A Long Short-Term Memory-based correlated traffic data prediction framework

Correlated traffic data refers to a collection of time series recorded simultaneously in different regions throughout the same transportation network route. Due to the presence of both temporal and spatial correlation properties between multiple time series data, accurate time series prediction becomes challenging. The accuracy of the traffic data prediction helps mitigating traffic congestion and establish a robust traffic management system. When a prediction algorithm fails to consider the correlations present in the dataset, the accuracy of the prediction results reduces. To overcome the prediction shortcomings, this study proposes a Long Short-Term Memory (LSTM)-based correlated traffic data prediction (LSTM-CTP) framework. The proposed LSTM-CTP framework was employed for two different real-time traffic datasets. These datasets were initially preprocessed to capture both temporal and spatial trends and the correlations between the collected data series. By employing LSTM, temporal and spatial trends were predicted. Further, the Kalman-filter approach was employed to obtain the final prediction by aggregating the temporal and spatial trend predictions. The performance of the proposed LSTM-CTP was evaluated using different performance metrics and compared with different time-series prediction algorithms. The proposed framework showed substantial improvements in prediction results compared to the other algorithms. Overall, the proposed LSTM-CTP framework can help control traffic congestion and ensure a more robust traffic management system in the future. (C) 2021 Elsevier B.V. All rights reserved.