Please use this identifier to cite or link to this item:
Title: AI-based traffic flow prediction
Authors: Su, Jingyi
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2020
Publisher: Nanyang Technological University
Abstract: In this paper we discuss an AI-based model for end-to-end traffic prediction tasks, which combines graph convolutional networks and gated recurrent units. The spatial feature of complex topologies and dynamic temporal features can be well extracted from spatial-temporal traffic data. Experiments with real-time traffic flow data sets show that this model has better performance compared to some baseline models.
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:EEE Theses

Files in This Item:
File Description SizeFormat 
  Restricted Access
2.07 MBAdobe PDFView/Open

Page view(s)

Updated on Feb 7, 2023


Updated on Feb 7, 2023

Google ScholarTM


Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.