Edit StudentProjectForm: Graph Neural Networks for Traffic Flow Forecasting

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Summary:	MANDATORY: (provide a one-line* summary of the project)*
Programme:	OPTIONAL: (Name and the number of credits)
Keywords:	OPTIONAL: (Give 4-5 keywords)
TimeFrame:	MANDATORY: (Indicate time for the project, for example, Fall 2024)
References:	Scarselli, F., Gori, M., Tsoi, A. C., Hagenbuchner, M., & Monfardini, G. (2008). The graph neural network model. IEEE transactions on neural networks, 20(1), 61-80. OPTIONAL: (Give 2-3 references for students to start with)
Prerequisites:	OPTIONAL: (Courses, any other important information)
Supervisor(s):	MANDATORY: (can be multiple names, coma-separated)
Examiner:	OPTIONAL: (Name of project Examiner)
Author(s):	OPTIONAL: (can be multiple names, coma-separated)
Level:	MANDATORY: choose one, preferably <Master>
Status:	MANDATORY: choose <Open> when the topic is ready for students to select

Project description (free text)

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Research question
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A very brief description of 3-4 work packages
Deliverables/outcomes/results

Traffic flow forecasting is one of the most critical topics in the Intelligent transportation system. Traffic flow forecasting focuses on predicting future traffic flow using past information. It is a challenging task due to the dynamic nature of the traffic network. Graph neural networks (GNN) have shown to be effective in modeling the dynamic nature of traffic data. The main goal of this project is to explore GNN for traffic flow forecasting.

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Edit StudentProjectForm: Graph Neural Networks for Traffic Flow Forecasting

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