# Detecting Transportation Mode Using Dense Smartphone GPS Trajectories and Transformer Models

arXiv: https://arxiv.org/abs/2603.00340 · HTML: https://mlsanity.com/paper/2603.00340/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 75 references that are in the map.
- First posted on arXiv 27 Feb 2026.

## Builds on (most cited first, 17 of 17)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [RoFormer: Enhanced Transformer with Rotary Position Embedding](https://mlsanity.com/paper/2104.09864/) (2021)
- [GLU Variants Improve Transformer](https://mlsanity.com/paper/2002.05202/) (2020)
- [GQA: Training Generalized Multi-Query Transformer Models from Multi-Head Checkpoints](https://mlsanity.com/paper/2305.13245/) (2023)
- [Long Short-Term Memory-Networks for Machine Reading](https://mlsanity.com/paper/1601.06733/) (2016)
- [Leveraging Language Foundation Models for Human Mobility Forecasting](https://mlsanity.com/paper/2209.05479/) (2022)
- [Inferring transportation modes from GPS trajectories using a convolutional neural network](https://mlsanity.com/paper/1804.02386/) (2018)
- [Trajectory Recovery From Ash: User Privacy Is NOT Preserved in Aggregated Mobility Data](https://mlsanity.com/paper/1702.06270/) (2017)
- [Future Directions in Human Mobility Science](https://mlsanity.com/paper/2408.00702/) (2024)
- [How do you go where? Improving next location prediction by learning travel mode information using transformers](https://mlsanity.com/paper/2210.04095/) (2022)
- [TrajectoryNet: An Embedded GPS Trajectory Representation for Point-based Classification Using Recurrent Neural Networks](https://mlsanity.com/paper/1705.02636/) (2017)
- [The Effect of Recency to Human Mobility](https://mlsanity.com/paper/1504.01442/) (2015)
- [Evidence of economic segregation from mobility lockdown during COVID-19 epidemic](https://mlsanity.com/paper/2004.05455/) (2020)
- [MobilityDL: A Review of Deep Learning From Trajectory Data](https://mlsanity.com/paper/2402.00732/) (2024)

## Abstract (arXiv)

Transportation mode detection is an important topic within GeoAI and transportation research. In this study, we introduce SpeedTransformer, a novel Transformer-based model that relies solely on speed inputs to infer transportation modes from dense smartphone GPS trajectories. In benchmark experiments, SpeedTransformer outperformed traditional deep learning models, such as the Long Short-Term Memory (LSTM) network. Moreover, the model demonstrated strong flexibility in transfer learning, achieving high accuracy across geographical regions after fine-tuning with small datasets. Finally, we deployed the model in a real-world experiment, where it consistently outperformed baseline models under complex built environments and high data uncertainty. These findings suggest that Transformer architectures, when combined with dense GPS trajectories, hold substantial potential for advancing transportation mode detection and broader mobility-related research.