Multiple membership multilevel model to estimate intersection crashes

Numerous studies have developed intersection crash prediction models to identify crash hotspots and evaluate safety countermeasures. These studies largely considered only micro-level crash contributing factors such as traffic volume, traffic signals, etc. Some recent studies, however, have attempted to include macro-level crash contributing factors, such as population per zone, to predict the number of crashes at intersections. As many intersections are located between multiple zones and thus affected by factors from the multiple zones, the inclusion of macro-level factors requires boundary problems to be resolved. In this study, we introduce an advanced multilevel model, the multiple membership multilevel model (MMMM), for intersection crash analysis. Our objective was to reduce heterogeneity issues between zones in crash prediction model while avoiding misspecification of the model structure. We used five years of intersection crash data (2009-2013) for the City of Regina, Saskatchewan, Canada and identified micro- and macro-level factors that most affected intersection crashes. We compared the fitting performance of the MMMM with that of two existing models, a traditional single model (SM) and a conventional multilevel model (CMM). The MMMM outperformed the SM and CMM in terms of fitting capability. We found that the MMMM avoided both the underestimation of macro-level variance and the type I statistical error that tend to occur when the crash data are analyzed using a SM or CMM. Statistically significant micro-level and macro-level crash contributing factors in Regina included major roadway AADT, four legs, traffic signals, speed, young drivers, and different types of land use.