mohammadamin ebrahimzadeh

Today, traffic accidents and the resulting damages are one of the major problems of road transportation in the country. The need to control these incidents in order to prevent accidents as much as possible and as a result to reduce many human and financial losses is fully felt. One of the important parameters on traffic accidents on the country's roads is road lighting. Moonlight brightens the road surface and increases drivers' visibility and sense of security, which can be considered as the positive effects of moonlight on reducing accidents. The purpose of this research is to investigate the effects of the moon on the number of road accidents during moonlit nights. A number of 9 out-of-town highways from Kerman, Kermanshah and Kurdistan provinces were used as a case study in this research, and the accident data of these 9 highways in the years 1996 to 1998 were used to compare the effect of moonlight on moonlit nights and at night. Non-moonshine was used. The result showed that with the increase of the crescent moon or the increase of the moonlight, accidents with damage, injury and death all decrease significantly. In general, by comparing the correlation of the crescent moon variable and the total number of accidents that happened at night, it shows that with the increase of the crescent moon, the total number of accidents decreases by -0.631.

Taking advantage of short-term traffic forecasts and using it in traffic management will reduce possible blockages and by reducing the travel time and distance traveled by cars, it will reduce noise pollution, air pollution and also consumption costs. It will be fueled. By knowing the traffic in different hours and predicting it, you can have a better management and planning for the roads of the country, the characteristics of the traffic flow in a road is one of the most important factors in decision making and traffic policy in a region. In this study, using traffic evaluation protocols by means of neural network, a traffic evaluation prediction model was built. Also, the technical evaluation and its economic benefits are discussed. In this regard, by using the traffic count in urban highways and by using the neural network model, the traffic prediction has been done. In the neural network, 4 input variables were used, low or high density, The volume of the number of cars, the type of vehicle, the flow speed, the number of 10 neurons were used in the hidden layer, and finally the numerical model was displayed as a numerical matrix. To predict the current density with proper accuracy, which was done here with r=0.93. Also, to compare the linear regression model, the accuracy of the regression model is lower than the neural network model and r=0.88 was obtained at a significance level of less than 0.05.

Drivers' behavior is one of the most important causes of road accidents and accidents, which can be influenced by countless individual differences such as driving knowledge, information processing, demographic variables and personality. The current research tries to accurately understand this phenomenon among public transport drivers. The sample studied in this research is the drivers of public transportation of goods and passengers in Karaj city. The research method, survey and data collection tool is a questionnaire, the data of which was collected in the form of individual interviews with 200 truck drivers, 220 taxi drivers, 100 inner-city bus drivers, and 68 suburban bus drivers. In this research, using the mentioned questionnaire, six main factors of patient driving, calmness in driving, tendency to fatigue, danger identification, anxious driving and excitement seeking were extracted as driver behavioral factors and the effect of demographic and personality variables on these factors was investigated. Pearson and Spearman correlation tests were also used to test the hypotheses. The results of this research show that the variables of age, level of education, ownership of the vehicle are related to the behavior of the driver, and there is a significant relationship between the personality variables and the driving behavior of professional drivers, and these different aspects of personality and behavior, the differences It justifies the drivers in taking risks and the frequency of accidents.

In recent years, research has been conducted on the adoption of autonomous vehicles and the environmental effects of this technology, but on the issue of the effect of utilizing autonomous vehicles on the share of urban vehicles in daily trips (through reducing parking space search time and share of urban vehicles in daily trips) little research has been done. In this research, the effect of autonomous vehicles system on parking space search time and the share of urban vehicles is discussed, and the effect is estimated by defining practical scenarios using a dynamic system model. In this research, after developing a flow diagram and simulating the system using two methods, model validity was evaluated. The model was used to analyze scenarios and estimate variations in important indicators. Based on the model results, the changes in the share of autonomous vehicles in daily trips show that different implementation strategies in utilizing autonomous vehicles and individuals’ preferences lead to change in the travel pattern.