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AuthorRamadan, Abdelrahman
AuthorElbery, Ahmed
AuthorZorba, Nizar
AuthorHassanein, Hossam S.
Available date2024-07-14T07:57:22Z
Publication Date2020
Publication NameIEEE International Conference on Communications
ResourceScopus
Identifierhttp://dx.doi.org/10.1109/ICC40277.2020.9149233
ISSN15503607
URIhttp://hdl.handle.net/10576/56618
AbstractTraffic forecasting is imperative to Intelligent Transportation Systems (ITS), and it has always been considered as a challenging research topic, due to the complex topological structure of the urban road network and the temporal stochastic nature of dynamic change. Popular sports events attract vast numbers of spectators travelling to the event, which will have a substantial effect on ITS, showing peaks on the network that can collapse a smart city's ITS. In this paper, we tackle traffic forecasting and use the Doha network in Qatar and the FIFA World Cup 2022 (FWC 2022) event as a case study. We propose a novel technique for embedding road network graphs into a Temporal-Graph Convolutional Network. The embedding process includes a modification to the graph weights based on graph theory and the properties of the line graph. Extensive simulations are carried out on a real-world calibrated dataset from Doha's road network. Our Temporal Line Graph Convolutional Network (TLGCN) proposal shows outstanding performance when compared to state-of-the-art techniques, not only for huge special events but also for the regular daily traffic.
SponsorACKNOWLEDGMENT This work was made possible by NPRP grant NPRP 9-185-2-096 from the Qatar National Research Fund (a member of the Qatar Foundation). The statements made herein are solely the responsibility of the authors.
Languageen
PublisherInstitute of Electrical and Electronics Engineers Inc.
SubjectLine Graphs
Spatiotemporal Dependence
T-GCN
Temporal Line Graph Convolutional Network
TLGCN
Traffic Forecasting
TitleTraffic Forecasting using Temporal Line Graph Convolutional Network: Case Study
TypeConference Paper
Volume Number2020-June
dc.accessType Abstract Only


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