Using the Python Programming in Developing Data Mining Models for the Accident Severity of Rural Crashes in Isfahan Province

Document Type : Original Article

Authors
Ali Tavakoli Kashani 1
Mahdi Hadinia 2
Mohammad Ali Salavati 3
1 Associate Professor, School of Civil Engineering, Iran University of Science and Technology, Tehran, Iran.
2 M.Sc., Student, School of Civil Engineering, Iran University of Science and Technology, Tehran, Iran.
3 Ph.D., Candidate, School of Civil Engineering, College of Engineering, University of Tehran, Tehran, Iran.
10.22034/road.2023.414608.2195
Abstract
According to Iran’s legal medical statistics, the death and injury rate due to traffic crashes has increased from 1396 to 1401. This trend is observable in the whole country and the provinces. It can be a warning sign for the authorities to analyze the factors on crashes and find solutions to reduce the death and in-jury rate. This research examines the factors affecting the severity of rural crashes in Isfahan province. Logistic regression model is used with 21 thousand data, and statistical analysis is performed on the results. The results show that variables such as month, time, type of road, type of collision, gender and cause of crash are significant. The probability of a severe crash is higher in the early hours of the day and in Farvardin than the rest of the months of the year; road violation can increase the probability of a severe crash by 5.27 times more than other causes (OR=5.27); and men have 41 percent higher probability of a severe crash than women. Some suggestions are made for further studies on some of these variables.
Keywords
Crash Severity
Logistic Regression
Data Mining
Rural Crashes
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