Mohamed Abdel-Aty, Ph.D., P.E.
University of Central Florida
Civil Engineering

Final Report

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Final Report Summary

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Connected Vehicles (CV) Transition and Market Penetration

The development of information and communication technologies have facilitated connected vehicle (CV) technologies, in which vehicles communicate with other vehicles (V2V), roadway infrastructures (V2I), and pedestrians (V2P) in real-time. CV is regarded as one of the most promising methods to improve traffic safety. According to NHTSA, at a full V2V adoption, CV technologies will annually prevent 439,000 to 615,000 crashes . Nevertheless, the full penetration of CV will not be accomplished until 20601. Hence, traffic flow will be a mixture of conventional vehicles and CVs for over 40 years. Some studies have found that the efficiency of CV technologies is heavily decided by the CV market penetration rate - . Thus, in the CV transition period, studying the market penetration on the safety impact of CV technologies is worthwhile. In addition to penetration rate, the level of connectivity might be important in determining the effect of CV. In this project, connectivity will be classified into four levels: no connection (Level 0), vehicles connects of infrastructures (Level 1), vehicles connect to vehicles (Level 2), and vehicles connect to vehicles and infrastructures (Level 3).

Under heavy fog condition, when vehicles are not connected (Level 0), if a heading vehicle reduces speed dramatically and its following vehicle cannot visually notice the speed reduction, these two vehicles might have a rear-end crash. Nevertheless, this condition could be improved by CV technologies since CV allows the drivers to “see through” vehicles under poor weather conditions. When the Connectivity Level is one (Level 1), the traffic condition at some specific locations can be obtained, then, traffic management strategies (such as speed recommendation) would help in improving traffic safety. When Connectivity Level is two (Level 2), the V2V technologies (e.g., emergency electronic brake lights) would be applied to prevent a following vehicle from being involved in a crash with its heading vehicle given these two vehicles are CVs. However, if one of the vehicles is not a CV or both are not CVs, other technologies based on the Connectivity Level three (Level 3) should be developed to improve safety. First, the V2I technology, which collects traffic information from roadway infrastructure (e.g., traffic detectors), can serve as a supplement of V2V technology. By combing V2V and V2I, the research team could propose algorithms to predict the travelling status of non-CVs and then apply corresponding strategies to CVs (e.g., advanced forward collision warning). Furthermore, according to the combined information, the research team will develop active traffic management strategies (such as variable speed limit) for all vehicles, including CVs and non-CVs. This project will attempt different CV market penetration rates and connectivity levels under fog condition, and we will study the impact of the rates and levels on the efficiency of the proposed strategies in microsimulation. Meanwhile, surrogate safety measurements would be used to find the strategies’ safety effect.

Meanwhile, based on Cooperative Adaptive Cruise Control (CACC), CV technology enables cooperatively driving on road, e.g., platoon-based driving pattern. The platoon-based driving may significantly improve traffic safety and efficiency because platoon has closer headways and lower speed variations compared to traditional traffic flow. The platoon-based cooperative driving system has been widely studied. However, there have not been enough studies that allocate managed-lane(s) to CV platoons. The benefit of managed-lanes for CV platoons should be highly related to CV penetration rates: when the rate is low, the managed-lane for CV platoons might cause waste of roadway resources; when the rate is high, more managed-lanes should be provided. In microsimulation, the research team will build the managed-lane CV platoon scenarios to obtain the number of platoon managed-lane for different CV penetration rates.

Supporting links:
UCF Data
TRID Record