What question did this study set out to answer?

This research aims to improve short-term traffic flow prediction accuracy using a Bayesian-optimized LSTM model.

July 2, 2026Open Access

A Bayesian-Optimized Long Short-Term Memory Deep Learning Model for Traffic Prediction in Intelligent Transportation

Key Points

This research aims to improve short-term traffic flow prediction accuracy using a Bayesian-optimized LSTM model.
An LSTM model is developed and optimized using Bayesian techniques for hyperparameter tuning.
Traffic data undergoes preprocessing through random sampling and scaling for enhanced robustness.
The model's performance is evaluated on three datasets, calculating RMSE, MAE, and MAPE.
Achieved RMSE values of 0.0442, 0.0216, and 0.4426 with improvements of 34.5%, 59.5%, and 32.6% over competitor models.
The MAE and MAPE for these datasets were also significantly lower compared to Attention-LSTM and TCN, highlighting accuracy.
The proposed model enhances dynamic traffic management and potentially reduces vehicle waiting times.

Abstract

Abstract In smart mobility networks, accurate vehicular flow forecasting is of critical importance, enabling efficient, robust, user- and environment-friendly management of devices, technologies, and systems. However, current short-term traffic prediction algorithms frequently face challenges of computational inefficiency and limited predictive precision. To overcome these limitations, this paper introduces an LSTM model optimized through Bayesian optimizer (BO-LSTM) to enhance prediction accuracy. The traffic data is first preprocessed through data augmentation using random sampling and scaling of traffic counts, which is particularly suitable for traffic time series data as it preserves temporal patterns while increasing data diversity and robustness against demand fluctuations. After this augmentation step, the data is standardized to bring the input features to a similar range. The LSTM model is trained using Bayesian optimization for hyperparameters tuning, including the learning rate, dense layers, number of iterations, and dropout rate, within an acceptable range. The root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) are computed for three different datasets, measured in number of vehicles per minute, yielding respective values of 0.0442, 0.0353, and 2.91%, 0.0216, 0.0173, and 1.84%, and 0.4426, 0.3541, and 2.63%. These results correspond to improvements of 34.5, 59.5, and 32.6% in RMSE, respectively, when compared against Attention-LSTM and temporal convolutional network (TCN) models. This proves the accuracy of the proposed model. The developed scheme outperforms counterpart models, advocating its potential to enhance dynamic traffic management and intelligent signal coordination systems. Such capability enables more accurate short-term flow estimates, thereby reducing average vehicle waiting times and improving intersection-level signal responsiveness.

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Cite This Study

Khan et al. (Tue,) studied this question.

synapsesocial.com/papers/6a4601009ed1343031310ec6 https://doi.org/https://doi.org/10.1007/s13369-026-11437-1

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