This approach focuses on relating crashes as a function of potential variables such as road characteristics, traffic level, and weather factors using historical records [1, 2, 3, 4, 5] and subsequently uses these models to identify relatively high-risk sections.
To investigate the crash experience as a function of ΔV, crash rate per mile per year was established for a three-tier ΔV classification for each severity-type crash in the two directions of travel.
Numerous studies have confirmed that the crash risk, as a function of age, conforms to a U-shaped relationship – crash rates, when based on vehicle miles traveled (VMT), are high among teenagers, are lower among middle-aged drivers, and increase again among older drivers".
The crash prediction model employed in this study, originally developed by Lee et al. [10, 11, 12], calculates crash frequency as a function of traffic conditions, external control factors and exposure in a log-linear modeling form.
The development of methods for real-time crash prediction as a function of current or recent traffic and roadway conditions is gaining increasing attention in the literature.
Accounting for the variability in crash severity as a function geometric design, traffic flow, and other roadway and roadside features is afforded by estimating statistical models.
This sounds tangible as being injured in a road crash is a function of risk, risk exposure and also the severity of exposure.
A potential explanation for the lack of an effect of helmet legislation is that our study examined injury risk, including both the chance of being in a crash, as well as the chance that the crash caused a head injury.
We propose an approach which decomposes the severe crash frequency into a function of the change in the total number of crashes and the probability of a crash becoming a severe crash before and after a countermeasure is implemented.
To a large extent, the crash was actually a function of the volatility created by that dizzying volume of transactions.
