Navigating Safer Car Routes Based on Measured Car Accidents

• Published in: Metrology Vol. 4; no. 4; pp. 517 - 533
• Main Authors: Gandur, Nazir L., Ekwaro-Osire, Stephen, Rasty, Jahan, Parker, Olin, Fernandes, Guilherme
• Format: Journal Article
• Language: English
• Published: 01-10-2024
• ISSN: 2673-8244; 2673-8244
• DOI: 10.3390/metrology4040032

Car accidents, a major US public safety issue, demand precise analysis and predictive models for mitigation. This study asks the following question: Can the safest car routes across the US be determined? The paper analyzes historical data to forecast future accidents and calculates the safest route between two locations. The study builds a predictive model utilizing statistical analyses, data mining, and machine learning. A joint probability density function (PDF) is devised to calculate the safest route for risk modeling, factoring in latitude and longitude. The model quantifies accident probabilities in areas and travel routes. Additionally, the safest direction can be determined using the gradient of the joint PDF curve. The predictive model enables policymakers to allocate resources proactively. The safest route selection enables drivers to navigate safer areas and routes, which can reduce the number of accidents. Through its analysis and joint PDF model, this research enriches accident analysis and prevention engineering, potentially fostering safer US roads.