Optimized Structure of the Traffic Flow Forecasting Model With a Deep Learning Approach

doi:10.1109/TNNLS.2016.2574840

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Optimized Structure of the Traffic Flow Forecasting Model With a Deep Learning Approach

Journal Article•DOI•

Optimized Structure of the Traffic Flow Forecasting Model With a Deep Learning Approach

Hao-Fan Yang¹, Tharam S. Dillon¹, Yi-Ping Phoebe Chen¹•Institutions (1)

La Trobe University¹

01 Oct 2017-IEEE Transactions on Neural Networks (IEEE)-Vol. 28, Iss: 10, pp 2371-2381

TL;DR: A novel model, stacked autoencoder Levenberg-Marquardt model, which is a type of deep architecture of neural network approach aiming to improve forecasting accuracy, and an optimized structure of the traffic flow forecasting model with a deep learning approach is presented.

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Abstract: Forecasting accuracy is an important issue for successful intelligent traffic management, especially in the domain of traffic efficiency and congestion reduction. The dawning of the big data era brings opportunities to greatly improve prediction accuracy. In this paper, we propose a novel model, stacked autoencoder Levenberg-Marquardt model, which is a type of deep architecture of neural network approach aiming to improve forecasting accuracy. The proposed model is designed using the Taguchi method to develop an optimized structure and to learn traffic flow features through layer-by-layer feature granulation with a greedy layerwise unsupervised learning algorithm. It is applied to real-world data collected from the M6 freeway in the U.K. and is compared with three existing traffic predictors. To the best of our knowledge, this is the first time that an optimized structure of the traffic flow forecasting model with a deep learning approach is presented. The evaluation results demonstrate that the proposed model with an optimized structure has superior performance in traffic flow forecasting.

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Remote Sensing And Image Interpretation

[...]

Ute Beyer

01 Jan 2016

TL;DR: The remote sensing and image interpretation is universally compatible with any devices to read and is available in the digital library an online access to it is set as public so you can get it instantly.

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Abstract: Thank you very much for downloading remote sensing and image interpretation. As you may know, people have look hundreds times for their favorite novels like this remote sensing and image interpretation, but end up in malicious downloads. Rather than reading a good book with a cup of tea in the afternoon, instead they are facing with some malicious virus inside their computer. remote sensing and image interpretation is available in our digital library an online access to it is set as public so you can get it instantly. Our book servers spans in multiple countries, allowing you to get the most less latency time to download any of our books like this one. Merely said, the remote sensing and image interpretation is universally compatible with any devices to read.

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1,802 citations

Journal Article•DOI•

A review of deep learning for renewable energy forecasting

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Huaizhi Wang¹, Zhenxing Lei¹, Xian Zhang², Bin Zhou³, Jianchun Peng¹ - Show less +1 more•Institutions (3)

Shenzhen University¹, Hong Kong Polytechnic University², Hunan University³

15 Oct 2019-Energy Conversion and Management

TL;DR: A comprehensive and extensive review of renewable energy forecasting methods based on deep learning to explore its effectiveness, efficiency and application potential and the current research activities, challenges, and potential future research directions are explored.

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537 citations

Journal Article•DOI•

Spatiotemporal Recurrent Convolutional Networks for Traffic Prediction in Transportation Networks.

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Haiyang Yu¹, Wu Zhihai¹, Shuqin Wang, Yunpeng Wang¹, Xiaolei Ma¹ - Show less +1 more•Institutions (1)

Beihang University¹

26 Jun 2017-Sensors

TL;DR: Wang et al. as mentioned in this paper proposed a spatiotemporal recurrent convolutional networks (SRCNs) for traffic forecasting, which inherit the advantages of deep CNNs and LSTM neural networks.

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Abstract: Predicting large-scale transportation network traffic has become an important and challenging topic in recent decades. Inspired by the domain knowledge of motion prediction, in which the future motion of an object can be predicted based on previous scenes, we propose a network grid representation method that can retain the fine-scale structure of a transportation network. Network-wide traffic speeds are converted into a series of static images and input into a novel deep architecture, namely, spatiotemporal recurrent convolutional networks (SRCNs), for traffic forecasting. The proposed SRCNs inherit the advantages of deep convolutional neural networks (DCNNs) and long short-term memory (LSTM) neural networks. The spatial dependencies of network-wide traffic can be captured by DCNNs, and the temporal dynamics can be learned by LSTMs. An experiment on a Beijing transportation network with 278 links demonstrates that SRCNs outperform other deep learning-based algorithms in both short-term and long-term traffic prediction.

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385 citations

Posted Content•

Spatiotemporal Recurrent Convolutional Networks for Traffic Prediction in Transportation Networks

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Haiyang Yu¹, Wu Zhihai¹, Shuqin Wang, Yunpeng Wang¹, Xiaolei Ma¹ - Show less +1 more•Institutions (1)

Beihang University¹

07 May 2017-arXiv: Learning

TL;DR: A network grid representation method that can retain the fine-scale structure of a transportation network and outperform other deep learning-based algorithms in both short-term and long-term traffic prediction is proposed.

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339 citations

Cites methods from "Optimized Structure of the Traffic ..."

...In addition, three prevailing deep learning NNs (i.e., LSTMs, DCNNs, and SAEs) and a classical machine learning method (SVM) were compared with the SRCNs for the same dataset....
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...The structures of other methods (LSTMs and SAEs) are established according to their papers....
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...The results of the SRCNs, LSTMs, SAEs, DCNNs, and SVM are listed in Figure 10 and Table 3....
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...In this section, we employ traffic speed data from Beijing, China, to evaluate our model—SRCNs— and compare them with other deep NNs, including LSTMs [36], SAEs [35], DCNNs, and SVM....
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...In this section, we compare SRCNs with four other algorithms (LSTMs, SAEs, DCNNs, and SVM) in terms of short term prediction....
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Journal Article•DOI•

LSTM-based traffic flow prediction with missing data

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Yan Tian¹, Kaili Zhang¹, Jianyuan Li, Xianxuan Lin¹, Bailin Yang¹ - Show less +1 more•Institutions (1)

Zhejiang Gongshang University¹

27 Nov 2018-Neurocomputing

TL;DR: A novel approach that is based on Long Short-Term Memory (LSTM) is proposed that obtains higher accuracy in traffic flow prediction compared with other approaches.

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310 citations

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References

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Open Access

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Journal Article•DOI•

Reducing the Dimensionality of Data with Neural Networks

[...]

Geoffrey E. Hinton¹, Ruslan Salakhutdinov¹•Institutions (1)

University of Toronto¹

28 Jul 2006-Science

TL;DR: In this article, an effective way of initializing the weights that allows deep autoencoder networks to learn low-dimensional codes that work much better than principal components analysis as a tool to reduce the dimensionality of data is described.

...read moreread less

Abstract: High-dimensional data can be converted to low-dimensional codes by training a multilayer neural network with a small central layer to reconstruct high-dimensional input vectors. Gradient descent can be used for fine-tuning the weights in such "autoencoder" networks, but this works well only if the initial weights are close to a good solution. We describe an effective way of initializing the weights that allows deep autoencoder networks to learn low-dimensional codes that work much better than principal components analysis as a tool to reduce the dimensionality of data.

...read moreread less

16,717 citations

Journal Article•DOI•

Deep learning in neural networks

[...]

Jürgen Schmidhuber¹•Institutions (1)

University of Lugano¹

01 Jan 2015-Neural Networks

TL;DR: This historical survey compactly summarizes relevant work, much of it from the previous millennium, review deep supervised learning, unsupervised learning, reinforcement learning & evolutionary computation, and indirect search for short programs encoding deep and large networks.

...read moreread less

14,635 citations

"Optimized Structure of the Traffic ..." refers background or methods in this paper

...That is, the SAE-LM model with a 240-min sampling time, SoftSign function for the autoencoders, 3 × log2(N) hidden nodes, five hidden layers (four autoencoders), and Purelin function for the last hidden set generates the most accurate predicted results....
[...]
...We follow the practice of using the same activation function for f (·) and g(·), and Sigmoid, Tanh, LogSig, and SoftSign functions are set as Levels 1, 2, 3, and 4, respectively....
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...The concept of deep learning is to use deep architectures (multiple layers of nonlinear processing units) to extract and transform the inherent features in the data from the lowest level to the highest level, and every continuous layer uses the output from the previous layer as input [4], [16]....
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...Therefore, if a large volume of traffic data is adopted, we may be able to avoid many failures caused by assumptions and the accuracy of traffic prediction can be improved by learning the information and correlations hidden in the data [3], [4]....
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...2) Design Factor ii: For the activation functions of the autoencoders, f (·) and g(·), the most commonly used transfer functions for deep structures are Sigmoid, Tanh, LogSig, and SoftSign [4], [34]....
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Book•

Learning Deep Architectures for AI

[...]

Yoshua Bengio¹•Institutions (1)

Université de Montréal¹

01 Jan 2009

TL;DR: The motivations and principles regarding learning algorithms for deep architectures, in particular those exploiting as building blocks unsupervised learning of single-layer modelssuch as Restricted Boltzmann Machines, used to construct deeper models such as Deep Belief Networks are discussed.

...read moreread less

Abstract: Can machine learning deliver AI? Theoretical results, inspiration from the brain and cognition, as well as machine learning experiments suggest that in order to learn the kind of complicated functions that can represent high-level abstractions (e.g. in vision, language, and other AI-level tasks), one would need deep architectures. Deep architectures are composed of multiple levels of non-linear operations, such as in neural nets with many hidden layers, graphical models with many levels of latent variables, or in complicated propositional formulae re-using many sub-formulae. Each level of the architecture represents features at a different level of abstraction, defined as a composition of lower-level features. Searching the parameter space of deep architectures is a difficult task, but new algorithms have been discovered and a new sub-area has emerged in the machine learning community since 2006, following these discoveries. Learning algorithms such as those for Deep Belief Networks and other related unsupervised learning algorithms have recently been proposed to train deep architectures, yielding exciting results and beating the state-of-the-art in certain areas. Learning Deep Architectures for AI discusses the motivations for and principles of learning algorithms for deep architectures. By analyzing and comparing recent results with different learning algorithms for deep architectures, explanations for their success are proposed and discussed, highlighting challenges and suggesting avenues for future explorations in this area.

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7,767 citations

"Optimized Structure of the Traffic ..." refers methods in this paper

..., the collected traffic data) are real numbers, we use the squared loss function L(x, z) to measure the reconstruction error [14]....
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...Deep learning is a type of machine learning method based on learning representations of data, and it has been successfully applied to assist in many fields, for instance, classification tasks, information processing, pattern recognition, and object detection [14], [15]....
[...]

Book•

Remote sensing and image interpretation

[...]

Thomas Martin Lillesand¹•Institutions (1)

University of Minnesota¹

01 Jan 1979

TL;DR: In this article, the authors present a textbook for introductory courses in remote sensing, which includes concepts and foundations of remote sensing; elements of photographic systems; introduction to airphoto interpretation; air photo interpretation for terrain evaluation; photogrammetry; radiometric characteristics of aerial photographs; aerial thermography; multispectral scanning and spectral pattern recognition; microwave sensing; and remote sensing from space.

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Abstract: A textbook prepared primarily for use in introductory courses in remote sensing is presented. Topics covered include concepts and foundations of remote sensing; elements of photographic systems; introduction to airphoto interpretation; airphoto interpretation for terrain evaluation; photogrammetry; radiometric characteristics of aerial photographs; aerial thermography; multispectral scanning and spectral pattern recognition; microwave sensing; and remote sensing from space.

...read moreread less

6,790 citations

Journal Article•DOI•

Remote Sensing and Image Interpretation

[...]

John K. Wright, Thomas M. Lillesand, Ralph W. Kiefer

01 Nov 1980-The Geographical Journal

TL;DR: In this article, the concept of remote sensing elements of photogrammetry was introduced. Butterfly, thermal, and hyperspectral sensors were used to interpret multispectral, thermal and hypererspectral images.

...read moreread less

Abstract: Concepts and Foundations of Remote Sensing Elements of Photographic Systems Basic Principles of Photogrammetry Introduction to Visual Image Interpretation Multispectral, Thermal, and Hyperspectral Sensing Earth Resource Satellites Operating in the Optical Spectrum Digital Image Processing Microwave and Lidar Sensing Appendix A: Radiometric Concepts, Terminology, and Units Appendix B: Remote Sensing Data and Information Resources Appendix C: Sample Coordinate Transformation and Resampling Procedures

...read moreread less

6,547 citations

"Optimized Structure of the Traffic ..." refers background in this paper

...Moreover, with widespread traffic sensing facilities and advancements in sensing technologies [11], [12], the amount of collected traffic flow data can be greatly increased....
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