Short-term traffic flow forecasting with spatial-temporal correlation in a hybrid deep learning framework

TL;DR

This paper introduces a hybrid deep learning model combining CNN and LSTM to improve short-term traffic flow forecasting by capturing spatial and temporal features, outperforming existing methods on open datasets.

Contribution

A novel deep architecture (CLTFP) that integrates CNN and LSTM for enhanced traffic flow prediction, with analysis of its properties using Granger Causality.

Findings

CLTFP outperforms other forecasting methods on open datasets.

The model effectively captures spatial and temporal traffic features.

Analysis reveals interesting properties of the hybrid model.

Abstract

Deep learning approaches have reached a celebrity status in artificial intelligence field, its success have mostly relied on Convolutional Networks (CNN) and Recurrent Networks. By exploiting fundamental spatial properties of images and videos, the CNN always achieves dominant performance on visual tasks. And the Recurrent Networks (RNN) especially long short-term memory methods (LSTM) can successfully characterize the temporal correlation, thus exhibits superior capability for time series tasks. Traffic flow data have plentiful characteristics on both time and space domain. However, applications of CNN and LSTM approaches on traffic flow are limited. In this paper, we propose a novel deep architecture combined CNN and LSTM to forecast future traffic flow (CLTFP). An 1-dimension CNN is exploited to capture spatial features of traffic flow, and two LSTMs are utilized to mine the short-term variability and periodicities of traffic flow. Given those meaningful features, the feature-level fusion is performed to achieve short-term forecasting. The proposed CLTFP is compared with other popular forecasting methods on an open datasets. Experimental results indicate that the CLTFP has considerable advantages in traffic flow forecasting. in additional, the proposed CLTFP is analyzed from the view of Granger Causality, and several interesting properties of CLTFP are discovered and discussed .