This paper leverages emerging highway vehicle platooning technology to improve the efficiency and effectiveness of fleeting trains at minimum headways under moving blocks. The research aims to better understand how closely following trains respond to different throttle and brake control algorithms, and, using insights gained from automobile and truck platooning technology, develop improved train control algorithms balancing fuel efficiency and train headway. To do so, a detailed multi-train performance simulator is developed to evaluate following train control algorithms and then adapt highway vehicle platooning control methods to the heavy haul freight rail domain. Five following train control algorithms under two different communication topologies are formulated to more intelligently consider information on the status of the train ahead when specifying throttle or brake settings for each following train. With string stability, following trains attenuate the actions of preceding trains and each successive train requires less aggressive acceleration and braking rates to maintain headways. The simulation results suggest that certain families of control laws are better than others at managing train separation and fuel consumption within train fleets. The results of this research will allow industry practitioners to develop improved locomotive driver advisory and semi-autonomous adaptive train cruise control systems for the operation of fleets of trains under moving blocks, and railroad operators to make more informed decisions regarding the potential fuel efficiency and capacity benefits of these systems.

本文利用新兴的公路车辆编队技术来提高在移动块下以最小车头时距驶过的列车的效率和有效性。该研究旨在更好地了解紧跟列车如何响应不同的油门和制动控制算法，并利用从汽车和卡车编队行驶技术中获得的见解，开发改进的列车控制算法，以平衡燃油效率和列车时距。为此，开发了一个详细的多列车性能模拟器来评估以下列车控制算法，然后将公路车辆队列控制方法应用于重载货运铁路领域。制定了两种不同通信拓扑下的以下五种列车控制算法，以便在为每辆后续列车指定油门或制动设置时更智能地考虑有关前方列车状态的信息。有了串稳定性，后面的列车会减弱前面列车的动作，并且每个后续列车需要更小的加速和制动率来保持车头时距。仿真结果表明，某些控制律系列在管理列车分离和列车车队内的燃料消耗方面优于其他系列。这项研究的结果将使行业从业者能够开发改进的机车司机咨询和半自主自适应列车巡航控制系统，用于在移动块下运行的列车车队，