政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/154753

English | 正體中文 | 简体中文 | Post-Print筆數 : 27 | Items with full text/Total items : 114104/145136 (79%)
Visitors : 52237356 Online Users : 467

RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.

Home ‧ Login ‧ Upload ‧ Help ‧ About ‧ Administer

政大機構典藏 > 資訊學院 > 資訊科學系 > 期刊論文 > Item 140.119/154753

Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/154753

Title:	Urban Traffic Flow Prediction Using LSTM and GRU
Authors:	張宏慶 Jang, Hung-Chin;Chen, Che-An
Contributors:	資訊系
Keywords:	traffic flow prediction;deep learning;LSTM (Long Short-Term Memory);GRU (Gated Recurrent Units)
Date:	2024-01
Issue Date:	2024-12-12 09:27:56 (UTC+8)
Abstract:	For smart cities, the issue of how to solve traffic chaos has always attracted public attention. Many studies have proposed various solutions for traffic flow prediction, such as ARIMA, ANN, and SVM. With the breakthrough of deep learning technology, the evolutionary models of RNN, such as LSTM (Long Short-Term Memory) and GRU (Gated Recurrent Units) models, have been proven to have excellent performance in traffic flow prediction. By using LSTM and GRU models, we explore more features and multi-layer models to increase the accuracy of traffic flow prediction. We compare the prediction accuracy of LSTM and GRU models in urban traffic flow prediction. The data collected in this study are divided into three categories, namely “regular traffic flow data”, “predictable episodic event data”, and “meteorological data”. The regular traffic flow data source is the “Vehicle Detector (VD) data of Taipei Open Data Platform”. Predictable episodic event data are predictable as non-routine events such as concerts and parades. We use a crawler program to collect this information through ticketing systems, tourism websites, news media, social media, and government websites and the meteorological data from the Central Meteorological Bureau. Through these three types of data, the accuracy in predicting traffic flow is enhanced to predict the degree of traffic congestion that may be affected.
Relation:	Engineering Proceedings (MDPI), Vol.55, No.1, 86
Data Type:	article
DOI 連結:	https://doi.org/10.3390/engproc2023055086
DOI:	10.3390/engproc2023055086
Appears in Collections:	[資訊科學系] 期刊論文

Files in This Item:

File	Description	Size	Format
index.html		0Kb	HTML	43	View/Open

社群 sharing

著作權政策宣告 Copyright Announcement

1.本網站之數位內容為國立政治大學所收錄之機構典藏，無償提供學術研究與公眾教育等公益性使用，惟仍請適度，合理使用本網站之內容，以尊重著作權人之權益。商業上之利用，則請先取得著作權人之授權。
The digital content of this website is part of National Chengchi University Institutional Repository. It provides free access to academic research and public education for non-commercial use. Please utilize it in a proper and reasonable manner and respect the rights of copyright owners. For commercial use, please obtain authorization from the copyright owner in advance.

2.本網站之製作，已盡力防止侵害著作權人之權益，如仍發現本網站之數位內容有侵害著作權人權益情事者，請權利人通知本網站維護人員(nccur@nccu.edu.tw)，維護人員將立即採取移除該數位著作等補救措施。
NCCU Institutional Repository is made to protect the interests of copyright owners. If you believe that any material on the website infringes copyright, please contact our staff(nccur@nccu.edu.tw). We will remove the work from the repository and investigate your claim.

Loading...