In Order to Forecast the Likelihood of a Traffic Collision, Machine Learning is Used

  • Deepa K.R Department of Master of Computer Applications, RajaRajeswari College of Engineering, Bengaluru
  • Abhishek S Department of Master of Computer Applications, RajaRajeswari College of Engineering, Bengaluru
Keywords: Road Accident, Traffic Accident, Machine Learning, K-means, Geo-Location

Abstract

Road accidents continue to be among the leading causes of death, disability, and hospitalisation inside the nation. This makes traffic accident risk prediction critical to be capable reduce them and save lives. A number of models had been developed to accomplish the same goal, ranging from ancient statistical models to modern models influenced by advent of machine learning. This work compares many of these models in an effort to examine and derive a helpful method to traffic accident risk prediction. Because drivers are in charge of the road, the study's goal is to give traffic accident risk prediction to drivers by examining information they would be aware of beforehand, as an instance vehicle type age, gender, time of day, weather, and so forth. additionally the usage of Random Forest and Logistic Regression, Optimal Classification Trees is a model capable of producing such outcomes that make intuitive sense to the driver. Furthermore, geo-location data analysis utilising the K-means clustering technique can offer information about high-accident locations.The road has gotten more difficult in the design and management sectors as the quantity of cars on the road has increased. Traffic accidents are a major source of worry on a worldwide scale because they have a considerable impact on people's safety, health, and well-being. The World Health Organisation (WHO) estimates that 1.35 million people die in automobile accidents each year. The result isthey represent a substantial field of research for utilisation of cutting-edge methodologies and algorithms for analysis and prediction. While many traffic accidents occur by external factors, some are caused by the driver. Unfavourable weather conditions, such as rain, clouds, and fog, for example, impair visibility and make driving on such roads difficult and often deadly. The present system prediction model evaluated only many probable causal factors.

Published
2023-07-01
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