Precision Scheduled Railroading and the Need for Improved Estimates of Yard Capacity and Performance Considering Traffic Complexity

Multiple North American freight railroads have adopted concepts of Precision Scheduled Railroading (PSR) that attempt to reduce costs by maximizing train length and minimizing railcar transit time. To achieve these objectives, PSR emphasizes pre-blocking traffic and operating general-purpose trains. These changes have altered the nature of operations at many classification yards, leading to yard closures and conversions to different yard types. Difficulty in implementing PSR-inspired operating practices at yards suggests the industry requires improved estimates of classification yard performance and capacity. While volume-based approaches may be adequate when yard operations are consistent with historical experience, it is hypothesized that approaches considering overall traffic complexity will offer improved predictions when changes are also made to the number of blocks and trains assembled in the yard. An original simulation model of a classification yard pull-down process is used to investigate this hypothesis. The simulation results suggest that a combination of factors describing yard traffic complexity can be a better predictor of yard performance than volume alone. The results are also transformed into a capacity constraint that describes the interaction between the maximum allowable daily number of railcars, blocks, and trains processed by a classification yard. Better understanding of these relationships can aid practitioners and researchers in improving network blocking models and developing train plans that properly use available yard capacity under PSR and other operating plans, reducing the likelihood of future network disturbances and congestion events.

Optimization of the Classification Yard Location Problem Based on Train Service Network

This paper investigates the problems of locating yards and allocating works of train formation among yards based on the train service network. It not only involves the modifications of the scales of existing yards, either improving, or downsizing or even demolishing, but also the determinations of building new yards in a given rail network. Besides, the train service network is also taken into consideration, so that the car-hour costs that are incurred from the reclassification and accumulation operations of railcars can be optimized simultaneously. The accumulation parameter setting is symmetrical on both directions of traffic flows. A binary integer programming model is first proposed for optimizing the train service network, the solution of which is employed as a benchmark of the further integrated optimization. Based on this, a nonlinear joint optimization model is developed aiming at striking a balance between the capital investment and car-hour consumptions, with the constraints of the reclassification capacities of yards and the number of sorting tracks, and multiple logical relations among decision variables. Corresponding linearization techniques are introduced for transforming the nonlinear models into the linear ones. Finally, an exact solving approach is presented with computational results that are based on a real-world based case to illustrate the efficacy of the linear model.

Optimizing Transport Scheme of High Value-Added Shipments in Regions without Express Train Services

In railway transportation, high value-added shipments in regions with large traffic volumes are generally delivered by express train services, since these freights need to be delivered in a short time. However, there are also high value-added shipments in areas where express train services are not available. If these freights are delivered by the traditional approaches (i.e., the freight cars are delivered to the adjacent classification yard by local trains, combined with other freight cars to form a train, and finally sent to the destination according to the transportation plan) with multiple reclassifications (a reclassification is when wagons are separated from their original train and then join another train in a classification yard), it will lead to delivery delays and economic losses to shippers and contribute to severe carbon emissions. In this context, this paper proposes an innovative method to deliver high value-added shipments in regions without express train services, which is called the method of reserving axle loads. The differences in assembling and transfer costs achieved by the method of reserving axle loads and traditional method are analyzed, especially the car-hours saved for the accumulation process of freight cars in a classification yard. Then, a corresponding mathematical model is established, which involves four scenarios: reserving axle loads for departing; reserving axle loads for arriving; reserving axle loads for both departing and arriving; and not reserving axle loads. Finally, the practicability and feasibility of the model was verified by two numerical experiments.

Macrolevel Classification Yard Capacity Modeling

Classification yards play a significant role in railroad freight transportation and are often considered bottlenecks for railroad networks. Based on a generic yard simulation model, the model in the presented study fits the Bureau of Public Roads function, which is widely used in highway capacity to represent the volume–dwell time relationship. The proposed analytical model incorporates major features of rail yards, such as the number and capacity of tracks in each area, the number of engines and humps, the humping speed, and the assemble rate. The model is validated by historical data from 16 classification yards of Class I railroads in the United States. The results show that the proposed model can generate precise capacity data of rail yard, as well as the dwell time of rail cars in yards. The dwell time increases sharply when the volume is greater than the capacity of a rail yard. The identified relationship may help a railroad analyze its network at the macro level and therefore improve the systemwide capacity and efficiency.