Exploring time variants for short-term passenger flow

Chen, Mu-Chen; Yu, Wei

doi:10.1016/j.jtrangeo.2010.04.003

Cited by 56 publications

(26 citation statements)

References 53 publications

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“…The predicted high frequency signal d cd m and the predicted low frequency signal d ca m are reconstructed to produce the signal d ca m À 1 . Continue the reconstruction process until d cd 1 and d ca 1 are reconstructed and the predicted passenger flow time series in target period b S is obtained, as illustrated in Fig. 3.…”

Section: Reconstructing the Predicted Seriesmentioning

confidence: 99%

“…During the past two decades, a large quantity of researchers have devoted to develop the forecasting models in the field of transportation [1]. The conventional travel forecasting models [2] have been successfully applied in the travel forecasting of the urban transportation systems, such as the inter-district travel by public transport in Sri Lanka [3], the local railway system in Copenhagen [4] and Chicago regional roads [2].…”

Section: Introductionmentioning

confidence: 99%

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

2015

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Section: Reconstructing the Predicted Seriesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

2015

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“…In order to improve the accuracy of traffic flow prediction, it is a very effective method to decompose the traffic flow data into different components. There are various methods to retrieve the different components in traffic flow data, including the simple average trend [37], principle component analysis [38], empirical mode decomposition (EMD) [39,40], wavelet methods [41], spectral analysis method [42,43], and the lowpass filter [44]. Since traffic flow data is characterized by stochastic and highly nonlinear patterns during the holidays, the existing traffic flow decomposition methods cannot achieve good performance, and it is difficult to apply traditional prediction methods to traffic prediction.…”

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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“…By using EMD, any complicated time series can be decomposed into a small number of Intrinsic Mode Functions (IMFs), which have simpler frequency components thus are easier and more accurate to forecast. Therefore, the EMD has been applied in many applications and hybrid model based on EMD with other model prove superior compare with individual forecasting model [4,7,9,15].…”

Section: Introductionmentioning

confidence: 99%

A modified EMD-ARIMA based on clustering analysis for fishery landing forecasting

Shabri¹

2016

ams

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This paper investigates the ability of a new hybrid forecasting model based on empirical mode decomposition (EMD), cluster analysis and Autoregressive Integrated Moving Average (ARIMA) model to improve the accuracy of fishery landing forecasting. In the first step, the original fishery landing was decomposed into a finite number of Intrinsic Mode Functions (IMFs) and a residual by EMD. The second stage, the cluster analysis was used to reconstruct the IMFs and residual into high frequency, medium frequency and low frequency components, and then every component are modelled using ARIMA model. Finally, these predicted results are aggregated into an ensemble result as final prediction. For illustration and verification purposes of the proposed model, monthly fishery landing record data from East Johor has been used as a cases study. The result shows that the proposed model obtained the best forecasting result compared with ARIMA and EMD-ARIMA models.

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Exploring time variants for short-term passenger flow

Cited by 56 publications

References 53 publications

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

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

Traffic Flow Prediction during the Holidays Based on DFT and SVR

A modified EMD-ARIMA based on clustering analysis for fishery landing forecasting

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