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Modeling Crash Frequency of Heavy Vehicles in Rural Freeways

Reza Imaninasab 1, Emad Sekhavati 2, andMansour Hajihoseinloo 2
1. School of Civil Engineering, Iran University of Science and Technology, Tehran, Iran
2. Department of Civil and Environmental Engineering, K.N. Toosi University of Technology Tehran, Iran

Abstract—In the recent years, Prediction models of crash frequency have been employed to estimate accident rate and have been remarkably taken into account for safety improvement plan. Heavy vehicles are one of the main causes of accidents worldwide as there is one out of 9 accidents that they were involved. In this research, a prediction model was developed using heavy vehicles accident data collected from Karaj-Qazvin freeway. In the model, important factors of occurrence of accidents including climatic condition, road surface condition, natural light condition, average vehicle speed, average daily traffic and 5 dangerous violations commitment were considered. Poison regression and negative binomial regression models were employed for modeling purposes. After evaluation of both models, factors including natural light condition, heavy vehicles daily volume, speed limit violation and vehicle defects, especially in lighting system during darkness, were found to be the most effective factors on heavy vehicles accident.

Index Terms—heavy vehicles accident frequency, poison regression model, negative binomial regression model

Cite: Reza Imaninasab, Emad Sekhavati, and Mansour Hajihoseinloo, "Modeling Crash Frequency of Heavy Vehicles in Rural Freeways," Journal of Traffic and Logistics Engineering, Vol. 4, No. 2, pp. 98-102, December 2016. doi: 10.18178/jtle.4.2.98-102
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