PREDICTIVE TRAFFIC ANALYSIS FOR ADVANCED TRANSPORT SYSTEMS UTILIZING MACHINE LEARNING

Abstract

Automobile manufacturers have introduced various safety features to mitigate the risk of
traffic accidents, yet accidents persist in both urban and rural areas. To enhance safety measures and
prevent accidents, accurate prediction models are crucial to identifying patterns associated with
different scenarios. Through these models, we can cluster accident scenarios and devise effective
safety strategies. Our objective is to achieve a substantial reduction in accidents using cost-effective
methods grounded in scientific research. To attain this objective, extensive data on traffic accidents
must be collected and analyzed, encompassing factors such as accident location, time, weather
conditions, and road features. Leveraging machine learning algorithms, we can automatically
discern patterns within the data and forecast accident scenarios based on these patterns.
Subsequently, these models can categorize accidents into distinct clusters, enabling the
development of tailored safety measures for each category. Through this approach, we can devise
efficient safety protocols adaptable to various settings. We are confident that this methodology
holds promise in significantly curbing the number of traffic accidents and enhancing safety for
drivers, passengers, and pedestrians alike.