A Novel Methodology for Measuring Individual-level Travel Regularity: A Joint Time-space-frequency Perspective
作者机构：School of Civil and Transportation Engineering， Hebei University of Technology School of Architecture and Art Design， Hebei University of Technology
出 版 物：《Journal of Traffic and Transportation Engineering(English Edition)》 (交通运输工程学报(英文版))
年 卷 期：2024年
核心收录：
学科分类：08[工学] 082303[工学-交通运输规划与管理] 082302[工学-交通信息工程及控制] 0823[工学-交通运输工程]
基 金：supported by the National Natural Science Foundation of China [grant numbers 52202387, 52172304] the Science and Technology Project of Hebei Education Department [grant number BJK2022044]
摘 要：Individual travel behaviors demonstrate a degree of regularity， particularly concerning time， space， and frequency repetition. Measuring the regularity of individual travel behavior contributes to understanding variations among individuals and provides opportunities for personalized transportation services. However， previous measurement methods have not comprehensively addressed these three aspects simultaneously or sufficiently captured the repetition of basic travel behaviors， while also relying on diverse data types without a comprehensive methodology covering multiple datasets. To address these research gaps， this paper presents an innovative method for measuring the regularity of individual users， which considers the repetition of basic travel behaviors along with temporal， spatial， and frequency dimensions. Applying this method to historical trip data from bike-sharing and subway systems has illustrated the effectiveness and applicability of the approach proposed in this paper. The findings suggest the broad applicability of this method across various data types， provided that the data allows for the extraction of information regarding users travel origins and destinations. Moreover， this method effectively produces a regularity value to assess and compare the overall regularity of users， surpassing previous limitations that solely relied on using frequency as an initial filter or distinguishing users as regular or irregular based solely on frequency thresholds.
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