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Scalable Urban Traffic Control

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Scalable Urban Traffic Control (SURTRAC)[1][2] is an adaptive traffic control system developed by researchers at the Robotics Institute, Carnegie Mellon University. SURTAC dynamically optimizes the control of traffic signals to improve traffic flow for both urban grids and corridors; optimization goals include less waiting, reduced traffic congestion, shorter trips, and less pollution. The core control engine combines schedule-driven intersection control[3] with decentralized coordination mechanisms.[4][5] Since June 2012, a pilot implementation of the SURTRAC system[6] has been deployed on nine intersections in the East Liberty neighborhood of Pittsburgh, Pennsylvania.[7] SURTRAC reduced travel times by more than 25% on average, and wait times were reduced by an average of 40%.[6][8] A second phase of the pilot program for the Bakery Square district has been running since October 2013.[9] In 2015, Rapid Flow Technologies[10] was formed to commercialize the SURTRAC technology.[11] The lead inventor of this technology, Dr. Xiao-Feng Xie, states that he has no association with and does not provide technical support for this company.[11]



The SURTRAC system design has three characteristics.[6] First, decision-making in SURTRAC proceeds in a decentralized manner. The decentralized control of individual intersections enables greater responsiveness to local real-time traffic conditions. Decentralization facilitates scalability by allowing the incremental addition of controlled intersections over time with little change to the existing adaptive network. It also reduces the possibility of a centralized computational bottleneck and avoids a single point of failure in the system.

A second characteristic of the SURTRAC design is an emphasis on real-time responsiveness to changing traffic conditions. SURTRAC adopts the real-time perspective of prior model-based intersection control methods[12] which attempt to compute intersection control plans that optimize actual traffic inflows. By reformulating the optimization problem as a single machine scheduling problem, the core optimization algorithm termed a schedule-driven intersection control algorithm,[3] is able to compute optimized intersection control plans over an extended horizon on a second-by-second basis.

A third characteristic of the SURTRAC design is to manage urban (grid-like) road networks, where multiple competing dominant flows shift dynamically through the day, and where specific dominant flows cannot be predetermined (as in arterial or major crossroad applications). Urban networks also often have closely spaced intersections requiring tight coordination of the intersection controllers. The combination of competing for dominant flows and densely spaced intersections presents a challenge for all adaptive traffic control systems. SURTRAC determines dominant flows dynamically by continually communicating projected outflows to downstream neighbors.[4] This information gives each intersection controller a more informed basis for locally balancing competing inflows while simultaneously promoting the establishment of larger "green corridors" when traffic flow circumstances warrant.



The SURTRAC system employs closed-circuit television (CCTV) cameras to monitor traffic conditions.[13] This use of CCTV networks in public spaces has sparked debate, with some critics arguing that such surveillance can contribute to an erosion of privacy and potentially facilitate more authoritarian forms of governance by reducing the anonymity of individuals in public areas.[14] Moreover, CCTV footage can be processed with technologies like automatic number plate recognition software, enabling the tracking of vehicles based on their license plates. Facial recognition software can also analyze these images to identify individuals by their facial features. However, it is noted that the resolution of the cameras utilized in the SURTRAC system is reportedly not high enough to enable the detection of license plates or the recognition of individual faces.[10]

There has also been discussion regarding the overall efficacy and impact of traffic optimization systems. Critics have suggested that the benefits of such systems have not been conclusively proven through scientific study. Additionally, concerns have been raised that these systems might inherently favor motorized traffic, potentially leading to disadvantages for pedestrians, bicyclists, and public transit users, and could inadvertently encourage increased use of automobiles.[15][16]

See also


Other adaptive traffic control systems



  1. ^ Xiao-Feng Xie, S. Smith, G. Barlow. Smart and Scalable Urban Signal Networks: Methods and Systems for Adaptive Traffic Signal Control. U.S. Patent No. 9,159,229, 2015.
  2. ^ Stephen F. Smith, Gregory J. Barlow, Xiao-Feng Xie. Smart and scalable urban signal networks: methods and systems for adaptive traffic signal control. U.S. Patent No. 9,830,813, 2017. (Continuation-in-part (CIP) to 9,159,229).
  3. ^ a b Xiao-Feng Xie, Stephen F. Smith, Liang Lu, Gregory J. Barlow. Schedule-driven intersection control. Transportation Research Part C: Emerging Technologies, 2012, 24: 168-189.
  4. ^ a b Xiao-Feng Xie, Stephen F. Smith, Gregory J. Barlow. Schedule-driven coordination for real-time traffic network control. International Conference on Automated Planning and Scheduling (ICAPS), Sao Paulo, Brazil, 2012: 323-331.
  5. ^ Hu, H-C and S.F. Smith, “Softpressure: A Schedule-Driven Backpressure Algorithm for Coping with Network Congestion”, Proceedings 27thInternational Joint Conference on Artificial Intelligence, Melbourne, Australia, August 2017
  6. ^ a b c Stephen F. Smith, Gregory J. Barlow, Xiao-Feng Xie, Zachary B. Rubinstein. Smart urban signal networks: Initial application of the SURTRAC adaptive traffic signal control system. International Conference on Automated Planning and Scheduling (ICAPS). Rome, Italy, 2013.
  7. ^ Stephen F. Smith, Gregory Barlow, Xiao-Feng Xie, and Zack Rubinstein. SURTRAC: Scalable Urban Traffic Control. Transportation Research Board 92nd Annual Meeting Compendium of Papers, 2013.
  8. ^ Walters, Ken (October 16, 2012). "Pilot Study on Traffic Lights Reduces Pollution, Traffic Clogs". CMU website. Carnegie Mellon University. Retrieved January 31, 2013.
  9. ^ Barlow, G.J., S.F. Smith, X-F Xie and Z.B. Rubinstein, “Real-Time Traffic Control for Urban Environments: Expanding the Surtrac Testbed Network”, 2014 World Congress on Intelligent Transportation Systems, Detroit, MI, September 2014.
  10. ^ a b "Rapid Flow Technologies". www.rapidflowtech.com. Retrieved 2018-06-02.
  11. ^ a b Xiao-Feng Xie (2018-07-03). "Statement on the Scalable Urban Traffic Control Technology". Retrieved 2018-07-03.
  12. ^ M. Papageorgiou, C. Diakaki, V. Dinopoulou, A. Kotsialos, and Y. Wang. Review of road traffic control strategies. Proceedings of the IEEE, 2003, 91(12):2043–2067.
  13. ^ Walters, Ken (2012-10-16). "Smart Signals: Pilot Study on Traffic Lights Reduces Pollution, Traffic Clogs". CMU Piper. Retrieved 2013-01-28.
  14. ^ Watt, Eliza (2017-05-23). "'The right to privacy and the future of mass surveillance'". The International Journal of Human Rights. 21 (7): 773–799. doi:10.1080/13642987.2017.1298091. ISSN 1364-2987. S2CID 148928418.
  15. ^ "Article: Traffic Signal Synchronization". www.imaja.com. Retrieved 2023-01-28.
  16. ^ Meyer, Robinson (2012-08-16). "Sorry, Los Angeles: Synchronizing Traffic Lights May Not Reduce Emissions". Theatlantic.com. Retrieved 28 January 2013.