Because of Its Importance In Saving Human Lives, Traffic Accident Prediction Is a Motor Vehicle
Traffic Challenge. There Are Various Studies In the Literature That Use Artificial Neural Networks (Anns),
Support Vector Machines (Svms), Decision Trees (Dts), and Other Categorization Approaches to Predict
The Severity of Traffic Accidents. Indeed, the Fundamental Shortcoming of Anns and Svms Is Their Lack Of
Human Interpretation, Whereas the Main Disadvantage of Traditional Dts Like C4.5, Id3, and Cart Is Their
Low Accuracy. to Solve These Flaws, We Present a Genetic Algorithm-Based Method to Predict Traffic
Accidents Based on User Preferences Instead of Traditional Dts In This Review.We Customised a Genetic
Algorithm, to Optimise and Find Rules Based on Support, Confidence, and Comprehensibility Metrics In
The Suggested Method. the Suggested Method's Goal Is to Provide Facilities For Users, Such As Traffic
Cops, Road and Transportation Engineers, to Make Use of Their Knowledge While Balancing All of The
Competing Objectives. a Traffic Accident Data Set of Accidents In Rural and Urban Roadways In Tehran
Province, Iran, Was Used to Assess the Suggested Technique During a Five-Year Period (2008–2013).
According to the Evaluation Results, the Proposed Technique Outperforms Classification Methods Such As
Ann, Svm, and Traditional Dts In Terms of Classification Metrics Such As Accuracy and Rule Performance
Metrics Such As Support and Confidence.