Performance and improvement of deep learning algorithms based on LSTM in traffic flow prediction
Abstract Existing traffic flow prediction research lacks adaptability to complex traffic scenarios and has limited prediction accuracy. This paper introduces an improved LSTM (Long Short-Term Memory) algorithm and sliding window technology to improve the accuracy and stability of traffic flow predic...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Springer
2025-03-01
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| Series: | Discover Applied Sciences |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s42452-025-06702-1 |
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| Summary: | Abstract Existing traffic flow prediction research lacks adaptability to complex traffic scenarios and has limited prediction accuracy. This paper introduces an improved LSTM (Long Short-Term Memory) algorithm and sliding window technology to improve the accuracy and stability of traffic flow prediction. This article discussed the enhancement of traffic flow (TF) prediction using a hybrid LSTM-BiGRU-Attention model aimed at improving the accuracy of traditional LSTM models. By integrating BiGRU (Bidirectional Gated Recurrent Unit) and Attention mechanisms, the proposed model captured complex TF patterns and long-term dependencies more effectively. The hybrid model is applied to Beijing urban road data with a time granularity (TG) of 10 min and a window size of 30 min, achieving an RMSE (root mean square error) of 4.478, an MAE (mean absolute error) of 3.609, and an R2 of 0.965. The LSTM-BiGRU-Attention model had similar prediction errors in TF during peak and off-peak periods and maintained high stability under different TF conditions. LSTM-BiGRU-Attention outperformed a single LSTM model and other benchmark models in multiple performance metrics. When the TG is 10 min, the window size of 30 min has the best performance, with 4.478, 3.609, and 0.965, respectively. The combination of LSTM, BiGRU, and Attention provides an effective solution for the development of intelligent transportation systems, which helps to achieve more accurate TF prediction and optimized management. |
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| ISSN: | 3004-9261 |