# Spatial-Temporal Attention Model for Traffic State Estimation with Sparse Internet of Vehicles

arXiv: https://arxiv.org/abs/2407.08047 · HTML: https://mlsanity.com/paper/2407.08047/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 45 references that are in the map.
- First posted on arXiv 10 Jul 2024.

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

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting](https://mlsanity.com/paper/1506.04214/) (2015)
- [Retentive Network: A Successor to Transformer for Large Language Models](https://mlsanity.com/paper/2307.08621/) (2023)
- [An Introduction to Convolutional Neural Networks](https://mlsanity.com/paper/1511.08458/) (2015)
- [T-GCN: A Temporal Graph ConvolutionalNetwork for Traffic Prediction](https://mlsanity.com/paper/1811.05320/) (2018)
- [SimVP: Simpler yet Better Video Prediction](https://mlsanity.com/paper/2206.05099/) (2022)
- [PredRNN: A Recurrent Neural Network for Spatiotemporal Predictive Learning](https://mlsanity.com/paper/2103.09504/) (2021)
- [Deep Learning for Spatio-Temporal Data Mining: A Survey](https://mlsanity.com/paper/1906.04928/) (2019)
- [Correlating sparse sensing for large-scale traffic speed estimation: A Laplacian-enhanced low-rank tensor kriging approach](https://mlsanity.com/paper/2210.11780/) (2022)
- [A simple contagion process describes spreading of traffic jams in urban networks](https://mlsanity.com/paper/1906.00585/) (2019)
- [Spatiotemporal Tensor Completion for Improved Urban Traffic Imputation](https://mlsanity.com/paper/2103.08323/) (2021)
- [Prediction of Traffic Flow via Connected Vehicles](https://mlsanity.com/paper/2007.05460/) (2020)
- [Generalized Global Ranking-Aware Neural Architecture Ranker for Efficient Image Classifier Search](https://mlsanity.com/paper/2201.12725/) (2022)
- [Digital Twin Based User-Centric Resource Management for Multicast Short Video Streaming](https://mlsanity.com/paper/2308.08995/) (2023)
- [Quality of Information in Mobile Crowdsensing: Survey and Research Challenges](https://mlsanity.com/paper/1709.03583/) (2017)

## Built on by (most cited first, 1 of 1)

- [One-Shot Federated Learning with Classifier-Free Diffusion Models](https://mlsanity.com/paper/2502.08488/) (2025)

## Abstract (arXiv)

The growing number of connected vehicles offers an opportunity to leverage internet of vehicles (IoV) data for traffic state estimation (TSE) which plays a crucial role in intelligent transportation systems (ITS). By utilizing only a portion of IoV data instead of the entire dataset, the significant overheads associated with collecting and processing large amounts of data can be avoided. In this paper, we introduce a novel framework that utilizes sparse IoV data to achieve cost-effective TSE. Particularly, we propose a novel spatial-temporal attention model called the convolutional retentive network (CRNet) to improve the TSE accuracy by mining spatial-temporal traffic state correlations. The model employs the convolutional neural network (CNN) for spatial correlation aggregation and the retentive network (RetNet) based on the attention mechanism to extract temporal correlations. Extensive simulations on a real-world IoV dataset validate the advantage of the proposed TSE approach in achieving accurate TSE using sparse IoV data, demonstrating its cost effectiveness and practicality for real-world applications.