标题:A Hybrid Method for Traffic Incident Duration Prediction Using BOA-Optimized Random Forest Combined with Neighborhood Components Analysis
作者:Shang, Qiang; Tan, Derong; Gao, Song; Feng, Linlin
作者机构:[Shang, Qiang; Tan, Derong; Gao, Song] Shandong Univ Technol, Sch Transportat & Vehicle Engn, Zibo 255049, Shandong, Peoples R China.; [Feng, Linlin 更多
通讯作者:Shang, Qiang;Shang, Q
通讯作者地址:[Shang, Q]Shandong Univ Technol, Sch Transportat & Vehicle Engn, Zibo 255049, Shandong, Peoples R China.
来源:JOURNAL OF ADVANCED TRANSPORTATION
出版年:2019
卷:2019
DOI:10.1155/2019/4202735
摘要:Predicting traffic incident duration is important for effective and real-time traffic incident management (TIM), which helps to minimize traffic congestion, environmental pollution, and secondary incident related to this incident. Traffic incident duration prediction methods often use more input variables to obtain better prediction results. However, the problems that available variables are limited at the beginning of an incident and how to select significant variables are ignored to some extent. In this paper, a novel prediction method named NCA-BOA-RF is proposed using the Neighborhood Components Analysis (NCA) and the Bayesian Optimization Algorithm (BOA)-optimized Random Forest (RF) model. Firstly, the NCA is applied to select feature variables for traffic incident duration. Then, RF model is trained based on the training set constructed using feature variables, and the BOA is employed to optimize the RF parameters. Finally, confusion matrix is introduced to measure the optimized RF model performance and compare with other methods. In addition, the performance is also tested in the absence of some feature variables. The results demonstrate that the proposed method not only has high accuracy, but also exhibits excellent reliability and robustness.
收录类别:EI;SCIE;SSCI
资源类型:期刊论文
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