SafeRoute: Learning to Navigate Streets Safely in an Urban Environment

TL;DR

SafeRoute is a deep reinforcement learning-based navigation system designed to help users find safer routes in urban environments by optimizing for safety and efficiency, trained on crime data from major US cities.

Contribution

It introduces a novel deep reinforcement learning approach for multi-criteria path-finding that incorporates safety considerations into urban navigation.

Findings

Improves safety by reducing proximity to crimes by up to 17%.

Reduces path length by up to 7% compared to state-of-the-art methods.

Successfully applied in Boston, New York, and San Francisco.

Abstract

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%.