عنوان مقاله [English]
In this research, we investigate the effective factors and types of pavement deteriorations affecting the number of crashes, including skid resistance, international roughness index (IRI), rutting, cracks, potholes, lane width, average annual daily traffic (AADT), length of segment and speed of vehicles investigated in 19 main urban centers in Tehran province and analyzed using SPSS software. Since investigating the effect of maintenance activities on crash reduction, was the other objective of this study; the mentioned relationship was re-evaluated after the maintenance activities. The regression model revealed a significant drop in crash rate. The results indicate that the deterioration of the pavement and among them the IRI are highly effective in crashes and the maintenance activities reduced crashes in the network by 60%. Since the present research is in the network level and all of the roads in the province of Tehran are considered, they will lead to proper management results. Based on the initial research questions, it can be concluded that there is a significant correlation between crash fatal, pavement deteriorations and traffic. Furthermore the developed relationships could be manipulated to predict after maintenance crashes. The pavement deteriorations and consequently the pavement condition are highly effective in crashes. It should be noted that these crashes are pavement-origin and the rate of reduction is also related to pavement-source accidents. Two regression and logarithmic analysis methods were used to match the variables by distributing existing models. Results revealed that the amount of crashes after maintenance has reached about 0.40 and 0.52 times the state before the maintenance activities, for regression and logarithmic analysis method respectively, which represents 60 and 50% reduction in crashes caused by deteriorations of pavements.
Existing enough entry data, both models are capable to predict crashes. The regression model is superior with 10% reduction in crashes.