期刊
ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION
卷 10, 期 3, 页码 -出版社
MDPI
DOI: 10.3390/ijgi10030148
关键词
activity-travel pattern mining; interpersonal and intrapersonal variability; network analysis; multilevel logit model; multiple-day trajectory data
资金
- National Natural Science Foundation of China [41801158]
- Shenzhen Municipal Basic Research Project (Free Exploration) [JCYJ20180302153551891]
- Shenzhen Municipal Natural Science Foundation [JCYJ20190808173611341]
This study used a network analysis approach to detect daily activity-travel patterns of 680 Beijing residents within a week, revealing significant day-to-day intrapersonal and interpersonal variabilities in the patterns. This suggests that applying a typical day of activity-travel behaviors to measure and represent longer-term behaviors may lead to bias.
Interpersonal and intrapersonal variabilities are two important perspectives to understand daily travel behaviors, while only a small number of studies incorporate them for understanding human dynamics. This paper employed a network analysis approach to detecting daily activity-travel patterns of 680 Beijing's residents within a week and then used a multilevel multinomial logit model to analyze the intrapersonal variability in patterns and the socioeconomic linkages behind them. Results suggest that most activity-travel patterns have significant day-to-day intrapersonal and interpersonal variabilities. This suggests that the application of a typical day of activity-travel behaviors to measure and represent a week's or even longer-term behaviors may be biased, due to the existence of day-to-day intrapersonal variability. This study also provides a hint for the selection of days of a week to conduct a diary survey for activity pattern mining or travel demand modeling.
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