Exposure based geographic analysis mode for estimating the expected pedestrian crash frequency in urban traffic zones; case study of Tehran.

Predicting pedestrian crashes on urban roads is one of the most important issues related to urban traffic safety. Due to the lack of spatial correlation and instability in the crash data, the statistical reliability of Empirical Bayesian method in the combination of the observed and predicted crash frequency is questionable. In this study, an EB model has been developed to estimate the expected frequency of pedestrian crashes in urban areas using the over-dispersion parameter taking into account the spatial correlation of crash data. The objective of this study is to estimate the expected geographical frequency of pedestrian crashes using the Empirical Bayesian (EB) approach using weighted geographical regression models for pedestrian crashes in Tehran. For doing so, four models of geographic weighted Poisson regression (GWPR), geographic weighted zero-inflated Poisson regression (GWZIPR), geographic weighted Negative Binomial regression (GWNBR) and the geographic weighted zero-inflated Negative Binomial regression (GWZINBR) have been used. In this study, the areas analyzed for the development of the EB model based on pedestrian exposure variables include traffic analysis zones (TAZs). Finally, the EB model was extended to the Geographic Empirical Bayesian (Ge-EB) model. The results showed that GWZIPR and GWZINBR models make more accurate predictions. These models had the lowest values of Akaike Information Criterion (AIC), the lowest values of Cross Validation and the lowest values of Root Mean Square Error (RMSE). The Moran and Variance Inflated Factor (VIF) indices were also within acceptable limits. The weighted negative binomial distribution could moderate the amount of heterogeneity of crash data to some extent. This study has shown the dispersion and density of pedestrian crashes without having the volume of pedestrians and thus can be done by taking safety measures in places prone to pedestrian crashes.

Composite performance indicators for helmet and seat-belt enforcement as imprecise data.

OBJECTIVES: Composite road safety performance indicators (RSPIs) are useful tools in regional road safety planning. Among the indicators and data calculated by the World Health Organization (WHO), information on the effectiveness of law enforcement on various risk factors for road casualties were provided, which can be considered as qualitative indicators. The purpose of this study is to analyze the performance indicators related to the percentage of helmet and seat-belt use versus the qualitative enforcement scores attributed by WHO. METHODS: This analysis was performed for 30 member states of WHO and will show how and with what degree of efficiency the qualitative output of the enforcement score acts versus the input percentage of seat-belt and helmet use. The qualitative nature of the output index has led us to depart the traditional analysis of crisp numerical indicators related to road safety performance and to consider data as imprecise or fuzzy indices. In this study we used two methods including imprecise DEA-based CIs and fuzzy DEA-based CIs, respectively. RESULTS: Results show that the clear score achieved by the Imprecise DEA-based CI model is easy to interpret and use. Whereas, in the Fuzzy DEA-based CI model, the fuzzy indicator scores obtained based on the level of several probabilities are strong in capturing the uncertainties related to human behavior. CONCLUSIONS: Both RSPIs are applicable with slight differences that were in the order of countries and the ease of reading the results. We also found that each method has different strengths and that the FDEA-based CIs method is more accurate and more in line with the inputs than the IDEA-based CIs method.