城市公共交通常乘客通勤出行提取方法

为挖掘公共交通通勤出行精准化特征,从追踪出行链的角度出发,利用公交与轨道多源数据研究常乘客通勤出行提取方法。通过选取潜在职住地设置高频职住地集合,提出公共交通常乘客职住地识别算法,结合出行链起讫站点与职住地空间信息匹配提取通勤出行链,并将常乘客出行分为home-work通勤、work-home通勤和非通勤出行。以北京市“回天地区”公交与轨道出行链数据为例,提取常乘客通勤出行。结果表明：常乘客职住地识别率达到85.9%,常乘客通勤出行和非通勤出行在出行时空分布和出行方式上存在明显差异,通勤出行提取可为北京市面向常乘客开展“预约出行”并分析其出行需求动态特征变化提供依据。

A Method for Extracting Commuting Trips of Frequent Passengers in Urban Public Transportation

In order to explore the precise characteristics of commuting trips in public transportation, this paper investigates an extraction method for commuting trips of frequent passengers using multi- source data of bus and rail transit from the perspective of tracking trip chains. The proposed algorithm is based on the selection of potential homework locations and the setting of a high-frequency home-work locations set. The extraction algorithm combines the spatial matching of the origin and destination of the trip chain with the home and work locations. And the commuters' trips are divided into home- work commuting trips, work- home commuting trips, and noncommuting trips. The results show that the home-work locations identification rate of frequent passengers reaches 85.9% , and there are significant differences in the spatial and temporal distribution of trip and trip mode between commuting and non- commuting trips, which can be used as a basis for Beijing's development for frequent passengers. It provides a basis for "reserved trips" and analyzing changes in the dynamic characteristics of their trip demand.