校园交通智能卡数据挖掘的个体出行模式与规则

Individual Travel Patterns and Rules Research Based on Campus Traffic Smart Card Data Mining

基于威斯康星大学-密尔沃基(UWM)UPass智能卡以及实时校园公交车辆自动定位技术(Automatic Vehicle Location, AVL)建立研究模型,采集数据并进行对比。通过对UWM UPass、AVL数据的挖掘构建出乘客出行链并进行相应分析,从而更好地了解乘客乘坐交通工具的特点。结合UWM UPass数据与AVL数据可以进一步研究更大时间范围内个体出行的模式,通过对大量出行链的计算从而进行规则的分类。这种分析模式及方法可为交通规划部门和经营者作出科学的公共交通规划和运营决策提供技术支撑,同时,基于本研究提出的方法也可为国内的交通规划及客流分析等提供一定的理论借鉴及方法参考,为分析获取准确、可靠的公交运营信息和乘客信息提供重要手段。

Establishing a research model based on the University of Wisconsin-Milwaukee(UWM)UPass smart card and real-time campus bus Automatic Vehicle Location(AVL)technology,collecting and comparing data.By mining UWM UPass and AVL data,constructing passenger travel chains,and conducting corresponding analyses,a better understanding of passenger transportation characteristics can be achieved.Combining UWM UPass data with AVL data enables further investigation into individual travel patterns over a larger time frame,facilitating rule classification through extensive travel chain computations.This analytical framework and methodology can provide technical support for transportation planning departments and operators to make scientific decisions regarding public transportation planning and operations.Additionally,the research methods outlined in this paper can serve as theoretical references and methodological guidance for domestic transportation planning and passenger flow analysis,thereby offering essential means for obtaining accurate and reliable public transit operation and passenger information.