عنوان مقاله [English]
Tunnels are one of the most important road networking facilities that cause stress for drivers and affect the way people drive. The main purpose of this research is to model the transverse displacement of cars in suburban tunnels based on their gender and age. In this study, neural-fuzzy network is used to model and predict the transverse movement of cars in tunnels. The results of this model are compared based on gender and age criteria.. In this study, changes in the speed of drivers and transverse displacements of cars while passing non-urban tunnels are examined. 30 different drivers, including 14 women and 16 men (young, middle-aged, and elderly) were examined in similar conditions to study the behaviors of drivers. The study was conducted in a Renault Logan with manual transmission. By using multivariate analyses of variance, the instantaneous speed of passing the tunnel, the variations in speed before the tunnel, and the transverse displacement of the vehicles were studied. Research graphs and results show that young men reduce their speed by about 8.97 km / h, middle-aged men by about 15.1 km / h and older men by about 20.76 km / h. Younger women reduce their speed by about 19.25 km / h, middle aged women about 17.93 km / h and older women about 14.07 km / h. The results show that the neural-fuzzy neural network method is able to predict the speed of the drivers' entrance to the tunnel with high accuracy. The results of this study are used to analyze the behavior of drivers in suburban tunnels. Given the importance of sudden changes in speed and transverse movement of cars, especially on two-way lanes, it can be possible to increase tunnel safety by reducing stressors in drivers.