Multi-Lane Short-Term Traffic Forecasting With Convolutional LSTM Network

Short-term traffic prediction consists a crucial component in intelligent transportation systems. With the explosion of automated traffic monitoring sensors and the flourishing of deep learning techniques, a growing body of deep neural network models have been employed to tackle this problem. In par...

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Bibliographic Details
Published in:IEEE access 2020, Vol.8, p.34629-34643
Main Authors: Ma, Yixuan, Zhang, Zhenji, Ihler, Alexander
Format: Article
Language:English
Subjects:
Artificial neural networks
Computer architecture
convolutional LSTM
Data models
Driving conditions
Hidden Markov models
Intelligent transportation systems
Machine learning
multi-lane traffic flow
Neural networks
Predictions
Predictive models
Short-term traffic flow prediction
Time series analysis
Traffic
Traffic control
Traffic flow
Transportation networks
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Summary:Short-term traffic prediction consists a crucial component in intelligent transportation systems. With the explosion of automated traffic monitoring sensors and the flourishing of deep learning techniques, a growing body of deep neural network models have been employed to tackle this problem. In particular, convolutional neural networks (CNN) and long short-term memory (LSTM) recurrent networks have demonstrated their advantages in modeling and predicting the spatiotemporal evolution of traffic flows. In this paper, we propose a novel Convolutional LSTM neural network architecture for multi-lane short-term traffic prediction. Compared to existing methods, we highlight the importance of (1) applying multiple features to characterize traffic conditions; (2) explicitly considering the routing between neighbouring lanes and downstream/upstream traffics; and (3) predicting multiple time-step traffic in a rolling-prediction manner. Experiments on 10 months 5-minute interval observations of the US I-101 Northern freeway at California Bay Area verify the proposed model. The results show that our model has considerable advantages in predicting multi-lane short-term traffic flow.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2020.2974575