Railway operations are subject to deviations from the planned schedule, i.e., delays. In those situations, high-quality traffic control actions are needed to reduce the delays. Existing studies mainly used prescriptive techniques (e.g., mathematical programming, heuristics) to identify the best control action. These methods have limitations in the firm reliance on deterministic parameters prescriptively or normatively determined beforehand, and little understandability by the practitioners. These drawbacks hinder their acceptance in practice. This study exploits instead past realization data to provide decision support for traffic control. The realized data describe the traffic control actions taken by human controllers, and their effects; those latter are more complex than a linear sum of predetermined parameters. We use decision graphs to identify which traffic control action leads to the best solution, in terms of reduction of delays, based on the past performance of the same action in similar conditions. We are also able to explain the reasons and the factors that lead to each suggested action. We focus on the relevant case of merging stations, where multiple lines merge as one line, deciding the relative order between two consecutive trains. The method determines the stochastic effects of the two possible decisions at merge points, which allows for choosing the best one. Compared within the framework of realized data, the action suggested is the best out of a series of benchmarks, including simple rules and optimization, improving (reducing delays) approximately 11.7% on the common benchmarks. The variables with the highest impact on the utility are the length of the planned dwell time and the planned presence of an overtaking. The variables influencing the utility most are the actual delays of trains, the train type, and the order actually implemented.

It has been argued that viscous dissipation from mantle flow in response to surface loading during glacial cycles can result in short-term heating and thus trigger transient volcanism or changes in mantle properties, which may in turn affect mantle dynamics. Furthermore, heating near the Earth's surface can also affect the stability of ice sheets. We have studied the magnitude and spatial-temporal distribution of viscous heating induced in the mantle by the realistic ice model ICE-6G and gravitationally consistent ocean loads. Three types of mantle rheologies, including linear, non-linear and composite rheologies are considered to see if non-linear creep can induce larger viscous heating than linear rheology. We used the Coupled-Laplace-Finite-Element model of Glacial Isostatic Adjustment (GIA) to compute the strain, stress and shear heating during a glacial cycle. We also investigated the upper bound of temperature change and surface heat flux change due to viscous heating. We found that maximum viscous heating occurs near the end of deglaciation near the edge of the ice sheet with amplitude as high as 120 times larger than that of the chondritic radioactive heating. The maximum heat flux due to viscous heating can reach 30 mW m^-2, but the area with large heat flux is small and the timescale of heating is short. As a result, the upper bound of temperature change due to viscous heating is small. Even if 30 glacial cycles are included, the largest temperature change can be of the order of 0.3 °C. Thus, viscous heating induced by GIA cannot induce volcanism and cannot significantly affect mantle material properties, mantle dynamics nor ice-sheet stability. The Author(s) 2017. Published by Oxford University Press. All rights reserved.