PSO-ANN in preventing traffic collisions: a comparative study

Traffic accident is a global threat which causes health and economic casualties all around the world. Due to expansion of transportation systems, congestion can lead spike road accident. Every year thousands people have died due traffic accidents. Various technologies been adopted by modern cities minimize Therefore, ensure people’s safety, concept smart city has introduced. In city, factors like road, light, weather conditions are important consider predict mishap. Several machine learning models implemented in existing literature determine collision. But accuracy not enough there exist lot challenges determining this paper, an approach particle swarm optimization with artificial neural network (PSO-ANN) proposed collision using dataset transport department United Kingdom. The performance PSO-ANN outperforms model. model be system counter issues. Random Forest, Naıve Bayes, Nearest Centroid, K-Nearest Neighbor classification used compare