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Spatio-temporal Trajectory Learning using Simulation Systems

Published: 17 October 2022 Publication History

Abstract

Spatio-temporal trajectories are essential factors for systems used in public transport, social ecology, and many other disciplines where movement is a relevant dynamic process. Each trajectory describes multiple state changes over time, induced by individual decision-making, based on psychological and social factors with physical constraints. Since a crucial factor of such systems is to reason about the potential trajectories in a closed environment, the primary problem is the realistic replication of individual decision making. Mental factors are often uncertain, not available or cannot be observed in reality. Thus, models for data generation must be derived from abstract studies using probabilities. To solve these problems, we present Multi-Agent-Trajectory-Learning (MATL), a state transition model to learn and generate human-like Spatio-temporal trajectory data. MATL combines Generative Adversarial Imitation Learning (GAIL) with a simulation system that uses constraints given by an agent-based model (Aℬℳ). We use GAIL to learn policies in conjunction with the Aℬℳ, resulting in a novel concept of individual decision making. Experiments with standard trajectory predictions show that our approach produces similar results to real-world observations.

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  • (2024)SynthCAT: Synthesizing Cellular Association Traces with Fusion of Model-Based and Data-Driven ApproachesProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36997308:4(1-24)Online publication date: 21-Nov-2024
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  • (2023)Unveiling the Dynamic Interactions between Spatial Objects: A Graph Learning Approach with Evolving ConstraintsProceedings of the 18th International Symposium on Spatial and Temporal Data10.1145/3609956.3609965(31-40)Online publication date: 23-Aug-2023

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cover image ACM Conferences
CIKM '22: Proceedings of the 31st ACM International Conference on Information & Knowledge Management
October 2022
5274 pages
ISBN:9781450392365
DOI:10.1145/3511808
  • General Chairs:
  • Mohammad Al Hasan,
  • Li Xiong
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Published: 17 October 2022

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  1. spatio-temporal mining
  2. trajectory-generation
  3. trajectory-learning

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View all
  • (2024)SynthCAT: Synthesizing Cellular Association Traces with Fusion of Model-Based and Data-Driven ApproachesProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36997308:4(1-24)Online publication date: 21-Nov-2024
  • (2024)Big Data (R)evolution in Geography: Complexity Modelling in the Last Two DecadesGeography Compass10.1111/gec3.7000918:11Online publication date: 6-Nov-2024
  • (2023)Unveiling the Dynamic Interactions between Spatial Objects: A Graph Learning Approach with Evolving ConstraintsProceedings of the 18th International Symposium on Spatial and Temporal Data10.1145/3609956.3609965(31-40)Online publication date: 23-Aug-2023

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