# TrajectoryNet: An Embedded GPS Trajectory Representation for Point-based Classification Using Recurrent Neural Networks

arXiv: https://arxiv.org/abs/1705.02636 · HTML: https://mlsanity.com/paper/1705.02636/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 May 2017.

## Built on by (most cited first, 10 of 10)

- [Trajectory Data Management and Mining: A Survey from Deep Learning to the LLM Era](https://mlsanity.com/paper/2403.14151/) (2024)
- [TrajAgent: An LLM-Agent Framework for Trajectory Modeling via Large-and-Small Model Collaboration](https://mlsanity.com/paper/2410.20445/) (2024)
- [CT-SGAN: Computed Tomography Synthesis GAN](https://mlsanity.com/paper/2110.09288/) (2021)
- [Towards Robust Trajectory Representations: Isolating Environmental Confounders with Causal Learning](https://mlsanity.com/paper/2404.14073/) (2024)
- [Enhancing Maritime Trajectory Forecasting via H3 Index and Causal Language Modelling (CLM)](https://mlsanity.com/paper/2405.09596/) (2024)
- [Traj-MLLM: Can Multimodal Large Language Models Reform Trajectory Data Mining?](https://mlsanity.com/paper/2509.00053/) (2025)
- [TraClets: Harnessing the power of computer vision for trajectory classification](https://mlsanity.com/paper/2205.13880/) (2022)
- [Modelling and Reasoning Techniques for Context Aware Computing in Intelligent Transportation System](https://mlsanity.com/paper/2107.14374/) (2021)
- [Forecasting COVID-19 Case Counts Based on 2020 Ontario Data](https://mlsanity.com/paper/2303.10294/) (2023)
- [Detecting Transportation Mode Using Dense Smartphone GPS Trajectories and Transformer Models](https://mlsanity.com/paper/2603.00340/) (2026)

## Abstract (arXiv)

Understanding and discovering knowledge from GPS (Global Positioning System) traces of human activities is an essential topic in mobility-based urban computing. We propose TrajectoryNet-a neural network architecture for point-based trajectory classification to infer real world human transportation modes from GPS traces. To overcome the challenge of capturing the underlying latent factors in the low-dimensional and heterogeneous feature space imposed by GPS data, we develop a novel representation that embeds the original feature space into another space that can be understood as a form of basis expansion. We also enrich the feature space via segment-based information and use Maxout activations to improve the predictive power of Recurrent Neural Networks (RNNs). We achieve over 98% classification accuracy when detecting four types of transportation modes, outperforming existing models without additional sensory data or location-based prior knowledge.