  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.
