Nov 29, 2023 · This paper aims to provide a robust traffic prediction solution with direct applications in smart cities and real-time traffic management.

Our research introduces a hybrid model, combining Long-Short Term Memory (LSTM) and the. Kalman filter-based Rauch-Tung-Striebel (RTS) noise reduction technique ...

Jul 31, 2024 · Our research introduces a hybrid model, combining Long-Short Term Memory (LSTM) and the Kalman filter-based Rauch-Tung-Striebel (RTS) noise ...

Enhancing Traffic Flow Prediction in Intelligent Cyber-Physical Systems: A Novel Bi-LSTM-Based Approach With Kalman Filter Integration. M Aljebreen, H Alamro ...

This study proposes a novel hybrid method, FVMD-WOA-GA, for enhancing traffic flow prediction in 5G-enabled intelligent transportation systems.

Enhancing Traffic Flow Prediction in Intelligent Cyber-Physical Systems: A Novel Bi-LSTM-Based Approach With Kalman Filter Integration · Author Picture ...

Enhancing Traffic Flow Prediction in Intelligent Cyber-Physical Systems: A Novel Bi-LSTM-Based Approach With Kalman Filter Integration. Article. Jan 2023.

A Kalman Filter Model is a method used in time series analysis to track past values, filter current values, and estimate future values by recursively ...

In this paper, we perform the prediction of IoT traffic in time series using LSTM - deep learning. the prediction accuracy has been evaluated using the RMSE as ...

A graph based strategic transport planning dataset, aimed at creating the next generation of deep graph neural networks for transfer learning.