The before-after study is still the most popular method used by traffic engineers and transportation safety analysts for evaluating the effects of an intervention. However, this kind of study may be plagued by important methodological limitations, which could significantly alter the study outcome. They include the regression-to-the-mean (RTM) and site-selection effects. So far, most of the research on these biases has focused on the RTM. Hence, the primary objective of this study consists of presenting a method that can reduce the site-selection bias when an entry criterion is used in before-after studies for continuous (e.g. speed, reaction times, etc.) and count data (e.g. number of crashes, number of fatalities, etc.). The proposed method documented in this research provides a way to adjust the Naive estimator by using the sample data and without relying on the data collected from the control group, since finding enough appropriate sites for the control group is much harder in traffic-safety analyses. In this study, the proposed method, a.k.a. Adjusted method, was compared to commonly used methods in before-after studies. The study results showed that among all methods evaluated, the Naive is the most significantly affected by the selection bias. Using the CG, the ANCOVA, or the EB method based on a control group (EBCG) method can eliminate the site-selection bias, as long as the characteristics of the control group are exactly the same as those for the treatment group. However, control group data that have same characteristics based on a truncated distribution or sample may not be available in practice. Moreover, site-selection bias generated by using a dissimilar control group might be even higher than with using the Naive method. The Adjusted method can partially eliminate site-selection bias even when biased estimators of the mean, variance, and correlation coefficient of a truncated normal distribution are used or are not known with certainty. In addition, three actual datasets were used to evaluate the accuracy of the Adjusted method for estimating site-selection biases for various types of data that have different mean and sample-size values.

Vehicles being driven for work purposes represent a large proportion of road collision and deaths in the workplace. These observations mean that people driving for work can impose a large burden on organisations and on society. In addition, previous studies identified a fleet driver effect for which there was greater collision risk for those who drive for work compared to the general driving population, even after controlling for exposure. This accentuates the need for both organisational and government policy makers to take steps to reduce the impact of these collisions. No single intervention has been found to solve issues around work-related road safety therefore a range of initiatives have been directed towards the risks associated with drivers, vehicles, journeys and organisations. Many of the interventions, however, lack robust evidence to support their use. The aim of this thesis is to assess organisational interventions to improve work-related road safety by using econometric models on real-world data. The data represents driving claims made between 2005 and 2012 by employees of a large UK company, with a fleet of approximately 35,000 vehicles. The drivers were employed in a variety of roles such as working in technical positions at customer sites or making sales visits. The company has applied a range of strategies to road safety resulting in annual claim reductions of 7.7% compared to only a 4.5% reduction in collisions nationally. The company s data are used to undertake three studies which focused on driver training, manager training and claim segmentation. Statistical models were employed to investigate the effect of two different driver training courses on the frequency of claims while controlling for other factors. The results indicated that driver training courses significantly reduced both the total number of claims and the claim types targeted by the training. The impacts of the interventions were also adjusted for the effects of non-random driver selection and other safety improvements initiated by the company or other agencies. An important finding of this work was that randomly inflated pre-training events accounted for between a third and a quarter of the observed reduction in claims following training. The second study evaluated the impact of management training on claims using multilevel models which allowed for correlation between observations. The study could not confirm that this training was an effective safety intervention. This null result provides an incentive to re-evaluate the implementation of the scheme. The final study identified homogeneous claim segments using statistical models and the impact of training was evaluated on these segments. Such claims were estimated to be reduced by between 32% to 55% following existing driver training courses. This thesis has helped close important gaps and contributed to knowledge in terms of both intervention methodology and the understanding of the effectiveness of work-related road safety interventions. The results, which are already being applied in the case study organisation, demonstrated that training employees in either safe and fuel efficient driving, or low speed manoeuvring, reduced vehicle insurance claims. Further work is necessary to verify the safety value of manager training including gathering detailed information on interactions between managers and drivers.