Rainfall‐integrated traffic speed prediction using deep learning method

Jia, Yunfeng; Wu, Jianping; Ben‐Akiva, Moshe; Seshadri, Ravi; Du, Yiman

doi:10.1049/iet-its.2016.0257

Cited by 100 publications

(52 citation statements)

References 34 publications

(47 reference statements)

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“…In the current work, we adopted LSSVR as prediction model because it has been widely used and shown promising results. However, it should be noticed that besides LSSVR, neural network based prediction models, such as deep learning, [23][24][25] have attracted much attention during the last few years. Comparing with LSSVR, deep learning is able to show better prediction accuracy given large number of samples.…”

Section: Resultsmentioning

confidence: 99%

“…ANN is widely used in traffic flow forecasting, especially deep learning [23][24][25] because it is able to approximate any complex function without prior knowledge of the problem. Although ANN is a powerful nonlinear modeling tool, it has some limitations, such as the difficulty to interpret the involved black-box operations, the determination of suitable network structure including the number of hidden layers and neurons.…”

Section: Intelligent Transportation System (Its)mentioning

confidence: 99%

“…On the whole, 10 sites are related to the target site 21 besides itself, which are 5,7,8,11,13,14,18,19,23, and 24. Among them, sites 8, 11, and 23 contribute to the target site 21 as the role of direct upstream sites.…”

Section: Interpretation Of Spatiotemporal Variablesmentioning

confidence: 99%

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Spatiotemporal variable and parameter selection using sparse hybrid genetic algorithm for traffic flow forecasting

Chen

Wei

Liu

et al. 2017

International Journal of Distributed Sensor Networks

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Short-term traffic flow forecasting is a difficult yet important problem in intelligent transportation systems. Complex spatiotemporal interactions between the target road segment and other road segments can provide important information for the accurate forecasting. Meanwhile, spatiotemporal variable selection and traffic flow prediction should be solved in a unified framework such that they can benefit from each other. In this article, we propose a novel sparse hybrid genetic algorithm by introducing sparsity constraint and real encoding scheme into genetic algorithm in order to optimize shortterm traffic flow prediction model based on least squares support vector regression. This method can integrate spatiotemporal variable selection, parameter selection as well as traffic flow prediction in a unified framework, indicating that the ''goodness,'' that is, contribution, of selected spatiotemporal variables and optimized parameters directly depends on the final traffic flow prediction accuracy. The real-world traffic flow data are collected from 24 observation sites located around the intersection of Interstate 205 and Interstate 84 in Portland, OR, USA. The experimental results show that the proposed sparse hybrid genetic algorithm-least square support vector regression prediction model can produce better performance but with much fewer spatiotemporal variables in comparison with other related models.

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Section: Resultsmentioning

confidence: 99%

Section: Intelligent Transportation System (Its)mentioning

confidence: 99%

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Spatiotemporal variable and parameter selection using sparse hybrid genetic algorithm for traffic flow forecasting

Chen

Wei

Liu

et al. 2017

International Journal of Distributed Sensor Networks

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“…A number of techniques and algorithms have been proposed for time series prediction, such as linear models, which include (but are not limited to) Auto-Regressive Integrated Moving Average (ARIMA) and its variants [1], support vector machines [2], statistical analysis [3] and, more recently, deep non-linear neural network algorithms like Recurrent Neural Networks (RNN) [4], LSTMs [5] and CNNs [6], which have been applied in many areas such as in financial prediction [7], [8], traffic prediction [9]- [11], machine fault prognosis/diagnosis [12] and anomaly detection [13], [14]. Although ARIMA and ARIMA-based model variants such as Seasonal ARIMA (SARIMA) [1], Vector ARIMA (ARIMAX) [15] have shown promising signs when applied towards univariate and multivariate time series prediction, they however show vulnerabilities when applied to non-linear, sequential, or time series data, such as traffic and stock prediction [9].…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning Framework for Univariate Time Series Prediction Using Convolutional LSTM Stacked Autoencoders

Essien

Giannetti

2019

2019 IEEE International Symposium on INnovations in Intelligent SysTems and Applications (INISTA)

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“…As a matter of fact, deep learning methods were designed to model complex systems with flexibility, simplicity, and strength using series of multilayer architectures. Fo instance, they are used to enhance intelligent transportation systems [22], [25], health informatics [23], human action recognition [26], detection of the cerebral microbleed voxels [20], classification of hearing loss images [21], and in fingerprints indoor positioning via WIFI [27]. Due to its broad applications, the Deep Belief Networks (DBNs) models, have received much attention from researchers recently.…”

Section: Introductionmentioning

confidence: 99%

Detecting Abnormal Ozone Measurements With a Deep Learning-Based Strategy

et al. 2018

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Abstract-Air quality management and monitoring are vital to maintaining clean air, which is necessary for the health of human, vegetation, and ecosystems. Ozone pollution is one of the main pollutants that negatively affect human health and ecosystems. This paper reports the development of an unsupervised and efficient scheme to detecting anomalies in unlabelled ozone measurements. This scheme combines a Deep Belief Networks (DBN) model and a one-class support vector machine (OCSVM). The DBN model accounts for nonlinear variations in the ground-level ozone concentrations, while OCSVM detects the abnormal ozone measurements. The performance of this approach is evaluated using real data from Isère in France. We also compare the detection quality of DBN-based detection schemes to that of deep stacked auto-encoders, Restricted Boltzmann Machinesbased OCSVM and DBN-based clustering procedures (i.e., Kmeans, Birch and Expectation Maximization). The results show that the developed strategy is able to identify anomalies in ozone measurements.

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Rainfall‐integrated traffic speed prediction using deep learning method

Cited by 100 publications

References 34 publications

Spatiotemporal variable and parameter selection using sparse hybrid genetic algorithm for traffic flow forecasting

Spatiotemporal variable and parameter selection using sparse hybrid genetic algorithm for traffic flow forecasting

A Deep Learning Framework for Univariate Time Series Prediction Using Convolutional LSTM Stacked Autoencoders

Detecting Abnormal Ozone Measurements With a Deep Learning-Based Strategy

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