Short-term traffic flow prediction method based on grey adaptive particle swarm support vector machine

2016 ◽  
Author(s):  
Mei Duo ◽  
E. Xu ◽  
Yan Qi
Keyword(s):  
Support Vector Machine ◽  
Traffic Flow ◽  
Particle Swarm ◽  
Prediction Method ◽  
Support Vector ◽  
Short Term ◽  
Traffic Flow Prediction ◽  
Flow Prediction

scholarly journals Traffic Flow Prediction using Combination of Support Vector Machine and Rough Set

2019 ◽  
Vol 8 (11) ◽  
pp. 3334-3338
Keyword(s):  
Support Vector Machine ◽  
Traffic Flow ◽  
Rough Set ◽  
Traffic Control ◽  
Support Vector ◽  
Traffic Data ◽  
Short Term ◽  
Traffic Flow Prediction ◽  
Flow Prediction ◽  
Threshold Variation

Congestion is the primary issue related to traffic flow. Avoiding congestion after getting into is not possible. So the only way is to make the informed decision by knowing the traffic situation in advance. This can be achieved with the help of traffic flow prediction. In the proposed work, short term traffic flow prediction is performed using support vector machine in combination with rough set. Traffic data used for analysis is collected from three adjacent intersections of Nagpur city and traffic flow is predicted at downstream junction. The work has attempted to study the effect of aggregation intervals and past samples on the prediction performance using MSE threshold variation. Rough set is used as a post processor to validate the prediction result. Accurate and timely prediction can provide reliability for optimized traffic control and guidance.

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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