MF-CNN: Traffic Flow Prediction Using Convolutional Neural Network and Multi-Features Fusion

Di YANG  Songjiang LI  Zhou PENG  Peng WANG  Junhui WANG  Huamin YANG  

IEICE TRANSACTIONS on Information and Systems   Vol.E102-D   No.8   pp.1526-1536
Publication Date: 2019/08/01
Online ISSN: 1745-1361
DOI: 10.1587/transinf.2018EDP7330
Type of Manuscript: PAPER
Category: Artificial Intelligence, Data Mining
Intelligent Transportation Systems,  traffic flow prediction,  convolutional neural networks,  multiple spatiotemporal features,  external factors,  

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Accurate traffic flow prediction is the precondition for many applications in Intelligent Transportation Systems, such as traffic control and route guidance. Traditional data driven traffic flow prediction models tend to ignore traffic self-features (e.g., periodicities), and commonly suffer from the shifts brought by various complex factors (e.g., weather and holidays). These would reduce the precision and robustness of the prediction models. To tackle this problem, in this paper, we propose a CNN-based multi-feature predictive model (MF-CNN) that collectively predicts network-scale traffic flow with multiple spatiotemporal features and external factors (weather and holidays). Specifically, we classify traffic self-features into temporal continuity as short-term feature, daily periodicity and weekly periodicity as long-term features, then map them to three two-dimensional spaces, which each one is composed of time and space, represented by two-dimensional matrices. The high-level spatiotemporal features learned by CNNs from the matrices with different time lags are further fused with external factors by a logistic regression layer to derive the final prediction. Experimental results indicate that the MF-CNN model considering multi-features improves the predictive performance compared to five baseline models, and achieves the trade-off between accuracy and efficiency.