Co-Evolving Traffic State Parameters Prediction Based on Mechanism-Data Blending Driven Deep Learning

Abstract

Traffic state prediction, a classical task for traffic management, is a central component of intelligent transport systems to maintain safe and efficient operation. While extensive and intensive research has been conducted on traffic state prediction, most studies have concentrated on enhancing the accuracy of specific traffic state parameters. However, traffic state is a co-evolutionary multivariate time series with various parameters such as flow, velocity, occupancy, etc. At the same time, traffic state data will inevitably be lost during collection. So accurate traffic prediction still faces the following challenges: First, how to deal with the complex missing situations in observational data? Second, how to learn the co-evolutionary relationships between different traffic state parameters while mining the high-dimensional spatio-temporal traffic state patterns? In this paper, we propose a mechanism-data blending-driven co-evolving traffic state parameter prediction method: multi-parameter hybrid tensor deep learning networks (MHT-Net), which consists of a multi-parameter tensor graph convolutional network (MTGCN) and a tensor recurrent neural network (T-RNN). MTGCN implements knowledge embedding of synergistic mechanisms between traffic parameters, ensuring that the road network spatial dependency and the synergistic influence relationship of the parameters can be obtained simultaneously; T-RNN is used to learn high-dimensional temporal features of traffic states. Experiment results on a real-world dataset from Jiangsu province outperform the state-of-the-art baselines, demonstrating the efficacy of the proposed method and providing an effective tool for traffic state prediction with missing values. A mechanism-data blending driven co-evolving traffic state parameter prediction method, multi-parameters hybrid tensor deep learning networks (MHT-Net) is proposed, which implements knowledge embedding of synergistic mechanisms between traffic parameters and learn the road network spatial dependency and the synergistic influence relationship of the parameters simultaneously. Experiment results demonstrate the efficacy of the proposed method and provide an effective tool for traffic state prediction with missing values.