数据挖掘在高速公路收费数据中的应用  

Data Mining applications to charge data of expressways

作　　者：郑长江[1] 沈金星[1] 

机构地区：[1]河海大学交通学院,南京210098

出　　处：《中国科技论文在线》2008年第10期742-745,共4页

基　　金：国家自然科学基金资助项目(50608018)

摘　　要：数据挖掘技术的发展为分析高速公路收费站海量统计资料提供了便利,通过对数据挖掘技术内容及其分类的简单介绍,运用关联规则并使用数据挖掘软件Weka对广靖锡澄高速公路无锡收费站的部分统计数据进行离散化处理,借助Apriori算法对影响收费站收费效率的收费时段、车辆进站速度、车辆收费平均时耗、收费车辆数、收费车型、收费费用、收费站进口道长度、天气等因素进行关联性挖掘,认为高速公路收费站的收费数据关联性较大的是收费时间、进站车速、收费车辆数、收费站规模等因素,对于解决收费排队过长、排队车辆控制、收费费率调整、收费站进出口道数量的设置、收费站规模设置等等提供了一定的参考价值。The developments of data mining methods provide the facilitator to analysis the massive statistics of expressways toll stations. Through introduced the content and classification of data mining method briefly, the authors use the Association Rules algorithm and Weka to discrete the statistics from Wuxi toll station in Guang Jing Xi Cheng expressway, mine the association rules among charge period、vehicle speed、average time consumed、total number of vehicles,vehicle type,total fees,length of import road and weather. At last, the authors make the conclusion that time, velocity, total number of cars and scale of fees station is related to each other more than other factors. It could offer reference to reduce queue length、control queue vehicle、adjustment toll rate、decisions length of import roads and set the scale of a toll station and so on.

关 键 词：交通信息工程及控制 数据挖掘 高速公路 关联规则 Weka软件 

分 类 号：U495[交通运输工程—交通运输规划与管理]