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題名 Urban Traffic Flow Prediction Using LSTM and GRU
作者 張宏慶
Jang, Hung-Chin;Chen, Che-An
貢獻者 資訊系
關鍵詞 traffic flow prediction; deep learning; LSTM (Long Short-Term Memory); GRU (Gated Recurrent Units)
日期 2024-01
上傳時間 12-十二月-2024 09:27:56 (UTC+8)
摘要 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.
關聯 Engineering Proceedings (MDPI), Vol.55, No.1, 86
資料類型 article
DOI https://doi.org/10.3390/engproc2023055086
dc.contributor 資訊系
dc.creator (作者) 張宏慶
dc.creator (作者) Jang, Hung-Chin;Chen, Che-An
dc.date (日期) 2024-01
dc.date.accessioned 12-十二月-2024 09:27:56 (UTC+8)-
dc.date.available 12-十二月-2024 09:27:56 (UTC+8)-
dc.date.issued (上傳時間) 12-十二月-2024 09:27:56 (UTC+8)-
dc.identifier.uri (URI) https://nccur.lib.nccu.edu.tw/handle/140.119/154753-
dc.description.abstract (摘要) 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.
dc.format.extent 105 bytes-
dc.format.mimetype text/html-
dc.relation (關聯) Engineering Proceedings (MDPI), Vol.55, No.1, 86
dc.subject (關鍵詞) traffic flow prediction; deep learning; LSTM (Long Short-Term Memory); GRU (Gated Recurrent Units)
dc.title (題名) Urban Traffic Flow Prediction Using LSTM and GRU
dc.type (資料類型) article
dc.identifier.doi (DOI) 10.3390/engproc2023055086
dc.doi.uri (DOI) https://doi.org/10.3390/engproc2023055086