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Buildings affect mobile patterns: developing a new urban mobility model

Authors:

Liang ZhangAuthors Info & Claims

BuildSys '18: Proceedings of the 5th Conference on Systems for Built Environments

Pages 83 - 92

https://doi.org/10.1145/3276774.3276780

Published: 07 November 2018 Publication History

Abstract

Urban Mobility Models (UMMs) are fundamental tools for estimating the population in urban sites and their spatial movements over time. They have great value for such applications as managing the resources of cellular networks, predicting traffic congestion, and city planning. Most existing UMMs were developed primarily in 2D. However, we argue that people's movements and living patterns involve 3D space, i.e., buildings, which can heavily affect the accuracy of UMMs.

In this paper, we for the first time conduct a comprehensive study on the impacts of buildings on human movements, and the effect on UMMs. In particular, we start from an extensive trace analysis of two different real-world datasets. Our key observation is that human patterns of movement among urban sites are affected by buildings, with buildings being able to "temporarily hold" human mobility. We innovatively capture this property by extending Markov processes, which have been widely used in developing UMMs, with semi-absorbing states. We then develop a Semi-absorbing Urban Mobility model (SUM) and theoretically prove its properties to capture the intrinsic impacts of buildings with an analysis of SUM on its difference from that of previous UMMs. Our evaluation also demonstrates that, as a basis for supporting mobile applications in an intracity and hourly scale, the SUM is far superior to previous UMMs. Our real-world case study on cellular network resource allocations further reveals the effectiveness of our SUM model. We show that the performance of the resource allocation scheme in a cellular network substantially improves by using SUM, with a reduction in the packet loss probability of 3.19 times.

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Cited By

Ren JZhou XJin XYe YCausone FFerrando MLi PShi X(2024)A Systematic Review of Occupancy Pattern in Urban Building Energy Modeling: From Urban to Building-scaleJournal of Building Engineering10.1016/j.jobe.2024.110307(110307)Online publication date: Jul-2024
https://doi.org/10.1016/j.jobe.2024.110307
Xiang CYang PXiao FFan XXiang CYang PXiao FFan X(2023)Urban Traffic Application: Traffic Volume PredictionMulti-dimensional Urban Sensing Using Crowdsensing Data10.1007/978-981-19-9006-9_5(113-150)Online publication date: 24-Mar-2023
https://doi.org/10.1007/978-981-19-9006-9_5
Egemose HHobson BOuf MKjærgaard M(2022)Occupancy Estimation Using Sparse Sensor CoverageProceedings of the 12th International Conference on the Internet of Things10.1145/3567445.3567449(104-111)Online publication date: 7-Nov-2022
https://dl.acm.org/doi/10.1145/3567445.3567449
Show More Cited By

Index Terms

Buildings affect mobile patterns: developing a new urban mobility model
1. Mathematics of computing
  1. Probability and statistics
    1. Stochastic processes
      1. Markov processes
2. Networks
  1. Network properties
    1. Network mobility

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Published In

cover image ACM Conferences

BuildSys '18: Proceedings of the 5th Conference on Systems for Built Environments

November 2018

211 pages

ISBN:9781450359511

DOI:10.1145/3276774

General Chair:
Rajesh Gupta
University of Califronia, San Diego
,
Program Chairs:
Polly Huang
National Taiwan University and Keio University
,
Marta Gonzalez
University of California, Berkeley

Copyright © 2018 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 07 November 2018

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NSF I/UCRC Grant
the Hong Kong Polytechnic University

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BuildSys '18

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BuildSys '18: The 5th ACM International Conference on Systems for Built Environments

November 7 - 8, 2018

Shenzen, China

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Overall Acceptance Rate 148 of 500 submissions, 30%

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Cited By

Ren JZhou XJin XYe YCausone FFerrando MLi PShi X(2024)A Systematic Review of Occupancy Pattern in Urban Building Energy Modeling: From Urban to Building-scaleJournal of Building Engineering10.1016/j.jobe.2024.110307(110307)Online publication date: Jul-2024
https://doi.org/10.1016/j.jobe.2024.110307
Xiang CYang PXiao FFan XXiang CYang PXiao FFan X(2023)Urban Traffic Application: Traffic Volume PredictionMulti-dimensional Urban Sensing Using Crowdsensing Data10.1007/978-981-19-9006-9_5(113-150)Online publication date: 24-Mar-2023
https://doi.org/10.1007/978-981-19-9006-9_5
Egemose HHobson BOuf MKjærgaard M(2022)Occupancy Estimation Using Sparse Sensor CoverageProceedings of the 12th International Conference on the Internet of Things10.1145/3567445.3567449(104-111)Online publication date: 7-Nov-2022
https://dl.acm.org/doi/10.1145/3567445.3567449
Trivedi ASilverstein KStrubell EShenoy PIyyer M(2021)WiFiMod: Transformer-based Indoor Human Mobility Modeling using Passive SensingProceedings of the 4th ACM SIGCAS Conference on Computing and Sustainable Societies10.1145/3460112.3471951(126-137)Online publication date: 28-Jun-2021
https://dl.acm.org/doi/10.1145/3460112.3471951
Wei PJiang X(2021)A Data-driven System for City-wide Energy Footprinting and ApportionmentACM Transactions on Sensor Networks10.1145/343363917:2(1-24)Online publication date: 23-Jan-2021
https://dl.acm.org/doi/10.1145/3433639
Fan XXiang CChen CYang PGong LSong XNanda PHe X(2021) BuildSenSys : Reusing Building Sensing Data for Traffic Prediction With Cross-Domain Learning IEEE Transactions on Mobile Computing10.1109/TMC.2020.297693620:6(2154-2171)Online publication date: 1-Jun-2021
https://doi.org/10.1109/TMC.2020.2976936
Wen JSheng MLi JHuang K(2020)Assisting Intelligent Wireless Networks with Traffic Prediction: Exploring and Exploiting Predictive Causality in Wireless TrafficIEEE Communications Magazine10.1109/MCOM.001.190021158:6(26-31)Online publication date: Jun-2020
https://doi.org/10.1109/MCOM.001.1900211
Prabowo AKoniusz PShao WSalim F(2019)COLTRANEProceedings of the 6th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation10.1145/3360322.3360853(21-30)Online publication date: 13-Nov-2019
https://dl.acm.org/doi/10.1145/3360322.3360853

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