Recent studies show that 85% of women have changed their traveled routes 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 with high foot traffic. 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%.

中文翻译：

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安全路线

最近的研究表明，85% 的女性已经改变了出行路线以避免骚扰和攻击。尽管如此，当前的地图绘制工具并没有赋予用户信息以负责他们的人身安全。我们提出了 SafeRoute，这是一种解决城市导航和避免街头骚扰和犯罪问题的新颖解决方案。与其他街道导航应用不同，SafeRoute 通过深度强化学习引入了一种新型路径生成。这使我们能够成功地优化多标准路径查找并将表示学习纳入我们的框架中。我们的代理学习选择有利的街道，以创建一条安全且短的路径，并具有结合安全性和效率的奖励功能。鉴于可以访问许多城市最近的犯罪报告，我们训练我们的模型在纽约波士顿进行实验，和旧金山。我们在这些城​​市的地区测试我们的模型，特别是人流量大的人口稠密的市中心地区。我们评估了 SafeRoute，并成功地将最先进的方法与最先进的方法相比，本地平均距离犯罪率提高了 17%，同时将路径长度缩短了 7%。