Data-Driven Graph Filter-Based Graph Convolutional Neural Network Approach for Network-Level Multi-Step Traffic Prediction

Lei Lin, Weizi Li and Lei Zhu ()
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Lei Lin: Goergen Institute for Data Science, 1209 Wegmans Hall, University of Rochester, Rochester, NY 14627, USA
Weizi Li: Department of Computer Science, The University of Memphis, Memphis, TN 38152, USA
Lei Zhu: Systems Engineering & Engineering Management, The University of North Carolina at Charlotte, 9201 University City Blvd., Charlotte, NC 28223, USA

Sustainability, 2022, vol. 14, issue 24, 1-16

Abstract: Accurately predicting network-level traffic conditions has been identified as a critical need for smart and advanced transportation services. In recent decades, machine learning and artificial intelligence have been widely applied for traffic state, including traffic volume prediction. This paper proposes a novel deep learning model, Graph Convolutional Neural Network with Data-driven Graph Filter (GCNN-DDGF), for network-wide multi-step traffic volume prediction. More specifically, the proposed GCNN-DDGF model can automatically capture hidden spatiotemporal correlations between traffic detectors, and its sequence-to-sequence recurrent neural network architecture is able to further utilize temporal dependency from historical traffic flow data for multi-step prediction. The proposed model was tested in a network-wide hourly traffic volume dataset between 1 January 2018 and 30 June 2019 from 150 sensors in the Los Angeles area. Detailed experimental results illustrate that the proposed model outperforms the other five widely used deep learning and machine learning models in terms of computational efficiency and prediction accuracy. For instance, the GCNN-DDGF model improves MAE, MAPE, and RMSE by 25.33%, 20.45%, and 29.20% compared to the state-of-the-art models, such as Diffusion Convolution Recurrent Neural Network (DCRNN), which is widely accepted as a popular and effective deep learning model.

Keywords: deep learning; graph convolutional gated recurrent neural network; data-driven graph filter; networkwide multi-step traffic prediction (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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