浙江大学学报(工学版)  2018, Vol. 52 Issue (3): 453-460    DOI: 10.3785/j.issn.1008-973X.2018.03.006
 土木与交通工程

1. 浙江大学 建筑工程学院, 浙江 杭州 310058;
2. 中国城市规划设计研究院, 北京 100037
Urban travel time prediction based on gradient boosting regression tress
GONG Yue1, LUO Xiao-Qin1, WANG Dian-hai1, YANG Shao-hui2
1. College of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, China;
2. China Academy of Urban Planning & Design, Beijing 100037, China
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Abstract:

A new method based on gradient boosting regression tress was proposed in order to improve the prediction accuracy of travel time, considering the correlation of the time series, and also took into account spatial correlation. First, massive data collected by license plate recognition equipment was preprocessed, and the missing data problem was solved by corresponding data completion algorithm. Then the travel time historical data set was established. By analyzing the correlation between the different influence factors and travel time, the feature vector was established. Moreover, in order to better understand the model, the importance of each feature vector was proposed by gradient boosting regression tress model. Finally, the actual data was used to evaluate the model, and the average absolute error percentage of the travel time is about 10.0%. Compared with SVM, ARIMA and other methods, the proposed method has higher accuracy.

 CLC: U491

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GONG Yue, LUO Xiao-Qin, WANG Dian-hai, YANG Shao-hui. Urban travel time prediction based on gradient boosting regression tress. JOURNAL OF ZHEJIANG UNIVERSITY (ENGINEERING SCIENCE), 2018, 52(3): 453-460.

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