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...

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Bibliographic Details
Published in:Journal of advanced transportation Vol. 2020; no. 2020; pp. 1 - 17
Main Authors: Ukkusuri, Satish V., Zhan, Xianyuan, Zhang, Yuliang, Yang, Chao, Chen, Yifan
Format: Journal Article
Language:English
Published: Cairo, Egypt Hindawi Publishing Corporation 2020
Hindawi
John Wiley & Sons, Inc
Wiley
Subjects:
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
Online Access:Get full text
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Summary: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.
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ISSN:0197-6729
2042-3195
DOI:10.1155/2020/5321385