Hybrid short-term freeway speed prediction methods based on periodic analysis

Zou, Yajie; Hua, Xuedong; Zhang, Yanru; Wang, Yinhai

doi:10.1139/cjce-2014-0447

Cited by 43 publications

(30 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For the original sequence (21)), q = 7, the Step 1-Step 8 rolling prediction obtained (21). The MAPE of these values was measured as a model performance criterion.…”

Section: Analysis Of Rsdgm(11) Model Prediction Resultsmentioning

confidence: 99%

“…Tang et al [20] proposed a hybrid prediction approach based on the weekly seasonality of traffic flow for different temporal scales, predicted future data using double exponential smoothing, and estimated the residual data using SVM. Zou et al [21] considered the cyclical characteristics of freeway speed data by introducing a trigonometric regression function to capture the periodic component. Furthermore, the weekly cycle of traffic emissions revealed by Barmpadimos et al [22] also reflect the weekly seasonality of traffic flow.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Grey Coupled Prediction Model for Traffic Flow with Panel Data Characteristics

Yang

Xiao

Mao

et al. 2016

Entropy

View full text Add to dashboard Cite

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.

show abstract

“…For the original sequence (21)), q = 7, the Step 1-Step 8 rolling prediction obtained (21). The MAPE of these values was measured as a model performance criterion.…”

Section: Analysis Of Rsdgm(11) Model Prediction Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Grey Coupled Prediction Model for Traffic Flow with Panel Data Characteristics

Yang

Xiao

Mao

et al. 2016

Entropy

View full text Add to dashboard Cite

show abstract

“…In the model identification step, the periodic features of time series are identified. The periodic features are regarded as the criteria for applying the model [33][34]53]. In the model estimation step, the model parameters are estimated using the maximum likelihood approach or least squares approach.…”

Section: Sarima Modelmentioning

confidence: 99%

Short-term traffic speed prediction under different data collection time intervals using a SARIMA-SDGM hybrid prediction model

Song

et al. 2019

PLoS ONE

View full text Add to dashboard Cite

Short-term traffic speed prediction is a key component of proactive traffic control in the intelligent transportation systems. The objective of this study is to investigate the short-term traffic speed prediction under different data collection time intervals. Traffic speed data was collected from an urban freeway in Edmonton, Canada. A seasonal autoregressive integrated moving average plus seasonal discrete grey model structure (SARIMA-SDGM) was proposed to perform the traffic speed prediction. The model performance of SARIMA-SDGM model was compared with that of the seasonal autoregressive integrated moving average (SARIMA) model, seasonal discrete grey model (SDGM), artificial neural network (ANN) model, and support vector regression (SVR) model. The results showed that SAR-IMA-SDGM model performs best with the lowest mean absolute error (MAE), mean absolute percentage error (MAPE), and the root mean square error (RMSE). The traffic speed prediction accuracy under different time intervals were compared based on the SARIMA-SDGM model. The results showed that the prediction accuracy improves with the increase in time interval. In addition, when the time interval is greater than 10 min, the prediction results yield stable prediction accuracy.

show abstract

“…These methods can be divided into model-based methods (ARIMA method [21], Bayesian method [22], multivariable linear regression [10], neural network prediction algorithm [23], Kalman filter algorithm [24], support vector machine [25], etc. ), and data-driven methods ( -nearest neighbor matching [5], nonparametric regression [26], etc.).…”

Section: Introductionmentioning

confidence: 99%

Highway Travel Time Prediction Using Sparse Tensor Completion Tactics and K-Nearest Neighbor Pattern Matching Method

Zhao

Gao

Tang

et al. 2018

Journal of Advanced Transportation

View full text Add to dashboard Cite

Remote transportation microwave sensor (RTMS) technology is being promoted for China's highways. The distance is about 2 to 5 km between RTMSs, which leads to missing data and data sparseness problems. These two problems seriously restrict the accuracy of travel time prediction. Aiming at the data-missing problem, based on traffic multimode characteristics, a tensor completion method is proposed to recover the lost RTMS speed and volume data. Aiming at the data sparseness problem, virtual sensor nodes are set up between real RTMS nodes, and the two-dimensional linear interpolation and piecewise method are applied to estimate the average travel time between two nodes. Next, compared with the traditional -nearest neighbor method, an optimal KNN method is proposed for travel time prediction. optimization is made in three aspects. Firstly, the three original state vectors, that is, speed, volume, and time of the day, are subdivided into seven periods. Secondly, the traffic congestion level is added as a new state vector. Thirdly, the cross-validation method is used to calibrate the value to improve the adaptability of the KNN algorithm. Based on the data collected from Jinggangao highway, all the algorithms are validated. The results show that the proposed method can improve data quality and prediction precision of travel time.

show abstract

Hybrid short-term freeway speed prediction methods based on periodic analysis

Cited by 43 publications

References 37 publications

Grey Coupled Prediction Model for Traffic Flow with Panel Data Characteristics

Grey Coupled Prediction Model for Traffic Flow with Panel Data Characteristics

Short-term traffic speed prediction under different data collection time intervals using a SARIMA-SDGM hybrid prediction model

Highway Travel Time Prediction Using Sparse Tensor Completion Tactics and K-Nearest Neighbor Pattern Matching Method

Contact Info

Product

Resources

About