An examination of the spatial and temporal transferability of crash prediction models in Florida

This research examines the spatial and temporal transferability of crash prediction models on rural multilane divided highways and rural two-lane roads in Florida. Spatial transferability is investigated through calibration of the Highway Safety Manual and the development of local safety performance functions across statewide conditions and three intra-state grouping methods, Florida Department of Transportation district boundaries, population density groups, and average annual rainfall groups. Crash prediction models are developed and applied across four different temporal combinations, two cases of model estimation and application from the same time period and two cases of model estimation and application from different time periods. To assess and compare the results of each crash prediction method, the mean squared error and variance of the squared error, as well as an error regression model, are analyzed.;Results of the error analysis prove that there is significant benefit in statewide calibration, but no additional improvement from calibration within intra-state groups. Locally developed safety performance functions result in improved crash prediction accuracy for population density based models on rural two-lane roads and district and population density based models on rural multilane divided highways. Further crash estimation improvements are evident using locally derived models with alternate functional forms; however, this result is only seen for rural multilane divided highways. All crash estimation methods show an increase in prediction error at higher traffic volumes.;Results of temporal transferability analysis show that there is no significant difference in crash prediction accuracy between any of the temporal estimation and application specifications. Crash estimation models estimated or calibrated based on the three years before or after the application time period were just as suitable as those based on the same application time period.