Equitable Estimation of Accurate High-Injury Network (HIN) for Vulnerable Road Users

A critical task for communities considering Vision Zero (VZ) implementation is to conduct a systemic analysis to identify and prioritize locations with the highest risk of dangerous traffic collisions and similar roadway characteristics for improvements. It is known as the High Injury Network (HIN). This collaborative research addresses critical questions from the literature that are relevant to identifying HINs and hence the VZ policy goals. Traditionally, the HIN estimation and validation are based on police crash and injury reports. Regarding reliable high-injury surface street networks, there is a significant concern that the traditional police-reports-based crash data are not fully capturing the extent of serious incidents and injuries. The challenge is especially acute for injury and crashes involving Vulnerable Road Users  (VRUs). 
A novel approach based on the analysis and processing of 911 call data using tools from AWS (Amazon Web Services via DxHub at Cal Poly) is proposed to address the issue. Based on this evaluation, we propose to obtain an alternative HIN that more accurately reflects VRU-collision patterns. The data-driven approaches to these critical questions associated with VZ implementation will allow communities to adopt a more informed and equitable VRU safety plan as part of their Vision Zero efforts.