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

TL;DR

This paper introduces MW-TGC, a novel graph convolutional network that effectively models complex spatial and temporal dependencies in urban traffic data, improving forecasting accuracy across diverse city environments.

Contribution

The study proposes MW-TGC, a multi-weighted graph convolutional network that captures spatial heterogeneity and integrates multiple features for enhanced traffic prediction.

Findings

Outperforms existing models in urban traffic forecasting

Reduces variance in heterogeneous urban environments

Provides robust performance across different city layouts

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.