Abstract
Identification of the most significant factors for evaluating road risk level is an important question in road safety research, predominantly for decision-making processes. However, model selection for this specific purpose is the most relevant focus in current research. In this paper, we proposed a new methodological approach for road safety risk evaluation, which is a two-stage framework consisting of data envelopment analysis (DEA) in combination with artificial neural networks (ANNs). In the first phase, the risk level of the road segments under study was calculated by applying DEA, and high-risk segments were identified. Then, the ANNs technique was adopted in the second phase, which appears to be a valuable analytical tool for risk prediction. The practical application of DEA-ANN approach within the Geographical Information System (GIS) environment will be an efficient approach for road safety risk analysis.
Highlights
Crash injury severity has always been a major concern in highway safety research
In order to find the factors influencing the road safety risk, artificial neural networks (ANNs) and multiple linear regression (MLR) were generated using the Road Traffic and Crash data obtained for European routes (E-313&E-314) of Limburg (Belgium)
After successfully applying the data envelopment analysis (DEA)-ANNs model for the road safety risk evaluation, we focused on the contributing factors used in the risk prediction
Summary
Crash injury severity has always been a major concern in highway safety research. Road safety research incorporates a broad exhibit of research territories, and the most successful of them is crash information investigation. Investigation of crash information remains the most broadly received way to deal with the safety of a transportation system (e.g., expressways, arterials, crossing points, etc.). The impact of the geometric design on the probability of a driver behavior has been very much archived in conventional safety studies. This course of research is useful in settling on choices in such things as installing cautioning signs on roadway areas, etc. Average Annual Daily Traffic (AADT) is a generally used indicator for measuring the traffic movement conditions, as it is recorded by most organizations around the nation/the world, is accessible to all roadway areas, and gives a measure of introduction to the specific
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