Urban Traffic Flow Forecast Based on FastGCRNN

TL;DR

This paper introduces FastGCRNN, a novel neural network model that efficiently captures spatial-temporal dependencies in large-scale urban traffic networks, reducing computational costs while maintaining high accuracy.

Contribution

The paper proposes FastGCRNN, combining importance sampling in FastGCN with GRU and Seq2Seq, to improve large-scale traffic flow forecasting efficiency.

Findings

Reduces computational complexity and memory usage

Maintains high accuracy in large-scale traffic datasets

Effective in modeling spatial-temporal traffic dependencies

Abstract

Traffic forecasting is an important prerequisite for the application of intelligent transportation systems in urban traffic networks. The existing works adopted RNN and CNN/GCN, among which GCRN is the state of art work, to characterize the temporal and spatial correlation of traffic flows. However, it is hard to apply GCRN to the large scale road networks due to high computational complexity. To address this problem, we propose to abstract the road network into a geometric graph and build a Fast Graph Convolution Recurrent Neural Network (FastGCRNN) to model the spatial-temporal dependencies of traffic flow. Specifically, We use FastGCN unit to efficiently capture the topological relationship between the roads and the surrounding roads in the graph with reducing the computational complexity through importance sampling, combine GRU unit to capture the temporal dependency of traffic flow, and embed the spatiotemporal features into Seq2Seq based on the Encoder-Decoder framework. Experiments on large-scale traffic data sets illustrate that the proposed method can greatly reduce computational complexity and memory consumption while maintaining relatively high accuracy.