Affiliation:
1. Department of Civil Engineering, Yarmouk University (YU), P.O. Box 566, Irbid 21163, Jordan
2. Department of Civil Engineering, Jordan University of Science & Technology (JUST), P.O. Box 3030, Irbid 22110, Jordan
3. Department of Computer Science, Yarmouk University (YU), P.O. Box 566, Irbid 21163, Jordan
Abstract
This study investigated the potential impacts on speeding violations in the United States, including the top ten states in terms of crashes: California, Florida, Georgia, Illinois, Michigan, North Carolina, Ohio, Pennsylvania, Tennessee, and Texas. Several variables connected to the driver, surroundings, vehicle, road, and weather were investigated. Three different machine learning algorithms—Random Forest (RF), Classification and Regression Tree (CART), and Multi-Layer Perceptron (MLP)—were applied to predict speeding violations. Accuracy, F-measure, Kappa statistic, Root Mean Squared Error (RMSE), Area Under Curve (AUC), and Receiver Operating Characteristic (ROC) were used to evaluate the algorithms’ performance. Findings showed that age, accident year, road alignment, weather, accident time, and speed limits are the most significant variables. The algorithms used showed excellent ability in analyzing and predicting speeding violations. The RF was the best method for analyzing and predicting speeding violations. Understanding how these factors affect speeding violations helps decision-makers devise ways to cut down on these violations and make the roads safer.
Subject
Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science
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