Analyzing Risky Behavior in Traffic Accidents

• Main Authors: Chaudhari, Mayank, Sarkar, Santonu, Sharma, Divyasheel
• Format: Conference Proceeding
• Language: English
• Subjects: Accident Risk Prediction; Accidents; Driver behavior; Neural networks; Predictive models; Random forests; Road safety; Support vector machines; Traffic Accidents; Vehicles
• ISSN: 2577-1655
• DOI: 10.1109/SMC42975.2020.9283330

Among all the transportation systems that people use, the public traffic-ways are most common and dangerous resulting in a significant number of fatalities per day worldwide. Statistics have shown that the mortality rates related to traffic accident are more among youth. Although various road safety strategies and rules are developed by the government and law-enforcement agencies to combat the situation, these methods mainly target design, operation, and usability of traffic-ways. Most of the recent data-driven analysis papers model the traffic patterns or predict accidents from the past data. In this paper, we consider a comprehensive, year long fatality analysis reporting system (FARS) data to analyze the role of various factors related to humans, weather and physical conditions (e.g., road surface, light condition etc.) involved in traffic accidents. We build an intelligent risk prediction model that can help decision-makers to ensure road safety. The proposed model estimates (i.) the accident risk over a future time frame, and (ii.) the risk associated with the drivers present on the traffic-way based on the driver's behavior, history, environmental conditions and physical conditions related to traffic-way.