Unplanned disruptions bring challenges to urban railway system operations because of their impacts on safety, operation efficiency, and service quality. Identifying the contributing factors of operation delays and affected areas under unplanned disruptions is critical for agencies to make effective and informed management decisions. Despite its importance, few studies have been reported on unplanned disruption analysis in urban railway systems or they have been limited in their analysis and modeling because of the lack of disruption data. This paper collects a complete set of unplanned disruption data for 7 years in Hong Kong and explores important factors affecting operation delays and affected areas. Quantile regression (QR) models are developed to explore the causes of operation delays under unplanned disruptions. The significant factors include the time of day, weather condition, signal control system (moving/fixed block), line types (urban/suburban), line operation direction, disruption location (underground/ground/elevated), the number of affected stations, and disruption types (e.g., tracing, locomotive and rolling stock, passengers, and operation). A binary logit model is developed to explore the variables contributing to the affected areas (single or multiple stations). The results show that the affected area is significantly influenced by the signal control system, line types, line operation direction, disruption location, terminal/departure station involved or not, transfer station involved or not, and disruption types. The findings provide useful insights into unplanned disruptions and support the development of engineering and policy countermeasures to prevent and mitigate unplanned disruption effects on operations and services.