AbstractAssessing the reliability and resiliency of transportation infrastructure is critical to improving the safety and sustainability of roadways. Such information, particularly when available on a network level helps transportation agencies identify vulnerable locations on their road network and make more informed decisions when managing infrastructure and when introducing design improvements. To that end, many design experts have promoted integrating the risk of failure of roadway design elements to satisfy road user demand (i.e., reliability measures) into the highway design process. In fact, previous work has established a link between design reliability and safety. Although exploring such relationships provides extremely valuable insights on the impacts of meeting or deviating from design requirements, it does not provide much information on where the most (and least) reliable road segments exist on a network. To overcome these critical shortcomings, this paper proposes the adoption of hot spot analysis and spatial interpolation to assess reliability of compliance with sight distance requirements on an aggregate network level. Light Detection and Ranging (LiDAR) data was first used to quantify available sight distance on 220 curved segments, and design reliability to sight distance requirements was then assessed at each location. Hot spot analysis and spatial interpolation using the inverse distance weighting method were then employed to identify regions of the Alberta road network where low or high design reliability existed or could be expected. The analysis revealed that the highest-risk regions on the Alberta highway network existed in areas of mountainous in the western region of the province as well as areas of rolling terrain in south Calgary. In contrast, it was found that curved road segments in the prairie region had a significantly higher design reliability to sight distance. The clustering of unreliable roadway segments in mountainous regions and regions of rolling terrain indicates that more effort is required to improve design reliability in those regions. Such important inferences are only possible when conducting an aggregate reliability assessment such as the one proposed in this paper.