Deep Learning Based Model to Recommend Safe Route Navigation System

The level of crime rates in practically every part of the world has risen to unprecedented levels. These are tough times that call for extreme methods to protect individuals, particularly those who must travel to unknown and known locations on a daily basis. Regardless of the mode of transportation used by the victim walking, driving one's own car, using a public transportation vehicle, autorickshaws, or taxis the majority of these crimes happen when the victim is traveling. This research suggests a Deep Learning Based Model To Recommend Safe Route Navigation System that shows the user a safe path shown on maps and based on the area's historical criminal history. Our approach is implemented in a hierarchical manner: a Decision Network is used to determine the user-specific attributes in the first place, and then that same Decision Network is used to activate the creation of safe routes via Geospatial Data Analysis. Users can view the navigated routes of the effective system as a colour coded map. Acknowledging that the real consequences which our research strive for should produce, we validated our model with specific data of San Francisco city for demonstrative purposes.