Urban Traffic Flow Forecast Based on FastGCRNN

• URL: http://arxiv.org/abs/2009.08087v1
• Date: Thu, 17 Sep 2020 06:05:05 GMT
• Title: Urban Traffic Flow Forecast Based on FastGCRNN
• Authors: Ya Zhang, Mingming Lu, Haifeng Li
• Abstract summary: It is hard to apply Fast Graph Contemporal Recurrent Neural Network (GCRN) to the large scale networks due to high computational complexity. We propose to abstract the road network into a geometric graph and build a Fast Graph Contemporal Recurrent Neural Network (GCRN) 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.
• Score: 14.445176586630465
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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