Spatial analysis of fatal and injury crashes in Pennsylvania.
Using injury and fatal crash data for Pennsylvania for 1996-2000, full Bayes (FB) hierarchical models (with spatial and temporal effects and space-time interactions) are compared to traditional negative binomial (NB) estimates of annual county-level crash frequency. Covariates include socio-demographics, weather conditions, transportation infrastructure and amount of travel. FB hierarchical models are generally consistent with the NB estimates. Counties with a higher percentage of the population under poverty level, higher percentage of their population in age groups 0-14, 15-24, and over 64 and those with increased road mileage and road density have significantly increased crash risk. Total precipitation is significant and positive in the NB models, but not significant with FB. Spatial correlation, time trend, and space-time interactions are significant in the FB injury crash models. County-level FB models reveal the existence of spatial correlation in crash data and provide a mechanism to quantify, and reduce the effect of, this correlation. Addressing spatial correlation is likely to be even more important in road segment and intersection-level crash models, where spatial correlation is likely to be even more pronounced.
Aguero-Valverde
Jonathan
J
Department of Civil and Environmental Engineering, Pennsylvania Transportation Institute, University Park, 16802-1408, USA.
Jovanis
Paul P
PP
D000063:Accidents, Traffic / Q000401:mortality*; D000293:Adolescent; D000368:Aged; D000369:Aged, 80 and over; D001336:Automobiles / Q000706:statistics & numerical data; D001499:Bayes Theorem; D002648:Child; D002675:Child, Preschool; D002980:Climate; D004779:Environment Design; D005843:Geography; D006801:Humans; D007223:Infant; D007231:Infant, Newborn; D015233:Models, Statistical; D010414:Pennsylvania / Q000453:epidemiology; D018570:Risk Assessment; D012307:Risk Factors; D014887:Weather; D014947:Wounds and Injuries / Q000401:mortality*