Constructing a Deep Regression Model Utilizing Cascaded Sparse Autoencoders and Stochastic Gradient Descent

Moussavi-Khalkhali, Arezou; Jamshidi, Mo

doi:10.1109/icmla.2016.0098

Cited by 5 publications

References 15 publications

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Survey of neural network‐based models for short‐term traffic state prediction

Loan

Taherifar

2018

WIREs Data Min & Knowl

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Traffic state prediction is a key component in intelligent transport systems (ITS) and has attracted much attention over the last few decades. Advances in computational power and availability of a large amount of data have paved the way to employ advanced neural network (NN) models for ITS, including deep architectures. There have been various NN‐based approaches proposed for short‐term traffic state prediction that are surveyed in this article, where the existing NN models are classified and their application to this area is reviewed. An in‐depth discussion is provided to demonstrate how different types of NNs have been used for different aspects of short‐term traffic state prediction. Finally, possible further research directions are suggested for additional applications of NN models, especially using deep architectures, to address the dynamic nature in complex transportation networks. This article is categorized under: Technologies > Prediction Technologies > Machine Learning Application Areas > Science and Technology

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Survey of neural network‐based models for short‐term traffic state prediction

Loan

Taherifar

2018

WIREs Data Min & Knowl

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Reflection on Four Decades of Contributions of My Graduate Students

Jamshidi

2015

Studies in Systems, Decision and Control

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Deep Learning in Transport Studies: A Meta-analysis on the Prediction Accuracy

Varghese

Chikaraishi

Urata

2020

J. Big Data Anal. Transp.

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Deep learning methods are being increasingly applied in transport studies, while the methods require modellers to go through a try-and-error model tuning process particularly on choosing neural network structure. Moreover, the accuracy level also depends on other factors such as the type of data, sample size, region of data collection, and time of prediction. To efficiently facilitate such a model tuning process, this study attempts to summarize the relationship between the prediction accuracy of deep learning models and the factors which influence it. We conducted a comprehensive review of the literature by adopting a detailed search strategy, followed by a meta-analysis on prediction accuracy. Four separate linear mixed effects models, taking into account unobserved heterogeneities in prediction accuracy across studies, were developed to statistically test the impacts of influential factors on prediction accuracy for (a) all observations (136 studies; 2314 cases), (b) studies with MAPE, MRE, and average accuracy indicators (86 studies; 1,878 cases), (c) classification-based studies with accuracy indicator (29 studies; 220 cases), and (d) traffic forecasting studies with MAPE, MRE, and average accuracy indicators (36 studies, 991 cases). The final model includes additional factors to test the influence of sample size and time horizon of prediction variables. The findings showed that, as expected, deep learning models, particularly ones that consider spatiotemporal dependencies of transport phenomena, show better prediction accuracies compared to conventional machine learning models. We also found that, on average, the prediction accuracy is improved by 5.90% with 100 million additional data, while the accuracy is reduced by 5.28% with 100 min increase in time horizon of prediction in traffic forecasting studies. We concluded this paper with a comprehensive summary of the existing findings on the applications of deep learning to transport studies.

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Constructing a Deep Regression Model Utilizing Cascaded Sparse Autoencoders and Stochastic Gradient Descent

Cited by 5 publications

References 15 publications

Survey of neural network‐based models for short‐term traffic state prediction

Survey of neural network‐based models for short‐term traffic state prediction

Reflection on Four Decades of Contributions of My Graduate Students

Deep Learning in Transport Studies: A Meta-analysis on the Prediction Accuracy

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