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Understanding Metropolitan Crowd Mobility via Mobile Cellular Accessing Data

Authors:

Jagan Sankaranarayanan,

Hanan SametAuthors Info & Claims

ACM Transactions on Spatial Algorithms and Systems (TSAS), Volume 5, Issue 2

Article No.: 8, Pages 1 - 18

https://doi.org/10.1145/3323345

Published: 25 July 2019 Publication History

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Abstract

Understanding crowd mobility in a metropolitan area is extremely valuable for city planners and decision makers. However, crowd mobility is a relatively new area of research and has significant technical challenges: lack of large-scale fine-grained data, difficulties in large-scale trajectory processing, and issues with spatial resolution. In this article, we propose a novel approach for analyzing crowd mobility on a “city block” level. We first propose algorithms to detect homes, working places, and stay regions for individual user trajectories. Next, we propose a method for analyzing commute patterns and spatial correlation at a city block level. Using mobile cellular accessing trace data collected from users in Shanghai, we discover commute patterns, spatial correlation rules, as well as a hidden structure of the city based on crowd mobility analysis. Therefore, our proposed methods contribute to our understanding of human mobility in a large metropolitan area.

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Sun LZhao JZhang JZhang FYe KXu C(2024) STRmt : A state transition based model for real‐time crowd counting in a metro system Concurrency and Computation: Practice and Experience10.1002/cpe.808636:14Online publication date: 5-Apr-2024
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Index Terms

Understanding Metropolitan Crowd Mobility via Mobile Cellular Accessing Data
1. Information systems
  1. Information systems applications
    1. Data mining
2. Social and professional topics
  1. User characteristics
    1. Geographic characteristics

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Published In

cover image ACM Transactions on Spatial Algorithms and Systems

ACM Transactions on Spatial Algorithms and Systems Volume 5, Issue 2

Special Issue on Urban Mobility: Algorithms and Systems

June 2019

133 pages

ISSN:2374-0353

EISSN:2374-0361

DOI:10.1145/3350424

Editor:
Walid G. Aref
Purdue University, USA

Issue’s Table of Contents

Copyright © 2019 ACM.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 25 July 2019

Accepted: 01 March 2019

Received: 01 December 2018

Published in TSAS Volume 5, Issue 2

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Funding Sources

National Science Foundation
the National Nature Science Foundation of China
Beijing National Research Center for Information Science and Technology
the National Key Research and Development Program of China
research fund of Tsinghua University - Tencent Joint Laboratory for Internet Innovation Technology

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https://doi.org/10.3390/fi16090342
Huang HHe SYang XTabatabaie M(2024) STICAP: Spatio-temporal Interactive Attention for Citywide Crowd Activity PredictionACM Transactions on Spatial Algorithms and Systems10.1145/360337510:1(1-22)Online publication date: 15-Jan-2024
https://dl.acm.org/doi/10.1145/3603375
Sun LZhao JZhang JZhang FYe KXu C(2024) STRmt : A state transition based model for real‐time crowd counting in a metro system Concurrency and Computation: Practice and Experience10.1002/cpe.808636:14Online publication date: 5-Apr-2024
https://doi.org/10.1002/cpe.8086
Thakur TMarchang N(2023)PAMDIJournal of Ambient Intelligence and Smart Environments10.3233/AIS-22047515:1(19-46)Online publication date: 1-Jan-2023
https://dl.acm.org/doi/10.3233/AIS-220475
Arulprakash ABaalamurugan KDhanaraj RSampath Kumar KGupta PRehman S(2022)Aggregation Technique Using Dynamic Cross-Propagation Clustering Algorithm in Wireless Body Sensor NetworksWireless Communications & Mobile Computing10.1155/2022/61025842022Online publication date: 1-Jan-2022
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Girolami MVitello PCapponi AFiandrino CFoschini LBellavista P(2022)A mobility-based deployment strategy for edge data centersJournal of Parallel and Distributed Computing10.1016/j.jpdc.2022.03.007164:C(133-141)Online publication date: 1-Jun-2022
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Fang ZYang YYang GXian YZhang FZhang D(2021)CellSenseProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/34780875:3(1-22)Online publication date: 14-Sep-2021
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Wang HLi YLin JCao HJin D(2021)Context-Aware Semantic Annotation of Mobility RecordsACM Transactions on Knowledge Discovery from Data10.1145/347704816:3(1-20)Online publication date: 22-Oct-2021
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