东南大学学报（英文版）2019，Vol.35Issue(4)：502-508,7.DOI:10.3969/j.issn.1003-7985.2019.04.013
基于海量车辆轨迹数据的机动车驾驶员驾驶行为评价
Evaluation of driving behavior based on massive vehicle trajectory data
摘要
Abstract
Based on the driver surveillance video data and controller area network (CAN) data,the methods of studying commercial vehicles' driving behavior is relatively advanced.However,these methods have difficulty in covering private vehicles.Naturalistic driving studies have disadvantages of small sample size and high cost,one new driving behavior evaluation method using massive vehicle trajectory data is put forward.An automatic encoding machine is used to reduce the noise of raw data,and then driving dynamics and selforganizing mapping (SOM) classification are used to give thresholds or the judgement method of overspeed,rapid speed change,rapid turning and rapid lane changing.The proportion of different driving behaviors and typical dangerous driving behaviors is calculated,then the temporal and spatial distribution of drivers' driving behavior and the driving behavior characteristics on typical roads are analyzed.Driving behaviors on accident-prone road sections and normal road sections are compared.Results show that in Shenzhen,frequent lane changing and overspeed are the most common unsafe driving behaviors;16.1％ drivers have relatively aggressive driving behavior;the proportion of dangerous driving behavior is higher outside the original economic special zone;dangerous driving behavior is highly correlated with traffic accident frequency.关键词
驾驶行为/GPS导航数据/自动编码机/自组织映射Key words
driving behavior/global positioning system (GPS) navigating data/automatic coding machine/self-organizing mapping (SOM)分类
交通工程引用本文复制引用
孙超,陈小鸿,张红军,张俊峰..基于海量车辆轨迹数据的机动车驾驶员驾驶行为评价[J].东南大学学报（英文版）,2019,35(4):502-508,7.基金项目
The National Natural Science Foundation of China (No.71641005),the National Key Research and Development Program of China(No.2018YFB1601105). （No.71641005）
