Balancing dependability and energy consumption is a critical challenge in modern railway systems, particularly for switch anti-icing mechanisms, where heating policies are used to prevent ice formation and ensure operational reliability while limiting energy use. In this context, we present an enhanced stochastic modeling framework for quantitative trade-off analysis of such competing objectives. Building upon prior work, the proposed framework enables a refined analysis of system behavior under varying environmental and operational conditions by introducing three advancements: i) more realistic descriptions of weather conditions, ii) integration of sensor measurement errors besides communication failures and forecast inaccuracies, and iii) novel modeling of heating control policies driven by actual train traffic patterns. We apply the framework to a comprehensive set of weather profiles and railway traffic scenarios to evaluate the impact of different heating strategies on reliability and energy efficiency. Results demonstrate how incorporating real train schedules into heating management can significantly influence the trade-off between energy consumption and dependability, offering actionable insights to decision-makers. The proposed framework provides a practical decision-support tool to guide the design of energy-efficient, reliable anti-icing systems that comply with operational and regulatory constraints.