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(V85) and alignment is analyzed. The paper includes two separate relevant analyses. The first analysis uses the regression models to investigate the relationships between V85as 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 V85for cars is the radius of curve. Also, for V85for trucks, the most influential variable is the median width.Finally, the derived models have statistics within the acceptable regions and they are conceptually reasonable. 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 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 (V85) and alignment The first includes uses of the regression models to investigate the relationships between V85as dependent variable, and horizontal alignment and roadway factors as independent variables. This analysis 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 V85for cars is the radius of curve. Also, for V85for trucks, the most influential variable is the median width. Finaally, the derived models have statistics within the acceptable regions and they are conceptually reasonable .