Fine-Grained Traffic Inference from Road to Lane via Spatio-Temporal Graph Node Generation

TL;DR

This paper introduces a novel task called Fine-grained Road Traffic Inference (FRTI) to generate lane-level traffic data from limited road data, utilizing a two-stage spatio-temporal graph framework for improved traffic management.

Contribution

The paper proposes the first FRTI task and a two-stage RoadDiff framework that effectively infers lane-level traffic using limited data, advancing traffic prediction methods.

Findings

RoadDiff outperforms baseline models on six datasets.

FRTI enables detailed lane traffic inference with limited data.

The approach improves traffic management accuracy.

Abstract

Fine-grained traffic management and prediction are fundamental to key applications such as autonomous driving, lane change guidance, and traffic signal control. However, obtaining lane-level traffic data has become a critical bottleneck for data-driven models due to limitations in the types and number of sensors and issues with the accuracy of tracking algorithms. To address this, we propose the Fine-grained Road Traffic Inference (FRTI) task, which aims to generate more detailed lane-level traffic information using limited road data, providing a more energy-efficient and cost-effective solution for precise traffic management. This task is abstracted as the first scene of the spatio-temporal graph node generation problem. We designed a two-stage framework--RoadDiff--to solve the FRTI task. solve the FRTI task. This framework leverages the Road-Lane Correlation Autoencoder-Decoder and the Lane Diffusion Module to fully utilize the limited spatio-temporal dependencies and distribution relationships of road data to accurately infer fine-grained lane traffic states. Based on existing research, we designed several baseline models with the potential to solve the FRTI task and conducted extensive experiments on six datasets representing different road conditions to validate the effectiveness of the RoadDiff model in addressing the FRTI task. The relevant datasets and code are available at https://github.com/ShuhaoLii/RoadDiff.