Modelling trends in road accident frequency— Bayesian inference for rates with uncertain exposure

Abstract

Several thousand people die as a result of a road accident each year in Great Britain and the trend in the number of fatal accidents is monitored closely to understand increases and reductions in the number of deaths. Results from analysis of these data directly influence Government road safety policy and ensure the introduction of effective safety interventions across the country.Overall accident numbers are important, but when disaggregating into various characteristics, accident risk (defined as the number of accidents relative to an exposure measure) is a better comparator. The exposure measure used most commonly for accident rate analysis is traffic flow which can be disaggregated into vehicle types, road type, and year. Here we want to assess the accident risk across different car types and car ages, and therefore alternative exposure sources are required. We disaggregate exposure to a further extent than possible with currently available data in order to take the increased variability within these new factors into account.Exposure data sources are mainly based on sample surveys and therefore have some associated uncertainty, however previous accident risk analysis has not, in general, taken this into account. For an explicit way to include this uncertainty we use a Bayesian analysis to combine three sources of exposure using a log-Normal model with model priors representing our uncertainty in each data source.Using further Bayesian models, we propagate this uncertainty through to accident rates and accident severity, determining important factors and interrelationships between factors to identify key features affecting accident trends, and we make the first exploration of the effect of the recent recession on road accidents.i ii

Application of Fuzzy Linguistic Rating and Entropy-Based GRA to Address Uncertainty in Safety Performance Index Construction

“…SPIs based on traffic violations that are used as indicators by some researchers, [20,21], are also a reason for the vagueness of the final score since this type of data can represent the approximate situation on roads but not the precise state of safety and drivers' behavior. Likewise, Lloyd and Forster [22] stated that risk exposures (especially accident risk, which is defined as the rate of accidents per vehicle kilometer traveled) represent uncertain information. Hence, the reliability of the performance index derived from this kind of data is questionable.…”

Causes of road accidents in Botswana: An econometric model

“…Vehicular mileage or amount of travel is commonly used to quantify exposure (Lloyd & Forster 2014;Naqvi, Quddus & Enoch 2020;Shen et al 2020). WHO (2004) argues that the risk for one journey may be small but that the amount of risk accumulates with each trip.…”

“…They concluded that fuel prices mediate fatal crashes by reducing exposure as a result of less travel and moderated driver behaviour like speed reduction. Lloyd and Forster (2014) argued that the use of vehicular mileage does not allow for disaggregation, and therefore to overcome this, they simulated both vehicle type and road types to estimate traffic flows. In order to deal with the limitation of disaggregation of travel distance, Shen et al (2020) used the length of driving time to measure exposure, arguing that it is a more reliable measure of exposure.…”