Ising-Traffic: Using Ising Machine Learning to Predict Traffic Congestion under Uncertainty


  • Zhenyu Pan University of Rochester
  • Anshujit Sharma University of Rochester
  • Jerry Yao-Chieh Hu Northwestern University
  • Zhuo Liu University of Rochester
  • Ang Li Pacific Northwest National Laboratory
  • Han Liu Northwestern University
  • Michael Huang University of Rochester
  • Tony Geng University of Rochester



ML: Classification and Regression, DMKM: Graph Mining, Social Network Analysis & Community Mining, DMKM: Mining of Spatial, Temporal or Spatio-Temporal Data, ML: Graph-based Machine Learning, ML: Time-Series/Data Streams


This paper addresses the challenges in accurate and real-time traffic congestion prediction under uncertainty by proposing Ising-Traffic, a dual-model Ising-based traffic prediction framework that delivers higher accuracy and lower latency than SOTA solutions. While traditional solutions face the dilemma from the trade-off between algorithm complexity and computational efficiency, our Ising-based method breaks away from the trade-off leveraging the Ising model's strong expressivity and the Ising machine's strong computation power. In particular, Ising-Traffic formulates traffic prediction under uncertainty into two Ising models: Reconstruct-Ising and Predict-Ising. Reconstruct-Ising is mapped onto modern Ising machines and handles uncertainty in traffic accurately with negligible latency and energy consumption, while Predict-Ising is mapped onto traditional processors and predicts future congestion precisely with only at most 1.8% computational demands of existing solutions. Our evaluation shows Ising-Traffic delivers on average 98X speedups and 5% accuracy improvement over SOTA.




How to Cite

Pan, Z., Sharma, A., Hu, J. Y.-C., Liu, Z., Li, A., Liu, H., Huang, M., & Geng, T. (2023). Ising-Traffic: Using Ising Machine Learning to Predict Traffic Congestion under Uncertainty. Proceedings of the AAAI Conference on Artificial Intelligence, 37(8), 9354-9363.



AAAI Technical Track on Machine Learning III