Dynamic Spatial Correlation in Graph WaveNet for Road Traffic Prediction
- 8 February 2023
- journal article
- research article
- Published by SAGE Publications in Transportation Research Record: Journal of the Transportation Research Board
- Vol. 2677 (7) , 90-100
Abstract
Road traffic prediction is a crucial area currently investigated under the umbrella of the intelligent transportation system. Timely and accurate traffic prediction is a challenging problem because of the diverse nature of roads, abrupt changes in speed, and the existence of dependencies between road segments. A critical component of this research is addressing the dynamic spatial and non-linear temporal dependencies in the road network. The traffic conditions in a traffic road network change continuously, and for precise predictions, time-varying spatial correlation needs to be integrated into the model. This study intends to incorporate dynamic spatial dependencies in the Graph WaveNet model by applying attention mechanisms that can compute attention scores for the self-adaptive adjacency matrix in the time domain. We compared the computation cost of our proposed model of a graph attention network with multi-head attention with Graph WaveNet for up to 60 min. Our model gave the best result for 60-min prediction with an average percentage decrease of 3.4% and 4.76% in root-mean-square error on PEMS-BAY and METR-LA datasets, respectively. However, the model training time is increased because of the added computation of attention scores.Keywords
This publication has 9 references indexed in Scilit:
- Spatial-Temporal Chebyshev Graph Neural Network for Traffic Flow Prediction in IoT-Based ITSIEEE Internet of Things Journal, 2021
- GMAN: A Graph Multi-Attention Network for Traffic PredictionProceedings of the AAAI Conference on Artificial Intelligence, 2020
- Spatial-Temporal Graph Attention Networks: A Deep Learning Approach for Traffic ForecastingIEEE Access, 2019
- Attention Based Spatial-Temporal Graph Convolutional Networks for Traffic Flow ForecastingProceedings of the AAAI Conference on Artificial Intelligence, 2019
- Real-Time Traffic Speed Estimation With Graph Convolutional Generative AutoencoderIEEE Transactions on Intelligent Transportation Systems, 2019
- Big data and its technical challengesCommunications of the ACM, 2014
- Short Term Traffic Flow Prediction for a Non Urban Highway Using Artificial Neural NetworkProcedia - Social and Behavioral Sciences, 2013
- Short-term Traffic Flow Forecasting based on Two-tier K-nearest Neighbor AlgorithmProcedia - Social and Behavioral Sciences, 2013
- The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domainsIEEE Signal Processing Magazine, 2013