• Deep Jadhav
• Janhavi Adhau
• Rashi Waghmare
• Smita Bhagwat
• Aparna Barbadekar

Road rash is a major contributing factor to traffic accidents, making road safety a major global problem. This research employs machine learning (ML) methods to construct a Rash Driving Pattern Detector. The main goal is to use attributes taken from vehicle data to identify and categorize patterns of reckless driving behavior. This study examines the following machine learning algorithms: Support Vector Machine (SVM), Gaussian Naive Bayes, Decision Tree, Random Forest, and k-Nearest Neighbors (k-NN). These algorithms are selected based on how well they perform classification tasks and how well they can identify intricate patterns in a variety of datasets. Axis orientation, speed, acceleration, and other pertinent characteristics are among the real- world vehicle data that make up the dataset that was used to train and test the model. The dataset has been meticulously annotated to differentiate between instances of reckless driving and typical driving behavior. The annotated dataset is used to train the suggested Rash Driving Pattern Detector with the chosen machine learning algorithms. Each algorithm's performance is assessed using measures including F1 score, accuracy, precision, and recall. The most effective technique for identifying and categorizing rash driving habits is found through comparative analysis. The goal of the study is to shed light on the advantages and disadvantages of each machine learning method when it comes to rash driving identification. The findings will guide the creation of a reliable and accurate real-time rash driving pattern recognition system, which might be included into intelligent transportation systems to improve traffic safety.

Rash Driving, Machine Learning, Random Forest, k-NN, Gaussian Naive Bayes, Decision Tree, Support Vector Machine, Road Safety, Pattern Detection.

"DRIVER’S RASH DRIVING DETECTOR USING MACHINE LEARNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 5, page no.g71-g78, May-2024, Available :http://www.jetir.org/papers/JETIR2405610.pdf

"DRIVER’S RASH DRIVING DETECTOR USING MACHINE LEARNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 5, page no. ppg71-g78, May-2024, Available at : http://www.jetir.org/papers/JETIR2405610.pdf