Fusing Mobile Phone and Travel Survey Data to Model Urban Activity Dynamics

A key issue to understand urban system is to characterize the activity dynamics in a city—when, where, what, and how activities happen in a city. To better understand the urban activity dynamics, city-wide and multiday activity participation sequence data, namely, activity chain as well as suitable...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Journal of advanced transportation Ročník 2020; číslo 2020; s. 1 - 17
Hlavní autoři: Ukkusuri, Satish V., Zhan, Xianyuan, Zhang, Yuliang, Yang, Chao, Chen, Yifan
Médium: Journal Article
Jazyk:angličtina
Vydáno: Cairo, Egypt Hindawi Publishing Corporation 2020
Hindawi
John Wiley & Sons, Inc
Wiley
Témata:
Activity patterns
Big Data
Cell phones
Cellular telephones
Chains
Cities
Clustering
Collaboration
Data mining
Datasets
Dynamics
Mobility
Participation
Pattern recognition
Public transportation
Smart cards
Tensors
Transportation planning
Travel
Trip surveys
Urban areas
ISSN:0197-6729, 2042-3195
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:A key issue to understand urban system is to characterize the activity dynamics in a city—when, where, what, and how activities happen in a city. To better understand the urban activity dynamics, city-wide and multiday activity participation sequence data, namely, activity chain as well as suitable spatiotemporal models, are needed. The commonly used household travel survey data in activity analysis suffers from limited sample size and temporal coverage. The emergence of large-scale spatiotemporal data in urban areas, such as mobile phone data, provides a new opportunity to infer urban activities and the underlying dynamics. However, the challenge is the absence of labeled activity information in mobile phone data. Consequently, how to fuse the useful information in household survey data and mobile phone data to build city-wide, multiday, and all-time activity chains becomes an important research question. Moreover, the multidimension structure of the activity data (e.g., location, start time, duration, type) makes the extraction of spatiotemporal activity patterns another difficult problem. In this study, the authors first introduce an activity chain inference model based on tensor decomposition to infer the missing activity labels in large-scale and multiday activity data, and then develop a spatiotemporal event clustering model based on DBSCAN, called STE-DBSCAN, to identify the spatiotemporal activity patterns. The proposed approaches achieved good accuracy and produced patterns with a high level of interpretability.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0197-6729
2042-3195
DOI:10.1155/2020/5321385