When dealing with customers, original equipment manufacturers (OEMs) classify vehicular usage by resorting to simplified, often colloquial, descriptions that allow for a rough understanding of the operating conditions and the user's needs. In this way, the information retrieved from the customers is exploited to guide their choices in terms of vehicle design and configuration, based on the characteristics of the transport application, labeled using intuitive metrics. However, a common problem in this context is the absence of any formal connection to lower levels of representation that might effectively be used to assess vehicular energy performance in simulation, or for design optimization using mathematical algorithms. Indeed, both processes require more accurate modeling of the surroundings, including exhaustive information about the local road, weather, and traffic conditions. Therefore, starting with a detailed statistical description of the environment, this paper proposes a method for mathematical classification of transport missions and applications within the theoretical framework of the operating cycle (OC). The approach discussed in the paper combines a collection of statistical models structured hierarchically, called a stochastic operating cycle (sOC), with a bird's-eye view description of the operating environment. The latter postulates the existence of different classes, which are representative of the usage and whose definition is based on simple metrics and thresholds expressed mathematically in terms of statistical measures. Algebraic expressions, called operating classes in the paper, are derived analytically for all the stochastic models presented. This establishes a connection between the two levels of representation, enabling to simulate longitudinal vehicle dynamics in virtual environments generated based on the characteristics of the intended application, using log data collected from vehicles and/or information provided by customers. Additionally, the relationships between the two descriptions are formalized using elementary probability operators, allowing for an intuitive characterization of a transport operation. An example is adduced to illustrate a possible application of the proposed method, using six sOCs parametrized from log data collected during real-world missions. The proposed approach may facilitate the interaction between OEMs, customers, and road operators, allowing for planning of maintenance, and optimization of transport missions, components, and configurations using standard procedures and routines.