Effective Trajectory Imputation using Simple Probabilistic Language Models

• Author / Creator

  Mohammed, Hayat S

• Trajectory data analysis refers to the systematic exploration of spatial and temporal movement patterns in trajectory datasets. Missing trajectory points pose a challenge as they affect downstream tasks that rely on these datasets, such as public transportation management, wildlife monitoring, and urban planning. Diverse approaches, from statistical methods to deep learning models, have been proposed to address the issue of missing data points in trajectories. In this thesis, we explore two approaches based on relatively simple probabilistic language models in order to address this problem, also known as trajectory imputation. Using a grid-based representation, we assign tokens to each point in a trajectory, representing each trajectory as a sequence of tokens akin to a sentence in natural language. This allows the application of language models for predicting missing points (tokens). Our experiments using a real dataset of over 200,000 taxi trips show that we can fill gaps of up to 2km with 85% precision. Furthermore, compared to large language models, probabilistic language models for imputing trajectory points offer a much simpler technique and enhance the interpretability of the results.

• Subjects / Keywords

  • trajectory data
  • trajectory imputation
  • language models

• Graduation date

  Spring 2024

• Type of Item

  Thesis

• Degree

  Master of Science

• DOI

  https://doi.org/10.7939/r3-q0x8-wy56

• License

  This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for non-commercial purposes. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.