Hein, Léo (Université Gustave Eiffel, IFP Energies nouvelles), De Nunzio, Giovanni (IFP Energies nouvelles), Chierchia, Giovanni (University Gustave Eiffel - ESIEE Paris), Pirayre, Aurélie (IFP Energies nouvelles), NAJMAN, Laurent (University Gustave Eiffel - ESIEE Paris)

Network-Wide Traffic Volume Estimation from Speed Profiles Using a Spatio-Temporal Graph Neural Network with Directed Spatial Attention

Scheduled for presentation during the Regular Session "S01c-Data-Driven Simulation and Modeling for Smart Mobility Systems" (WE-LA-T1), Wednesday, November 19, 2025, 16:00−16:20, Southport 1

2025 IEEE 28th International Conference on Intelligent Transportation Systems (ITSC), November 18-21, 2025, Gold Coast, Australia

This information is tentative and subject to change. Compiled on October 19, 2025

Abstract

Existing traffic volume estimation methods typically address either forecasting traffic on sensor-equipped roads or spatially imputing missing volumes using nearby sensors. While forecasting models generally disregard unmonitored roads by design, spatial imputation methods explicitly address network-wide estimation; yet this approach relies on volume data at inference time, limiting its applicability in sensor-scarce cities. Unlike traffic volume data, probe vehicle speeds and static road attributes are more broadly accessible and support full coverage of road segments in most urban networks. In this work, we present the Hybrid Directed-Attention Spatio-Temporal Graph Neural Network (HDA-STGNN), an inductive deep learning framework designed to tackle the network-wide volume estimation problem. Our approach leverages speed profiles, static road attributes, and road network topology to predict daily traffic volume profiles across all road segments in the network. To evaluate the effectiveness of our approach, we perform extensive ablation studies that demonstrate the model’s capacity to capture complex spatio-temporal dependencies and highlight the value of topological information for accurate network-wide traffic volume estimation without relying on volume data at inference time.