Urban mobility is constantly challenged by congestion and unpredictable disruptions, making it more crucial than ever to understand and improve the resilience of urban road networks. Growing cities have necessitated reliable and efficient transportation systems, highlighting the importance of a thorough resilience study that analyzes the dynamics and complexities of road networks. Improving resilience is not only about addressing short-term traffic problems; it also aims to ensure the long-term sustainability and adaptability of urban infrastructure. This thesis presents a data-driven approach to investigate the spatiotemporal impact of daily non-recurring disruptions and the resilience of urban road networks. The underlying traffic propagation dynamics and recovery time, vulnerability, and resilience of an urban road network are examined using multi-year observed travel time and incident data. The study develops a statistical method to estimate event occurrence, restoration, and recovery times and formulates a new resilience metric inspired by the resilience triangle concept and complex network theory. The analysis captures the microscopic dynamics of affected road links in detail and allows for an accurate estimation of an incident’s occurrence, restoration, and recovery time. The results indicate that incidents are often detected earlier than reported, but the impact of those incidents remains on the network for a longer period than reported. In addition, areas with low resilience tend to be geographically clustered, often near high-demand regions that have low network densities, indicating inefficiency in the network and low resilience. This study demonstrates that the proposed methodology captures network responses to disruptions accurately and provides valuable insights for transport policy, including the strategic placement of recovery resources, such as police units, during disruptions. The findings of this study have significant implications for the improvement of urban road network resilience.