Trajectory Optimization for Heavy-Haul Trains Under Varying Wheel-Rail Adhesion Conditions

The operation of heavy-haul trains (HHTs) with heavy loads and long carriages faces significant challenges in complex weather conditions, such as rain and snow, as well as in environments with extended downhill sections. As operational demands increase, the optimization of trajectories for HHTs under varying wheel-rail contact conditions becomes critical. This study proposes a dynamic model for HHTs to optimize train trajectories in real railway environments. By analyzing variations in the wheel-rail adhesion coefficient (WRAC) under four scenarios—dry, wet with sand scattering, wet without sand scattering, and oil contamination—the study evaluates the impact of adhesion coefficient changes on train control and braking distance. Furthermore, using dry conditions and wet conditions without sand scattering as case studies, the study investigates the influence of adhesion coefficients on train operation and energy consumption. Compared to dry conditions, under wet conditions without sand scattering, the average train running time increases by 5%, while energy consumption decreases by approximately 10% (with a maximum reduction of 15%), and average acceleration drops by 22.5%. When the train brakes from 80 to 0 km/h, the braking distance under wet conditions is 200% of that under dry conditions. The experiments employ an adaptive large neighborhood search (ALNS) algorithm, which reduces computation time by 50% compared to traditional solvers such as CPLEX. This study investigates the optimization of trajectories under varying adhesion coefficients and provides robust support for the safe and punctual operation of trains in extreme weather conditions such as rain and snow.