The paper presents an analysis of roadway factors and posted speed limits that affect the operating speed at multi-lane highways in Egypt. Field data on multi-lane highways in Egypt are used in this investigation. The analysis considers two categories of highways. The first consists of two desert roads (Cairo–Alexandria and Cairo–Ismailia desert roads) and the second consists of two agricultural roads (Cairo–Alexandria and Tanta–Damietta agricultural roads). The paper includes three separate relevant analyses. The first analysis uses the regression models to investigate the relationships between operating speed (V85) as dependent variable, and roadway factors and posted speed as independent variables. The road factors are lane width, shoulder width, pavement width, median width, number of lanes in each direction, and existence of side access along each section. The second analysis uses the Artificial Neural Network (ANN) to explore the previous relationships while the third one examines the suitability of the posted speed limits on the roads under study. It is found that the ANN modeling gives the best model for predicting the operating speed and the most influential variables on V85 are the pavement width, followed by the median width and the existence of side access along section. It is also found that the posted speed limit has a very small effect on the operating speed due to the bad behavior of drivers in Egypt. These results are so important for controlling V85 on multi-lane rural highways in Egypt.
Horizontal alignment greatly affects the speed of vehicles at rural roads. Therefore, it is necessary to analyze and predict vehicles speed on curve sections. Numerous studies took rural two-lane as research subjects and provided models for predicting operating speeds. However, less attention has been paid to multi-lane highways especially in Egypt. In this research, field operating speed data of both cars and trucks on 78 curve sections of four multi-lane highways is collected. With the data, correlation between operating speed (V 85) and alignment is analyzed. The paper includes two separate relevant analyses. The first analysis uses the regression models to investigate the relationships between V 85 as dependent variable, and horizontal alignment and roadway factors as independent variables. This analysis proposes two predicting models for cars and trucks. The second analysis uses the artificial neural networks (ANNs) to explore the previous relationships. It is found that the ANN modeling gives the best prediction model. The most influential variable on V 85 for cars is the radius of curve. Also, for V 85 for trucks, the most influential variable is the median width. Finally, the derived models have statistics within the acceptable regions and they are conceptually reasonable.
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