Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/146155
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
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.
URI: https://hdl.handle.net/10356/146155
DOI: 10.1109/ITSC45102.2020.9294470
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: https://doi.org/10.1109/ITSC45102.2020.9294470
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Conference Papers

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