Setting research priorities to achieve long-term national road safety goals in Iran.

Background: Road traffic crashes (RTCs) and its associated injuries are one of the most important public health problems in the world. In Iran, RTCs rank second in terms of mortality. To address this issue, there is a need for research-based interventions. Prioritizing researches using a variety of approaches and frameworks to determine the most effective interventions is a key nodal point in the RTCs' research policy planning cycle. Thus, this study aims to generate and prioritize research questions in the field of RTCs in Iran. Methods: By adapting the Child Health and Nutrition Research Initiative (CHNRI) method, this study engaged 25 prominent Iranian academic leaders having role in setting Iran's long-term road safety goals, a group of research funders, and policymakers. The experts' proposed research questions were independently scored on a set of criteria: feasibility, impact on health, impact on the economy, capacity building, and equity. Following the prioritization of Research Questions (RQs), they were all classified using the 5 Pillar frameworks. Results: In total, 145 Research Questions were systematically scored by experts against five criteria. Iran's top 20 road traffic safety priorities were established. The RQs related to "road safety management" and "road and infrastructure" achieved a high frequency. Conclusions: The top 20 research questions in the area of RTCs in Iran were determined by experts. The majority of these RQs were related to "road safety management". The results of this study may contribute to the optimal use of resources in achieving long-term goals in the prevention and control of road traffic crashes and its related injuries. Considering these RQs as research investment options will improve the current status of Road Traffic Injuries (RTIs) at a national level and further advance toward compliance with international goals. If these research priorities are addressed, and their findings are implemented, we can anticipate a significant reduction in the number of crashes, injuries, and deaths.

Development of crash frequency models for safety promotion of urban collector streets.

The merits for development and application of crash frequency prediction models for safety promotion on any road type, with a focus on urban collector streets, are presented in this article. The city of Yazd, a medium-sized city in the middle of Iran, was selected as a case study and the data required for modelling crash frequencies along five collector streets comprising 31 street sections were collected. Six models including Poisson and negative binomial models and their deviations along with a hybrid artificial neural networks (ANN) model were developed to predict crash frequency along each street section. The overfitting problem was addressed using appropriate sensitivity analysis methods which were also used to identify the input variables with significant impact on the model performance. The results indicated that the developed hybrid ANN model provided the best performance in terms of accuracy and the number of input variables. The application of hybrid ANN model to evaluate the safety impacts of four different strategies, each resembled by one of the input variables of this model, indicated that these models can successfully be used for this purpose.