October 27, 2023
Conference Paper

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


This paper addresses the challenges in accurate and realtime traffic congestion prediction with uncertainty by proposing Ising-Traffic, a novel quantum-inspired dual-model Ising based traffic prediction framework which delivers higher accuracy and lower latency than SOTA solutions. While traditional and deep learning methods face the trade-off between algorithm complexity and computational efficiency, our Ising-based method leverages Ising’s inherent and unique capability of finding the state of a system with the lowest energy and applying it to traffic prediction. In this work, traffic prediction under uncertainty is formulated into two separate Ising models: Reconstruct-Ising and Predict-Ising. Reconstruct-Ising is mapped onto modern Ising machine 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 98× speedups and 5% accuracy improvement over SOTA.

Published: October 27, 2023


Pan Z., A. Sharma, J. Hu, Z. Liu, A. Li, H. Liu, and M. Huang, et al. 2023. Ising-Traffic: Using Ising Machine Learning to Predict Traffic Congestion under Uncertainty. In Proceedings of the AAAI Conference on Artificial Intelligence, February 7-14, 2023, Washington, D.C., 37, 9354-9363. Washington, District Of Columbia:AAAI Press. PNNL-SA-176751. doi:10.1609/aaai.v37i8.26121