基于个体出行数据的公共交通出行链提取方法

公共交通个体出行信息的提取对掌握公共交通出行的时空特征,改善居民通勤出行效率具有重要意义.研究从公交刷卡数据、公交定位数据、轨道AFC数据等海量公共交通多源数据的关联匹配与处理方法入手,提出了公共交通出行链信息提取中,换乘关系判断、通勤行为判别及出行起讫点匹配的方法与规则,标定了出行链匹配阈值参数,建立了基于个体出行数据的公共交通通勤出行链提取模型.提取模型的准确度验证表明:出行链结构提取及通勤出行判别的成功率均达到100%,出行阶段起讫点匹配成功率为87.5%,准确性为97.1%,满足了公共交通出行特征提取的需求.该方法为公共交通通勤出行判别及基于个体的微观通勤出行时空特征的深入分析奠定了基础.

Extraction Method of Public Transit Trip Chains Based on the Individual Riders’Data

The extraction of public transit passengers’travel information has great significance to master the time-space characteristics of public transit travel and to improve the efficiency of residents' commuting. Through matching and processing the multi-source public transport data which derived from bus smart card data, bus location data and subway AFC system data, this paper mainly studies the methods and rules of the transfer relationship judgment, commuting travel identification and trip starting point matching, which are essential steps for the extraction of public transportation trip chain information. The thresholds for trip chain matching and connecting is also calibrated, and the public transit commuting chain extraction model is established based on the individual riders’travel data. The results of the extract model validation show that the success rate of trip chain structure extraction is, and commuting travel identification reach 100%, and the success rate of origin stop and destination stop of passengers’trip is 87.5% and the accuracy is up to 97.1% The study provide essential foundation for the public transport commuter travel identification and the public transit trip chains time-space features analysis based on the individual travelers’ridership data.