A Hybrid Deep Learning Model for Short-Term Traffic Flow Pre-Diction Considering Spatiotemporal Features

Shenghan Zhou, Chaofan Wei, Chaofei Song, Yu Fu, Rui Luo, Wenbing Chang and Linchao Yang ()
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Shenghan Zhou: School of Reliability and Systems Engineering, Beihang University, Beijing 100191, China
Chaofan Wei: School of Reliability and Systems Engineering, Beihang University, Beijing 100191, China
Chaofei Song: School of Reliability and Systems Engineering, Beihang University, Beijing 100191, China
Yu Fu: School of Reliability and Systems Engineering, Beihang University, Beijing 100191, China
Rui Luo: School of Reliability and Systems Engineering, Beihang University, Beijing 100191, China
Wenbing Chang: School of Reliability and Systems Engineering, Beihang University, Beijing 100191, China
Linchao Yang: School of Reliability and Systems Engineering, Beihang University, Beijing 100191, China

Sustainability, 2022, vol. 14, issue 16, 1-14

Abstract: Traffic flow prediction is one of the basic, key problems with developing an intelligent transportation system since accurate and timely traffic flow prediction can provide information support and decision support for traffic control and guidance. However, due to the complex characteristics of traffic information, it is still a challenging task. This paper proposes a novel hybrid deep learning model for short-term traffic flow prediction by considering the inherent features of traffic data. The proposed model consists of three components: the recent, daily and weekly components. The recent component is integrated with an improved graph convolutional network (GCN) and bi-directional LSTM (Bi-LSTM). It is designed to capture spatiotemporal features. The remaining two components are built by multi-layer Bi-LSTM. They are developed to extract the periodic features. The proposed model focus on the important information by using an attention mechanism. We tested the performance of our model with a real-world traffic dataset and the experimental results indicate that our model has better prediction performance than those developed previously.

Keywords: traffic flow prediction; hybrid deep learning; Bi-LSTM; graph convolution network (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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