Route-The Safe: A Robust Model for Safest Route Prediction Using Crime and Accidental Data

Abstract

     Crimes are rising day by day & thus safety & security is becoming a major concern for people today. Even while travelling, people should be aware & choose the route which is safest to travel from. People who are new to the city, have no idea about the safe routes. Though people rely on google maps for planning their routes; yet it only provides the shortest path & give no consideration for safety of the path. Although several other route planning apps exist which provide the safest route, but these do not consider all the factors that account for safety of the path. Apart from other navigation apps, this paper describes an innovative method to find safest route having lowest risk score. This paper uses updated crime and accident data available on NYC OpenData to determine average risk score of clusters/regions. Machine learning algorithms are used to generate the risk score of a path based upon average risk score of nearby clusters/regions. Also, one can get better results by increasing the number of factors that affect the safety of the path. In future, a better prediction algorithm can be introduced through which traveler can identify probable crimes which he/she might face while travelling on a specific route.