Road Accident Modeling by Fuzzy Logic based on Physical and Mental Health of Drivers

Drivers are vulnerable to musculoskeletal and psychological disorders because of substantially harmful agents in this stressful occupation. This study aimed to investigate the influence of driver’s physical and psychological health on the risk of road accidents using fuzzy logic approach. Two input variables including musculoskeletal disorders (MSDs) and mental health, alongside accident risk levels as output variables were fuzzed using a fuzzy inference system (FIS). Triangular and trapezoid membership functions were used to graphically define outputs related to low, moderate, high and very high, in fuzzy sets. A Mamdani-type FIS was applied to represent all the rules in the IF-THEN format and the patterns of linguistic variables were designed using AND, o‎r and NOT operators. There was significant relationship between MSDs and psychological health with road accidents involving drivers of heavy vehicles (P<0.05). Besides, surface graphs illustrated the relationship between MSDs, psychological health and accident risks. FIS as a novel approach was used for predication of accident risk levels involving drivers of heavy vehicles based on health factors. Physical and psychological health can influence the safe operation of heavy vehicle drivers. The fuzzy inference system provided a method that is advantageous and with promising results for modeling of road accident risk levels on the basis of driver’s physical and mental health.