Rapid Flow Technologies

Products

Intelligent Transportation for Smart Cities
Surtrac

Surtrac is an innovative approach to real-time traffic signal control, combining research from artificial intelligence and traffic theory. Surtrac optimizes the performance of signals for the traffic that is actually on the road, improving traffic flow for both urban grids and corridors and leading to less waiting, reduced congestion, shorter trips, less pollution, and happier drivers.

Phaenon

Effective automatic vehicle identification (AVI) sensing in urban areas requires ubiquitous sensor deployment in an urban network. We have developed the Phaenon sensor to be inexpensive enough for dense, urban deployments, allowing measurement of cruising for parking, traffic signal performance, traffic congestion, and many other applications.

Measuring cruising for parking

Drivers searching extensively for parking, a behavior known as cruising, leads to excess congestion and pollution. Many recent smart parking interventions have attempted to address this issue, though the problem of effectively detecting and measuring cruising remained largely unsolved. We are developing solutions to detect and measure the frequency of cruising for parking and its effects on congestion.

Measuring traffic signal performance

We are building technologies to measure system performance of surface road networks, particularly dense urban networks. This technology could be used for performance measurement of traffic signal systems or roadway infrastructure improvements, congestion detection for traffic management systems, discovery of OD pairs for planning purposes, and other applications.

Innovative technologies for intelligent transportation

Surtrac

Intelligent Traffic Signal Control

Surtrac is the most advanced adaptive traffic signal control system on the market today. Using patented technology developed in the Robotics Institute at Carnegie Mellon University, Surtrac combines state of the art research in artificial intelligence and traffic theory.

Surtrac provides a true real-time response to changing traffic conditions, optimizing traffic flow second-by-second, unlike other adaptive systems which may take minutes to respond to changes in traffic. Surtrac coordinates traffic flows on grids, not just on corridors like conventional systems. Surtrac also optimizes traffic for many modes of travel, keeping vehicles, cyclists, pedestrians, and transit moving.

Surtrac is proven in the field to yield drastic improvement over conventional traffic signal timing. Travel times are reduced 25%, time spent waiting at signals is down 40%, stops are down over 30%, and emissions are reduced more than 20%.

  • Travel

    Reduced 25% by eliminating stops and reducing wait time, not by increasing travel speeds.

  • Delay

    Over 40% less time waiting at intersections leads to less delay.

  • Stops

    30-40% fewer stops, means less wear and tear on roads and tires.

  • Emissions

    By reducing stops and idling, vehicles produce fewer harmful emissions, improving air quality.

Our Team

Stephen Smith, Ph.D.
Stephen Smith, Ph.D.
Chief Executive Officer
Steve is one of the inventors of the Surtrac adaptive traffic signal control system. He is a AAAI Fellow and Research Professor in the Robotics Institute at Carnegie Mellon University, where he is Director of the Intelligent Coordination and Logistics Laboratory.
Greg Barlow, Ph.D.
Greg Barlow, Ph.D.
Chief Technology Officer
Greg is one of the inventors of the Surtrac intelligent traffic signal system, and is leading the development of the Phaenon sensor network technology and measurement of cruising for parking.
Isaac Isukapati, Ph.D.
Isaac Isukapati, Ph.D.
Isaac is leading algorithm development for the Phaenon sensor network system, including detecting and classifying cruising for parking.
Allen Hawkes
Allen Hawkes
Software Engineer
Allen is the lead developer for the Surtrac scheduler. He has an M.S. in Robotics from Carnegie Mellon and a B.S. from Duke.
Joe Zhou, Ph.D.
Joe Zhou, Ph.D.
Pablo Nanez, Ph.D.
Pablo Nanez, Ph.D.
Zack Rubinstein, Ph.D.
Zack Rubinstein, Ph.D.

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