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The Trajectory Interval Forest Classifier for Trajectory Classification

Authors:

Cristiano Landi,

Riccardo Guidotti,

Anna MonrealeAuthors Info & Claims

SIGSPATIAL '23: Proceedings of the 31st ACM International Conference on Advances in Geographic Information Systems

Article No.: 67, Pages 1 - 4

https://doi.org/10.1145/3589132.3625617

Published: 22 December 2023 Publication History

Abstract

GPS devices generate spatio-temporal trajectories for different types of moving objects. Scientists can exploit them to analyze migration patterns, manage city traffic, monitor the spread of diseases, etc. Many current state-of-the-art models that use this data type require a not negligible running time to be trained. To overcome this issue, we propose the Trajectory Interval Forest (TIF) classifier, an efficient model with high throughput. TIF works by calculating various mobility-related statistics over a set of randomly selected intervals. These statistics are used to create a tabular representation of the data, which can be used as input for any classical classifier. Our results show that TIF is comparable to or better than state-of-art in terms of accuracy and is orders of magnitude faster.

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Cited By

Wang MHaworth JIlyankou IChristie N(2024)CycleTrajectory: An End-to-End Pipeline for Enriching and Analyzing GPS Trajectories to Understand Cycling Behavior and EnvironmentProceedings of the 2nd ACM SIGSPATIAL Workshop on Sustainable Urban Mobility10.1145/3681779.3696838(27-32)Online publication date: 29-Oct-2024
https://dl.acm.org/doi/10.1145/3681779.3696838
Xie YZhang YYin YZhang SZhang YShah RZimmermann RXiao GCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)Traj2Former: A Local Context-aware Snapshot and Sequential Dual Fusion Transformer for Trajectory ClassificationProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3681340(8053-8061)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3681340

Index Terms

The Trajectory Interval Forest Classifier for Trajectory Classification
1. Computing methodologies
  1. Artificial intelligence
    1. Knowledge representation and reasoning
  2. Machine learning
    1. Learning paradigms
      1. Supervised learning
        Supervised learning by regression
2. Information systems
  1. Information systems applications

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Published In

cover image ACM Conferences

SIGSPATIAL '23: Proceedings of the 31st ACM International Conference on Advances in Geographic Information Systems

November 2023

686 pages

ISBN:9798400701689

DOI:10.1145/3589132

Copyright © 2023 Owner/Author(s).

This work is licensed under a Creative Commons Attribution International 4.0 License.

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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: 22 December 2023

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SIGSPATIAL

SIGSPATIAL '23: 31st ACM International Conference on Advances in Geographic Information Systems

November 13 - 16, 2023

Hamburg, Germany

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Cited By

Wang MHaworth JIlyankou IChristie N(2024)CycleTrajectory: An End-to-End Pipeline for Enriching and Analyzing GPS Trajectories to Understand Cycling Behavior and EnvironmentProceedings of the 2nd ACM SIGSPATIAL Workshop on Sustainable Urban Mobility10.1145/3681779.3696838(27-32)Online publication date: 29-Oct-2024
https://dl.acm.org/doi/10.1145/3681779.3696838
Xie YZhang YYin YZhang SZhang YShah RZimmermann RXiao GCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)Traj2Former: A Local Context-aware Snapshot and Sequential Dual Fusion Transformer for Trajectory ClassificationProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3681340(8053-8061)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3681340

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