Journal of Transportation Research

Journal of Transportation Research

Holidays Traffic Volume Prediction Using Neural Network (Case Study: Iran Rural Roads)

Document Type : Original Article

Authors
Sina Sahebi 1
Mana Meskar 2
Mohammad Bafandeh 3
1 Assistant Professor, Civil, Water and Environmental Engineering, Shahid Beheshti University‌, Tehran, Iran.
2 Ph.D., Student, Industrial Engineering, Sharif University of Technology, Tehran, Iran.
3 M.Sc., Student, Civil, Water and Environmental Engineering, Shahid Beheshti University‌, Tehran, Iran.
10.22034/tri.2024.398914.3158
Abstract
Traffic volume represents one of the most critical parameters in transportation analyses, influencing various aspects such as design, planning, policy-making, and model development. During holidays, characterized by a peak in out-of-town trips, significant changes occur in the traffic volume pattern across the country's rural road network. In Iran, holidays follow different patterns based on lunar and solar occasions, resulting in notable fluctuations in traffic volume on suburban roads. This study aims to investigate the pattern of changes in traffic volume during holidays and to provide a model for predicting traffic volume during such periods. For this purpose, an artificial neural network model was developed to forecast traffic volume on the suburban roads of the country's provinces, with sensitivity to holidays. The models are designed on a daily time scale and are developed separately for each province. The evaluation metric used for the models is the mean absolute percentage error. The average of this metric across all models was 9.28%, with an example from Mazandaran province showing 8.89%. The results of this research aid policymakers in predicting traffic volume in the transportation networks of the country at the province-day level during different times of the year and enable them to take proactive measures to address traffic congestion during special occasions and holidays. Moreover, the developed model allows for the examination of the effect of different holiday scenarios on the country's traffic volume.
Keywords
Traffic Volume Prediction
Holidays
Artificial Neural Network
Subjects
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