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

TL;DR

This paper presents a novel spatial-temporal attention model called CRNet that effectively estimates traffic states using sparse IoV data, reducing data collection costs while maintaining high accuracy in intelligent transportation systems.

Contribution

The paper introduces CRNet, a convolutional retentive network that leverages spatial-temporal attention to improve traffic state estimation with limited IoV data.

Findings

CRNet achieves high accuracy with sparse IoV data.

The model effectively captures spatial-temporal traffic correlations.

Simulation results validate cost-effectiveness and practicality.

Abstract

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.