Quantifying the Influence of Volume Variability on Railway Hump Classification Yard Performance with AnyLogic Simulation

Abstract

On North American freight railways, railcars transported in manifest (carload) freight trains are required to stop and be resorted at multiple intermediate classification (marshalling) yards before reaching their destination. Yard dwell time comprises the majority of total railcar transit time, and railway yard congestion can further promote mainline train delay. However, most of the literature on railroad performance has focused on the mainline, and few of the previous yard studies specifically examined the yard performance impacts of vast volume fluctuation because of variable shipping demand. To further understand yard behavior under these conditions, this paper explores the influence of inbound traffic volume variability on classification yard performance and outbound train size using a novel AnyLogic simulation model. A series of simulation experiments quantify the interaction between inbound and outbound train size variability as measured by different yard performance metrics under different overall traffic volumes and blocking patterns. The results quantify the impact of volume variation on railway classification yard level of service, and suggest that forming longer blocks leads to shorter dwell times but larger sensitivity to volume variation compared to forming multiple short blocks. Comparison of arrival and departure train size distributions indicates that classification yards moderate volume fluctuations while causing departure delays. These results serve as a quantitative foundation for further study of yard performance recovery patterns following traffic volume surges and the resulting interactions between yards and mainlines in the railway network, and can inform railroad operating plan adjustments to better manage yard traffic and utilize existing resources.