An empirical assessment of the effects of economic recessions on pedestrian-injury crashes using mixed and latent-class models

摘要: This study explores the differences in pedestrian injury severity three distinct economic time periods from recent global recession (the Great Recession): pre-recession, recession, and post-recession. Using data crashes Chicago, Illinois over an eight-year period, separate time-period models of pedestrian-injury severities (with possible outcomes severe injury, moderate minor injury) were estimated using latent-class logit mixed models. Likelihood ratio tests conducted to examine overall stability model estimates across marginal effects each explanatory variable also considered investigate temporal effect individual parameter on injury-severity probabilities. A wide range variables potentially affecting was including time, location, crashes, as well roadway environmental conditions, characteristics, crash characteristics. Our findings show significant instability, which likely results a combination long-term evolution influence factors that affect severity. Understanding explicitly modeling driver behavior is promising direction for future research, but this would unfortunately require far more extensive than currently available traditional safety databases. Language: en