Integrating Machine Learning Into Intelligent Transportation Systems For Real-Time Traffic Prediction

Abstract

Despite the numerous safety features that automakers have created to reduce the likelihood of traffic accidents, accidents nevertheless happen regularly in both urban and rural locations. The development of precise prediction models that can recognize patterns connected to various events is essential to avert mishaps and enhance safety protocols. We may group accident possibilities and create efficient safety precautions by employing these models. By taking scientific steps, we hope to reduce accidents as much as possible with minimal funding. In order to accomplish this, we must gather and examine a great deal of information about traffic incidents, including the location, time, weather, and characteristics of the roads. Data patterns may be automatically found using machine learning algorithms, which can then be utilized to forecast accident scenarios based on these trends. After that, these models can be used to group mishaps into distinct groups and create safety protocols for each group. This method allows us to create affordable safety precautions that may be used in a range of situations. We think that this strategy might greatly lower the frequency of traffic accidents and increase the safety of pedestrians, drivers, and passengers.