# Latent Space Model for Road Networks to Predict Time-Varying Traffic

arXiv: https://arxiv.org/abs/1602.04301 · HTML: https://mlsanity.com/paper/1602.04301/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Feb 2016.

## Built on by (most cited first, 22 of 22)

- [Towards Spatio-Temporal Aware Traffic Time Series Forecasting--Full Version](https://mlsanity.com/paper/2203.15737/) (2022)
- [Traffic Flow Forecasting with Spatial-Temporal Graph Diffusion Network](https://mlsanity.com/paper/2110.04038/) (2021)
- [Spatio-Temporal Joint Graph Convolutional Networks for Traffic Forecasting](https://mlsanity.com/paper/2111.13684/) (2021)
- [Stability of Graph Convolutional Neural Networks to Stochastic Perturbations](https://mlsanity.com/paper/2106.10526/) (2021)
- [Spectral embedding for dynamic networks with stability guarantees](https://mlsanity.com/paper/2106.01282/) (2021)
- [STGC-GNNs: A GNN-based traffic prediction framework with a spatial-temporal Granger causality graph](https://mlsanity.com/paper/2210.16789/) (2022)
- [Rethinking Sensors Modeling: Hierarchical Information Enhanced Traffic Forecasting](https://mlsanity.com/paper/2309.11284/) (2023)
- [Origin-Destination Network Generation via Gravity-Guided GAN](https://mlsanity.com/paper/2306.03390/) (2023)
- [Passenger Mobility Prediction via Representation Learning for Dynamic Directed and Weighted Graph](https://mlsanity.com/paper/2101.00752/) (2021)
- [Modeling Heterogeneous Relations across Multiple Modes for Potential Crowd Flow Prediction](https://mlsanity.com/paper/2101.06954/) (2021)
- [Learning Stochastic Graph Neural Networks with Constrained Variance](https://mlsanity.com/paper/2201.12611/) (2022)
- [Detecting Vulnerable Nodes in Urban Infrastructure Interdependent Network](https://mlsanity.com/paper/2307.09866/) (2023)
- [Spatio-Temporal Graph Neural Network for Urban Spaces: Interpolating Citywide Traffic Volume](https://mlsanity.com/paper/2505.06292/) (2025)
- [Spatial-temporal Conv-sequence Learning with Accident Encoding for Traffic Flow Prediction](https://mlsanity.com/paper/2105.10478/) (2021)
- [Latent Evolution Model for Change Point Detection in Time-varying Networks](https://mlsanity.com/paper/2212.08818/) (2022)
- [Multi-fold Correlation Attention Network for Predicting Traffic Speeds with Heterogeneous Frequency](https://mlsanity.com/paper/2104.09083/) (2021)
- [HTTE: A Hybrid Technique For Travel Time Estimation In Sparse Data Environments](https://mlsanity.com/paper/2301.05293/) (2023)
- [Adaptive Graph Convolution Networks for Traffic Flow Forecasting](https://mlsanity.com/paper/2307.05517/) (2023)
- [BiDepth: A Bidirectional-Depth Neural Network for Spatio-Temporal Prediction](https://mlsanity.com/paper/2501.08411/) (2025)
- [Causality Enhanced Origin-Destination Flow Prediction in Data-Scarce Cities](https://mlsanity.com/paper/2503.06398/) (2025)
- [Uniform Inductive Spatio-Temporal Kriging](https://mlsanity.com/paper/2603.05301/) (2026)
- [A Multi-View Coupled Tensor Decomposition for Lightweight Online Adaptive Traffic Prediction](https://mlsanity.com/paper/2608.25498/) (2026)

## Abstract (arXiv)

Real-time traffic prediction from high-fidelity spatiotemporal traffic sensor datasets is an important problem for intelligent transportation systems and sustainability. However, it is challenging due to the complex topological dependencies and high dynamics associated with changing road conditions. In this paper, we propose a Latent Space Model for Road Networks (LSM-RN) to address these challenges. In particular, given a series of road network snapshots, we learn the attributes of vertices in latent spaces which capture both topological and temporal properties. As these latent attributes are time-dependent, they can estimate how traffic patterns form and evolve. In addition, we present an incremental online algorithm which sequentially and adaptively learn the latent attributes from the temporal graph changes. Our framework enables real-time traffic prediction by 1) exploiting real-time sensor readings to adjust/update the existing latent spaces, and 2) training as data arrives and making predictions on-the-fly with given data. By conducting extensive experiments with a large volume of real-world traffic sensor data, we demonstrate the utility superiority of our framework for real-time traffic prediction on large road networks over competitors as well as a baseline graph-based LSM.