Detour: Understanding the Application of Artificial Intelligence Based Models in Forecasting Safe Travel Routes

Abstract

This paper delves into the development of an Al-driven sys- tem designed to predict secure and safe travel routes. Given the escalat- ing levels of criminal activities and traffic incidents, the need for reliable and accurate tools to determine secure journey paths has become in- creasingly paramount. The proposed system utilizes machine learning algorithms and real-time data from diverse sources, including traffic up-dates and local crime statistics, to provide users with personalized and secure travel routes. Implementing such a system aims to safeguard the well-being of individuals navigating alone, particularly during nighttime, by steering them away from potentially unsafe areas, even if it means opting for a longer route. In addition to reviewing various techniques and strategies employed in similar applications proposed by others, we propose future directions and introduce our algorithm for advancing the study and development of this field, pinpointing areas for enhancement and innovation. Our suggested approach involves identifying a secure route through a town using hotspot analysis, applying Kernel Density Estimation to identify high-crime areas and assigning risk levels to dif- ferent paths based on this information, thereby minimizing the use of routes with higher risks. The discoveries presented in this paper carry substantial implications for the transportation landscape, presenting an effective tool to enhance road safety.