scholarly journals An Improved Double-layer K-nearest Neighbor Nonparametric Regression Method for Short-time Traffic Flow Prediction

2016 ◽  
Vol 14 (2A) ◽  
pp. 298
Author(s):  
Wang Cheng ◽  
Pang Xiyu ◽  
Huang Guolin
Keyword(s):  
Traffic Flow ◽  
Nonparametric Regression ◽  
Double Layer ◽  
Nearest Neighbor ◽  
Regression Method ◽  
K Nearest Neighbor ◽  
Traffic Flow Prediction ◽  
Flow Prediction ◽  
Short Time

scholarly journals An Improved K-nearest Neighbor Model for Short-term Traffic Flow Prediction

2013 ◽  
Vol 96 ◽  
pp. 653-662 ◽  
Author(s):  
Lun Zhang ◽  
Qiuchen Liu ◽  
Wenchen Yang ◽  
Nai Wei ◽  
Decun Dong
Keyword(s):  
Traffic Flow ◽  
Nearest Neighbor ◽  
K Nearest Neighbor ◽  
Short Term ◽  
Traffic Flow Prediction ◽  
Flow Prediction

Support Vector Machine for Short-Term Traffic Flow Prediction and Improvement of Its Model Training using Nearest Neighbor Approach

2020 ◽  
pp. 036119812098043
Author(s):  
Trinh Dinh Toan ◽  
Viet-Hung Truong
Keyword(s):  
Support Vector Machine ◽  
Traffic Flow ◽  
Prediction Accuracy ◽  
Nearest Neighbor ◽  
Support Vector ◽  
Rolling Horizon ◽  
K Nearest Neighbor ◽  
Short Term ◽  
Traffic Flow Prediction ◽  
Flow Prediction

Short-term prediction of traffic flow is essential for the deployment of intelligent transportation systems. In this paper we present an efficient method for short-term traffic flow prediction using a Support Vector Machine (SVM) in comparison with baseline methods, including the historical average, the Current Time Based, and the Double Exponential Smoothing predictors. To demonstrate the efficiency and accuracy of the SVM method, we used one-month time-series traffic flow data on a segment of the Pan Island Expressway in Singapore for training and testing the model. The results show that the SVM method significantly outperforms the baseline methods for most prediction intervals, and under various traffic conditions, for the rolling horizon of 30 min. In investigating the effect of the input-data dimension on prediction accuracy, we found that the rolling horizon has a clear effect on the SVM’s prediction accuracy: for the rolling horizon of 30–60 min, the longer the rolling horizon, the more accurate the SVM prediction is. To look for a solution for improvement of the SVM’s training performance, we investigate the application of k-Nearest Neighbor method for SVM training using both actual data and simulated incident data. The results show that the k- Nearest Neighbor method facilitates a substantial reduction of SVM training size to accelerate the training without compromising predictive performance.

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