United States Department of Transportation
i
Impact of Connected and Automated Vehicles (CAVs) on Freeway Capacity
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2019-09-01
[PDF-2.04 MB]
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Corporate Contributors:University of North Carolina at Charlotte. Center for Advanced Multimodal Mobility Solutions and Education ; United States. Department of Transportation. University Transportation Centers (UTC) Program ; United States. Department of Transportation. Office of the Assistant Secretary for Research and Technology
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Edition:Final Report
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Abstract:Connected and automated vehicle (CAV) technologies are combination technologies of connected vehicle and automated vehicle. As widely known, CAVs can bring with them many benefits including improving safety, reducing emissions and increasing mobility of the transportation system. CAV only needs a smaller lane width and headway which will lead to a higher roadway capacity. CAVs may have coordinated weaving maneuvers which will increase the capacity on weaving sections. For an intersection, instead of using a stop- or signal-controlled method, CAV can have coordinated through or turning movements to avoid collisions. In short, there is no doubt that the CAV technologies will significantly change the future transportation system. As the CAVs start to penetrate into the market, the current Highway Capacity Manual (HCM) methods cannot be used to evaluate freeway capacity due to the fact that they did not account for the impacts of CAV strategies in the HCM. To quantify the impact of CAVs on freeway capacity, new guidelines should be established in order to be suitable for use in conducting various types of analyses involving CAV strategies. The impact of different CAV penetration rates in the highway system on various facilities under different scenarios should be examined. In order to be better prepared for both CAV planning and operations under varying levels of market penetration and traffic demand, there is a critical need to develop and establish the HCM capacity adjustments. This research will develop guidelines for and make recommendations on estimating and predicting freeway capacity in the presence of CAVs or AVs, and therefore will lead to a better understanding of how CAVs or AVs improve mobility in the freeway system. In the case study conducted in this research, four different freeway scenarios are chosen from the Caltrans Performance Measurement System (PeMS). To obtain valid results, various driving behavior parameters are calibrated to the real traffic conditions for human-driven vehicles by using VISSIM, a commonly used traffic microsimulation tool. In particular, the calibration is conducted using genetic algorithm for driving behavior parameters such as standstill distance and minimum headway between vehicles. After the calibration process, the simulation is conducted on basic freeway segments in the mixed traffic environment including regular human-driven vehicles, AVs, and CAVs. Simulation results are discussed in detail. Overall, the results of this study can help traffic engineers and stakeholders better understand how different market penetration levels of CAV and AV influence freeway capacity and therefore can help improve freeway traffic management.
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