Modeling crash frequencies by transportation mode using micro/macro level variables

Document Type : Research Article


1 PhD in Civil Engineering, Barzin Eskan Paya (BEP) Consulting Engineering, Yazd, Iran

2 Department of Civil & Environmental Engineering, Tarbiat Modares University, Tehran, Iran.

3 Islamic Azad University, Science and Research Branch, Tehran

4 Department of Civil Engineering, Faculty of Engineering, Ardakan University, Ardakan, Iran



In this study, traffic and geometric factors affecting accidents occurring in road segments are investigated across different transportation modes (vehicle, motorcycle, and pedestrian) using micro and macro levels variables simultaneously while accounting for the effect of intra-zone correlation due to the same independent variables for accidents occurring within a zone. The data related to 14903 accidents that had occurred in 96 Traffic Analysis Zones (TAZ) in Tehran were collected and imported into Geographic Information System (GIS) application. Negative Binomial models and multilevel models were adopted to predict the number of traffic accidents. Due to considering the multilevel structure of the data in multilevel models, it showed a better performance in explaining the factors affecting accidents. Moreover, based on the results obtained from analyzing the sensitivity analysis of variables for final models, the effect size of one variable in accidents varies across different modes of transport. This discloses the necessity of investigating accidents across modes of transport. According to the results, variables like high intensity of intersection in one TAZ and the length of the road segment increases the number of traffic accidents in all three modes of transport. Variable of the ratio of the principal arterial length to total roads available in one zone has almost 3.2 times stronger effect on motorcycle accident than vehicle and pedestrian accidents. So that adding 1 unit to this variable increases the number of vehicle and pedestrian accidents by a factor of 1.7, whereas, this variable increases motorcycle accidents by a weight of 5.4.


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