Driving Speed Modeling Under Different Climate Conditions(Case Study:‌Tehran – Qom Freeway)

Various environmental conditions could have a significant effect on traffic flow. Understanding the effects of environmental conditions on a vehicle’s speed could provide an accurate estimation of travel speed and traffic management strategies adoption to prevent traffic crashes. In this paper, the speed of vehicles had been investigated based on different variables, such as the effect of some weather conditions (such as rainy, foggy, cloudy and sunny), air temperature, speed variations during nights and days, heavy vehicles percentages and vehicle volumes. In this research, the effects of these variables on vehicle speeds have been studied in order to decrease suburban crash frequencies, using a multiple linear regression model. Required data have been gathered from Iran Road Maintenance & Transportation Organization and Meteorological Organization Government agency in 2014. According to 7541 hourly samples at Tehran-Qom freeway, the results of the proposed model shows that rainy weather and night and day speed variation caused 1.355 and less than 1 kilometer per hour reduction in speed, respectively. Also, the proposed model has an acceptable and reliable error in speed prediction and the evaluation criteria such as mean absolute error, mean absolute percentage error and root mean square error were 3.579, 4.034 and 4.996, respectively.