Urban Traffic Volume Prediction using LSTM and Bi-LSTM: Performance Evaluation on the Metro Interstate Dataset

Urban traffic forecasting underpins the mitigation of congestion, enhancement of road safety, and reduction of emissions in intelligent transportation systems. We benchmark Long Short-Term Memory (LSTM) and Bidirectional LSTM (Bi-LSTM) models on the Metro Interstate Traffic Volume dataset under an i...

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書誌詳細

主要な著者:	Andri Pranolo, Shoffan Saifullah, Agung Bella Utama Putra, Rafał Dreżewski, Aji Prasetya Wibawa
フォーマット:	Artigo
言語:	Inglês
出版事項:	Fakultas Ilmu Komputer UMI 2025-10-01
シリーズ:	Ilkom Jurnal Ilmiah
主題:	lstm bi-lstm deep learning time series forecasting urban traffic
オンライン･アクセス:	https://jurnal.fikom.umi.ac.id/index.php/ILKOM/article/view/3001
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