基于时间序列关系的GBRT交通事故预测模型  

GBRT Traffic Accident Prediction Model Based on Time Series Relationship

作　　者：杨文忠[1] 张志豪 吾守尔·斯拉木[1] 温杰彬 富雅玲 王丽花 王婷 YANG Wen-zhong;ZHANG Zhi-hao;WUSHOUER Silamu;WEN Jie-bin;FU Ya-ling;WANG Li-hua;WANG Ting(College of Infonnation Science and Engineering,Xinjiang University Urumqi 830046;School of Software,Xinjiang University Urumqi 830046)

机构地区：[1]新疆大学信息科学与工程学院,乌鲁木齐830046 [2]新疆大学软件学院,乌鲁木齐830046

出　　处：《电子科技大学学报》2020年第4期615-621,共7页Journal of University of Electronic Science and Technology of China

基　　金：新疆维吾尔自治区自然科学基金(2017D01C042)。

摘　　要：道路交通事故是道路交通安全水平的具体表现。在当前交通事故预测工作中,存在对数据中时间序列关系的挖掘不充分、预测的周期宏观、交通事故相关的影响因素考虑不全等问题。该文提出一种基于时间序列关系的梯度提升回归树(GBRT)交通事故模型。该模型对英国Leicester的2005-2015年每天的交通事故数、死亡人数、涉事的车辆数进行预测。实验结果显示,引入时间序列关系有助于提升模型预测精度。预测结果为交通管理部门的决策起到参考作用,建模方式为同类型预测问题的建模工作带来了积极的参考意义。Road traffic accidents are a concrete manifestation of road traffic safety levels.In the current traffic accident prediction work,there is an insufficient mining of the time series relationship in the data,the predicted time period is too macroscopic,and the influencing factors related to traffic accidents are missing.Aiming at the above problems,a gradient boosted regression tree(GBRT)traffic accident model based on time series relationship is proposed.The model predicts the number of daily traffic accidents,deaths,and the number of vehicles involved in Leicester,England,from 2005 to 2015.Experimental results show that adding the time series relationship helps to improve the prediction accuracy of the model.The prediction results serve as a reference for the decision-making of the traffic management department.The modeling method brings positive reference significance to the modeling work of the same type of prediction problems.

关 键 词：梯度提升回归树 预测 时间序列 交通事故 

分 类 号：U495[交通运输工程—交通运输规划与管理]