research-article

Pedestrian-movement prediction based on mixed Markov-chain model

Authors:

Akinori Asahara,

Kishiko Maruyama,

Kouichi SetoAuthors Info & Claims

GIS '11: Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

Pages 25 - 33

https://doi.org/10.1145/2093973.2093979

Published: 01 November 2011 Publication History

Abstract

A method for predicting pedestrian movement on the basis of a mixed Markov-chain model (MMM) is proposed. MMM takes into account a pedestrian's personality as an unobservable parameter. It also takes into account the effects of the pedestrian's previous status. A promotional experiment in a major shopping mall demonstrated that the highest prediction accuracy of the MMM method is 74.4%. In comparison with methods based on a Markov-chain model (MM) and a hidden-Markov model (HMM) (i.e., prediction rates of about 45% and 2%, respectively), the proposed MMM-based prediction method is substantially more accurate. This pedestrian-movement prediction based on MMM using tracking data will make it possible to provide so-called "adaptive mobile services" with proactive functions.

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Cited By

Zhang JGuo LWang GYu JZheng XMei YHan B(2025)A dual-level graph attention network and transformer for enhanced trajectory prediction under road network constraintsExpert Systems with Applications10.1016/j.eswa.2024.125510261(125510)Online publication date: Feb-2025
https://doi.org/10.1016/j.eswa.2024.125510
Song JZhang XJiang W(2024)Sea Surface Temperature Prediction in China Sea Based on SAM-LSTM ApproachAmerican Journal of Environmental Science and Engineering10.11648/j.ajese.20240802.118:2(14-22)Online publication date: 28-Apr-2024
https://doi.org/10.11648/j.ajese.20240802.11
Musleh MMokbel M(2024)Let's Speak Trajectories: A Vision to Use NLP Models for Trajectory Analysis TasksACM Transactions on Spatial Algorithms and Systems10.1145/365647010:2(1-25)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1145/3656470
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Index Terms

Pedestrian-movement prediction based on mixed Markov-chain model
1. Human-centered computing
  1. Visualization
    1. Visualization application domains
      1. Geographic visualization
2. Information systems
  1. Information systems applications
    1. Data mining
    2. Spatial-temporal systems

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Published In

cover image ACM Conferences

GIS '11: Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

November 2011

559 pages

ISBN:9781450310314

DOI:10.1145/2093973

Editors:
Divyakant Agrawal
University of California at Santa Barbara
,
Isabel Cruz
University of Illinois at Chicago
,
Christian S. Jensen
Aarhus University, Denmark
,
Eyal Ofek
Microsoft, USA
,
Egemen Tanin
University of Melbourne, Australia

Copyright © 2011 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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SIGSPATIAL: ACM Special Interest Group on Spatial Information

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SIGGRAPH: ACM Special Interest Group on Computer Graphics and Interactive Techniques

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 November 2011

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GIS '11

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SIGSPATIAL

GIS '11: 19th SIGSPATIAL International Conference on Advances in Geographic Information Systems

November 1 - 4, 2011

Illinois, Chicago

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Overall Acceptance Rate 220 of 1,116 submissions, 20%

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Cited By

Zhang JGuo LWang GYu JZheng XMei YHan B(2025)A dual-level graph attention network and transformer for enhanced trajectory prediction under road network constraintsExpert Systems with Applications10.1016/j.eswa.2024.125510261(125510)Online publication date: Feb-2025
https://doi.org/10.1016/j.eswa.2024.125510
Song JZhang XJiang W(2024)Sea Surface Temperature Prediction in China Sea Based on SAM-LSTM ApproachAmerican Journal of Environmental Science and Engineering10.11648/j.ajese.20240802.118:2(14-22)Online publication date: 28-Apr-2024
https://doi.org/10.11648/j.ajese.20240802.11
Musleh MMokbel M(2024)Let's Speak Trajectories: A Vision to Use NLP Models for Trajectory Analysis TasksACM Transactions on Spatial Algorithms and Systems10.1145/365647010:2(1-25)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1145/3656470
Zhang ZWong WSikdar B(2024)A Hybrid Reinforcement Learning-Based Method for Generating Privacy-Preserving Trajectories in Low-Density Traffic EnvironmentsIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2024.340031225:10(14740-14757)Online publication date: Oct-2024
https://doi.org/10.1109/TITS.2024.3400312
Papathanasopoulou VPerakis HSpyropoulou IGikas V(2024)Pedestrian Simulation Challenges: Modeling Techniques and Emerging Positioning Technologies for ITS ApplicationsIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2024.340028025:10(12876-12892)Online publication date: 1-Oct-2024
https://dl.acm.org/doi/10.1109/TITS.2024.3400280
Uhlik OOkrinova PTokarevskikh AApeltauer TApeltauer J(2024)Real-Time RSET Prediction Across Three Types of Geometries and Simulation Training Dataset: A Comparative Study of Machine Learning ModelsDevelopments in the Built Environment10.1016/j.dibe.2024.100461(100461)Online publication date: May-2024
https://doi.org/10.1016/j.dibe.2024.100461
Zhao SLi ZZhu ZChang CLi XChen YYang B(2024)An integrated framework for accurate trajectory prediction based on deep learningApplied Intelligence10.1007/s10489-024-05724-354:20(10161-10175)Online publication date: 8-Aug-2024
https://doi.org/10.1007/s10489-024-05724-3
YAO CSHI WLIU CCHEN HCHEN Q(2023)An overview of mobile robot navigation technologySCIENTIA SINICA Informationis10.1360/SSI-2022-042053:12(2303)Online publication date: 11-Dec-2023
https://doi.org/10.1360/SSI-2022-0420
Zhang PKoutsopoulos HMa Z(2023)DeepTrip: A Deep Learning Model for the Individual Next Trip Prediction With Arbitrary Prediction TimesIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2023.325204324:6(5842-5855)Online publication date: Jun-2023
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Greer RDesai SRakla LGopalkrishnan AAlofi ATrivedi M(2023)Pedestrian Behavior Maps for Safety Advisories: CHAMP Framework and Real-World Data Analysis2023 IEEE Intelligent Vehicles Symposium (IV)10.1109/IV55152.2023.10186648(1-8)Online publication date: 4-Jun-2023
https://doi.org/10.1109/IV55152.2023.10186648
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