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The seasonal distribution coefficients of traffic volume have not been yet taken into account in asphalt pavement design in China. Through comprehensive analysis on the assessed data and the factors affecting monthly distribution coefficients of traffic volume in different areas, the monthly and seasonal distribution coefficients of traffic volume in Harbin city, China has been predicted and determined using a back propagation (BP) neural network algorithm. The research contents involve data investigation and analysis of monthly distribution coefficients of traffic volume, analysing the factors affecting the mentioned coefficients, prediction and determination of monthly and seasonal distribution coefficients of traffic volume, and application of those coefficients, etc. Through the application of monthly and seasonal distribution coefficients, the seasonal traffic volumes in a year were calculated, and the application results demonstrated that the seasonal distribution of traffic volume has a scientific rationality. In addition, this study indicated that the seasonal traffic volume could accurately evaluate the actual conditions about axle loads on the pavement, resulting in the asphalt pavement design to be more rational. Eventually, the findings could appropriately meet the requirements of actual vehicle loads passing on the road surface.

Determination and application of seasonal distribution coefficients of traffic volume in pavement design