Rural Road Safety Monitoring Using Crash Severity Predictive Models: A Case Study of Khorasan Razavi Province in Iran

Document Type : Research Article


1 MSc of transportation planning, Tarbiat Modares University, Tehran, Iran

2 Transportation Planning, Civil engineering department, Tarbiat Modares University, Tehran, Iran

3 Associate Professor of Transportation Engineering, Tarbiat Modares University, Tehran, Iran,


The high severity of crashes caused by high-speed vehicles is one of the drawbacks of intercity transportation. Heavy costs are linked to severe crashes, including death, injuries, and damage to the road, road equipment, and vehicles, as well as major psychological effects. This study uses predictable traffic characteristics to forecast the severity of crashes on suburban highways using logit family models. As a result, the traffic data from the traffic detectors on the roadways is integrated with the crash data in the first stage before being evaluated and modeled. Spatial-temporal scenarios are combined with these two datasets. In this investigation, the ordered logit (OL) and multinominal logit (MNL) models were used. The data, which refers to the roads in Iran's north-eastern province of Khorasan Razavi, was collected over a four-year period. Results indicate that the MNL model performs better than the OL model with more significant traffic parameters.


Main Subjects

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