# Self-supervised Trajectory Representation Learning with Temporal Regularities and Travel Semantics

arXiv: https://arxiv.org/abs/2211.09510 · HTML: https://mlsanity.com/paper/2211.09510/

## Key facts

- Cited by 58 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 47 references that are in the map.
- First posted on arXiv 17 Nov 2022.

## Builds on (most cited first, 15 of 15)

- [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)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [SimCSE: Simple Contrastive Learning of Sentence Embeddings](https://mlsanity.com/paper/2104.08821/) (2021)
- [node2vec: Scalable Feature Learning for Networks](https://mlsanity.com/paper/1607.00653/) (2016)
- [Representation Degeneration Problem in Training Natural Language Generation Models](https://mlsanity.com/paper/1907.12009/) (2019)
- [ConSERT: A Contrastive Framework for Self-Supervised Sentence Representation Transfer](https://mlsanity.com/paper/2105.11741/) (2021)
- [Self-Supervised Learning for Recommender Systems: A Survey](https://mlsanity.com/paper/2203.15876/) (2022)
- [STDEN: Towards Physics-Guided Neural Networks for Traffic Flow Prediction](https://mlsanity.com/paper/2209.00225/) (2022)
- [Unsupervised Path Representation Learning with Curriculum Negative Sampling](https://mlsanity.com/paper/2106.09373/) (2021)
- [Empowering A* Search Algorithms with Neural Networks for Personalized Route Recommendation](https://mlsanity.com/paper/1907.08489/) (2019)
- [DETECT: Deep Trajectory Clustering for Mobility-Behavior Analysis](https://mlsanity.com/paper/2003.01351/) (2020)
- [Self-supervised Representation Learning for Trip Recommendation](https://mlsanity.com/paper/2109.00968/) (2021)

## Built on by (most cited first, 30 of 58)

- [Foundation Models for Time Series Analysis: A Tutorial and Survey](https://mlsanity.com/paper/2403.14735/) (2024)
- [Spatio-Temporal Graph Neural Networks for Predictive Learning in Urban Computing: A Survey](https://mlsanity.com/paper/2303.14483/) (2023)
- [PDFormer: Propagation Delay-Aware Dynamic Long-Range Transformer for Traffic Flow Prediction](https://mlsanity.com/paper/2301.07945/) (2023)
- [Continuous Trajectory Generation Based on Two-Stage GAN](https://mlsanity.com/paper/2301.07103/) (2023)
- [Towards Urban General Intelligence: A Review and Outlook of Urban Foundation Models](https://mlsanity.com/paper/2402.01749/) (2024)
- [LibCity: A Unified Library Towards Efficient and Comprehensive Urban Spatial-Temporal Prediction](https://mlsanity.com/paper/2304.14343/) (2023)
- [More Than Routing: Joint GPS and Route Modeling for Refine Trajectory Representation Learning](https://mlsanity.com/paper/2402.16915/) (2024)
- [UniTraj: Learning a Universal Trajectory Foundation Model from Billion-Scale Worldwide Traces](https://mlsanity.com/paper/2411.03859/) (2024)
- [TrajFM: A Vehicle Trajectory Foundation Model for Region and Task Transferability](https://mlsanity.com/paper/2408.15251/) (2024)
- [BIGCity: A Universal Spatiotemporal Model for Unified Trajectory and Traffic State Data Analysis](https://mlsanity.com/paper/2412.00953/) (2024)
- [A Survey of Generative Techniques for Spatial-Temporal Data Mining](https://mlsanity.com/paper/2405.09592/) (2024)
- [MM-Path: Multi-modal, Multi-granularity Path Representation Learning -- Extended Version](https://mlsanity.com/paper/2411.18428/) (2024)
- [Bridging Traffic State and Trajectory for Dynamic Road Network and Trajectory Representation Learning](https://mlsanity.com/paper/2502.06870/) (2025)
- [POI-Enhancer: An LLM-based Semantic Enhancement Framework for POI Representation Learning](https://mlsanity.com/paper/2502.10038/) (2025)
- [Unraveling Spatio-Temporal Foundation Models via the Pipeline Lens: A Comprehensive Review](https://mlsanity.com/paper/2506.01364/) (2025)
- [Micro-Macro Spatial-Temporal Graph-based Encoder-Decoder for Map-Constrained Trajectory Recovery](https://mlsanity.com/paper/2404.19141/) (2024)
- [Self-Supervised Representation Learning for Geospatial Objects: A Survey](https://mlsanity.com/paper/2408.12133/) (2024)
- [Full Bayesian Significance Testing for Neural Networks](https://mlsanity.com/paper/2401.13335/) (2024)
- [RED: Effective Trajectory Representation Learning with Comprehensive Information](https://mlsanity.com/paper/2411.15096/) (2024)
- [AutoHFormer: Efficient Hierarchical Autoregressive Transformer for Time Series Prediction](https://mlsanity.com/paper/2506.16001/) (2025)
- [Blurred Encoding for Trajectory Representation Learning](https://mlsanity.com/paper/2511.13741/) (2025)
- [Grid and Road Expressions Are Complementary for Trajectory Representation Learning](https://mlsanity.com/paper/2411.14768/) (2024)
- [Geo2Vec: Shape- and Distance-Aware Neural Representation of Geospatial Entities](https://mlsanity.com/paper/2508.19305/) (2025)
- [CoBAD: Modeling Collective Behaviors for Human Mobility Anomaly Detection](https://mlsanity.com/paper/2508.14088/) (2025)
- [Semantic-Enhanced Representation Learning for Road Networks with Temporal Dynamics](https://mlsanity.com/paper/2403.11495/) (2024)
- [Towards Robust Trajectory Representations: Isolating Environmental Confounders with Causal Learning](https://mlsanity.com/paper/2404.14073/) (2024)
- [TrajCogn: Leveraging LLMs for Cognizing Movement Patterns and Travel Purposes from Trajectories](https://mlsanity.com/paper/2405.12459/) (2024)
- [UniTE: A Survey and Unified Pipeline for Pre-training Spatiotemporal Trajectory Embeddings](https://mlsanity.com/paper/2407.12550/) (2024)
- [TransferTraj: A Vehicle Trajectory Learning Model for Region and Task Transferability](https://mlsanity.com/paper/2505.12672/) (2025)
- [UVTM: Universal Vehicle Trajectory Modeling with ST Feature Domain Generation](https://mlsanity.com/paper/2402.07232/) (2024)

## Abstract (arXiv)

Trajectory Representation Learning (TRL) is a powerful tool for spatial-temporal data analysis and management. TRL aims to convert complicated raw trajectories into low-dimensional representation vectors, which can be applied to various downstream tasks, such as trajectory classification, clustering, and similarity computation. Existing TRL works usually treat trajectories as ordinary sequence data, while some important spatial-temporal characteristics, such as temporal regularities and travel semantics, are not fully exploited. To fill this gap, we propose a novel Self-supervised trajectory representation learning framework with TemporAl Regularities and Travel semantics, namely START. The proposed method consists of two stages. The first stage is a Trajectory Pattern-Enhanced Graph Attention Network (TPE-GAT), which converts the road network features and travel semantics into representation vectors of road segments. The second stage is a Time-Aware Trajectory Encoder (TAT-Enc), which encodes representation vectors of road segments in the same trajectory as a trajectory representation vector, meanwhile incorporating temporal regularities with the trajectory representation. Moreover, we also design two self-supervised tasks, i.e., span-masked trajectory recovery and trajectory contrastive learning, to introduce spatial-temporal characteristics of trajectories into the training process of our START framework. The effectiveness of the proposed method is verified by extensive experiments on two large-scale real-world datasets for three downstream tasks. The experiments also demonstrate that our method can be transferred across different cities to adapt heterogeneous trajectory datasets.