A time-use activity-pattern recognition model for activity-based travel demand modeling

This study develops a new comprehensive pattern recognition modeling framework that leverages activity data to derive clusters of homogeneous daily activity patterns, for use in activity-based travel demand modeling. The pattern recognition model is applied to time use data from the large Halifax ST...

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Vydáno v:	Transportation (Dordrecht) Ročník 46; číslo 4; s. 1369 - 1394
Hlavní autoři:	Hafezi, Mohammad Hesam, Liu, Lei, Millward, Hugh
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York Springer US 01.08.2019 Springer Nature B.V
Témata:	Acknowledgment Activity patterns Applied behavior analysis Cluster analysis Clustering Demographic variables Demographics Demography Economic Geography Economics Economics and Finance Engineering Economics Innovation/Technology Management Learning algorithms Logistics Machine learning Marketing Modelling Organization Pattern recognition Population (statistical) Recognition Regional/Spatial Science Salt Sequences Sociodemographics Temporal distribution Three dimensional models Time Time use Traffic models Travel Travel demand Fuzzy C-means clustering algorithm Time-use Activity-based model Activity pattern recognition Machine learning
ISSN:	0049-4488, 1572-9435
On-line přístup:	Získat plný text
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