Spatio-temporal Trajectory Learning using Simulation Systems

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.