<![CDATA[Public transportation systems are vital components of urban infrastructure, shaping mobility and development. The emergence of Bus Rapid Transit (BRT) systems offers a promising solution to challenges faced by traditional bus services. However, delays within BRT systems can compromise their efficiency and reliability. The goal of this study is to investigate and analyze the critical factors influencing delays in Bus Rapid Transit (BRT) systems, specifically focusing on the Istanbul Metrobus, in order to provide actionable insights for optimizing operations and enhancing service reliability in BRT operations. Decision Trees identify critical parameters affecting delays, while Bayesian Networks elucidate causal dependencies among variables. The proposed Bayesian Precedence Network integrates these methodologies. This study employed a range of diverse data sources analyzed through advanced software tools like GeNIe Modeler. The results underscore the effectiveness of decision analysis in quantifying uncertainties and assessing critical factors that inform transit planning and optimization. The findings reveal that a passenger occupancy rate of 43% results in a 76% probability of no delays, while high traffic flow decreases this probability to 55%. Conversely, clear weather conditions enhance this probability to 80%, whereas rainy conditions and non-optimized operational efficiency heighten the risk of delays. Overall, this study provides a blueprint for addressing public transportation challenges, empowering transportation planners and policymakers to create more efficient and reliable transit networks.]]>
