SafeRoute: Learning to Navigate Streets Safely in an Urban Environment
  Recent studies show that 85% of women have changed their traveled route to
avoid harassment and assault. Despite this, current mapping tools do not
empower users with information to take charge of their personal safety. We
propose SafeRoute, a novel solution to the problem of navigating cities and
avoiding street harassment and crime. Unlike other street navigation
applications, SafeRoute introduces a new type of path generation via deep
reinforcement learning. This enables us to successfully optimize for
multi-criteria path-finding and incorporate representation learning within our
framework. Our agent learns to pick favorable streets to create a safe and
short path with a reward function that incorporates safety and efficiency.
Given access to recent crime reports in many urban cities, we train our model
for experiments in Boston, New York, and San Francisco. We test our model on
areas of these cities, specifically the populated downtown regions where
tourists and those unfamiliar with the streets walk. We evaluate SafeRoute and
successfully improve over state-of-the-art methods by up to 17% in local
average distance from crimes while decreasing path length by up to 7%.
