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Government agencies and the medical, insurance and automotive industries all have an interest in understanding the socio-economic costs of road crashes. These costs are estimated in most countries, and their computation methodology are continuously progressing as more refined costing methods, are used. This paper outlines two recent studies in Iran and Australia in order to compare crash cost estimation approaches. The analytical approaches and the results of similar studies in some other countries are also discussed It is shown that different approaches to estimating human costs and its components have considerable effects on the results. In both studies, the contribution to the total costs are human costs (50 to 60 percent), vehicle costs (30 percent) with the remaining 10 to 20 percent covering general costs.

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In spite of significant advances in highways safety, a lot of crashes in high severities still occur in highways. Investigation of influential factors on crashes enables engineers to carry out calculations in order to reduce crash severity. Therefore, this paper deals with the models to illustrate the simultaneous influence of human factors, road, vehicle, weather conditions and traffic features including traffic volume and flow speed on the crash severity in urban highways. This study uses a series of artificial neural networks to model and estimate crash severity and to identify significant crash-related factors in urban highways. Applying artificial neural networks in engineering science has been proved in recent years. It is capable to predict and present desired results in spite of limited data sets, which is the remarkable feature of the artificial neural networks models. Obtained results illustrate that the variables such as highway width, head-on collision, type of vehicle at fault, ignoring lateral clearance, following distance, inability to control the vehicle, violating the permissible velocity and deviation to left by drivers are most significant factors that increase crash severity in urban highways.