Please use this identifier to cite or link to this item:
Title: Attention based graph Bi-LSTM networks for traffic forecasting
Authors: Zhao, Han
Yang, Huan
Wang, Yu
Wang, Danwei
Su, Rong
Keywords: Engineering
Issue Date: 2020
Source: Zhao, H., Yang, H., Wang, Y., Wang, D., & Su, R. (2020). Attention based graph Bi-LSTM networks for traffic forecasting. Proceedings of the IEEE International Conference on Intelligent Transportation Systems (ITSC). doi:10.1109/ITSC45102.2020.9294470
Conference: 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)
Abstract: Traffic forecasting is of great importance to vehicle routing, traffic signal control and urban planning. However, traffic forecasting task is challenging due to several factors, such as complex spatial topological structure and dynamic changing of traffic status. Most existing methods have limited ability to capture both spatial and temporal dependence of traffic data. In this paper, we propose a novel end-to-end deep learning model, Attention based Graph Bi-LSTM networks (AGBN) to perform the traffic forecasting task. It uses graph convolutional network (GCN) to extract spatial features and bidirectional long short-term memory networks (Bi-LSTM) to capture the temporal dependence. The attention mechanism is used to select relevant features at all time steps. Experiments show that our model could extract both spatial and temporal dependence well and outperforms other baselines on real-world traffic datasets.
DOI: 10.1109/ITSC45102.2020.9294470
Schools: School of Electrical and Electronic Engineering 
Research Centres: ST Engineering-NTU Corporate Lab 
Rights: © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at:
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Conference Papers

Files in This Item:
File Description SizeFormat 
Attention Based Graph Bi-LSTM Networks for Traffic Forecasting.pdf2.78 MBAdobe PDFThumbnail

Citations 20

Updated on Apr 8, 2024

Web of ScienceTM
Citations 50

Updated on Oct 24, 2023

Page view(s)

Updated on Apr 21, 2024

Download(s) 20

Updated on Apr 21, 2024

Google ScholarTM




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