This system is a real-time adaptive traffic control system, which combines artificial intelligence (AI) and traffic theory to optimize highly dynamic traffic flow in complex real-world urban road networks.
- Xiao-Feng Xie, et al. Smart and Scalable Urban Signal Networks: Methods and Systems for Adaptive Traffic Signal Control. U.S. Patent No. 9,159,229, 2015.
As the lead inventor of the system, Dr. Xie has created its core control engine, which combines schedule-driven intersection control (SchIC) with decentralized coordination mechanisms (in the sense of Internet of Smart Intersections, an instance of smart IoT). He has also designed and realized the strengthening strategies to enable the real-world operations of the system in the field.
His relevant research work also includes: multimodal traffic control (assisted with machine learning and computer vision techniques), integration with decentralized route choice models and dynamic congestion pricing protocols, vehicle-to-infrastructure (V2I) communication with connected vehicles, energy efficiency optimization, and data-driven self-learning and active congestion management based on performance measurement.
– (East Liberty) Deployed the the initial smart traffic lights for a network with nine intersections in the East Liberty neighborhood, Pittsburgh, PA. The system has been running since June 2012. Improvements of over 25% reductions in travel times, over 40% reductions in idle time, and a projected reduction in emissions of over 21% were achieved.
– (Bakery Square) Expanded the intelligent traffic signal control system further to include nine more intersections in the Bakery Square neighborhood, Pittsburgh, PA. The expanded system has been running since Oct 2013. Improvements of over 24% reductions in travel times, over 41% reductions in idle time, and a projected reduction in emissions of over 20% were achieved.
Disclaimer: Strong domain expertise is required to configure the smart and scalable urban traffic control system for achieving its optimal operations and improving urban traffic in the real world. Dr. Xie takes no responsibility for, and will not be liable for, any problems caused by inappropriate configuration of this system, for any installation or adjustment without his participation or guidance.
- [Oct 13, 2015] As the lead inventor, obtained the U.S. patent for the smart and scalable urban traffic control system.
- [Oct 10, 2014] Presented the work on multimodal urban traffic control at the IEEE International Conference on Intelligent Transportation Systems, Qingdao, China.
- [Oct 03, 2014] CMU SCS 25th Anniversary: The smart, adaptable traffic signal system was listed as 25 Great Things about computer science at Carnegie Mellon.
- Xiao-Feng Xie, S. Smith, Ting-Wei Chen, G. Barlow. Real-time traffic control for sustainable urban living. IEEE International Conference on Intelligent Transportation Systems (ITSC), Qingdao, China, 2014: 1863-1868. [DOI]
- Xiao-Feng Xie, Yiheng Feng, S. Smith, K. Larry Head. Unified route choice framework: Specification and application to urban traffic control. Transportation Research Record: Journal of the Transportation Research Board, 2014, 2466: 105-113. [DOI]
- Xiao-Feng Xie, S. Smith, G. Barlow, Ting-Wei Chen. Coping with real-world challenges in real-time urban traffic control. Transportation Research Board (TRB) Annual Meeting, Washington, DC, USA, 2014.
- Xiao-Feng Xie, S. Smith, G. Barlow. Schedule-driven coordination for real-time traffic network control. International Conference on Automated Planning and Scheduling (ICAPS), Sao Paulo, Brazil, 2012: 323-331.
- Xiao-Feng Xie, S. Smith, G. Barlow. Coordinated look-ahead scheduling for real-time traffic signal control. International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS), Valencia, Spain, 2012: 1271-1272. [DOI]
- Xiao-Feng Xie, S. Smith, Liang Lu, G. Barlow. Schedule-driven intersection control. Transportation Research Part C: Emerging Technologies, 2012, 24: 168-189. [DOI]
- Xiao-Feng Xie, G. Barlow, S. Smith, Z. Rubinstein. Platoon-based self-scheduling for real-time traffic signal control. IEEE International Conference on Intelligent Transportation Systems (ITSC), Washington, DC, USA, 2011, 879-884. [DOI]
– This work was supported in part by the Robotics Institute of CMU, with support from the Hillman Foundation, the Heinz Endowments, and the R. K. Mellon Foundation.