XGBoost-Based Heuristic Path Planning Algorithm for Large Scale Air–Rail Intermodal Networks

Abstract

It is particularly important to develop efficient air–rail intermodal path planning methods for making full use of the advantages of air–rail intermodal networks and providing passengers with richer and more reasonable travel options. A Time-Expanded Graph (TEG) is used to model the timetable information of public transportation providing a theoretical basis for public transportation path planning. However, if the TEG includes a large amount of data such as train stations, airports, train and air schedules, the network scale will become very large, making path planning extremely time-consuming. This study proposes an XGBoost-based heuristic path planning algorithm (XGB-HPPA) for large scale air–rail intermodal networks, which use the XGBoost model to predict transfer stations before path planning, and quickly eliminate unreasonable transfer edges by adding a heuristic factor, reducing the network scale, thus accelerating the computation speed. Comparative results indicate that XGB-HPPA can markedly enhance computational speed within large-scale networks, while obtaining as many valid solutions as possible and approximating the optimal solution.