大数据环境下基于职住地识别的公交通勤行为判断与特征分析

Public transport commuting behavior judgment and characteristicanalysis based on workplace and residence identification underbig data environment

在大量的关于公交通勤行为特征的研究中存在乘客下车站点推算准确度不高、通勤出行行为识别率较低等问题。为此,对交通小区级别的公交大数据进行挖掘,构建基于乘客职住地识别的公交通勤行为分析方法,并展开相应的实证研究。结果表明:与基于出行链的研究方法相比,本文方法识别和提取出的通勤出行链更具完整性;识别出的通勤乘客与非通勤乘客的出行时空特征差异显著。研究为交通规划部门提供了一种大数据环境下精度较高的公交通勤行为分析方法。

In a large number of studies on characteristics of public transport commuting behavior,there exist some problems such as low estimation accuracy of passenger get-off stations and low recognition rate of commuting behaviors.Therefore,the public transport big data on the community level are mined,the public transport commuting behavior analysis method based on the passengers workplace and residence identification is constructed,and the corresponding empirical research is carried out.The results show that:compared with the research method based on travel chains,the commuter travel chains identified and extracted by the method are more complete;the identified travel space-time characteristics of commuters and non-commuters are significantly different.The study provides a high precision analysis method of public transport commuting behaviors in the big data environment for the transportation planning department.