Data-driven predictive model for dynamic expected travel time estimation in rail freight networks: A case study

Rail freight is vital for economic growth due to its efficiency and environmental benefits, but its lack of fixed schedules due to various delay factors poses challenges for accurate Expected Travel Time (ETT) predictions. This research addresses the need for real-time, accurate and dynamic ETT predictions crucial for maintaining efficient supply chains by developing a novel predictive model that leverages real-time data. The model ensembles Graph Convolutional Network-Long Short-Term Memory (GCN-LSTM) and Kalman Filters (KF) models to capture the complex spatiotemporal interactions and diverse traction behaviours within the freight train railway network. The methodology comprises three phases: modeling, schedule generation, and dynamic updating. In the modeling phase, historical train movement data is used to develop predictive models, with KF handling state-space representation and GCN-LSTM capturing spatial and temporal dependencies. These models are ensembled to enhance prediction accuracy. The schedule generation phase estimates travel times using the ensembled model, the dynamic updating phase refines predictions using real-time data, while congestion is assessed by clustering congested areas with Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and propagating these clusters through KF. The proposed model is compared with different state-of-art predictive models. The methodology’s effectiveness was validated using real-world data from Indian Railway freight operations. The proposed model demonstrated superior accuracy, with Mean Absolute Percentage Error of 19.51%, while the moving average-based model which was previously being used by the Indian Railway had an error of 44.34%. This approach, implemented as a decision support system for Indian Railways’ daily operations, provides advanced planning solutions to manage the growing complexities of rail freight logistics effectively.