Efficient Road Structural Design And Traffic Accident Analysis Using Supervised Learning Algorithms

The flow of traffic analysis can be helpful for to improving the traffic quality and protect it from the conditions to cause danger, risk, or injury. Our goal is about reducing the accidents risk which involves designing the roads and principles. The previous road designing patterns of highly accidental risk. Especially some of the circumstances of accidents may be related to the driver such as driver fault, health problems, or also vehicles such as brake failure, steering, or the road itself such as poor sidewalks, and lack of road equipment, and safety measures. In this paper, we talk about some of the best road designing techniques in the world. ‘Double crossover diamond interchange (DCD)’, is a true way to go more people reduce the vehicle striking violently against another. One more design is that a ‘roundabout’, is a circular common intersection where the vehicles can travel anticlockwise around that center part. In This Modern roundabout, there is no need for traffic signals or speed breakers. When the vehicles enter the roundabout zone, if traffic is there in the roundabout, then go to the circulating roadway area and go to their desired street. Find the exact models to predict the deaths and severe injuries caused by road accidents. This analysis needs to classify the various models of machine learning and data science algorithms for instance Linear Regression, Decision Tree, Random Forest (RF), and Naive Bayes Classification and Support Vector Machine (SVM). The decision tree algorithm has a root node and contain child nodes. But Random Forest is having multiple decision trees. Random Forest has the best performance with 79% accuracy then decision tree with 70% accuracy, SVM with 75%, Logic Regression with 72%, and Naive Bayes with 73% accuracy.