articleIEEE Transactions on Intelligent Transportation SystemsMar 20, 2023HYBRID OA

Graph Neural Networks for Intelligent Transportation Systems: A Survey

SRSaeed RahmaniABAsiye BaghbaniNBNizar BouguilaZPZachary Patterson

Delft University of Technology · Concordia University

Indexed incrossref

Abstract

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…

Citation impact

266
total citations
FWCI
38.95
Percentile
100%
References
241
Citations per year

Authors

4

Topics & keywords

Topics
Keywords
  • Computer science
  • Intelligent transportation system
  • Categorization
  • Variety (cybernetics)
  • Intersection (aeronautics)
  • Graph
  • Domain (mathematical analysis)
  • Advanced Traffic Management System
UN Sustainable Development Goals
  • Sustainable cities and communities
No related works found for this paper.

Funding