顾及轨迹还原与分类的渣土车作业行为提取方法  

Extraction of Muck Truck Operation Behavior Considering Trajectory Restoration and Classification

作　　者：庄汉宸 张亚茹 王文轩 张书亮[1,2] ZHUANG Hanchen;ZHANG Yaru;WANG Wenxuan;ZHANG Shuliang(Key Laboratory of VGE of Ministry of Education,Nanjing Normal University,Nanjing 210023,China;Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application,Nanjing 210023,China)

机构地区：[1]南京师范大学虚拟地理环境教育部重点实验室,南京210023 [2]江苏省地理信息资源开发与利用协同创新中心,南京210023

出　　处：《地球信息科学学报》2023年第4期754-765,共12页Journal of Geo-information Science

基　　金：国家自然科学基金项目(42071364)。

摘　　要：渣土车轨迹数据蕴含丰富的行为模式信息,包括停车行为、运输路径、异常活动、渣土装载与倾倒OD点等关键特征,已逐渐成为渣土车运行监测与作业行为监管的主要数据来源。但是目前在渣土车作业行为提取中仍主要采用车载GPS数据结合工地电子围栏的传统签到系统,存在电子围栏与道路相互包含、重叠等一系列问题。针对传统渣土车作业行为提取中存在的车辆作业误判问题,本文提出一种顾及轨迹还原与分类的渣土车作业行为提取方法。①从运动状态和几何形态2个方面理解并识别渣土车作业行为模式;②利用顾及时间与距离的停留点提取算法提取车辆停留点,处理停留点与轨迹的映射关系,完成基于停留点的轨迹匹配;③构建平均相似值函数对轨迹进行语义信息增强;④提出SR-LGBM算法,筛选作业轨迹与非作业轨迹,实现渣土车的作业行为提取。采用南京市渣土车轨迹数据进行测试,结果表明,本文方法的准确率达97.29%,相比GaussianNB、Logistic Regression等传统分类算法其准确率与召回率均得到不同程度的提高,有效解决了电子围栏与道路重叠或多个围栏交叉造成的误判问题,可实现准确、高效的作业行为提取。The muck truck trajectory data contain rich behavior pattern information,including key features such as parking behavior,transportation paths,abnormal activities,and muck loading and dumping OD points,etc.It has gradually become the main data source for operation monitoring and operation behavior supervision of muck trucks.However,at present,the traditional sign-in system of vehicle-mounted GPS data combined with site electronic fences is still commonly used in extraction of the muck truck operation behavior,which has a series of problems such as mutual inclusion and overlap between the electronic fence and the road.Aiming at the problem of misjudgment of vehicle operation in traditional extraction of muck truck operation behavior,this paper proposes a muck truck operation behavior extraction method considering trajectory restoration and classification.Firstly,the operation behavior pattern of muck trucks is recognized from two aspects:motion state and geometric form.Secondly,the stopping point extraction algorithm taking into account time and distance is used to extract the vehicle stopping point,and the mapping relationship between the stopping point and the trajectory is processed to complete the trajectory matching based on the stopping point.Then,the average similarity function is constructed to enhance the semantic information of the trajectory.Finally,the SR-LGBM algorithm is proposed to filter the operating trajectory and non-operating trajectory and extract the operation behavior of muck trucks.The test results show that the accuracy of the proposed method is 97.29%,which is significantly improved compared with the traditional classification algorithms such as GaussianNB and Logistic Regression.Our proposed methods effectively solve the misjudgment problem caused by the overlap of electronic fences and roads or multiple fences,and can accurately and efficiently extract the operation behavior of muck trucks.

关 键 词：渣土车轨迹数据 作业行为 作业行为模式 停留点 平均相似值函数 语义增强 轨迹特征参数 SR-LGBM算法 

分 类 号：U495[交通运输工程—交通运输规划与管理] TP311.13[交通运输工程—道路与铁道工程]