نوع مقاله : مقاله پژوهشی
1 دانش آموخته کارشناسی ارشد، گروه حمل و نقل، دانشکده عمران و محیط زیست، دانشگاه صنعتی امیرکبیر، تهران، ایران
2 دانش آموخته کارشناسی ارشد، گروه راه و ترابری، دانشکده عمران و محیط زیست، دانشگاه صنعتی امیرکبیر، تهران، ایران
3 استاد، گروه راه و ترابری زیست، دانشگاه صنعتی امیرکبیر، تهران، ایران
عنوان مقاله [English]
Climate variations, especially rainfall, have a high effect on the behavior of drivers and their decision. Due to the high effect of weather conditions on driving as well as human fatalities on road accidents, the importance of investigating driver behavior as a result of climate change is highlighted. This paper examines the variation of hourly average speed in the different weather conditions by considering the latter traffic factors influencing the speed. Rasht-Khomam highway is considered as the study area. Rasht as a coastal city has the highest rainfall in all the cities of Iran. The linear regression model has been used in this study to examine the variation of hourly average speed, considering traffic factors and weather conditions as independent variables. The Most important traffic factors that were included in the modeling are flow rate and percentage of heavy vehicles. Also, variables representing weather conditions include rainfall, horizontal vision and daylight. The independent variables were trans-formed into dummy variables to investigate the nonlinear relationship between them and the dependent variable. The results showed that the flow rate of more than 2100 veh/h reduces the speed of the vehicles up to 10.81 km/h, which is 12.72% of permitted speed on this highway. Another interesting finding of this research was that the amount of rainfall in ordered levels has a nonlinear effect on the speed. Heavy rain, moderate rain, and light rain reduce speeds by about 5.3, 4 and 2 km/h, respectively. Daylight was also shown to affect speed. In other words, driving at night can reduce speed up to 3 km/h. Horizontal vision variables were not recognized as significant in the model. This may be due to the drivers being familiar with the area and as a result, this parameter cannot affect their performance.