TRAFFIC SPEED PREDICTION AND CONGESTION LEVEL IDENTIFICATION USING FLOATING CAR DATA

Traffic congestion poses significant challenges to urban mobility and transportation infrastructure worldwide. Accurate prediction of traffic speed and timely identification of congestion levels are crucial for effective traffic management and planning. Owing to the widespread adoption of telecommunication technologies, various traffic datasets have become available, such as Floating Car Data (FCD), which collect real-time information from vehicles in transit, providing a rich and dynamic dataset for analyzing traffic speed. However, predicting traffic speed and identifying congestion levels using FCD remains challenging due to the complexities of traffic dynamics and the non-linear nature of traffic flow. In response, multiple solutions have been proposed using deep learning methods. This study addresses the persistent issue of FCD data sparsity and its limitations in providing consistent, accurate traffic speed predictions. The present work focuses on constructing an LSTM-based method, called LSTM-C, to predict traffic speed. In the proposed LSTM-C method, a new Contrast measure is introduced and incorporated to enhance the prediction of traffic speed across candidate road segments. The LSTM-C model demonstrates a significant improvement in both prediction accuracy and congestion level identification, outperforming existing models such as those by Majumdar et al. and Gao et al. Subsequently, traffic rules are applied to the predicted speeds to determine congestion levels for each segment. The experimental results demonstrate that the proposed model achieves a high level of accuracy, reaching up to 96.697%, which represents an improvement of 1.6% and 1.79% in accuracy compared to the two benchmark LSTM methods employed for speed prediction.