Online-SVR for short-term traffic flow prediction under typical and atypical traffic conditions

Castro-Neto, Manoel Mendonça de; Jeong, Young-Seon; Jeong, Myong K.; Lee, Han

doi:10.1016/j.eswa.2008.07.069

Cited by 676 publications

(311 citation statements)

References 42 publications

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“…Most studies compare the quality of a new prediction method with previous models such as ARIMA and a neural network model as parametric and non-parametric methods, respectively [1]. The historical mean average model is a time series model with fixed and equal weights, ARIMA is an accurate and useful time series model, and MLP as a popular neural network model; these models were selected for comparison with the proposed model.…”

Section: Contribution and Structure Of The Papermentioning

confidence: 99%

“…An intelligent transportation system (ITS) is an advanced application that provides innovative services for different modes of traffic management. The term ITS was first introduced as an umbrella term to cover all technologies in information technology, communications, and control [1]. A solution to prevent traffic congestion using ITS is one that predicts traffic parameters such as traffic flow, speed, and density.…”

Section: Introduction 11 Statement Of the Problemmentioning

confidence: 99%

See 1 more Smart Citation

Traffic Flow Prediction Using MI Algorithm and Considering Noisy and Data Loss Conditions: An Application to Minnesota Traffic Flow Prediction

Hosseini

Moshiri

Rahimi-Kian

et al. 2014

PROMET

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Section: Contribution and Structure Of The Papermentioning

confidence: 99%

Section: Introduction 11 Statement Of the Problemmentioning

confidence: 99%

Traffic Flow Prediction Using MI Algorithm and Considering Noisy and Data Loss Conditions: An Application to Minnesota Traffic Flow Prediction

Hosseini

Moshiri

Rahimi-Kian

et al. 2014

PROMET

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“…SVR is a nonlinear prediction model that is based on SVM theory. It has been widely applied in short-term prediction problems, such as traffic flow forecasting (Castro-Neto et al, 2009) and electric load forecasting . Descriptions of identification algorithms for the SVR model can be found in (Castro-Neto et al, 2009;Fan and Tang, 2013;Huo et al, 2014).…”

Section: Formulation Descriptions Of Svr Modelmentioning

confidence: 99%

“…However, empirical mode decomposition (EMD) technique is capable of processing nonlinear and non-stationary time series. Therefore, it has been popularly employed in developing hybrid models for various time series predictions (Castro-Neto et al, 2009;Fan and Tang, 2013;Cheng and Wei, 2014;Wang et al, 2015). Hou and Qi (2011) developed an EMD based on a radial basis function neural network (EMD-RBFNN) model to handle the nonlinearity and non-stationarity in ship motions.…”

Section: Introductionmentioning

confidence: 99%

A hybrid AR-EMD-SVR model for the short-term prediction of nonlinear and non-stationary ship motion

Duan

Huang

Han

et al. 2015

J. Zhejiang Univ. Sci. A

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Accurate and reliable short-term prediction of ship motions offers improvements in both safety and control quality in ship motion sensitive maritime operations. Inspired by the satisfactory nonlinear learning capability of a support vector regression (SVR) model and the strong non-stationary processing ability of empirical mode decomposition (EMD), this paper develops a hybrid autoregressive (AR)-EMD-SVR model for the short-term forecast of nonlinear and non-stationary ship motion. The proposed hybrid model is designed by coupling the SVR model with an AR-EMD technique, which employs an AR model in ends extension. In addition to the AR-EMD-SVR model, the linear AR model, non-linear SVR model, and hybrid EMD-AR model are also studied for comparison by using ship motion time series obtained from model testing in a towing tank. Prediction results suggest that the non-stationary difficulty in the SVR model is overcome by using the AR-EMD technique, and better predictions are obtained by the proposed AR-EMD-SVR model than other models.

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“…The most commonly used nonparametric method is the artificial neural network (ANN), which has been used widely in traffic forecasting (Van Lint et al, 2005), (Vlahogianni et al, 2005). Other commonly used methods include various forms of nonparametric regression (Smith et al, 2002); (Clark, 2003) and kernel methods (Chun-Hsin Wu et al, 2004); (Castro-Neto et al, 2009)). Each approach has its strengths and weaknesses, and (Karlaftis and Vlahogianni, 2011) provide a good overview, but the task is the same: to create a model that can effectively describe the spatio-temporal evolution of the process.…”

Section: Space-time Forecastingmentioning

confidence: 99%

A Comparison of Neighbourhood Selection Techniques in Spatio-Temporal Forecasting Models

Haworth

Cheng

2014

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Spatio-temporal neighbourhood (STN) selection is an important part of the model building procedure in spatio-temporal forecasting. The STN can be defined as the set of observations at neighbouring locations and times that are relevant for forecasting the future values of a series at a particular location at a particular time. Correct specification of the STN can enable forecasting models to capture spatio-temporal dependence, greatly improving predictive performance. In recent years, deficiencies have been revealed in models with globally fixed STN structures, which arise from the problems of heterogeneity, nonstationarity and nonlinearity in spatio-temporal processes. Using the example of a large dataset of travel times collected on London's road network, this study examines the effect of various STN selection methods drawn from the variable selection literature, varying from simple forward/backward subset selection to simultaneous shrinkage and selection operators. The results indicate that STN selection methods based on penalisation are effective. In particular, the maximum concave penalty (MCP) method selects parsimonious models that produce good forecasting performance.* Corresponding author. This is useful to know for communication with the appropriate person in cases with more than one author.

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Online-SVR for short-term traffic flow prediction under typical and atypical traffic conditions

Cited by 676 publications

References 42 publications

Traffic Flow Prediction Using MI Algorithm and Considering Noisy and Data Loss Conditions: An Application to Minnesota Traffic Flow Prediction

Traffic Flow Prediction Using MI Algorithm and Considering Noisy and Data Loss Conditions: An Application to Minnesota Traffic Flow Prediction

A hybrid AR-EMD-SVR model for the short-term prediction of nonlinear and non-stationary ship motion

A Comparison of Neighbourhood Selection Techniques in Spatio-Temporal Forecasting Models

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