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

Engineering notes

The developed scheme outperforms counterpart models, advocating its potential to enhance dynamic traffic management and intelligent signal coordination systems.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。

Original 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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