United States Department of Transportation
i
Evaluating and Comparing the Impact of Connected and Autonomous Vehicles on Conventional Intersections and Superstreets
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2022-09-01
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[PDF-1.29 MB]
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Edition:Final Report
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Abstract:Road congestion has been a major source of economic loss and environmental pollution in the transportation arena. There are different approaches developed by transportation professionals to mitigate this issue, to name a few, signal optimization, innovative intersection design, variable speed limit control, and ramp metering. These methods have been proved to have the ability to significantly improve the performances of existing transportation infrastructure. Among these strategies, innovative intersection design is often featured with a displaced left turn and channelize right of ways. With considerable construction cost, the innovative intersection can significantly increase the traffic efficiency especially when there is relatively large traffic volume from the main road and less traffic volume from the minor road. According to Hummer (2014), there have been numerous superstreets constructed in the states of North Carolina and Maryland. According to the existing investigation on the performances of superstreet, conclusions were made that superstreet can provide both travel time and safety benefits. Nevertheless, it was also pinpointed that superstreet may confuse drivers who are not familiar with superstreet designs. Hence, in the implementation stage, proper road signs and signal indications play important roles in the superstreet operations. Recently, as a new technology trend, connected and autonomous vehicles have come to reality thanks to the development of information and computation technologies. Many researchers have devoted their efforts to investigating the benefits of CAVs in different transportation environments, including freeways, ramps, roundabouts, and intersections. Nevertheless, the knowledge on the performances of CAV in the innovative intersection design is limited. The lack of this knowledge may produce biased prediction on the future influence of CAVs in the existing transportation infrastructure. Hence this research is designed to mitigate this gap by evaluating and comparing the performance difference of the superstreet and conventional intersection. In these studies, advanced CAV behavior models are developed and tested against traditional HDVs such as trajectory planning and platooning. The mechanisms of trajectory planning vary in different transportation settings. For example, in the signal-controlled intersection, trajectory planning relies on the communication of vehicles and traffic signal controllers. On the other hand, in the non-signalized intersection, the trajectory planning will rely on communication with other vehicles so that the optimal sequence of entering the intersection can be planned. In addition, car-following models of CAVs often differ from the ones of HDVs. The car-following models of CAVs have an intuitively measurable parameter, whose values are made feasible through Radar or LiDAR of CAVs. Platooning and adaptive signal control are two advanced CAV features which rely on vehicle to vehicle (V2V) and vehicle to infrastructure (V2I) communication technologies. This project also develops relevant models to test these CAV features in the superstreet environment.
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