A Machine Learning-Based Approach to Railway Logistics Transport Path Optimization

Abstract

With the rapid development of high-speed railways, the continuous improvement of the road network, and the leapfrog increase in demand for cross-line passenger travel, the connectivity between cities is also increasing. The network structure has gradually evolved from a single line structure to a complex network structure, and the routes connecting city nodes are no longer unique. At this stage, there are multiple effective routes between destinations on the network, and the choice of train routes has diversified. Different options for interline trains have different degrees of impact on line and station operations. Therefore, this study constructed a deep learning CNN-GRU combined model to predict the traffic speed of railway logistics. The model used a convolutional neural network (CNN) to extract the spatial characteristics of speed data and GRU to extract the time characteristics of speed data. The experiment proved that the prediction accuracy of the combined model was better than that of the single GRU and CNN models. Finally, a multi-objective optimization model with the shortest total train running distance, the shortest total train running time, and the least influence on road sections is constructed using the prediction results.

Last-Mile Optimization Using Neural Networks

“…The experiment demonstrated superior accuracy compared to individual GRU and CNN models. The prediction results were further utilized in a multiobjective optimization model for efficient train route planning [33].…”

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