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Realizing the traffic volume at the present time is frequently one of the concerns that occupies the planners’ minds in transportation. Knowing the current volume plays an important role in reflecting the performance of transportation system in the future. Traffic studies are based on observations and interpretations of the current circumstances .Since the present observations cannot be represented for the future status, it should be predicted by means of determined conditions. Annual Average Daily Traffic is one the measure to be used for the traffic volume, which has been mentioned in the codes. The fixed or non-fixed automated counters serve to count this volume. In Iran, Road Maintenance & Transportation Organization is responsible to count daily through different ways. In the present study, the data collected from the selected axes of Mazandaran Province was utilized to make a predictive model for traffic volume. It is fitted by data, linear and logarithmic regression models and also neural network model.

Evaluation of the Efficiency of Neural Networks and Statistical Models to Determine Daily Traffic Volume of the Suburban Roads of Mazandaran Province