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

doi:10.1609/aaai.v37i8.26121

journal article Jun 26, 2023

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

Proceedings of the AAAI Conference on Artificial Intelligence Vol. 37 No. 8 pp. 9354-9363 · Association for the Advancement of Artificial Intelligence (AAAI)

View at Publisher Save 10.1609/aaai.v37i8.26121

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.

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Details

Published: Jun 26, 2023
Vol/Issue: 37(8)
Pages: 9354-9363

Authors

Research Center for Grassland and Animal Husbandry Engineering Technology of Ningxia Province Ningxia University Yinchuan Ningxia China; Key Laboratory for Model Innovation in Forage Production Efficiency, Ministry of Agriculture and Rural Affairs, P.R. China Ningxia University Yinchuan Ningxia China; Northern Yanchi Desert Steppe Observation and Research Station of Ningxia Wuzhong Ningxia China

Department of Operations Research and Financial Engineering, Princeton University Princeton College Park MD, 20742, USA

Cite This Article

Zhenyu Pan, Anshujit Sharma, Jerry Yao-Chieh Hu, et al. (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. https://doi.org/10.1609/aaai.v37i8.26121

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

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