Skip to content
Log in
Open access

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

Hamza Murad Khan Anwar Khan Muhammad Imran Majid Sana ul Haq M. H. Zafar
Jun 2026 · The Arabian journal for science and engineering · 0 citations · 15 references

TL;DR

The developed scheme outperforms counterpart models, advocating its potential to enhance dynamic traffic management and intelligent signal coordination systems and enables more accurate short-term flow estimates, thereby reducing average vehicle waiting times and improving intersection-level signal responsiveness.

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.

DOI Semantic Scholar
Read PDF

Similar papers

Conference Aug 2026

Intelligent Traffic Flow Forecasting using Optimized Deep Learning Frameworks: Current Trends and Future Directions

Accurate Traffic Flow Forecasting (TFF) is important for emerging Intelligent Transportation Systems (ITS) that support active traffic management, optimize routes, and reduce congestion. In this paper, Deep Learning (DL) methods for TFF, with an emphasis on models like Recurrent Neural Networks (RNN) reinforced with attention mechanism, Bidirectional Long Short-Term Memory (Bi-LSTM), as well as Stacked Autoencoder (SAE) is used for ITS. In complex traffic situations, these strategies improve prediction accuracy and remove nonlinear spatial-temporal networks. Bio-inspired optimization methods, such as the Fruit Fly Optimization Algorithm (FFOA), Philippine Eagle Optimization (PEO) and Kookaburra Optimization Algorithm (KOA) are reviewed for adaptive learning, weight initialization and optimal parameter adjustment in order to further improve model performance. The model architectures, optimization techniques, and assessment criteria discussed in recent research are compared in this review to show how they contribute to precise RMSE, MAPE, MAE traffic forecasts. With a focus on multi-source data fusion, real-time adaptability and interpretable AI frameworks for next-generation ITS, it concludes by identifying research gaps and future creativities.

V. Poornima, M. Subashini · 0 citations
Open access Jul 2026

Integrating Bayesian Inference with BP-Transformer for Accurate Traffic Flow Prediction

A novel hybrid deep learning model that combines Bayesian inference with backpropagation and a Transformer architecture, improving prediction accuracy while maintaining the model's generalization ability is proposed.

A. Feofilova, V. Fialkin, Jixiao Jiang · 0 citations
Open access Jun 2026

Comparative analysis of statistical and deep learning models for short-term LTE cell traffic prediction

A comparative analysis of the statistical and deep learning models for real-time, cell-level LTE eNodeB traffic forecasting across diverse geospatial environments using real-world data, with practical validation through LSTM- based traffic prediction for network optimization.

Janak Dangi, Nanda Bikram Adhikari · 0 citations
Review Open access Aug 2026

Advances in intelligent transportation systems: ML and DL techniques for traffic congestion forecasting

A key contribution of this study lies in its comparative synthesis of ML and DL models, revealing that hybrid and graph-based DL architectures consistently outperform traditional ML methods when handling large-scale, heterogeneous traffic datasets.

Thabo Matue, A. A. Akinyelu, Mase Mokotsolane · 0 citations
Open access Jul 2026

Mixed non linear programming model for optimizing and training long short-term memory architectures: application in traffic flow forecasting

It is demonstrated that framing LSTM architecture optimization as a mixed-variable combinatorial problem, coupled with PSO-based optimization, substantially improves forecasting performance, offering a robust and versatile strategy for accurate traffic prediction and other complex sequential data applications.

Taoufyq Elansari, Hamza H. Sulimani · 0 citations
Open access Jul 2026

Prediction and Modeling of Traffic Status at Road Intersection Using Deep-Learning Models

This study proposes a deep-learning-based approach for short-term traffic-state classification using real-world traffic data collected during 2022 at the Alésia intersection in Paris, and demonstrates that recurrent architectures substantially outperform the ANN baseline, highlighting the importance of temporal dependencies in traffic-state classification.

Chaymae Chouiekh, Ali Yahyaouy, M. A. Sabri et al. · 0 citations