Incorporating dynamicity of transportation network with multi-weight traffic graph convolutional network for traffic forecasting

Abstract

Traffic forecasting problem remains a challenging task in the intelligent transportation system due to its spatio-temporal complexity. Although temporal dependency has been well studied and discussed, spatial dependency is relatively less explored due to its large variations, especially in the urban environment. In this study, a novel graph convolutional network model, Multi-Weight Traffic Graph Convolutional (MW-TGC) network, is proposed and applied to two urban networks with contrasting geometric constraints. The model conducts graph convolution operations on speed data with multi-weighted adjacency matrices to combine the features, including speed limit, distance, and angle. The spatially isolated dimension reduction operation is conducted on the combined features to learn the dependencies among the features and reduce the size of the output to a computationally feasible level. The output of multi-weight graph convolution is applied to the sequence-to-sequence model with Long Short-Term Memory units to learn temporal dependencies. When applied to two urban sites, urban-core and urban-mix, MW-TGC network not only outperformed the comparative models in both sites but also reduced variance in the heterogeneous urban-mix network. We conclude that MW-TGC network can provide a robust traffic forecasting performance across the variations in spatial complexity, which can be a strong advantage in urban traffic forecasting.

11 pages, 7 figures, Accepted to IEEE Transactions on Intelligent Transportation Systems (2020)

References in corpus (6)

• Sequence to Sequence Learning with Neural Networks
• Semi-Supervised Classification with Graph Convolutional Networks
• Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting
• T-GCN: A Temporal Graph ConvolutionalNetwork for Traffic Prediction
• Deep Multi-View Spatial-Temporal Network for Taxi Demand Prediction
• PCNN: Deep Convolutional Networks for Short-term Traffic Congestion Prediction

Cited by in corpus (5)

• Graph Neural Network for Traffic Forecasting: A Survey
• How to Build a Graph-Based Deep Learning Architecture in Traffic Domain: A Survey
• Traffic Prediction using Artificial Intelligence: Review of Recent Advances and Emerging Opportunities
• Spatiotemporal Tile-based Attention-guided LSTMs for Traffic Video Prediction
• A Comparative Study on Basic Elements of Deep Learning Models for Spatial-Temporal Traffic Forecasting