01708179

Component

https://doi.org/10.1177/0361198119851747

This paper describes a method for fitting predictive models that relate vehicle impact speeds to pedestrian injuries, in which results from a national sample are calibrated to reflect local injury statistics. Three methodological issues identified in the literature, outcome-based sampling, uncertainty regarding estimated impact speeds, and uncertainty quantification, are addressed by (i) implementing Bayesian inference using Markov Chain Monte Carlo sampling and (ii) applying multiple imputation to conditional maximum likelihood estimation. The methods are illustrated using crash data from the NHTSA Pedestrian Crash Data Study coupled with an exogenous sample of pedestrian crashes from Minnesota’s Twin Cities. The two approaches produced similar results and, given a reliable characterization of impact speed uncertainty, either approach can be applied in a jurisdiction having an exogenous sample of pedestrian crash severities.

The Standing Committee on Pedestrians (ANF10) peer-reviewed this paper (19-03641). © National Academy of Sciences: Transportation Research Board 2019.

English

pp 583-592

2019-11

Transportation Research Record: Journal of the Transportation Research Board

Web

References (28)

Calibration; Injury severity; Markov chains; Maximum likelihood method; Monte Carlo method; Pedestrian vehicle crashes; Uncertainty

Bayesian approach; Impact speed; Local conditions; Multiple imputation; Quantification

Highways; Pedestrians and Bicyclists; Safety and Human Factors

TRIS, TRB, ATRI

Jun 18 2019 3:04PM