What do autonomous vehicles mean to congestion and crash? Network traffic flow modeling and simulations for autonomous vehicles
Transportation infrastructure is quickly moving towards revolutionary changes to accommodate the deployment of AV. On the other hand, the transition to new vehicle technologies will be shaped in large part by changes in performance of roadway infrastructure. This research aims at understanding the relationship between AV technology and infrastructure performance, which leads to fundamentals of future infrastructure design.
We attempt to tackle two fundamental questions:
1. How would vehicle automation/communication, with different sensing and control specifications, change the vehicle speed and headway under various traffic conditions, and therefore change traffic congestion and crash patterns in the network?
2. How would the vehicular technology change the flow capacity of the roadway infrastructure networks, under different crash rates that are expected to be achieved by different vehicular control strategies? How does the change vary at different levels of AV penetration rates?
As a seed project, this research primarily addresses the mobility concerns of AVs. We propose to study potential car-following behavior of AV, which results in a new fundamental diagram of vehicle density, speed and volume. We develop a novel two-class traffic flow model where both AVs and conventional vehicles are mixed. We examine four types of network topology, lane drop, merge junctions, diverge junctions and non-stop intersections. The expected outcome of this research is to lay out a framework of traffic flow modeling in the presence of AVs, and ultimately to allow flexible extensions of various vehicular control specifications for systematic assessment of mobility and safety. Insights on AV impact to the network flow capacity and congestion patterns will be provided for policy indications and infrastructure design.