Connected Vehicle Infrastructure University Transportation Center
The objective of speed harmonization is to dynamically adjust and coordinate maximum appropriate vehicle speeds in response to downstream congestion, incidents, and weather or road surface conditions in order to prevent/reduce vehicle crashes and maximize traffic throughput. The proposed research effort builds on and extends existing research efforts being conducted at FHWA by developing a novel speed harmonization algorithm that is predictive as opposed to reactive in nature. Specifically, the speed harmonization algorithm extends research being conducted at the Saxton Traffic Operations Laboratory (STOL) by developing an algorithm that predicts the occurrence of shockwaves up to five minutes before their occurrence using a combination of macroscopic traffic modeling and Bayesian filtering techniques. The wireless communication between vehicles and vehicle to roadside equipment (RSE) provide a unique environment to collect traffic data in addition to traditional sensor data. Traffic data gathered from connected vehicles as probes will be combined with fixed-location traffic sensor data to predict downstream recurrent and non-recurrent traffic conditions including speed and shockwave temporal-spatial evolutions. Based on the predicted traffic conditions, as well as weather and road surface conditions, the speed harmonization algorithm will make recommendations on the optimum course of action in an attempt to reduce vehicle crashes, maximize traffic throughput, and reduce vehicle emissions. A field test will be conducted to validate the proposed algorithm using the test vehicles that will be developed as part of the FHWA STOL effort and the connected vehicle test-bed in Northern Virginia.
The goal of this research effort is to develop a dynamic speed harmonization application (SPD-HARM) that makes use of the frequently collected and rapidly disseminated multi-source data drawn from connected travelers, roadside sensors, and infrastructure, as depicted in Figure 1. The application may be a vehicle-integrated device (e.g., a vehicle manufacturer-installed or aftermarket integrated device), a personal wireless application (e.g., a smartphone or other handheld device), or another application capable of collecting, receiving, and disseminating movement and locational information. The goal of SPD-HARM would be to improve the nature, accuracy, precision, and speed of dynamic decision making by both system managers and system users.
In achieving the identified goal the objective of the project is to develop speed decision algorithms to achieve the mobility, safety, and environmental goals of dynamic speed harmonization. A connected vehicle environment will enable systems and algorithms that can generate traffic condition predictions, alternative scenarios, and solution evaluations in real-time. This would entail developing a simulation-based optimization tool to compute the optimum speed recommendations. Note that this requires an increase in computational capability as well as long-term storage of historical data. Performance measurement will play an important role in evaluating and improving dynamic speed harmonization algorithms and methods.
Elhenawy M. and Rakha H. (2015), “Matrix Projection Approach for Predicting Freeway State Evolution and Dynamic Travel Times,” Published within the proceedings of the 94th Transportation Research Board Annual Meeting, Washington DC, January 11-15, CD-ROM [Paper # 15-0843].
Chen H. and Rakha H. (2014), “Real-time travel time prediction using particle filtering with a non-explicit state-transition model,” Transportation Research: Part C, Vol. 43 Issue Part 1, pp112-126.
Chen H. and Rakha H. (2014), “Data-driven Particle Filter for Multi-step Look-ahead Travel Time Prediction,” Published within the proceedings of the 93rd Transportation Research Board Annual Meeting, Washington DC, January 12-16, CD-ROM [Paper # 14-0824].
Chen H. and Rakha H. (2014), “Agent-Based Modeling Approach to Predict Experienced Travel Times,” Published within the proceedings of the 93rd Transportation Research Board Annual Meeting, Washington DC, January 12-16, CD-ROM [Paper # 14-3851].
Yang H. and Rakha H. (2017), “Reinforcement Learning Ramp Metering Control for Weaving Sections in a Connected Vehicle Environment,” 96th Transportation Research Board Annual Meeting, Washington DC, January 7-12. [Paper # 17-03689]
Elhenawy M. and Rakha H. (2015), “Matrix Projection Approach for Predicting Freeway State Evolution and Dynamic Travel Times,” Presented at the 94th Transportation Research Board Annual Meeting, Washington DC, January 11-15, CD-ROM [Paper # 15-0843].
Chen H. (2014), “Real-time Traffic State Prediction: Modeling and Applications,” Ph.D. Dissertation, Virginia Tech.
Chen H. and Rakha H. (2014), “Data-driven Particle Filter for Multi-step Look-ahead Travel Time Prediction,” Presented at the 93rd Transportation Research Board Annual Meeting, Washington DC, January 12-16, CD-ROM [Paper # 14-0824].
Chen H. and Rakha H. (2014), “Agent-Based Modeling Approach to Predict Experienced Travel Times,” Presented at the 93rd Transportation Research Board Annual Meeting, Washington DC, January 12-16, CD-ROM [Paper # 14-3851].
Chen H., Rakha H., and McGhee C. (2013), “Dynamic Travel Time Prediction using Pattern Recognition,” 20th ITS World Congress, Tokyo, Japan, October 14-18 [Paper: 4108].
Research and Innovative Technology Administration
University Transportation Centers Program
Department of Transportation
1200 New Jersey Avenue, SE
Washington, DC 20590
USA
Harwood, Leslie
Phone: 540-231-9530
Email: lharwood@vtti.vt.edu
Virginia Polytechnic Institute and State University, Blacksburg
Virginia Tech Transportation Institute
3500 Transportation Research Plaza
Blacksburg, Virginia 24061
USA
Chen, Hao
Rakha, Hesham
RiP URL
Project Poster
TriD Format