poster

20 Years of Mobility Modeling & Prediction: Trends, Shortcomings & Perspectives

Authors:

Vaibhav Kulkarni,

Benoît GarbinatoAuthors Info & Claims

SIGSPATIAL '19: Proceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

Pages 492 - 495

https://doi.org/10.1145/3347146.3359110

Published: 05 November 2019 Publication History

Abstract

In this paper, we present the insights drawn from a comprehensive survey of human-mobility modeling research based on 1680 articles that can serve as a roadmap for research and practice in this area. Mobility modeling research has accelerated the advancement of several fields of studies such as urban planning, epidemic modeling, traffic engineering and contributed to the development of location-based services. However, while the application of mobility models in different domains has increased, the credibility of the research results has decreased. We highlight two significant shortfalls observed in our reviewed studies: (1) data-agnostic model selection resulting in a poor tradeoff between modeling accuracy vs. computational complexity, and (2) failure in identifying the source of empirical gains, due to adoption of erroneous validation methodologies. We also observe troubling trends with respect to the application of Markov models for modeling mobility, despite the questionable association between Markov processes and mobility dynamics. We offer the literature meta-data and the associated tools to the community in order to improve the reliability and credibility of human mobility modeling research.

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Cited By

Tsanakas SHameed AViolos JLeivadeas A(2024)A light-weight edge-enabled knowledge distillation technique for next location prediction of multitude transportation meansFuture Generation Computer Systems10.1016/j.future.2023.12.025154:C(45-58)Online publication date: 1-May-2024
https://dl.acm.org/doi/10.1016/j.future.2023.12.025
Miao QLi MLin WWang ZShao HXie JShu NQiao Y(2022)Human Mobility Prediction with Calibration for Noisy TrajectoriesElectronics10.3390/electronics1120336211:20(3362)Online publication date: 18-Oct-2022
https://doi.org/10.3390/electronics11203362
Chen MChiang HYang K(2022)Constructing Cooperative Intelligent Transport Systems for Travel Time Prediction With Deep Learning ApproachesIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2022.314826923:9(16590-16599)Online publication date: Sep-2022
https://doi.org/10.1109/TITS.2022.3148269
Show More Cited By

Index Terms

20 Years of Mobility Modeling & Prediction: Trends, Shortcomings & Perspectives
1. Information systems
  1. Information systems applications
    1. Spatial-temporal systems

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cover image ACM Conferences

SIGSPATIAL '19: Proceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

November 2019

648 pages

ISBN:9781450369091

DOI:10.1145/3347146

Copyright © 2019 Owner/Author.

Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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SIGSPATIAL: ACM Special Interest Group on Spatial Information

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 05 November 2019

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Poster
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Refereed limited

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Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung

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SIGSPATIAL '19

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SIGSPATIAL

SIGSPATIAL '19: 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

November 5 - 8, 2019

IL, Chicago, USA

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SIGSPATIAL '19 Paper Acceptance Rate 34 of 161 submissions, 21%;

Overall Acceptance Rate 257 of 1,238 submissions, 21%

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Cited By

Tsanakas SHameed AViolos JLeivadeas A(2024)A light-weight edge-enabled knowledge distillation technique for next location prediction of multitude transportation meansFuture Generation Computer Systems10.1016/j.future.2023.12.025154:C(45-58)Online publication date: 1-May-2024
https://dl.acm.org/doi/10.1016/j.future.2023.12.025
Miao QLi MLin WWang ZShao HXie JShu NQiao Y(2022)Human Mobility Prediction with Calibration for Noisy TrajectoriesElectronics10.3390/electronics1120336211:20(3362)Online publication date: 18-Oct-2022
https://doi.org/10.3390/electronics11203362
Chen MChiang HYang K(2022)Constructing Cooperative Intelligent Transport Systems for Travel Time Prediction With Deep Learning ApproachesIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2022.314826923:9(16590-16599)Online publication date: Sep-2022
https://doi.org/10.1109/TITS.2022.3148269
Talavera FLera IGuerrero C(2022)Analysis of wireless network access logs for a hierarchical characterization of user mobilityJournal of King Saud University - Computer and Information Sciences10.1016/j.jksuci.2022.03.01434:6(2471-2487)Online publication date: Jun-2022
https://doi.org/10.1016/j.jksuci.2022.03.014
Hayes DRos DAlay ÖTeymoori PVister T(2022)Investigating predictive model-based control to achieve reliable consistent multipath mmWave communicationComputer Communications10.1016/j.comcom.2022.07.011Online publication date: Jul-2022
https://doi.org/10.1016/j.comcom.2022.07.011
Hayes DRos DAlay ÖTeymoori PIgartua MGiannelli CZheng J(2021)Reliable Consistent Multipath mmWave CommunicationProceedings of the 24th International ACM Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems10.1145/3479239.3485684(149-158)Online publication date: 22-Nov-2021
https://dl.acm.org/doi/10.1145/3479239.3485684
Sun JKim J(2021)Joint prediction of next location and travel time from urban vehicle trajectories using long short-term memory neural networksTransportation Research Part C: Emerging Technologies10.1016/j.trc.2021.103114128(103114)Online publication date: Jul-2021
https://doi.org/10.1016/j.trc.2021.103114
Terroso-Sáenz FMuñoz A(2021)Nation-wide human mobility prediction based on graph neural networksApplied Intelligence10.1007/s10489-021-02645-3Online publication date: 19-Jul-2021
https://doi.org/10.1007/s10489-021-02645-3

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