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Densely connected convolutional networks with attention LSTM for crowd flows prediction

Li, Wei, Tao, Wei, Qiu, Junyang, Liu, Xin, Zhou, Xingyu and Pan, Zhisong 2019, Densely connected convolutional networks with attention LSTM for crowd flows prediction, IEEE Access, vol. 7, pp. 140488-140498, doi: 10.1109/ACCESS.2019.2943890.

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Title Densely connected convolutional networks with attention LSTM for crowd flows prediction
Author(s) Li, Wei
Tao, Wei
Qiu, Junyang
Liu, Xin
Zhou, Xingyu
Pan, Zhisong
Journal name IEEE Access
Volume number 7
Start page 140488
End page 140498
Total pages 11
Publisher Institute of Electrical and Electronics Engineers
Place of publication Piscataway, N.J.
Publication date 2019
ISSN 2169-3536
Keyword(s) Science & Technology
Technology
Computer Science, Information Systems
Engineering, Electrical & Electronic
Telecommunications
Computer Science
Engineering
Spatiotemporal phenomena
Correlation
Feature extraction
Predictive models
Urban areas
Meteorology
Time series analysis
Data mining
spatiotemporal modeling
crowd flow prediction
densely connected convolutional network
long short-term memory
attention mechanism
MODELS
Summary With the rapid progress of urbanization, predicting citywide crowd flows has become increasingly significant in many fields, such as traffic management and public security. However, influenced by the complex spatiotemporal relations in raw data and other factors, such as events and weather, obtaining a precise prediction is challenging. Some previous works attempted to address this problem using various ways, such as autoregressive integrated moving average, vector auto-regression and some deep learning models. However, seldom can these methods comprehensively capture the spatiotemporal correlations. In this paper, we propose a novel spatio-temporal prediction model that is based on densely connected convolutional networks and attention long short-term memory (ST-DCCNAL), to simultaneously predict the inflow and outflow of the crowds in regions divided within a specific city. The ST-DCCNAL model consists of three parts: spatial part, external factors part and temporal part. In the spatial part, we employ densely connected convolutional networks to extract spatial characteristics at different levels. The external factors part utilizes a fully connected network to extract features from auxiliary information. In the last part, an attention-based long short-term memory module is leveraged to capture the temporal pattern. To demonstrate the practicality and effectiveness of the proposed model, we evaluate it using two separate real-world datasets of taxis in Beijing and bikes in New York. The experimental results confirm that the performance of our model is better than that of other baseline methods.
Language eng
DOI 10.1109/ACCESS.2019.2943890
Indigenous content off
HERDC Research category C1 Refereed article in a scholarly journal
Free to Read? Yes
Persistent URL http://hdl.handle.net/10536/DRO/DU:30133128

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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.