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Abstract
Highway accidents are a significant concern worldwide, leading to substantial loss of life, property, and economic productivity. This study aims to conduct a comprehensive analysis of highway accidents by investigating their frequency, underlying causes, and exploring effective preventive measures. Through an extensive review of historical accident data, this research will assess key factors contributing to highway accidents, including human behavior, vehicle conditions, environmental influences, and road infrastructure. Special attention will be given to identifying accident-prone areas (blackspots) and determining the most common types of accidents, as well as the timeframes in which they frequently occur. To better understand the root causes of these accidents, the study will employ statistical analysis and machine learning techniques on data sourced from government databases and highway safety reports. The study will focus on critical factors such as driver error, vehicle malfunctions, poor road design, adverse weather conditions, and inadequate traffic management systems. It will also analyze the effectiveness of existing safety measures like traffic signals, road signage, and speed limits, while proposing new, data-driven interventions for improving highway safety. The expected results of this research include a clearer identification of high-risk areas and times for highway accidents, as well as the discovery of key accident causative factors. Based on these findings, the study will propose targeted solutions, such as enhancing road infrastructure, increasing public awareness campaigns on safe driving, implementing stricter vehicle inspection protocols, and adopting advanced traffic monitoring systems. Ultimately, the results are expected to provide actionable insights for policymakers and highway authorities to reduce accident rates and improve overall road safety.
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