The retrieval of intra-day trend and its influence on traffic prediction

Chen, Chenyi; Yin, Wei; Li, Zhiheng; Hu, Jianming; Zuo, Zhigang

doi:10.1016/j.trc.2011.12.006

Cited by 165 publications

(94 citation statements)

References 65 publications

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“…The prediction results of Wavelet-SVM with several standard evaluation measures are almost the same with the original time series and the residual time series. The experimental results are opposite to the outcomes by Chen et al [29]. We find the reason is that the single linear and non-linear prediction algorithms are applied in [29], while the prediction models evaluated in this paper are hybrid methods, which combine with analysis and prediction parts.…”

Section: Introductioncontrasting

confidence: 72%

“…As discussed by [29], some prediction models that work well for workdays data may yield unsatisfactory results for weekends or holidays data. In order to compare and evaluate different state-ofthe-art short-time passenger flow forecasting approaches with the proposed method in this paper comprehensively, the test data is collected from 13rd February to 25th March 2012 in Beijing subway system, containing not only the weekends, but also the Valentine's Day (14th February).…”

Section: The Test Datamentioning

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: Introductioncontrasting

confidence: 72%

Section: The Test Datamentioning

confidence: 99%

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

2015

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“…Due to this paper processing 15min interval short-term traffic flow forecasting, so the 5min data must be processed into 15min interval traffic flow data. According to the literature [17], to improve the forecasting precision, the rest days data are excluded, and only the working days data are used. So, there are actually 22 days of data for the experiment, with total number of data 2112.…”

Section: Experiments and Results Analysis Experiments Data And Environmentmentioning

confidence: 99%

Short-term traffic flow forecasting based on feature selection with mutual information

Yuan¹,

Tu²

2017

AIP Conference Proceedings

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Abstract. Traffic flow forecasting is related to many traffic variables, and how to select appropriate traffic variable combination is very important to traffic flow forecasting, which can reduce the cost of calculation and improve the forecasting precision. In this paper, a feature selection technique with mutual information is proposed for this purpose. Firstly, the mutual information is used to evaluate the relevance and redundancy of variables, and feature selection is used to select the relevant variables and filter out the redundancy between the selected variables. Secondly, BP neural network is used as the forecasting engine. Finally, a numerical example of traffic flow data from Pems is used to verify the forecasting performance of the proposed method, the results indicate that the proposed method can effectively reduce the cost of calculation and also improve the model forecasting precision.

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“…(2) According to Equation (19), the corresponding nearness grey relational degree and are obtained: (4) Equation (18) is used to solve the time-series and cross-sectional data coupled prediction:…”

Section: Rsdgm(11)-arima Coupled Modelmentioning

confidence: 99%

“…Kamarianakis et al [3] used smooth-transition regressions to characterize the daily cycles of urban traffic flow. Chen et al [19] analyzed the retrieval of intra-day trends for traffic flow series to address missing data and to improve traffic predictions. The weekly seasonality of traffic flow has been recognized and used by many scholars.…”

Section: Introductionmentioning

confidence: 99%

Grey Coupled Prediction Model for Traffic Flow with Panel Data Characteristics

Yang

Xiao

Mao

et al. 2016

Entropy

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This paper studies the grey coupled prediction problem of traffic data with panel data characteristics. Traffic flow data collected continuously at the same site typically has panel data characteristics. The longitudinal data (daily flow) is time-series data, which show an obvious intra-day trend and can be predicted using the autoregressive integrated moving average (ARIMA) model. The cross-sectional data is composed of observations at the same time intervals on different days and shows weekly seasonality and limited data characteristics; this data can be predicted using the rolling seasonal grey model (RSDGM (1,1)). The length of the rolling sequence is determined using matrix perturbation analysis. Then, a coupled model is established based on the ARIMA and RSDGM(1,1) models; the coupled prediction is achieved at the intersection of the time-series data and cross-sectional data, and the weights are determined using grey relational analysis. Finally, numerical experiments on 16 groups of cross-sectional data show that the RSDGM(1,1) model has good adaptability and stability and can effectively predict changes in traffic flow. The performance of the coupled model is also better than that of the benchmark model, the coupled model with equal weights and the Bayesian combination model.

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The retrieval of intra-day trend and its influence on traffic prediction

Cited by 165 publications

References 65 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

Short-term traffic flow forecasting based on feature selection with mutual information

Grey Coupled Prediction Model for Traffic Flow with Panel Data Characteristics

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