Development of a New Risk Assessment Methodology for Light Goods Vehicles on Two-Lane Road Sections

Abstract

Increasing mobility directly affects traffic frequency and thus increases the possible risk of traffic accident occurrences. Taking this into account, it is necessary to create models for determining risk and to act preventively based on these models; this is of great importance both to society and science. In this paper, six measuring sections of a road network are considered on the basis of eight geometric-exploitation road parameters, taking into account the data for light goods vehicles. An original methodology is proposed for identifying risk levels of road sections through their evaluation. For identifying risk levels, the Dombi Logarithmic Methodology of Additive Weights (D’LMAW) was used, which was combined with the Measurement Alternatives and Ranking according to the Compromise Solution (MARCOS) method. Statistical indicators were processed using a hybrid methodology based on the application of rough numbers and Dombi–Bonferroni functions. The performance of the presented methodology was verified on a real-world example, processing the statistical parameters of six two-lane road sections, with the sixth measuring section showing the best performance, since it had the minimum risk. Research has shown that measuring sections with increasing longitudinal gradients are safer. The analysis of measuring sections from fall to rise reduces the deviation of speeds from the speed limit on the roads. The effectiveness, rationality, and robustness of the solution of the proposed methodology was confirmed through a sensitivity analysis.

Development of the MCDM fuzzy LMAW-grey MARCOS model for selection of a dump truck

“…Fuzzy LMAW New Risk Assessment Methodology for Light Goods Vehicles on Two-Lane Road Sections (Subotić et al, 2021).…”

Modification of the Logarithm Methodology of Additive Weights (LMAW) by a Triangular Fuzzy Number and Its Application in Multi-Criteria Decision Making

“…This method was developed in order to define weight coefficients and rank alternatives. So far, the LMAW method has been applied in a number of papers only to define weight coefficients of criteria, both in its basic and modified form [23][24][25]. In this paper, the LMAW method was modified using fuzzy numbers (FLMAW) and was used both for the calculation of weight coefficients of criteria and for the evaluation of alternative solutions.…”