Random Parameter Model Used to Explain Effects of Built-Environment Characteristics on Pedestrian Crash Frequency

Abstract

Pedestrian safety has been a major concern for megacities such as New York City. Although pedestrian fatalities show a downward trend, these fatalities constitute a high percentage of overall traffic fatalities in the city. Data from New York City were used to study the factors that influence the frequency of pedestrian crashes. Specifically, a random parameter, negative binomial model was developed for predicting pedestrian crash frequencies at the census tract level. This approach allows the incorporation of unobserved heterogeneity across the spatial zones in the modeling process. The influences of a comprehensive set of variables describing the sociodemographic and built-environment characteristics on pedestrian crashes are reported. Several parameters in the model were found to be random, which indicates their heterogeneous influence on the numbers of pedestrian crashes. Overall, these findings can help frame better policies to improve pedestrian safety.