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A platform for research: civil engineering, architecture and urbanism
Short-Term traffic condition prediction of urban road network based on improved SVM
In this fast developing world, the number of motor vehicles is increasing rapidly but road resources remain limited, causing severe congestion problem of city traffic. In order to predict short-term traffic condition accurately, we propose a short-term traffic condition prediction method for urban road network based on improved support vector machine. As outliers inevitably exist in collected traffic data, our method can effectively deal with the negative effects of outliers, thus enhance the robustness of the model and improve the generalization ability. Experimental results show that the improved method has better classification accuracy than other machine learning methods, which further validates the effectiveness of our method.
Short-Term traffic condition prediction of urban road network based on improved SVM
In this fast developing world, the number of motor vehicles is increasing rapidly but road resources remain limited, causing severe congestion problem of city traffic. In order to predict short-term traffic condition accurately, we propose a short-term traffic condition prediction method for urban road network based on improved support vector machine. As outliers inevitably exist in collected traffic data, our method can effectively deal with the negative effects of outliers, thus enhance the robustness of the model and improve the generalization ability. Experimental results show that the improved method has better classification accuracy than other machine learning methods, which further validates the effectiveness of our method.
Short-Term traffic condition prediction of urban road network based on improved SVM
Yan, He (author) / Yu, Dong-Jun (author)
2017-09-01
181430 byte
Conference paper
Electronic Resource
English
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