# Jointly Contrastive Representation Learning on Road Network and Trajectory

arXiv: https://arxiv.org/abs/2209.06389 · HTML: https://mlsanity.com/paper/2209.06389/

## Key facts

- Cited by 34 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 48 references that are in the map.
- First posted on arXiv 14 Sep 2022.

## Builds on (most cited first, 23 of 23)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [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)
- [node2vec: Scalable Feature Learning for Networks](https://mlsanity.com/paper/1607.00653/) (2016)
- [Understanding Contrastive Representation Learning through Alignment and Uniformity on the Hypersphere](https://mlsanity.com/paper/2005.10242/) (2020)
- [Learning deep representations by mutual information estimation and maximization](https://mlsanity.com/paper/1808.06670/) (2018)
- [Deep Graph Infomax](https://mlsanity.com/paper/1809.10341/) (2018)
- [Learning Representations by Maximizing Mutual Information Across Views](https://mlsanity.com/paper/1906.00910/) (2019)
- [MINE: Mutual Information Neural Estimation](https://mlsanity.com/paper/1801.04062/) (2018)
- [Distributed Representations of Sentences and Documents](https://mlsanity.com/paper/1405.4053/) (2014)
- [Contrastive Multi-View Representation Learning on Graphs](https://mlsanity.com/paper/2006.05582/) (2020)
- [Deep Graph Contrastive Representation Learning](https://mlsanity.com/paper/2006.04131/) (2020)
- [Adaptive Graph Convolutional Recurrent Network for Traffic Forecasting](https://mlsanity.com/paper/2007.02842/) (2020)
- [Graph Contrastive Learning with Adaptive Augmentation](https://mlsanity.com/paper/2010.14945/) (2020)
- [InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximization](https://mlsanity.com/paper/1908.01000/) (2019)
- [Graph Self-Supervised Learning: A Survey](https://mlsanity.com/paper/2103.00111/) (2021)
- [Graph Representation Learning via Graphical Mutual Information Maximization](https://mlsanity.com/paper/2002.01169/) (2020)
- [Unsupervised Path Representation Learning with Curriculum Negative Sampling](https://mlsanity.com/paper/2106.09373/) (2021)
- [Tensor Completion for Weakly-dependent Data on Graph for Metro Passenger Flow Prediction](https://mlsanity.com/paper/1912.05693/) (2019)
- [Graph Convolutional Networks for Road Networks](https://mlsanity.com/paper/1908.11567/) (2019)
- [Long-Short Term Spatiotemporal Tensor Prediction for Passenger Flow Profile](https://mlsanity.com/paper/2004.11022/) (2020)
- [Contrastive Instruction-Trajectory Learning for Vision-Language Navigation](https://mlsanity.com/paper/2112.04138/) (2021)
- [Trajectory Forecasting from Detection with Uncertainty-Aware Motion Encoding](https://mlsanity.com/paper/2202.01478/) (2022)

## Built on by (most cited first, 30 of 34)

- [Deep Learning for Cross-Domain Data Fusion in Urban Computing: Taxonomy, Advances, and Outlook](https://mlsanity.com/paper/2402.19348/) (2024)
- [Towards Urban General Intelligence: A Review and Outlook of Urban Foundation Models](https://mlsanity.com/paper/2402.01749/) (2024)
- [More Than Routing: Joint GPS and Route Modeling for Refine Trajectory Representation Learning](https://mlsanity.com/paper/2402.16915/) (2024)
- [KITS: Inductive Spatio-Temporal Kriging with Increment Training Strategy](https://mlsanity.com/paper/2311.02565/) (2023)
- [BIGCity: A Universal Spatiotemporal Model for Unified Trajectory and Traffic State Data Analysis](https://mlsanity.com/paper/2412.00953/) (2024)
- [Bridging Traffic State and Trajectory for Dynamic Road Network and Trajectory Representation Learning](https://mlsanity.com/paper/2502.06870/) (2025)
- [MM-DAG: Multi-task DAG Learning for Multi-modal Data -- with Application for Traffic Congestion Analysis](https://mlsanity.com/paper/2306.02831/) (2023)
- [Dynamic Causal Graph Convolutional Network for Traffic Prediction](https://mlsanity.com/paper/2306.07019/) (2023)
- [Road Network Representation Learning with the Third Law of Geography](https://mlsanity.com/paper/2406.04038/) (2024)
- [Correlated Time Series Self-Supervised Representation Learning via Spatiotemporal Bootstrapping](https://mlsanity.com/paper/2306.06994/) (2023)
- [Self-Supervised Representation Learning for Geospatial Objects: A Survey](https://mlsanity.com/paper/2408.12133/) (2024)
- [RED: Effective Trajectory Representation Learning with Comprehensive Information](https://mlsanity.com/paper/2411.15096/) (2024)
- [Holistic Semantic Representation for Navigational Trajectory Generation](https://mlsanity.com/paper/2501.02737/) (2025)
- [Blurred Encoding for Trajectory Representation Learning](https://mlsanity.com/paper/2511.13741/) (2025)
- [Road Planning for Slums via Deep Reinforcement Learning](https://mlsanity.com/paper/2305.13060/) (2023)
- [Grid and Road Expressions Are Complementary for Trajectory Representation Learning](https://mlsanity.com/paper/2411.14768/) (2024)
- [Non-Neighbors Also Matter to Kriging: A New Contrastive-Prototypical Learning](https://mlsanity.com/paper/2401.12681/) (2024)
- [Low-Rank Robust Subspace Tensor Clustering for Metro Passenger Flow Modeling](https://mlsanity.com/paper/2404.04403/) (2024)
- [Choose A Table: Tensor Dirichlet Process Multinomial Mixture Model with Graphs for Passenger Trajectory Clustering](https://mlsanity.com/paper/2310.20224/) (2023)
- [A Critical Perceptual Pre-trained Model for Complex Trajectory Recovery](https://mlsanity.com/paper/2311.02631/) (2023)
- [Semantic-Enhanced Representation Learning for Road Networks with Temporal Dynamics](https://mlsanity.com/paper/2403.11495/) (2024)
- [VisionTraj: A Noise-Robust Trajectory Recovery Framework based on Large-scale Camera Network](https://mlsanity.com/paper/2312.06428/) (2023)
- [Skin Cancer Detection utilizing Deep Learning: Classification of Skin Lesion Images using a Vision Transformer](https://mlsanity.com/paper/2407.18554/) (2024)
- [Context-Enhanced Multi-View Trajectory Representation Learning: Bridging the Gap through Self-Supervised Models](https://mlsanity.com/paper/2410.13196/) (2024)
- [Jointly Learning Representations for Map Entities via Heterogeneous Graph Contrastive Learning](https://mlsanity.com/paper/2402.06135/) (2024)
- [Deep Causal Learning to Explain and Quantify The Geo-Tension's Impact on Natural Gas Market](https://mlsanity.com/paper/2407.10878/) (2024)
- [Structure-preserving contrastive learning for spatial time series](https://mlsanity.com/paper/2502.06380/) (2025)
- [Dual-branch Spatial-Temporal Self-supervised Representation for Enhanced Road Network Learning](https://mlsanity.com/paper/2511.06633/) (2025)
- [Transformer-based Drum-level Prediction in a Boiler Plant with Delayed Relations among Multivariates](https://mlsanity.com/paper/2407.11180/) (2024)
- [Exploring Genre and Success Classification through Song Lyrics using DistilBERT: A Fun NLP Venture](https://mlsanity.com/paper/2407.21068/) (2024)

## Abstract (arXiv)

Road network and trajectory representation learning are essential for traffic systems since the learned representation can be directly used in various downstream tasks (e.g., traffic speed inference, and travel time estimation). However, most existing methods only contrast within the same scale, i.e., treating road network and trajectory separately, which ignores valuable inter-relations. In this paper, we aim to propose a unified framework that jointly learns the road network and trajectory representations end-to-end. We design domain-specific augmentations for road-road contrast and trajectory-trajectory contrast separately, i.e., road segment with its contextual neighbors and trajectory with its detour replaced and dropped alternatives, respectively. On top of that, we further introduce the road-trajectory cross-scale contrast to bridge the two scales by maximizing the total mutual information. Unlike the existing cross-scale contrastive learning methods on graphs that only contrast a graph and its belonging nodes, the contrast between road segment and trajectory is elaborately tailored via novel positive sampling and adaptive weighting strategies. We conduct prudent experiments based on two real-world datasets with four downstream tasks, demonstrating improved performance and effectiveness. The code is available at https://github.com/mzy94/JCLRNT.