Optimisation of road safety treatment strategies through crash modification factors and simulation

Optimisation of road safety treatment strategies through crash modification factors and simulation

Road safety has become an intensively studied topic with an overarching aim of better understanding why road crashes occur and thus to reduce both frequency and severity. If it is known why road crashes occur, agencies should be able to better apply more effective and efficient road safety improvement strategies. The aim of the traffic engineer is to design and provide a safe travel environment to the road user. While road crashes cannot be completely prevented, a sound understanding of the causative factors helps to minimise crash rate. Crash occurrences can be viewed as a result of the interaction of numerous variables including road geometry, vehicle condition, and operational conditions such as speed and traffic volume.

The main objective of this research was to evaluate traffic and geometric road features and their influences on the safety performance of road intersections, roundabouts, and road segments by estimating suitable crash modification factors (CMFs). To accomplish the study objective, crash prediction models (CPMs) were developed using a generalised linear model (GLM) technique, i.e. Poisson or negative binomial (NB) distribution. The regional area of Toowoomba City, Australia was adopted as the case study. Traffic, geometric, and crash data on 106 road intersections for the years 2008-2015, as well as 49 roundabouts and 84 roadway segments for years the 2010-2015 were used for crash modelling and evaluation. The NB distribution was adopted in preference to Poisson distribution as the data showed over-dispersion. Several goodness-of-fit (GOF) tests were performed on the developed models to identify the better-fitting models. These models were then validated using both the estimation and validation datasets.

An accurate identification of hazardous road locations (HRLs) prevents wasted resources that may result if possible improvements at such locations are identified with less accuracy. The Empirical-Bayes (EB) approach was employed to identify the HRLs in the study area. This approach was adopted to provide more accurate safety estimation by accounting for the regression-to-the-mean bias usually associated with the road crash data. The HRLs were then ranked based on their potential for safety improvement (PSI) value, which is the difference between the expected and predicted road crashes at each location. The top 10 poorly performing locations for each of theroad intersections, roundabouts, and road segments were identified for further investigation.

The CMFs identify any change in the safety performance resulting from implementing a particular treatment. In this study, CMFs were used to estimate the effect of the various proposed safety treatments at identified HRLs. The cross-sectional method (regression approach) was applied to estimate CMFs for individual safety treatment. This method has been considered recently and has not been extensively applied, however, it can be considered as a viable alternative method to estimate the CMFs in cases where observational before-and-after studies are not practical due to data restrictions.

In order to estimate the variation in the values of CMF with different sites characteristics, the crash modification functions (CMFunctions) were developed. Using CMFunctions, the safety effects of various traffic and geometric elements of different road facilities (i.e., intersections, roundabouts, and roadway segments) were investigated. The study also notes that while there has been substantial research in the broad area, very few studies have been undertaken to estimate CMFs for the combined effect of multiple safety treatments. However, the four most suitable techniques for estimating combined CMFs were reviewed and applied together to propose effective safety measures for the HRLs. Since there were variations in the estimation of combined CMFs using the four techniques, the average values were adopted as the best approach to estimate the effect of combined treatments. The results demonstrated that multiple treatments have higher safety effects (i.e., lower CMF) than single treatments. The results also indicated that the effect of treatments on road safety does not depend on the number of treatments that have been applied but rather depend on environment.

The traffic simulation software PTV VISSIM 9.0 was employed to assess the traffic operational performance before and after safety treatment implementation. The top 10 HRLs for each of the road facilities were simulated and evaluated under different scenarios in terms of level of service (LOS), traffic delay, travel time, and average speed. The results showed that there is no significant degradation of traffic operations expected at treated locations.

Finally, a benefit analysis was conducted to estimate the savings during the 10 years after applying the proposed treatments. The crash reduction factors and crash costs were utilised to estimate the crash cost reduction that was associated with single and combined treatments. Such estimation can support road authorities and practitioners to select the final treatment plans for the identified HRLs by undertaking benefit-cost analysis to assist the decision-making process.

Contributions of this research can be summarised as: (i) to develop CPMs for different types of road facilities, (ii) to develop CMFunctions to estimate the variation in the values of CMF with different sites characteristics, (iii) to propose a methodology to identify the most appropriate safety treatments (single and multiple treatments) using CMFs, costing and simulation packages. The research has also identified some important aspects for future research to extend the present work.