This research develops a system optimal dynamic traffic assignment (DTA) model for mixed traffic of human drivers and automated vehicles (AVs) and investigates network level mobility and energy impacts for different market shares of AVs. A methodology based on vehicle-specific-energy is proposed to estimate the energy consumption from the embedded spatial-queuing traffic flow model within the DTA formulation. Results with a test network indicate that potential travel time and energy consumption reductions are possible with increased AV market share in transportation networks. Results also report a decrease in travel time as high as 49% and energy consumption as high as 28% at the system level. The developed DTA model will be able to assist in transportation planning and the investment decision process by estimating the mobility and energy impacts in future transportation networks with mixed traffic of human drivers and AVs.
Robust and Reactive Traffic Engineering for Dynamic Traffic Demands
