Safe and Efficient Automated Freeway Traffic Control - Phase 3

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 traffic flow. 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 proposal builds from earlier Safety21 research. Phase 1 developed the transition from unstable stop and go traffic to a smoother trajectory for the first CAV. It uses current conditions along the corridor to estimate the optimal trajectory for the CAV encountering congestion. Phase 2 extended the control to a platoon of CAV, suppressing major disturbances, typically with the 15th vehicle exhibiting a nearly constant speed trajectory even while the 1st vehicle in the platoon encounters stop waves. The proposed research for Phase 3 seeks to address several key aspects. First, extend the methodology to actively manage multi-lane flows, including sequencing and optimizing lane change maneuvers. In all phases of this work, the HDV dynamics ahead of the first CAV come from empirical microscopic data sets. Phase 2 simply accepts the lane changes when and where the HDV happened to change lanes. But these maneuvers disrupt the platoons and degrade performance. Phase 3 will actively handle the lane change maneuvers so that they no longer disrupt platoons and in fact, can be used to robustly support the platoons. Another limitation of Phase 2 is that the empirical data only covers 1/3 of a mile, as a result, it is difficult to match demand of vehicles from upstream with the variable supply of capacity downstream. This research will develop ways to use multiple platoons to buffer against capacity fluctuations and provide finer control, e.g., dynamically advancing vehicles from upstream platoons when downstream capacity exceeds forecasts and slowing platoons when it falls below forecasts. In this way, the proposed research anticipates and develops the management strategies needed to maintain the smoothly flowing queued traffic with CAV.

• Record URL:
  • https://ppms.cit.cmu.edu/projects/detail/575

Language

• English

Project

• Status: Completed
• Funding: $137,891.00
• Contract Numbers:

  69A3552344811

  69A3552348316

• 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:

  Safety21 University Transportation Center

  Carnegie Mellon University
  Pittsburgh, PA  United States  15213

  Carnegie Mellon University

  Pittsburgh, PA  United States 
• Performing Organizations:

  Ohio State University

  Columbus, OH  United States 
• Principal Investigators:

  Coifman, Benjamin

  Liu, Yuan

• Start Date: 20250701
• Expected Completion Date: 20260831
• Actual Completion Date: 20260831
• USDOT Program: University Transportation Centers

Subject/Index Terms

• TRT Terms: Autonomous vehicles; Connected vehicles; Highway capacity; Lane changing; Rear end crashes; Traffic congestion; Traffic control; Traffic platooning; Traffic safety; Vehicle trajectories
• Subject Areas: Highways; Operations and Traffic Management; Planning and Forecasting; Vehicles and Equipment;

Filing Info

• Accession Number: 02002845
• Record Type: Research project
• Source Agency: Safety21 University Transportation Center
• Contract Numbers: 69A3552344811, 69A3552348316
• Files: UTC, RIP
• Created Date: Sep 17 2026 2:04PM