Surtrac 2.0
Working at the request of City of Pittsburgh DOMI to prepare the City for its planned expansion of the adaptive signal network, this project will upgrade the Surtrac Pittsburgh deployment to incorporate the latest technology advances (Surtrac 2.0).
Advances include: (1) new coordination of traffic signals with pedestrian walk signals that will significantly increase pedestrian crossing time at intersections, (2) redesigned, web-based performance monitoring capabilities, and (3) enhanced predictive modeling of traffic flows from detector information, which improves overall traffic flow optimization.
Project Update (April 2020)
Highlights:
- Reinstallation of the entire Surtrac 2.0 system stack (from the operating system up) at all 50 intersections
- Detection zones for portions of the network (e.g., Centre/Negley/Baum triangle) whose traffic lanes and patterns have been redesigned since the original deployment have been updated
- Extensions to the Surtrac 2.0 system have been made to insure proper coordination between tightly spaced intersections (e.g., Penn Avenue and Kirkland Avenue along Centre Avenue)
Based on the preliminary analysis at selected intersections, the pedestrian upgrade designed to increase pedestrian walk time is expected to increase the percentage of pedestrian walk time at each intersection anywhere from 20-70%, depending on how much the Surtrac-generated signal timing plans vary over time from their fixed-time counterparts.
The web-based operator interface to the system, which is called “Rapid View”, will allow City Department of Public Works employees to:
- Monitor individual intersections and adjust signal timing parameters such as phase minimum and maximum times as appropriate
- See the operational status of the network and receive alerts when specific system components (e.g., video cameras) at different intersections are not functioning properly
- View statistics (e.g., split times, delay) on how well a given traffic signal is performing over the past n cycles
- Visualize actual traffic and congestion at each intersection and throughout the network in real-time.
Faculty
Project Team
Stephen F. Smith
Research Professor, Robotics Institute, 一本道无码
sfs@cs.cmu.edu
Isaac Isukapati
Project Scientist, Robotics Institute, 一本道无码
isaaci@andrew.cmu.edu
Project Collaborators
Western Pennsylvania Regional Data Center (WPRDC)