Ising-Traffic: Using Ising Machine Learning to Predict Traffic Congestion under Uncertainty
Zhenyu Pan, Anshujit Sharma, Jerry Yao-Chieh Hu, Zhuo Liu, Ang Li, Han Liu, Michael Huang, Tony Geng
Abstract
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.
BibTeX
@article{Pan_Sharma_Hu_Liu_Li_Liu_Huang_Geng_2023, title={Ising-Traffic: Using Ising Machine Learning to Predict Traffic Congestion under Uncertainty}, volume={37}, url={https://ojs.aaai.org/index.php/AAAI/article/view/26121}, DOI={10.1609/aaai.v37i8.26121}, abstractNote={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.}, number={8}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Pan, Zhenyu and Sharma, Anshujit and Hu, Jerry Yao-Chieh and Liu, Zhuo and Li, Ang and Liu, Han and Huang, Michael and Geng, Tony}, year={2023}, month={Jun.}, pages={9354-9363} }