A novel wavelet-SVM short-time passenger flow prediction in Beijing subway system

Sun, Yuqing; Leng, Biao; Guan, Wei

doi:10.1016/j.neucom.2015.03.085

Cited by 261 publications

(140 citation statements)

References 74 publications

(97 reference statements)

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“…The parametric approach includes the historical average (HA) method, autoregressive moving average method (ARIMA) [6,7], seasonal autoregressive integrated moving average method (SARIMA) [8][9][10], and Kalman filter (KF) [11,12]. The nonparametric approach includes artificial neural networks (ANNS) [13][14][15][16][17], k-nearest neighbor (KNN) [18][19][20][21][22], support vector regression (SVR) [23,24], and the Bayesian model [25,26]. The hybrid approach mainly combines the parametric approach with the nonparametric approach [27][28][29][30][31][32][33][34][35].…”

Section: Introductionmentioning

confidence: 99%

Traffic Flow Prediction during the Holidays Based on DFT and SVR

Luo

Zhang

2019

Journal of Sensors

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With the implementation of the freeway free policy during the holidays, traffic congestion in the freeway becomes a common phenomenon. In order to alleviate traffic pressure, traffic flow prediction during the holidays has become a problem of great concern. This paper proposes a hybrid prediction methodology combining discrete Fourier transform (DFT) with support vector regression (SVR). The common trend in the traffic flow data is extracted using DFT by setting an appropriate threshold, which is predicted by extreme extrapolation of the historical trend. The SVR method is applied to predict the residual series. The experimental results with measured data collected from the toll stations in Jiangsu province of China show that the proposed algorithm has higher accuracy compared with the traditional method, and it is an efficient method for traffic flow prediction during the holidays.

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Section: Introductionmentioning

confidence: 99%

Traffic Flow Prediction during the Holidays Based on DFT and SVR

Luo

Zhang

2019

Journal of Sensors

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“…Then the short-term load prediction of the next 24 h in one region was achieved by the SVM model based on the historical load data as input. Support vector machine algorithm has strong generalization ability, fast convergence speed, and can avoid falling into local optimal solution [15,16]. At present, many optimization algorithms, such as particle swarm optimization (PSO), simulated annealing (SA) and genetic algorithm (GA), are proposed for the optimization of SVM parameters.…”

Section: Introductionmentioning

confidence: 99%

Daily Peak Load Forecasting Based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and Support Vector Machine Optimized by Modified Grey Wolf Optimization Algorithm

2018

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Daily peak load forecasting is an important part of power load forecasting. The accuracy of its prediction has great influence on the formulation of power generation plan, power grid dispatching, power grid operation and power supply reliability of power system. Therefore, it is of great significance to construct a suitable model to realize the accurate prediction of the daily peak load. A novel daily peak load forecasting model, CEEMDAN-MGWO-SVM (Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and Support Vector Machine Optimized by Modified Grey Wolf Optimization Algorithm), is proposed in this paper. Firstly, the model uses the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) algorithm to decompose the daily peak load sequence into multiple sub sequences. Then, the model of modified grey wolf optimization and support vector machine (MGWO-SVM) is adopted to forecast the sub sequences. Finally, the forecasting sequence is reconstructed and the forecasting result is obtained. Using CEEMDAN can realize noise reduction for non-stationary daily peak load sequence, which makes the daily peak load sequence more regular. The model adopts the grey wolf optimization algorithm improved by introducing the population dynamic evolution operator and the nonlinear convergence factor to enhance the global search ability and avoid falling into the local optimum, which can better optimize the parameters of the SVM algorithm for improving the forecasting accuracy of daily peak load. In this paper, three cases are used to test the forecasting accuracy of the CEEMDAN-MGWO-SVM model. We choose the models EEMD-MGWO-SVM ( The experimental results fully demonstrate the reliability and effectiveness of the CEEMDAN-MGWO-SVM model proposed in this paper for daily peak load forecasting, which shows the strong generalization ability and robustness of the model. Keywords: daily peak load forecasting; complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN); modified grey wolf optimization (MGWO); support vector machine (SVM)

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“…Wei and Chen [2] developed a hybrid forecasting approach which combines empirical mode decomposition and back-propagation neural networks and found that the approach performs well and stably in forecasting the short-term metro passenger flow. Sun et al [9] proposed a hybrid model of Wavelet Support Vector Machine (SVM). The method first decomposes the passenger flow data into different high frequency and low frequency series by wavelet and then predicted these series by SVM separately.…”

Section: Introductionmentioning

confidence: 99%

Short-Term Origin-Destination Based Metro Flow Prediction with Probabilistic Model Selection Approach

Dai

Sun

2018

Journal of Advanced Transportation

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Reliable prediction of short-term passenger flow could greatly support metro authorities' decision processes, help passengers to adjust their travel schedule, or, in extreme cases, assist emergency management. The inflow and outflow of the metro station are strongly associated with the travel demand within metro networks. The purpose of this paper is to obtain such prediction. We first collect the origin-destination information from the smart-card data and explore the passenger flow patterns in a metro system. We then propose a data driven framework for short-term metro passenger flow prediction with the ability to utilize both spatial and temporal related information. The approach adopts two forecasts as basic models and then uses a probabilistic model selection method, random forest classification, to combine the two outputs to achieve a better forecast. In the experiments, we compare the proposed model with four other prediction models, i.e., autoregressive-moving-average, neural networks, support vector regression, and averaging ensemble model, as well as the basic models. The results indicate that the proposed approach outperforms the others in most cases. The origin-destination flows extracted from smart-card data can be successfully exploited to describe different metro travel patterns. And the framework proposed here, especially the probabilistic combination method, can improve the performance of short-term transportation prediction.

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A novel wavelet-SVM short-time passenger flow prediction in Beijing subway system

Cited by 261 publications

References 74 publications

Traffic Flow Prediction during the Holidays Based on DFT and SVR

Traffic Flow Prediction during the Holidays Based on DFT and SVR

Daily Peak Load Forecasting Based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and Support Vector Machine Optimized by Modified Grey Wolf Optimization Algorithm

Short-Term Origin-Destination Based Metro Flow Prediction with Probabilistic Model Selection Approach

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