An analysis by researchers at the University of Delaware and the Oak Ridge National Laboratory examined the impact of optimally controlled CAVs on the performance of a transportation network. It is broadly understood that the use of CAVs at traffic bottlenecks such as merging roadways, on-ramps, and speed reduction zones can reduce or eliminate disruptions by regulating drivers' responses. CAV technologies have been shown to make traffic safer and more efficient in such scenarios; however, there is relatively less study on their impact on the overall traffic network.
The authors of the paper offer a mathematical model of an optimal control framework for CAVs at intersections, in which vehicles are guided by a centralized coordinator. Then, by applying the model to a VISSIM network that was based on a traffic environment in Newark, Delaware, they determined the benefits and limitations of CAV implementation.
The study specifically compared a baseline human-driver case with the hypothetical optimized CAV case. Four scenarios were examined:
- Fully human-operated vehicle fleet with a long following distance and without the use of the centralized controller
- Fully CAV fleet with shorter following distance and without the use of the centralized controller
- Fully CAV fleet with a long following distance and with the use of the centralized controller
- Fully CAV fleet with shorter following distance and with the use of the centralized controller
The lengthened following distance in the third scenario was intended to provide a closer comparison to the human-operated case. The authors note that the scenarios only examine 0 percent and 100 percent market penetration by CAVs, and thus do not examine interaction between human-operated and automated vehicles.
The analysis then tested the effectiveness of the control framework under varying levels of demand, ranging from 10 percent to 200 percent of current existing demand, to understand how differing levels of traffic would affect behavior.
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