基于大数据的违法超限超载车辆源头企业区域分析  

Source Enterprise Area of Illegal Overrun and Overloaded Vehicles Based on Big Data

作　　者：杨曼 陈爱伟 陈玉飞 YANG Man;CHEN Aiwei;CHEN Yufei(China Design Group Co.,Ltd,Nanjing 210014,China)

机构地区：[1]华设设计集团股份有限公司,南京210014

出　　处：《交通与运输》2021年第6期73-77,共5页Traffic & Transportation

基　　金：江苏省交通运输科技项目(2020Y16基于大数据的违法超限超载运输治理技术应用)。

摘　　要：为从根本上遏制车辆的超限超载运输,精准打击与管控超限源头区域的违法行为,对违法超限超载车辆源头企业区域进行数据挖掘分析研究。以南京市为例,基于超限车辆清单数据、车辆定位数据,采用大数据分析处理技术对超限车辆轨迹进行划分,提取满足停止区域条件的轨迹点,选取Quick Bundles算法进行聚类,得到不同的分类区域,判定为超限车辆潜在源头企业或半路加装区域,再将聚类区域的中心位置与现有重点企业清单进行匹配分析,并剔除掉服务区、加油站等干扰区域,得到剩余区域可判定为潜在可疑源头区域,需执法部门重点关注,为执法突击检查和信用体系完善提供参考依据。In order to fundamentally curb the overrun and overload transportation of vehicles and accurately crack down on and control the illegal acts in the source area of overrun and overload vehicles,innovatively conducted data mining analysis and research on the source enterprise area of illegal overrun and overload vehicles.Taking Nanjing as an example,based on the list data and vehicle positioning data of overrun vehicles,the trajectory of overrun vehicles is divided by using big data analysis and processing technology,the trajectory points that meet the conditions of stopping area are extracted,and the quick bundles(QB)algorithm is selected for clustering to obtain different classified areas,which can be determined as the potential source enterprise or half way installation area of overrun vehicles,Then,the central position of the cluster area is matched with the list of existing key enterprises,and the interference areas such as service areas and gas stations are removed.It is found that the remaining areas can be determined as potential suspicious source areas,which need the focus of law enforcement departments,so as to provide reference basis for law enforcement surprise inspection and credit system improvement.

关 键 词：大数据 超限超载车辆 源头治超 Quick Bundles(QB)算法 数据挖掘分析 

分 类 号：U491[交通运输工程—交通运输规划与管理]