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

Intelligent transportation systems (ITS) are pivotal in modern urban development by enhancing mobility and transit efficiency. However, the challenges of accurate short-term traffic prediction persist due to real-time traffic data's dynamic, nonlinear, and non-stationary nature. In this study, the author addresses these challenges and proposes a novel approach to improve traffic flow prediction. Our research introduces a hybrid model, combining Long-Short Term Memory (LSTM) and the Kalman filter-based Rauch-Tung-Striebel (RTS) noise reduction technique, tailored to mitigate the limitations of low market penetration of connected vehicles and data availability. This paper aims to provide a robust traffic prediction solution with direct applications in smart cities and real-time traffic management. To evaluate our model's efficacy, The author conducted an empirical case study using the Enhanced Next Generation Simulation (NGSIM) dataset, which offers highly granular vehicle trajectory data. The author rigorously analyses our methodology and algorithms to assess the quality of traffic predictions. Our results show that the proposed model outperforms traditional LSTM models. In particular, the Bi-LSTM/RTS model achieved a remarkable accuracy of 99% in predicting traffic patterns during sunny weather conditions, signifying a significant advancement in short-term traffic prediction accuracy. The evaluation metrics demonstrate that Bi-LSTM outperforms LSTM by a wide margin, with a coefficient of determination value of 0.99 against 0.97. The trend anticipated by Bi-LSTM (8.6%) is more in line with the actual trend than that projected by LSTM (8.9%), as seen by the smaller MAPE. In conclusion, Bi-LSTM outperforms LSTM at predicting traffic patterns.