The objective of this research was to develop a decision-support system to help road safety policy makers make the right choices in road safety planning based on the efficiency of previously implemented safety measures. The measures considered for each region in the study include performance indicators about police operations, treated black spots, freeway and highway facility supplies, speed control cameras, emergency medical services and road lighting projects. To this end, an inefficiency measure is calculated, defined by the proportion of fatality rates in relation to the combined measure of road safety performance indicators, which should be minimised. The relative inefficiency for each region is modelled using the data envelopment analysis (DEA) technique, which follows a benchmarking and target-setting process. In the next step, a fuzzy decision-making system is constructed to convert the information obtained from the DEA into a rule-based system that can be used by policy makers to evaluate the expected outcomes of certain alternative investment strategies in road safety. Using the resultant fuzzy decision-support system, policy makers can analyse alternative strategies in addition to those unique targets suggested by the DEA benchmarking and target-setting process.

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In order to develop a sustainable, safe, and dynamic transportation system, proper attention must be paid to the safety of pedestrians. The purpose of this study is to analyze the surrogate measures related to pedestrian crash exposure in urban roads, including the use of sociodemographic characteristics, land use, and geometric characteristics of the network. This study develops pedestrian exposure models using geographical spatial models including geographically weighted regression (GWR), geographically weighted Poisson regression (GWPR), and geographically weighted Gaussian regression (GWGR). In general, the results of the GWPR model show that the presence of a bus station, population density, type of residential use, average number of lanes, number of traffic control cameras, and sidewalk width are negatively associated with increasing the number of crashes. In this study, in order to identify traffic analysis zones (TAZ) based on the observed and predicted crash data, spatial distance-based methods using GWPR outputs have been used. This study shows the dispersion and density of pedestrian crashes without possessing the volume of pedestrians. Comparison of the performance of GWPR and Poisson models shows a significant spatial heterogeneity in the analysis.

Driving above the speed limit is one of the factors that significantly affect safety. Many studies examined the factors affecting the speed of vehicles in the simulated environment. The present study aimed to analyze drivers’ characteristics, time and weather conditions, and geometric features’ effect on mean speed in simulated conditions simultaneously. In this regard, the simulator experiment data of 70 drivers were collected in a two-lane rural highway at six different times, and weather scenarios and their socioeconomic characteristics were collected by a questionnaire. Structural equation modeling (SEM) was used to capture the complex relationships among related variables. Eleven variables were grouped into four latent variables in the structural model. Latent variables including “Novice Drivers,” “Experienced Drivers,” “Sight Distance,” and “Geometric Design” were defined and found significant on their mean speed. The results showed that “Novice Drivers” have a positive correlation with the mean speed. Meanwhile, “Experienced Drivers,” who drive 12% slower than the novice group, negatively affect the mean speed with a standard regression weight of −0.08. This relation means that young and novice drivers are more inclined to choose higher speeds. Among variables, the latent variable “Sight Distance” has the most significant effect on the mean speed. This model shows that foggy weather conditions strongly affect the speed selection behavior and reduce the mean speed by 40%. Nighttime also reduces mean speed due to poor visibility conditions. Furthermore, “Geometric design” as the latent variable indicates the presence of curves on the simulated road, and it can be concluded that the existence of a curve on the road encourages drivers to slow down, even young drivers. It is noteworthy that the parts of the simulated road with a horizontal curve act as a speed reduction tool for drivers.

Background: Road traffic injuries (RTIs) impose a significant social and economic burden. Objectives: The objective of this study was to estimate the medical costs and economic burden caused by RTI in Iran Methods: The major components included in this study were medical costs, lost output, and indirect costs. Cost components and their values in 2011 were obtained using previous data collected during the study. A general approach that included a consideration of capital was used to calculate the cost of RTIs.