Conceptual Framework for Accident Prone Hotspot Identification and Removal using Historical Data Analytics

Traffic management in a smart city is a significant challenge due to various causes, and among the significant reasons is traffic congestion. Unexpected accidents occur due to multiple factors like bad weather, driving speed, road friction, peak hours, etc. and cause disabilities, health injuries and sometimes loss of life. Therefore, it becomes necessary to discover the relationship among attributes or identify the factor that may lead to the accident under specific situation. In this paper, various relevant papers are reviewed to investigate the accident analysis using traffic data and also a conceptual framework is proposed to predict the accident-prone hotspot using historical data analytics. Further, it is also proposed that alert must be generated for users about the possible precautions by analyzing various factors that could be responsible for the accident.