# Generating Individual Trajectories Using GPT-2 Trained from Scratch on Encoded Spatiotemporal Data

arXiv: https://arxiv.org/abs/2308.07940 · HTML: https://mlsanity.com/paper/2308.07940/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 29 references that are in the map.
- First posted on arXiv 14 Aug 2023.

## Builds on (most cited first, 4 of 4)

- [Neural Machine Translation with Byte-Level Subwords](https://mlsanity.com/paper/1909.03341/) (2019)
- [A Survey on Deep Learning for Human Mobility](https://mlsanity.com/paper/2012.02825/) (2020)
- [Generative Models for Simulating Mobility Trajectories](https://mlsanity.com/paper/1811.12801/) (2018)
- [Privacy in trajectory micro-data publishing : a survey](https://mlsanity.com/paper/1903.12211/) (2019)

## Built on by (most cited first, 1 of 1)

- [Generating In-store Customer Journeys from Scratch with GPT Architectures](https://mlsanity.com/paper/2407.11081/) (2024)

## Abstract (arXiv)

Following Mizuno, Fujimoto, and Ishikawa's research (Front. Phys. 2022), we transpose geographical coordinates expressed in latitude and longitude into distinctive location tokens that embody positions across varied spatial scales. We encapsulate an individual daily trajectory as a sequence of tokens by adding unique time interval tokens to the location tokens. Using the architecture of an autoregressive language model, GPT-2, this sequence of tokens is trained from scratch, allowing us to construct a deep learning model that sequentially generates an individual daily trajectory. Environmental factors such as meteorological conditions and individual attributes such as gender and age are symbolized by unique special tokens, and by training these tokens and trajectories on the GPT-2 architecture, we can generate trajectories that are influenced by both environmental factors and individual attributes.