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

•The effects of potentially recession-induced behavioral shifts on pedestrian-injury severities were studied.•Mixed logit and latent class logit approaches were used to capture unobserved heterogeneity.•Pedestrian injury severities in vehicle-pedestrian crashes were found to be unstable over time.•A long-term temporal instability in pedestrian injuries being caused by some underlying fundamental shifts.

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