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Article

Self-Organizing Traffic Flow Prediction with an Optimized Deep Belief Network for Internet of Vehicles

1
Advanced Informatics School, Universiti Teknologi Malaysia Kuala Lumpur (UTM), Jalan Semarak, Kuala Lumpur 54100, Malaysia
2
School of Computer Science and Electronic Engineering, University of Essex, Colchester CO4 3SQ, UK
3
Faculty of Computing, Universiti Teknologi Malaysia Kuala Lumpur (UTM), Skudai, Johor 81310, Malaysia
*
Author to whom correspondence should be addressed.
Sensors 2018, 18(10), 3459; https://doi.org/10.3390/s18103459
Received: 18 August 2018 / Revised: 25 September 2018 / Accepted: 10 October 2018 / Published: 15 October 2018
(This article belongs to the Special Issue Future Research Trends in Internet of Things and Sensor Networks)
To assist in the broadcasting of time-critical traffic information in an Internet of Vehicles (IoV) and vehicular sensor networks (VSN), fast network connectivity is needed. Accurate traffic information prediction can improve traffic congestion and operation efficiency, which helps to reduce commute times, noise and carbon emissions. In this study, we present a novel approach for predicting the traffic flow volume by using traffic data in self-organizing vehicular networks. The proposed method is based on using a probabilistic generative neural network techniques called deep belief network (DBN) that includes multiple layers of restricted Boltzmann machine (RBM) auto-encoders. Time series data generated from the roadside units (RSUs) for five highway links are used by a three layer DBN to extract and learn key input features for constructing a model to predict traffic flow. Back-propagation is utilized as a general learning algorithm for fine-tuning the weight parameters among the visible and hidden layers of RBMs. During the training process the firefly algorithm (FFA) is applied for optimizing the DBN topology and learning rate parameter. Monte Carlo simulations are used to assess the accuracy of the prediction model. The results show that the proposed model achieves superior performance accuracy for predicting traffic flow in comparison with other approaches applied in the literature. The proposed approach can help to solve the problem of traffic congestion, and provide guidance and advice for road users and traffic regulators. View Full-Text
Keywords: deep belief network; historical time traffic flows; restricted Boltzmann machine; optimization; traffic flow prediction deep belief network; historical time traffic flows; restricted Boltzmann machine; optimization; traffic flow prediction
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MDPI and ACS Style

Goudarzi, S.; Kama, M.N.; Anisi, M.H.; Soleymani, S.A.; Doctor, F. Self-Organizing Traffic Flow Prediction with an Optimized Deep Belief Network for Internet of Vehicles. Sensors 2018, 18, 3459. https://doi.org/10.3390/s18103459

AMA Style

Goudarzi S, Kama MN, Anisi MH, Soleymani SA, Doctor F. Self-Organizing Traffic Flow Prediction with an Optimized Deep Belief Network for Internet of Vehicles. Sensors. 2018; 18(10):3459. https://doi.org/10.3390/s18103459

Chicago/Turabian Style

Goudarzi, Shidrokh, Mohd Nazri Kama, Mohammad Hossein Anisi, Seyed Ahmad Soleymani, and Faiyaz Doctor. 2018. "Self-Organizing Traffic Flow Prediction with an Optimized Deep Belief Network for Internet of Vehicles" Sensors 18, no. 10: 3459. https://doi.org/10.3390/s18103459

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