Highlights • A new data-driven network vulnerability approach is presented. • A novel approach is developed to identify the spatiotemporal impact of incidents. • The proposed approach monitors the dynamic propagation of congestion patterns. • The spatiotemporal impact of incidents is used to derive a new vulnerability index. • The new vulnerability index considers probability and consequences of link failure.

Abstract This paper proposes a new data-driven impact area vulnerability analysis approach that considers both the probability of impact of incidents and their resulting effects on an impact area. Multi-year observed travel time and incident data are deployed to investigate the underlying traffic propagation dynamics. These datasets contain a wealth of information that reflects the occurrences of link closure/disruption by space and time and the network-wide impacts stemming from these disruptions. A novel approach is developed to identify the resulting spatial and temporal impacts of non-recurrent incidents. Rather than focusing solely on the travel time changes of the link subject to incident, this approach captures all aspects of the impacts of incidents by monitoring the dynamic propagation of congestion patterns in the set of links that are in the vicinity of the link subject to incident (i.e., impact area). These spatial and temporal dimensions of the impacts are subsequently used in a new vulnerability analysis. The performance of the developed approach was examined using historical travel time and incident data from the City of Calgary, Canada. The results indicate that the recorded temporal impact of incidents is not representative enough of the true impact of incidents because the dynamic spatial propagation of the effect of incidents on the impact area is overlooked. The proposed approach is capable of capturing the enduring spatiotemporal impact of incidents in a large-scale road network. The primary improvement of the developed vulnerability index is its ability to model multi-dimensional aspects of non-recurrent travel delays caused by non-current incidents in a road network.