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Title	A Multi-Modal Graph Neural Network Approach To Traffic Risk Forecasting In Smart Urban Sensing
ID_Doc	2817
Authors	Zhang Y.; Dong X.; Shang L.; Zhang D.; Wang D.
Year	2020
Published	Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks workshops
DOI	http://dx.doi.org/10.1109/SECON48991.2020.9158447
Abstract	Forecasting traffic accidents at a fine-grained spatial scale is essential to provide effective precautions and improve traffic safety in smart urban sensing applications. Current solutions primarily rely on complete historical traffic accident records and/or accurate real-time traffic sensor data for traffic risk prediction. These solutions are prone to various limitations (e.g., facility availability, privacy and legal constraints). In this paper, we address those limitations by exploring two types of widely available and complementary data sources: social media sensing and remote sensing. However, three important technical challenges exist: i) the social media data and remote sensing data often have different data modalities and characteristics, which makes it difficult to effectively fuse them for traffic risk forecasting; ii) the social media data is often noisy, providing fuzzy evidence to capture the spatialoral dynamics of traffic accident occurrences; iii) the remote sensing data often contains complex visual features, making it a non-trivial task to explore the accident occurrence correlation between different locations. To address the above challenges, we propose GraphCast, a multi-modal graph neural network framework to accurately forecast the traffic risks in a city by jointly exploring the social media sensing and remote sensing paradigms. The evaluation results on a real-world case study in New York City show that our GraphCast scheme significantly outperforms the state-of-the-art conventional and deep learning baselines in accurately forecasting the traffic risks in a city. © 2020 IEEE.
Author Keywords	Graph Neural Network; Remote Sensing; Smart Cities; Social Sensing; Traffic Risk Forecasting

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