Abstract:

The accurate real-time traffic prediction is the fundamental technological challenge in realizing an intelligent transportation system.Current prediction methods overlook the varying degree of spatial dependence between roads when considering the spatio-temporal characteristics of traffic information,leading to a lack of differentiated design in prediction models and inaccurate predictions for individual roads.To better analyze the differences in spatial features between roads,a quantum effect traffic prediction model is designed for intuitive reasoning.This paper introduces the concept of intuitive reasoning to encode,combine,and compare road network structures,identifying highly correlated road clusters based on spatial features.The quantum annealing algorithm optimizes clustering results towards approximating global optimal solutions.Prediction models are built using the Huawei Cloud's MindSpore framework based on different clusters,focusing on the spatio-temporal characteristics within each cluster.Experiments conducted on real datasets from Los Angeles freeways in 2012 and Tokyo's 1843 freeways in 2021 are compared with various baseline models such as the History Average model,Autoregressive Integrated Moving Average model,Graph Convolutional Network,Gate Recurrent Unit,and Temporal Graph Convolutional Network.The root mean squared error performance on the two real data sets is improved by 11.32%和13.86% compared with the Temporal Graph Convolutional Network,which provides a new and effective solution to the current traffic prediction problem.

Key words: intuitive reasoning, quantum computers, quantum annealing algorithm, deep learning, traffic prediction