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

Chao Yang,Xianyuan Zhan,Yifan Chen,Satish V. Ukkusuri,Yuliang Zhang

doi:10.1155/2020/5321385

Abstract

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.

Highlights

Cities, which are complex sociotechnical systems, are home to more than fifty percent of the world population [1]
We introduce activity event distance to measure the similarity between activities, and further develop a spatiotemporal event clustering algorithm based on DBSCAN (STE-DBSCAN) to integrate the multidimension information of the activity data and solve our spatiotemporal activity pattern extraction problem
To evaluate the performance of the model, mean absolute percentage error (MAPE) and root mean squared error (RMSE) are used, which are computed as xi′jk G×AAi∗×BBj∗×CCk∗, MAPE

Summary

Introduction

Cities, which are complex sociotechnical systems, are home to more than fifty percent of the world population [1]. Compared with the previous urban data sources, mobile phone data are massive in scale and have large spatiotemporal coverage and population representativeness, which is ideal for city-wide inference possible. Is approach allows us to take the advantage of both the detailed activity information from datasets with limitations (i.e., travel survey) and the good coverage, representativeness, and scale offered in the mobile phone data. Another challenge is how to model activity dynamics (spatiotemporal activity pattern) at the city level. We develop a novel approach based on tensor-based collaborative filtering framework to infer large-scale individual-based activity chains by fusing mobile phone data and travel survey data. E following sections review the related works, introduce the data and the urban context feature extraction, describe the methodologies of the paper, present the experiment results, and conclude this work

Related Works

Data and Urban Context Feature Extraction

Activity Chain Inference

Experimental Results and Discussion

Ntrain

Conclusions