic-scorecard-us/>. (January 30, 2021).
Cambridge Systematics. (2005). “Traffic Congestion and Reliability: Trends and Advanced Strategies for Congestion Mitigation.”. Federal Highway Administration, United States.
Chen, H., Rakha, H. A., and McGhee, C. C. (2013). “Dynamic Travel Time Prediction Using Pattern Recognition.” In 20th World Congress on Intelligent Transportation Systems. TU Delft.
Donnell, E. T., Hines, S. C., Mahoney, K. M., Porter, R. J., and McGee, H. (2009). Speed Concepts: Informational Guide. United States Department of Transportation, Washington.
Duvvuri, S. V., Mathew, S., Gouribhatla, R., and Pulugurtha, S. S. (2020). “Identifying Road Links and Variables Influencing the Applicability of Variable Speed Limits Using Supervised Machine Learning and Travel Time Data.” Journal of Modern Mobility Systems, 1, 125-130.
ESRI. “Forest-Based Classification and Regression (Spatial Statistics).” ≤https://pro.arcgis.com/en/pro-app/tool-reference/spatial-statistics/forestbasedclassificationregression.htm≥, (April 28, 2020a).
ESRI. “Forest-based Classification and Regression, Spatial Statistics.” <https://pro.arcgis.com/en/pro-app/tool-reference/spatial-statistics/forestbasedclassificationregression.htm> (Nov. 25, 2020b).
ESRI. “How Multivariate Clustering Works.” ≤https://pro.arcgis.com/en/pro-app/tool-reference/spatial-statistics/how-multivariate-clustering-works.htm≥. (April 28, 2020c).
Federal Highway Administration (FHWA). (2020). “ITS Probe Vehicle Technologies.” ≤https://www.fhwa.dot.gov/ohim/handbook/chap5.pdf>. (April 12, 2020).
Fitzpatrick, K., Shamburger, B., and Fambro, D. (1996). “Design Speed, Operating Speed, And Posted Speed Survey.” Transportation Research Record, 1523(1), 55-60.
Ghods, A. H., Kian, A. R., and Tabibi, M. (2007). “A Genetic-Fuzzy Control Application to Ramp Metering and Variable Speed Limit Control.” In 2007 IEEE International Conf. on Systems, Man and Cybernetics, 1723-1728.
Hadiuzzaman, M., and Qiu, T. Z. (2013). “Cell Transmission Model Based Variable Speed Limit Control for Freeways.” Canadian Journal of Civil Engineering, 40(1), 46-56.
Hadiuzzaman, M., Qiu, T. Z., and Lu, X. Y. (2013). “Variable Speed Limit Control Design for Relieving Congestion Caused by Active Bottlenecks.” Journal of Transportation Engineering, 139(4), 358-370.
Hegyi, A., De Schutter, B., and Hellendoorn, J. (2003). “Optimal Coordination of Variable Speed Limits to Suppress Shock Waves.” Transportation Research Record, 1852(1), 167-174.
Hegyi, A., De Schutter, B., and Hellendoorn, J. (2005a). “Optimal Coordination of Variable Speed Limits to Suppress Shock Waves.” IEEE Transactions on Intelligent Transportation Systems, 6(1), 102-112.
Hegyi, A., De Schutter, B., and Hellendoorn, H. (2005b). “Model Predictive Control for Optimal Coordination of Ramp Metering and Variable Speed Limits.” Transportation Research Part C: Emerging Technologies, 13(3), 185-209.
Katz, B., Ma, J., Rigdon, H., Sykes, K., Huang, Z., Raboy, K., and Chu, J. (2017). “Synthesis of Variable Speed Limit Signs.”. Federal Highway Administration.
Papageorgiou, M., Ben-Akiva, M., Bottom, J., Bovy, P. H., Hoogendoorn, S. P., Hounsel, N. B., and McDonald, M. (2007). “ITS and Traffic Management.” Handbooks in Operations Research and Management Science, 14, 715-774.
Robinson, M. (2000). “Examples of Variable Speed Limit Applications.” Speed Management Workshop at 79th Annual Meeting of Transportation Research Board, Washington, D.C.
Schrank, D., Ersele, B., and Lomax, T. (2019). 2019 Urban Mobility Report. Texas Transportation Institute, Texas A&M University, College Station, TX.
Sun, R., Hu, J., Xie, X., and Zhang, Z. (2014). “Variable Speed Limit Design to Relieve Traffic Congestion Based on Cooperative Vehicle Infrastructure System.” Procedia-Social and Behavioral Sciences, 138, 427-438.
Transportation to America. (2020). “The Congestion Con: How More Lanes and More Money Equals More Traffic.” https://t4america.org/wp-content/uploads/2020/03/Congestion-Report-2020-FINAL.pdf. (January 30, 2021).
Wilson, A. (2019). “A Brief Introduction to Supervised Learning.” Towards Data Science. ≤https://towardsdatascience.com/a-brief-introduction-to-supervised-learning-54a3e3932590>. (March 10, 2020).
Yu, R., and Abdel-Aty, M. (2014). “An Optimal Variable Speed Limits System to Ameliorate Traffic Safety Risk.” Transportation Research Part C: Emerging Technologies, 46, 235-246.
Zhang, Y., and Haghani, A. (2015). “A Gradient Boosting Method to Improve Travel Time Prediction.” Transportation Research Part C: Emerging Technologies, 58, 308-324.
Information & Authors
Information
Published In
International Conference on Transportation and Development 2021
Pages: 50 - 61
Copyright
© 2021 American Society of Civil Engineers.
History
Published online: Jun 4, 2021
Authors
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.