Weather Curvature and Service Demand under an Evidence Budget
Abstract
Weather variation changes the workload that a service design must accommodate, but estimating that variation does not identify the preferred design. This article combines a secondary analysis of daily public bicycle rentals with a methodological synthesis of simulation selection and adaptive artificial intelligence. Calendar-adjusted ordinary least squares reveals nonlinear temperature association and substantial weather-related demand differences. Dependence-aware uncertainty is necessary because adjacent daily observations share operating conditions. The empirical model describes an input environment rather than a service intervention. The central argument is that prediction, feasibility, selection loss, and computation cost need separate evaluation before a demand model can support a resource decision. Parameter-efficient adaptation is considered within that broader accounting problem.
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