Safe and efficient automated freeway traffic control

Shockwaves are a naturally emerging phenomena in freeway traffic, but they represent one of the largest safety risks on freeways. Freeway drivers do not expect to encounter abrupt drops in speed or stopped traffic, as a result, shockwaves sharply increase the accident rates, particularly in the context of rear end collisions. For example, US interstate highways in 2021 saw the following rear-end collision numbers: Fatality 985, Injury-Only 71,408, Property-Damage-Only 152,011. Rear end collision severity is directly related to the relative speed between the involved vehicles, shockwaves increase these relative speeds, and thus, they also increase accident severity. Shockwaves also reduce freeway capacity and have a detrimental impact on fuel consumption and emissions because accelerating engines are less efficient than when cruising. Connected and autonomous vehicles (CAV) hold the promise to attenuate and eliminate shockwaves (and thus, also reduce the severity and number of accidents), but only if the system is explicitly designed to do so. The very factors that give rise shockwaves in human driven vehicles (HDV) will also do so in CAV. While CAV offer new ways to manage traffic dynamics, an automated freeway will still be subject to traffic dynamics. For example, even with perfectly driven CAV, trucks and cars behave differently and the ever-changing mix of different vehicle types will give rise to rapidly varying bottleneck capacity. The real challenge is designing the CAV system so that it ensures the safest possible operation, and then within those bounds, the greatest operational efficiency (maximizing capacity, minimizing delays, etc.). This research will approach CAV traffic control by first establishing the desired macroscopic traffic states along a freeway corridor and will use a rolling horizon to continually update the desired states in response to perturbations in the macroscopic traffic stream. Under this macroscopic framework, the CAV will know what behavior they should take simply by knowing where they are in space relative to the set of desired states. The main objective of the macro to micro control scheme is that the system can efficiently anticipate and respond to disturbances over large distances. It is this macroscopic look-ahead that will allow the system to detect and attenuate shockwaves. Although communications bandwidth is not the focus of this work, the macro to micro control scheme also has the potential to greatly reduce the necessary communication bandwidth to control the freeway traffic. For this initial project the focus will be on detecting and dissipating large shockwaves after they have formed and begun propagating. The research will include developing the macroscopic framework to anticipate, detect and respond to shockwaves; developing the means to convey the macro to micro control scheme for the CAV to realize the macroscopic traffic states; and finally demonstrating the methodology using microscopic vehicle trajectory data from real shockwaves as both the initial conditions and bounding constraints of how the system can respond. If successful, it is anticipated that future research will explore mitigating shockwaves at the source- accommodating the variable bottleneck capacity and other unstable traffic dynamics.

• Record URL:
  https://ppms.cit.cmu.edu/projects/detail/424

Language

• English

Project

• Status: Active
• Funding: $118754
• Contract Numbers:

  69A3552344811

• Sponsor Organizations:

  Carnegie Mellon University

  Safety21 National UTC for Promoting Safety
  Pittsburgh, PA  United States  15213

  Office of the Assistant Secretary for Research and Technology

  University Transportation Center Program
  ,    
• Managing Organizations:

  Carnegie Mellon University

  Safety21 National UTC for Promoting Safety
  Pittsburgh, PA  United States  15213
• Project Managers:

  Stearns, Amy

• Performing Organizations:

  The Ohio State University

  ,    
• Principal Investigators:

  Coifman, Benjamin

• Start Date: 20230701
• Expected Completion Date: 20240630
• Actual Completion Date: 0
• USDOT Program: University Transportation Centers

Subject/Index Terms

• TRT Terms: Autonomous vehicles; Bottlenecks; Connected vehicles; Freeways; Macroscopic traffic flow; Rear end crashes; Vehicle trajectories
• Subject Areas: Highways; Operations and Traffic Management; Safety and Human Factors; Vehicles and Equipment;

Filing Info

• Accession Number: 01900236
• Record Type: Research project
• Source Agency: Safety21
• Contract Numbers: 69A3552344811
• Files: UTC, RIP
• Created Date: Nov 20 2023 7:52PM