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

Goudarzi, Shidrokh; Kama, Nazri; Anisi, Mohammad Hossein; Soleymani, Seyed Ahmad; Doctor, Faiyaz

doi:10.3390/s18103459

Cited by 42 publications

(29 citation statements)

References 45 publications

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“…To accomplish this, Wang et al (2016a) have tried to combine an RNN with a CNN to pay attention to both the temporal and spatial aspects of traffic. Fouladgar et al (2017), Du et al (2017) and Goudarzi et al (2018) have combined the power of LSTM + CNN to understand both temporal and local dependencies to predict different traffic characteristics. Yao et al (2018a) have considered two challenges, the first being the dynamic dependency of traffic on temporal features, that is, in different hours of the day, this dependency may differ from one direction of traffic flow to another direction.…”

Section: Traffic Characteristics Predictionmentioning

confidence: 99%

Applications of Deep Learning in Intelligent Transportation Systems

Haghighat

Ravichandra-Mouli

Chakraborty

et al. 2020

J. Big Data Anal. Transp.

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In recent years, Intelligent Transportation Systems (ITS) have seen efficient and faster development by implementing deep learning techniques in problem domains which were previously addressed using analytical or statistical solutions and also in some areas that were untouched. These improvements have facilitated traffic management and traffic planning, increased safety and security in transit roads, decreased costs of maintenance, optimized public transportation and ride-sharing company's performance, and advanced driver-less vehicle development to a new stage. This papers primary objective was to provide a review and comprehensive insight into the applications of deep learning models on intelligent transportation systems accompanied by presenting the progress of ITS research due to deep learning. First, different techniques of deep learning and their state-of-the-art are discussed, followed by an in-depth analysis and explanation of the current applications of these techniques in transportation systems. This enumeration of deep learning on ITS highlights its significance in the domain. The applications are furthermore categorized based on the gap they are trying to address. Finally, different embedded systems for deployment of these techniques are investigated and their advantages and weaknesses over each other are discussed. Based on this systematic review, credible benefits of deep learning models on ITS are demonstrated and directions for future research are discussed.

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Section: Traffic Characteristics Predictionmentioning

confidence: 99%

Applications of Deep Learning in Intelligent Transportation Systems

Haghighat

Ravichandra-Mouli

Chakraborty

et al. 2020

J. Big Data Anal. Transp.

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“…We provide as open source the tool we have developed for downloading road traffic data from PeMS. 2 We also provide the downloaded dataset in an open repository. 3 PeMS provides the traffic flow as number of vehicles every 5 minutes.…”

Section: Datasetmentioning

confidence: 99%

“…As an The associate editor coordinating the review of this manuscript and approving it for publication was Shaohua Wan . example, [2] and [3] propose using deep belief networks and recurrent neural networks for traffic flow and traffic speed prediction, respectively. Several deep neural network techniques have been proposed to date, and it is not possible to conclude from the existing literature which has the best prediction accuracy.…”

Section: Introductionmentioning

confidence: 99%

A Comprehensive Evaluation of Deep Learning-Based Techniques for Traffic Prediction

Mena-Oreja

Gozálvez

2020

IEEE Access

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Deep learning-based techniques are the state of the art in road traffic prediction or forecasting. Several deep neural networks have been proposed to predict the traffic but they have not been evaluated under common datasets. Current studies analyze their capacity to predict road traffic in general but do not focus on their capacity to predict the formation of congestions. This is critical for avoiding congestions or mitigate their negative impact. This paper progresses the current state of the art by presenting a comprehensive comparison of the state-of-the-art deep neural networks for road traffic prediction. The comparison is conducted using the same real traffic datasets, and under normal and congested traffic conditions. The evaluation includes new deep neural networks and error recurrent models. Our study first demonstrates that accurately predicting the traffic overall does not imply that a deep neural network can accurately predict the traffic when congestions are being formed. This reinforces the idea that prediction techniques must also be evaluated under congestion conditions. Our analysis also shows that exploiting the spatiotemporal evolution of the traffic (and not just the temporal one) provides better prediction accuracy overall and in particular under congestion conditions. The study also demonstrates that error recurrent models outperform deep neural networks that do not utilize an error feedback both under normal and congested traffic conditions. In particular, our study shows that the error recurrent model eRCNN is the deep learning technique that achieves to date the best traffic prediction accuracy. It is also important emphasizing that error recurrent models achieve better prediction accuracy with shallower neural networks and therefore lower computational cost.

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“…A typical DL model can accept input data in raw format and automatically discover the required features level by level, which greatly simplifies feature engineering. With the DL-based model, there was a clear improvement of traffic prediction [12][13][14][15]. The LSTM [16] is a special kind of deep recurrent neuron network (RNN), which dynamically feeds the output of the previous step back into the input layer of the current step in sequence.…”

Section: Introductionmentioning

confidence: 99%

Short-Term Prediction of Bus Passenger Flow Based on a Hybrid Optimized LSTM Network

Han

Wang

Ren

et al. 2019

IJGI

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The accurate prediction of bus passenger flow is the key to public transport management and the smart city. A long short-term memory network, a deep learning method for modeling sequences, is an efficient way to capture the time dependency of passenger flow. In recent years, an increasing number of researchers have sought to apply the LSTM model to passenger flow prediction. However, few of them pay attention to the optimization procedure during model training. In this article, we propose a hybrid, optimized LSTM network based on Nesterov accelerated adaptive moment estimation (Nadam) and the stochastic gradient descent algorithm (SGD). This method trains the model with high efficiency and accuracy, solving the problems of inefficient training and misconvergence that exist in complex models. We employ a hybrid optimized LSTM network to predict the actual passenger flow in Qingdao, China and compare the prediction results with those obtained by non-hybrid LSTM models and conventional methods. In particular, the proposed model brings about a 4%–20% extra performance improvements compared with those of non-hybrid LSTM models. We have also tried combinations of other optimization algorithms and applications in different models, finding that optimizing LSTM by switching Nadam to SGD is the best choice. The sensitivity of the model to its parameters is also explored, which provides guidance for applying this model to bus passenger flow data modelling. The good performance of the proposed model in different temporal and spatial scales shows that it is more robust and effective, which can provide insightful support and guidance for dynamic bus scheduling and regional coordination scheduling.

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

Cited by 42 publications

References 45 publications

Applications of Deep Learning in Intelligent Transportation Systems

Applications of Deep Learning in Intelligent Transportation Systems

A Comprehensive Evaluation of Deep Learning-Based Techniques for Traffic Prediction

Short-Term Prediction of Bus Passenger Flow Based on a Hybrid Optimized LSTM Network

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