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

Goudarzi, S and Kama, MN and Anisi, MH and Soleymani, SA and Doctor, F (2018) 'Self-Organizing Traffic Flow Prediction with an Optimized Deep Belief Network for Internet of Vehicles.' Sensors, 18 (10). ISSN 1424-2818

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Abstract

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.

Item Type: Article
Uncontrolled Keywords: deep belief network; historical time traffic flows; restricted Boltzmann machine; optimization; traffic flow prediction
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Science and Health > Computer Science and Electronic Engineering, School of
Depositing User: Elements
Date Deposited: 30 Oct 2018 09:35
Last Modified: 30 Oct 2018 09:35
URI: http://repository.essex.ac.uk/id/eprint/23358

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