Safe and efficient automated freeway traffic control- Phase 2

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. 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 essentially seeks to take conventionally unstable queued traffic and bring it to a stable flow while queued. It 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. This proposal is for a continuation of a first year project. Year 1 is focusing on the initial transition from unstable stop and go traffic to the first follower with a smooth trajectory. It uses current conditions along the corridor to estimate the trajectory of a vehicle just entering the corridor and calculate the optimal trajectory that maintains the same departure time while reducing acceleration/deceleration. Then continually update the forecasted trajectory over time based on evolving conditions. The year 2 focus will be extending and refining the smooth trajectories upstream of the first vehicle, addressing unanticipated disturbances (from minor lane change maneuvers to major vehicle failures), and anticipating the management strategies needed to maintain the smoothly flowing queued traffic.

• Record URL:
  • https://ppms.cit.cmu.edu/projects/detail/500

Language

• English

Project

• Status: Active
• Funding: $128735
• Contract Numbers:

  69A3552344811

• Sponsor Organizations:

  Office of the Assistant Secretary for Research and Technology

  University Transportation Centers Program
  Department of Transportation
  Washington, DC  United States  20590
• Managing Organizations:

  Carnegie Mellon University

  Pittsburgh, PA  United States 

  Safety21 University Transportation Center

  Carnegie Mellon University
  Pittsburgh, PA  United States  15213
• Project Managers:

  Stearns, Amy

• Performing Organizations:

  Ohio State University

  Columbus, OH  United States 
• Principal Investigators:

  Coifman, Benjamin

• Start Date: 20240701
• Expected Completion Date: 20250630
• Actual Completion Date: 0
• USDOT Program: University Transportation Centers

Subject/Index Terms

• TRT Terms: Autonomous vehicles; Connected vehicles; Macroscopic traffic flow; Traffic characteristics; Traffic congestion; Traffic queuing; Traffic safety; Vehicle trajectories
• Subject Areas: Highways; Operations and Traffic Management; Vehicles and Equipment;

Filing Info

• Accession Number: 01933401
• Record Type: Research project
• Source Agency: Safety21 University Transportation Center
• Contract Numbers: 69A3552344811
• Files: UTC, RIP
• Created Date: Oct 13 2024 9:24AM