Ma, Xiaolan (Beijing Jiaotong university), Zhou, Min (Beijing Jaotong University), Song, Haifeng (Beihang University), Wu, Wei (Beijing Jiaotong University), Dong, Hairong (Beijing Jaotong University)

Virtual Coupling-Enabled Trajectory Optimization for Heavy-Haul Train Group: A Goal-Oriented MADDPG Approach

Scheduled for presentation during the Video Session "On-Demand Video Presentations" (VP-VP), Saturday, November 22, 2025, 08:00−18:00, On-Demand Platform

2025 IEEE 28th International Conference on Intelligent Transportation Systems (ITSC), November 18-21, 2025, Gold Coast, Australia

This information is tentative and subject to change. Compiled on April 2, 2026

Abstract

The virtual coupling-based train group operation presents promising prospects to enhance the capacity of freight railways. Adopting a leader-follower control mechanism enables coordinated multi-train operations. However, inhomogeneous environmental disturbances demand-optimized group-level operational states and trajectories during emergencies, particularly on heavy-haul lines. This paper addresses this challenge by developing a mixed-integer quadratic programming (MIQP) model that incorporates safety constraints, train dynamics, speed limits, and tracking interval requirements. A goal-oriented reinforcement learning method based on Multi-Agent Deep Deterministic Policy Gradient (GO-MADDPG) is proposed to achieve the train group's safe, efficient, energy-saving, and stable trajectories. Agents output continuous acceleration and deceleration control actions, while a cooperative collision avoidance mechanism and action masking ensure both safety and feasibility. Numerical experiments demonstrate that the proposed method can efficiently derive efficiency-enhanced solutions within short computation times.