Deep Spatial–Temporal Graph Modeling of Urban Traffic Accident Prediction

Huang, Yong-Xian; Zhang, Fan; Hu, Jinhui

doi:10.1007/978-981-16-6963-7_41

Cited by 2 publications

(1 citation statement)

References 17 publications

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“…Huang et al [211] proposed a gated graph convolutional multi-task (GGCMT) framework for city-wide traffic accident prediction. They divided the study area into squares of the same size and constructed a weighted graph of these virtual regions.…”

Section: G Transportation Safetymentioning

confidence: 99%

Graph Neural Networks for Intelligent Transportation Systems: A Survey

Rahmani

Baghbani

Bouguila

et al. 2023

IEEE Trans. Intell. Transport. Syst.

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Graph neural networks (GNNs) have been extensively used in a wide variety of domains in recent years. Owing to their power in analyzing graph-structured data, they have become broadly popular in intelligent transportation systems (ITS) applications as well. Despite their widespread applications in different transportation domains, there is no comprehensive review of recent advancements and future research directions that covers all transportation areas. Accordingly, in this survey, for the first time, we provide an overview of GNN studies in the general domain of ITS. Unlike previous surveys, which have been limited to traffic forecasting problems, we explore how GNN frameworks have evolved for different ITS applications, including traffic forecasting, demand prediction, autonomous vehicles, intersection management, parking management, urban planning, and transportation safety. Also, we micro-categorize the studies based on their transportation application to identify domainspecific research directions, opportunities, and challenges, which have been missing in previous surveys. Moreover, we identify unique and undiscussed research opportunities and directions, which is the result of reviewing a wide range of transportation applications. The neglected role of edge and graph learning in ITS applications, developing multi-modal models, and exploiting the power of unsupervised and reinforcement learning methods for developing more powerful GNNs are some examples of such new discussions in this survey. Finally, we have identified popular baseline models and datasets in each transportation domain, which facilitate the development and evaluation of future GNNbased frameworks.

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Section: G Transportation Safetymentioning

confidence: 99%