Transferring crash modification factors to automated vehicle environments using surrogate endpoints: Theoretical considerations

Although the Highway Safety Manual was developed primarily from statistical summaries of conditions prevailing on North American roads, engineers in other nations have expressed interest in applying, or “transferring,” its predictive methods to places other than those providing the source data. More recently, an emerging issue concerns the application of crash modification factors (CMF) estimated for recent conditions to possibly different conditions in the future, which could change significantly if and when automated vehicles increase their market share. This leads to the question of how the past investment in safety research might be leveraged with a limited experience of newer conditions in order to support reasonable decision-making. The main claim of this paper is that when background knowledge regarding a type of road crash can be reliably represented by a directed acyclic graph, the graph’s connectivity structure can be used to identify a set of surrogate endpoints that will support transfer of a CMF estimated in one situation to a different situation. We present two analytic results that explicate this claim and then use simulation to illustrate the potential applicability of these results. We end with suggestions for further research to help make this approach practical.