What Do Autonomous Vehicles Mean to Traffic Congestion and Crash? Network Traffic Flow Modeling and Simulation for Autonomous Vehicles

Transportation infrastructure is quickly moving towards revolutionary changes to accommodate the deployment of autonomous vehicles (AVs). 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 revolutionary change in transportation infrastructure design in the both short and long term. For nearly a century, traffic flow in the roadway networks is operated purely by human beings. Human’s reactions to preceding vehicles and side vehicles almost dominate the driving behavior, which is to be replaced by vehicle automation/communication in the future. The technology for autonomous and connected vehicles is rapidly approaching the point of commercial implementation. AVs are cars that can be fully controlled by computers, instead of people, relying upon on-board advanced sensors and computers to observe and interpret road conditions and determine a safe course of action. Connected vehicles, on the other hand, receive data from other vehicles, or a central system, that then instructs them how to operate safely. Generally there is a significant developmental overlap between the two, with future autonomous connected vehicles able to receive data from itself, other cars and systems, and capable of driving themselves or accepting control from external systems. With an assigned time and path, these lightweight, self-guided cars would proceed steadily through crowded infrastructure without all the stop-and-go that chokes roadways and saps fuel efficiency. Many of the enabling technologies, such as adaptive cruise control and lane departure warning systems, already exist. The project team envisions that the pathways of AV and connected vehicle development are likely to converge in the long run. This research uses ‘autonomous vehicle (AV)’ to represent ‘autonomous connected vehicle’. To assess the vehicular technology impact to the traffic flow, two of the most important questions the project team attempts to tackle in this research are, 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 network, 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? This project primarily addresses the mobility concerns of AVs, while establishing a modeling framework that allows future extensions to assess both mobility and safety. In particular, this research proposes a multi-class traffic flow model that captures the car-following behavior of both regular vehicles and AVs. The research helps determine the impact of vehicle automation on the effective road capacity and operating efficiency of transportation networks. It also provides insights for design of the vehicle control strategies targeting mobility and safety. With the traffic flow model mixing both AV and regular vehicles, future research will be devoted to address knowledge gaps related to the operations of automated vehicles and the existing road infrastructure, and the policy implications for transportation planning, system design, and the economy.


    • English


    • Status: Completed
    • Sponsor Organizations:

      Carnegie Mellon University

      Pittsburgh, PA  United States 

      Office of the Assistant Secretary for Research and Technology

      University Transportation Centers Program
      Department of Transportation
      Washington, DC  United States  20590
    • Project Managers:

      Ehrlichman, Courtney

    • Principal Investigators:

      Qian, Sean

    • Start Date: 20160101
    • Expected Completion Date: 0
    • Actual Completion Date: 0

    Subject/Index Terms

    Filing Info

    • Accession Number: 01595816
    • Record Type: Research project
    • Source Agency: Technologies for Safe and Efficient Transportation University Transportation Center
    • Files: UTC, RiP
    • Created Date: Apr 8 2016 2:27PM