Abstract
The NATO Reference Mobility Model (NRMM) is a comprehensive means of predicting the speeds of military vehicles in on-road, off road, and gap-crossing contexts. The model has been in service for many years and helps user communities concerned with vehicle design, wargaming, and strategic planning. Recent developments in computer hardware and software are creating an opportunity for NRMM to serve a tactical role on the battlefield. Adaptation of NRMM to this role requires that its users come to grips with the collection of digital data to describe vehicle, terrain, and scenario data in a real-time environment. This paper discusses the performance of NRMM when selected inputs and algorithms contain random components. A developmental pathway is outlined that leads from current deterministic mobility forecasts to stochastic forecasts capable of suggesting the risks taken when speed predictions must be made in the presence of data and algorithm errors. Concepts that express measures of confidence for wide-area mobility forecasts when errors are known with small-area detail are described. Several numerical examples are given.
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