Analysing the complexity of day-to-day individual activity-travel patterns using a multidimensional sequence alignment model: A case study in the Bandung Metropolitan Area, Indonesia
Using a panel regression model and a multidimensional three-week household time-use and activity diary, this study analyses the complexity of the day-to-day variability in individuals’ activity-travel patterns by applying a multidimensional sequence alignment model. It is found that the variability...
| Main Authors: | Dharmowijoyo, D.B.E., Susilo, Y.O., Karlström, A. |
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| Format: | Article |
| Institution: | Universiti Teknologi Petronas |
| Record Id / ISBN-0: | utp-eprints.19341 / |
| Published: |
Elsevier Ltd
2017
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| Online Access: |
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85027346442&doi=10.1016%2fj.jtrangeo.2017.08.001&partnerID=40&md5=c73220b90e344d1795bcba39b95e68ac http://eprints.utp.edu.my/19341/ |
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| Summary: |
Using a panel regression model and a multidimensional three-week household time-use and activity diary, this study analyses the complexity of the day-to-day variability in individuals’ activity-travel patterns by applying a multidimensional sequence alignment model. It is found that the variability between weekend and weekday pairs is much greater than between weekday-weekday pairs or weekend-weekend pairs. The variability of other household members’ activity-travel patterns is found to significantly influence an individual's activity-travel patterns. The results also show that the variability in the activity-travel patterns of workers and students is greater when conducting a particular discretionary activity on weekdays. Due to performing discretionary activities more often and for longer, non-workers tend to have more predictable activity-travel patterns. Undertaking multitasking activities within different activities on weekdays significantly impacted the different degrees of variability in an individual's activity-travel patterns. Having different health and built environment characteristics also corresponds with different degrees of predictability of the activity-travel patterns, particularly in the worker/student case. © 2017 |
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