46,198 research outputs found

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

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    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

    Integrated Traffic and Communication Performance Evaluation of an Intelligent Vehicle Infrastructure Integration (VII) System for Online Travel Time Prediction

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    This paper presents a framework for online highway travel time prediction using traffic measurements that are likely to be available from Vehicle Infrastructure Integration (VII) systems, in which vehicle and infrastructure devices communicate to improve mobility and safety. In the proposed intelligent VII system, two artificial intelligence (AI) paradigms, namely Artificial Neural Networks (ANN) and Support Vector Regression (SVR), are used to determine future travel time based on such information as current travel time, VII-enabled vehicles’ flow and density. The development and performance evaluation of the VII-ANN and VII-SVR frameworks, in both of the traffic and communications domains, were conducted, using an integrated simulation platform, for a highway network in Greenville, South Carolina. Specifically, the simulation platform allows for implementing traffic surveillance and management methods in the traffic simulator PARAMICS, and for evaluating different communication protocols and network parameters in the communication network simulator, ns-2. The study’s findings reveal that the designed communications system was capable of supporting the travel time prediction functionality. They also demonstrate that the travel time prediction accuracy of the VII-AI framework was superior to a baseline instantaneous travel time prediction algorithm, with the VII-SVR model slightly outperforming the VII-ANN model. Moreover, the VII-AI framework was shown to be capable of performing reasonably well during non-recurrent congestion scenarios, which traditionally have challenged traffic sensor-based highway travel time prediction methods

    A NEURAL NETWORK BASED TRAFFIC-FLOW PREDICTION MODEL

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    Prediction of traffic-flow in Istanbul has been a great concern for planners of the city. Istanbul as being one of the most crowded cities in the Europe has a rural population of more than 10 million. The related transportation agencies ill Istanbul continuously collect data through many ways thanks to improvements in sensor technology and communication systems which allow to more closely monitor the condition of the city transportation system. Since monitoring alone cannot improve the safety or efficiency of the system, those agencies actively inform the drivers continuously through various media including television broadcasts, internet, and electronic display boards on many locations on the roads. Currently, the human expertise is employed to judge traffic-flow on the roads to inform the public. There is no reliance on past data and human experts give opinions only on the present condition without much idea on what will be the likely events in the next hours. Historical events such as school-timings, holidays and other periodic events cannot be utilized for judging the future traffic-flows. This paper makes a preliminary attempt to change scenario by using artificial neural networks (ANNs) to model the past historical data. It aims at the prediction of the traffic volume based on the historical data in each major junction in the city. ANNs have given very encouraging results with the suggested approach explained in the paper

    Optimisation of Mobile Communication Networks - OMCO NET

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    The mini conference “Optimisation of Mobile Communication Networks” focuses on advanced methods for search and optimisation applied to wireless communication networks. It is sponsored by Research & Enterprise Fund Southampton Solent University. The conference strives to widen knowledge on advanced search methods capable of optimisation of wireless communications networks. The aim is to provide a forum for exchange of recent knowledge, new ideas and trends in this progressive and challenging area. The conference will popularise new successful approaches on resolving hard tasks such as minimisation of transmit power, cooperative and optimal routing

    Enhancing Traffic Prediction with Learnable Filter Module

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    Modeling future traffic conditions often relies heavily on complex spatial-temporal neural networks to capture spatial and temporal correlations, which can overlook the inherent noise in the data. This noise, often manifesting as unexpected short-term peaks or drops in traffic observation, is typically caused by traffic accidents or inherent sensor vibration. In practice, such noise can be challenging to model due to its stochastic nature and can lead to overfitting risks if a neural network is designed to learn this behavior. To address this issue, we propose a learnable filter module to filter out noise in traffic data adaptively. This module leverages the Fourier transform to convert the data to the frequency domain, where noise is filtered based on its pattern. The denoised data is then recovered to the time domain using the inverse Fourier transform. Our approach focuses on enhancing the quality of the input data for traffic prediction models, which is a critical yet often overlooked aspect in the field. We demonstrate that the proposed module is lightweight, easy to integrate with existing models, and can significantly improve traffic prediction performance. Furthermore, we validate our approach with extensive experimental results on real-world datasets, showing that it effectively mitigates noise and enhances prediction accuracy
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