Short Term Traffic Flow Prediction of Urban Road Using Time Varying Filtering Based Empirical Mode Decomposition

Wang, Yanpeng; Zhao, Liang; Liu, Shuqing; Wen, Xinyu; Xiong, Ye

doi:10.3390/app10062038

Cited by 19 publications

(11 citation statements)

References 30 publications

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“…In order to make the results more convincing, this article regards two-thirds of the statistical data as the training set, and the remaining data are the test set [36].…”

Section: Case Studymentioning

confidence: 99%

A novel hybrid model of ARIMA‐MCC and CKDE‐GARCH for urban short‐term traffic flow prediction

Zhao

Wen

Wang

et al. 2021

IET Intelligent Trans Sys

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The linearity and heteroscedasticity are the important characteristics of short-term traffic flow data. Generally, the autoregressive integrated moving average (ARIMA) model and the generalized autoregressive conditional heteroscedasticity (GARCH) model are respectively used to explain these characteristics. However, the prerequisite for the use of ARIMA is that the training residuals should follow the standard Gaussian distribution, which is hard to be satisfied in practice. Meanwhile, the variance in the GARCH model usually neglects the time-varying characteristic. To address these problems, this paper proposes an innovative method based on the combination of ARIMA, maximum correntropy criterion (MCC), conditional kernel density estimation (CKDE), and GARCH. Specifically, the MCC method is first employed to estimate the coefficients of the ARIMA model (i.e. ARIMA-MCC), by which the linear prediction is conducted. Then, the CKDE model is established to describe the training residuals obtained by ARIMA-MCC and estimate the time-varying variance in the GARCH model (i.e. GARCH-CKDE). Case studies based on four groups of data measured in the urban road are used to evaluate the performance of the proposed method. Compared with the traditional ARIMA model, the improvement by the proposed method reaches 14.45% in terms of the evaluation criterion of mean absolute percentage error.

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“…In order to make the results more convincing, this article regards two-thirds of the statistical data as the training set, and the remaining data are the test set [36].…”

Section: Case Studymentioning

confidence: 99%

A novel hybrid model of ARIMA‐MCC and CKDE‐GARCH for urban short‐term traffic flow prediction

Zhao

Wen

Wang

et al. 2021

IET Intelligent Trans Sys

Self Cite

View full text Add to dashboard Cite

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“…A hybrid method incorporating filtering-based empirical mode decomposition is proposed in [9]. A deep learning-based method with non-parametric regression is proposed in [10], a novel deep neural network architecture with multisegents (recurrent and convolutional layers) is proposed in [11].…”

Section: Related Workmentioning

confidence: 99%

InterTwin: Deep Learning Approaches for Computing Measures of Effectiveness for Traffic Intersections

2021

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Microscopic simulation-based approaches are extensively used for determining good signal timing plans on traffic intersections. Measures of Effectiveness (MOEs) such as wait time, throughput, fuel consumption, emission, and delays can be derived for variable signal timing parameters, traffic flow patterns, etc. However, these techniques are computationally intensive, especially when the number of signal timing scenarios to be simulated are large. In this paper, we propose InterTwin, a Deep Neural Network architecture based on Spatial Graph Convolution and Encoder-Decoder Recurrent networks that can predict the MOEs efficiently and accurately for a wide variety of signal timing and traffic patterns. Our methods can generate probability distributions of MOEs and are not limited to mean and standard deviation. Additionally, GPU implementations using InterTwin can derive MOEs, at least four to five orders of magnitude faster than microscopic simulations on a conventional 32 core CPU machine.

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“…SVM is a classic traffic flow prediction method. Scholars improved the SVM algorithm and obtained its improved model, such as support vector regression (SVR) [22], least-squares support vector machine (LSSVM) [23,24], and least-squares support vector regression (LSSVR) [25]. In recent years, inspired by neural networks, new technologies such as deep neural networks and deep learning have been developing rapidly, and traffic flow prediction technologies are also constantly updated and improved [26][27][28].…”

Section: Introductionmentioning

confidence: 99%

An Improved Hybrid Highway Traffic Flow Prediction Model Based on Machine Learning

et al. 2020

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For intelligent transportation systems (ITSs), reliable and accurate real-time traffic flow prediction is an important step and a necessary prerequisite for alleviating traffic congestion and improving highway operation efficiency. In this paper, we propose an improved hybrid predicting model including two steps: decomposition and prediction to predict highway traffic flow. First, we adopted the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) method to adaptively decompose the original nonlinear, nonstationary, and complex highway traffic flow data. Then, we used the improved weighted permutation entropy (IWPE) to obtain new reconstructed components. In the prediction step, we used the gray wolf optimizer (GWO) algorithm to optimize the least-squares support vector machine (LSSVM) prediction model established for each reconstruction component and integrate the prediction results of each subsequence to obtain the final prediction result. We experimentally validated the effectiveness of the proposed approach. The research results reveal that the proposed model is useful for predicting traffic flow and its changing trends and also allowing transportation officials to make more effective traffic decisions.

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Short Term Traffic Flow Prediction of Urban Road Using Time Varying Filtering Based Empirical Mode Decomposition

Cited by 19 publications

References 30 publications

A novel hybrid model of ARIMA‐MCC and CKDE‐GARCH for urban short‐term traffic flow prediction

A novel hybrid model of ARIMA‐MCC and CKDE‐GARCH for urban short‐term traffic flow prediction

InterTwin: Deep Learning Approaches for Computing Measures of Effectiveness for Traffic Intersections

An Improved Hybrid Highway Traffic Flow Prediction Model Based on Machine Learning

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