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

被引：0
|
作者：
Peng F. [1 ]
Song G.-H. [1 ]
Zhu S. [2 ]
机构：
[1] Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, Beijing Jiaotong University, Beijing
[2] Beijing Transport Institute, Beijing
来源：
Jiaotong Yunshu Xitong Gongcheng Yu Xinxi/Journal of Transportation Systems Engineering and Information Technology | 2021年 / 21卷 / 02期
基金：
中国国家自然科学基金;
关键词：
Commuting trip; Frequent passengers; Home-work locations identification; Public transportation; Urban traffic;
D O I：
10.16097/j.cnki.1009-6744.2021.02.023
中图分类号：
学科分类号：
摘要：
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. Copyright © 2021 by Science Press.
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页码：158 / 165and172
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