Quasi-induced exposure revisited

Lyles, Richard W; Stamatiadis, Polichronis; Lighthizer, Dale R

doi:10.1016/0001-4575(91)90005-p

Cited by 66 publications

(40 citation statements)

References 2 publications

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“…• quasi-induced exposure improves on induced exposure techniques as responsible drivers in single vehicle accidents and multi-vehicle accidents are not assumed to be from the same population (Lyles et al 1991;Stamatiadis and Deacon 1996);…”

Section: Discussionmentioning

confidence: 99%

“…• not at fault drivers seem to be approximately a random sample from the general driving population (Lyles et al 1991);…”

Section: Discussionmentioning

confidence: 99%

“…• validation of this technique is no more challenging that other techniques and quasi-induced exposure data are an important improvement over other less readily available data (Lyles et al 1991);…”

Section: Discussionmentioning

confidence: 99%

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

Lloyd

Forster

2014

Computational Statistics & Data Analysis

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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

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Section: Discussionmentioning

confidence: 99%

“…• not at fault drivers seem to be approximately a random sample from the general driving population (Lyles et al 1991);…”

Section: Discussionmentioning

confidence: 99%

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

Lloyd

Forster

2014

Computational Statistics & Data Analysis

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“…The small differences between the victims struck by male and female at-fault drivers were not statistically significant. Based on these and similar findings broken down by roadway type, vehicle type, and age groupings, Lyles et al (1991) concluded that not-at-fault drivers in two-vehicle crashes represent "a random sample of all those on the road under the specified conditions [males vs. females]--otherwise they would not be expected to be similar" (p. 284).…”

Section: * Empirical Tests Of the Quasi-induced Exposure Methodsmentioning

confidence: 99%

Toward a Better Benchmark: Assessing the Utility of Not-at-Fault Traffic Crash Data in Racial Profiling Research

Alpert

Smith

Dunham

2004

Justice Research and Policy

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As studies on racial profiling and biased policing have begun to proliferate, researchers are debating which benchmark is most appropriate for comparison with police traffic stop data. Existing benchmark populations, which include populations estimated from census figures, licensed drivers, arrestees, reported crime suspects, and observed drivers and traffic violators, all have significant limitations. This article offers a new, alternative benchmark for police traffic stops, a benchmark that has not been previously applied or tested in a racial profiling research setting. The analysis presented compares traffic observation data, gathered at selected, high volume intersections during an ongoing racial profiling study in Miami-Dade County, Florida, to not-at-fault driver demographic data from two-vehicle crashes at those same intersections. Findings indicate that non-responsible drivers in two-vehicle crashes appear to represent a reasonably accurate estimate of the racial composition of drivers on the roadways at selected intersections and within areas of varying racial composition. The implications of this finding for racial profiling research are discussed, and suggested areas for future inquiry are identified.

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“…Lyles, Stamatiadis and Lighthizer (1991) demonstrated, at least in terms of driver gender, that those not responsible in two-vehicle crashes represented a random sample of drivers regardless of the gender of the responsible group. Davis and Gao (1993) concluded that "the induced exposure model offers a promising approach for estimating the differential in accident risk experience by subgroups of drivers……."…”

Section: Introductionmentioning

confidence: 99%

The efficiency of using non-culpable crash-claim involvements from insurance data as a means of estimating travel exposure for road user sub-groups

Cooper¹,

Meckle²,

Andersen³

2010

Journal of Safety Research

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Quasi-induced exposure revisited

Cited by 66 publications

References 2 publications

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

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

Toward a Better Benchmark: Assessing the Utility of Not-at-Fault Traffic Crash Data in Racial Profiling Research

The efficiency of using non-culpable crash-claim involvements from insurance data as a means of estimating travel exposure for road user sub-groups

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