Incorporating behavioral variables into crash count prediction by severity: A multivariate multiple risk source approach

Publication date: August 2019Source: Accident Analysis & Prevention, Volume 129Author(s): Mohammad Razaur Rahman Shaon, Xiao Qin, Amir Pooyan Afghari, Simon Washington, Md Mazharul HaqueAbstractThe frequency and severity of traffic crashes have commonly been used as indicators of crash risk on transport networks. Comprehensive modeling of crash risk should account for both frequency and injury severity—capturing both the extent and intensity of transport risk for designing effective safety improvement programs. Previous research has revealed that crashes are correlated across severity categories because of the combined influence of risk factors, observed or unobserved. Moreover, crashes are the outcomes of a multitude of factors related to roadway design, traffic operations, pavement conditions, driver behavior, human factors, and environmental characteristics, or in more general terms: factors reflect both engineering and non-engineering risk sources. Perhaps not surprisingly, engineering risk sources have dominated the list of variables in the mainstream modeling of crashes whereas non-engineering sources, in particular, behavioral factors, are crucially omitted. It is plausible to assume that crash contributing factors from the same risk source affect crashes in a similar manner, but their influences vary across different risk sources. Conventional crash frequency modeling hypothesizes that the total crash count at any roadway site is well-approximated by a single risk s...
Source: Accident Analysis and Prevention - Category: Accident Prevention Source Type: research