Papagelis, ManosLi, Jing2024-03-182024-03-182024-03-16https://hdl.handle.net/10315/41931Trajectory prediction involves estimating an object's future path using its current state and historical data, with applications in autonomous vehicles, robotics, and human motion analysis. Deep learning methods trained on historical data have been applied to this task, but they struggle with complex spatial dependencies due to the intricate nature of trajectory data and dynamic environments. We introduce TrajLearn, a novel trajectory prediction model using generative models and higher-order mobility flow representations (hexagons). TrajLearn, given a trajectory's recent history and current state, predicts its next k steps. It employs a variant of beam search for exploring multiple paths, ensuring spatial continuity. Our experiments demonstrate that TrajLearn surpasses current leading methods and other baselines by about 60% on various real-world datasets. We also explore different prediction horizons (k values), perform resolution sensitivity analysis, and conduct an ablation study to evaluate the contributions of different model components.Author owns copyright, except where explicitly noted. Please contact the author directly with licensing requests.Computer scienceElectronic Thesis or Dissertation2024-03-16Human mobilityTrajectory predictionDeep generative models