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E Hauer

Publications and source records attributed to E Hauer.

12 recordsLinked to original sources
PubMed2001

Overdispersion in modelling accidents on road sections and in empirical bayes estimation.

In multivariate statistical models of road safety one usually finds that the accident counts are 'overdispersed'. The extent of the overdispersion is itself subject to estimation. It is shown that the assumption one makes about the nature of overdispersion will affect the maximum likelihood estimates of model parameters. If one assumes that the same overdispersion parameter applies to all road sections in the data base, then, the maximum likelihood estimate of parameters will be unduly influenced by very short road sections and insufficiently influenced by long road sections. The same assumption about the overdispersion parameter also leads to an inconsistency when one estimates the safety of a road section by the Empirical Bayes method. A way to avoid both problems is to estimate an overdispersion parameter (phi) that applies to a unit length of road, and to set the overdispersion parameter for a road section of length L to phiL. How this would change the estimates of regression parameters for road section models now in use requires examination. Safety estimation by the Empirical Bayes method is altered substantially.

Accidents, Traffic↗
PubMed1997

Crash reductions related to traffic signal removal in Philadelphia.

The effect on intersection crashes of converting one-way street intersections in Philadelphia from signal to multiway stop sign control was estimated. Using crash and traffic volume data for a comparison group, regression models were computed to represent the normal crash experience of signal controlled intersections of one-way streets, by impact type, as a function of traffic volume. An empirical Bayesian procedure was used to estimate what would have been the expected number of crashes at the converted intersections had they not been converted. The empirical Bayesian estimates were compared with actual counts of crashes after conversion. Estimates were obtained for different classes of crashes categorized by impact type, day/night condition, and impact severity. Aggregate results indicate that replacing signals by multiway stop signs on one-way streets is associated with a reduction in crashes of approximately 24%, combining all severities, light conditions, and impact types.

Accidents, Traffic↗
PubMed1995

[The need for work--experiences from a day-care center].

The concept of a vocational day-care centre for persons with chronic mental illness is discussed. Work is done with clay, which enables a supply with both very simple and very sophisticated tasks. Clients are able to clearly realize the results of their joint efforts. Analysis of attendance at the centre reveals that especially those who had spent longer periods of time in psychiatric hospital and are already receiving a disability pension came to work most regularly. These findings show that even for those who have definitely withdrawn from gainful employment, individually adapted work opportunities nevertheless remain of considerable importance.

Adult↗
PubMed1992

Empirical Bayes approach to the estimation of "unsafety": the multivariate regression method.

There are two kinds of clues to the unsafety of an entity: its traits (such as traffic, geometry, age, or gender) and its historical accident record. The Empirical Bayes approach to unsafety estimation makes use of both kinds of clues. It requires information about the mean and the variance of the unsafety in a "reference population" of similar entities. The method now in use for this purpose suffers from several shortcomings. First, a very large reference population is required. Second, the choice of reference population is to some extent arbitrary. Third, entities in the reference population usually cannot match the traits of the entity the unsafety of which is estimated. To alleviate these shortcomings the multivariate regression method for estimating the mean and variance of unsafety in reference populations is offered. Its logical foundations are described and its soundness is demonstrated. The use of the multivariate method makes the Empirical Bayes approach to unsafety estimation applicable to a wider range of circumstances and yields better estimates of unsafety. The application of the method to the tasks of identifying deviant entities and of estimating the effect of interventions on unsafety are discussed and illustrated by numerical examples.

Accidents↗
PubMed1991

Prediction in road safety studies: an empirical inquiry.

Studies about the road safety effect of interventions are usually retrospective quasi-experiments. In these, one key task is to predict what would have been the safety of the treated group without the intervention. Such predictions can be made by several methods, one of which is to use a "comparison group." We use 26 yearly counts of reported injury accidents for the Canadian provinces to examine which of several simple methods of prediction would have historically predicted best. We find that the use of more data does not always improve prediction. How well one predicts depends not only on the amount of data used but also on the extent to which the prediction method is in accord with the unknown time trend behind the accident counts. We also find that the use of a comparison group to predict is not always better than predicting that this year's count will be the same as last year's. In addition, the intuitive notion that a good comparison group is that which is thought similar to the treated group is too simple. Both similarity and size (as measured by the number of accidents) are important. Moreover, whatever preconceived notions of similarity we had, were contradicted by the data. If the history of accident counts on the treatment group and on several possible comparison groups is available, a simple method to select the most suitable comparison group is suggested.

Accidents, Traffic↗
PubMed1991

Comparison groups in road safety studies: an analysis.

When the safety effect of some treatment is to be estimated, one has to answer the question: "what would have been the ratio of the expected number of accidents 'before' and 'after' had the treated group been left without treatment?" In retrospective observational studies, the count of accidents on a "comparison group" is often used to provide an answer. In consequence, the estimate of the safety effect depends on which of several comparison groups is chosen. It is therefore important to have guidance for this choice. The empirical inquiry in the companion paper led to several conclusions. The explanation of what has been noted by the examination of data requires analysis and is taken up in this paper.

Accidents, Traffic↗
PubMed1986

Research into the validity of the traffic conflicts technique.

The use of near-misses and conflicts as an indirect measure of safety in widespread. The Traffic Conflicts Technique is one procedure for the indirect measurement of safety which has achieved a degree of formalization and attained a measure of popularity in many countries. However, doubts about the validity of the Traffic Conflicts Technique persist. In this paper we examine the conceptual foundations of indirect safety measurement when as data serve observation of events such as near-misses or conflicts. The key problem addressed is that of "validity." A definition of what validity is to mean is suggested. The statistical machinery for the measurement of validity on the basis of empirical evidence is formulated and tested. With this, it may now be possible to move towards rational concensus about the potential of the Traffic Conflicts Technique and similar procedures for the indirect measurement of safety. As a by-product of the analytical results, a yardstick has been created by which to judge the relative performance of the many extant variants of the Traffic Conflicts Technique.

Accident Prevention↗
PubMed1986

On the estimation of the expected number of accidents.

We show that similar entities (drivers, intersections, bus companies, rail crossings) which in one period recorded "x" accidents do not have, on the average, "x" accidents in the subsequent period. The difference is large and systematic. This leads us to conclude that in circumstances in which only the safety estimates in these two periods matter, use of "x" to estimate the expected number of accidents has definite shortcomings. Better estimators are suggested, explored and their use is illustrated. We note that the suggested estimators are similar to what is used when estimation is based on a "treatment-control" type experimental design. It is hoped that the suggested estimators will alleviate some practical problems in the structuring of controlled experiments in safety research, eliminate bias-by-selection from uncontrolled studies and in general enhance the accuracy of safety estimates.

Accidents, Traffic↗
PubMedDate not supplied

Estimating the accident potential of an Ontario driver.

To run a "demerit point" program, one uses routinely available information about drivers to identify those who are most likely to have an accident in the near future. On the basis of a four-year record for a large sample of Ontario drivers, we have examined several tools for the identification of such drivers and investigated how they perform. Each driver is thought to have an expected number of accidents, m. In a group of drivers with common traits (such as age, gender, record of convictions and accidents) the ms have a mean E(m) and a variance VAR(m). Estimates of E(m) and VAR(m) for all combinations of traits can be obtained within the framework of a multivariate statistical model. The same estimates can then be used to judge how well a model identifies drivers who have a large m. In such a multivariate model it is important to use data about previous accidents and convictions. However, the accuracy with which the m of a driver can be estimated is not improved much by distinguishing between offence type or between accidents as being "at fault" or "not at fault". Without much loss in estimation accuracy, one may attach a weight 1 to a conviction and 2 to an accident. Model performance is described in tangible terms: how many accidents are recorded by the drivers identified by a model, what proportion of identified drivers are "false positives," how many drivers with high m remain unidentified. We conclude that by using a multivariate statistical model one can do substantially better than by using a demerit point scheme in which points are assigned to offenses on the basis of their perceived seriousness. However, even when the best model is used to identify a large group of drivers, many will be false positives.

Accidents, Traffic↗