Leveraging Spatio-Temporal Patterns for Predicting Citywide Traffic Crowd Flows Using Deep Hybrid Neural Networks

Predicting the accurate traffic crowd flows is of practical importance for intelligent transportation systems (ITS). However, it is challenging because traffic flows are affected by multiple complex factors, such as spatial and temporal dependencies of regions and external factors. In this paper, we...

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Published in:2019 IEEE 25th International Conference on Parallel and Distributed Systems (ICPADS) pp. 125 - 132
Main Authors: Ali, Ahmad, Zhu, Yanmin, Chen, Qiuxia, Yu, Jiadi, Cai, Haibin
Format: Conference Proceeding
Language:English
Published: IEEE 01.12.2019
Subjects:
Convolutional neural network
crowd flows prediction
Deep learning
Long short term memory
Spatio-temporal dynamics
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Abstract Predicting the accurate traffic crowd flows is of practical importance for intelligent transportation systems (ITS). However, it is challenging because traffic flows are affected by multiple complex factors, such as spatial and temporal dependencies of regions and external factors. In this paper, we propose a deep hybrid spatio-temporal dynamic neural network, called DHSTNet, to predict both inflows and outflows in every region of a city. More specifically, it consists of four main components, i.e., closeness influence taking the instantaneous variations of traffic flows, period influence regularly identifying daily changes of traffic crowd flows, weekly component identifying the patterns of weekly traffic flows and external component acquiring external factors. We design a branch of deep hybrid recurrent convolutional neural network units to model the first three temporal properties, i.e., closeness, period influence, and weekly influence. The external components are feed into two fully connected neural networks. For different branches, our proposed model assigns different weights and then combines the output of the four components. Experimental results based on two large-scale real-world datasets demonstrate the superiority of our model over the existing state-of-the-art methods.
AbstractList Predicting the accurate traffic crowd flows is of practical importance for intelligent transportation systems (ITS). However, it is challenging because traffic flows are affected by multiple complex factors, such as spatial and temporal dependencies of regions and external factors. In this paper, we propose a deep hybrid spatio-temporal dynamic neural network, called DHSTNet, to predict both inflows and outflows in every region of a city. More specifically, it consists of four main components, i.e., closeness influence taking the instantaneous variations of traffic flows, period influence regularly identifying daily changes of traffic crowd flows, weekly component identifying the patterns of weekly traffic flows and external component acquiring external factors. We design a branch of deep hybrid recurrent convolutional neural network units to model the first three temporal properties, i.e., closeness, period influence, and weekly influence. The external components are feed into two fully connected neural networks. For different branches, our proposed model assigns different weights and then combines the output of the four components. Experimental results based on two large-scale real-world datasets demonstrate the superiority of our model over the existing state-of-the-art methods.
Author Yu, Jiadi
Ali, Ahmad
Cai, Haibin
Zhu, Yanmin
Chen, Qiuxia
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  fullname: Cai, Haibin
  organization: East China Normal University
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Snippet Predicting the accurate traffic crowd flows is of practical importance for intelligent transportation systems (ITS). However, it is challenging because traffic...
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StartPage 125
SubjectTerms Convolutional neural network
crowd flows prediction
Deep learning
Long short term memory
Spatio-temporal dynamics
Title Leveraging Spatio-Temporal Patterns for Predicting Citywide Traffic Crowd Flows Using Deep Hybrid Neural Networks
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