Mainstream navigation software, like Google and Apple Maps, often lacks the ability to provide routes prioritizing safety. However, safety remains a paramount concern for many. Our aim is to strike a balance between safety and efficiency. To achieve this, we're devising an Integer Programming model that takes into account both the shortest path and the safest route. We will harness machine learning to derive safety coefficients, employing methodologies such as generalized linear models, linear regression, and recurrent neural networks. Our evaluation will be based on the Root Mean Square Error (RMSE) across various subway stations, helping us identify the most accurate model for safety coefficient estimation. Furthermore, we'll conduct a comprehensive review of different shortest-path algorithms, assessing them based on time complexity and real-world data to determine their appropriateness in merging both safety and time efficiency.

翻译：主流导航软件（如谷歌地图和苹果地图）往往无法提供优先考虑安全性的路线。然而，安全性仍是许多用户的首要关切。本研究旨在平衡安全性与效率。为此，我们设计了一个整数规划模型，该模型综合考量最短路径与最安全路线。我们将利用机器学习方法推导安全系数，具体采用广义线性模型、线性回归和循环神经网络等技术。评估将基于各地铁站点的均方根误差（RMSE）进行，以识别最精确的安全系数估计模型。此外，我们将全面评述不同的最短路径算法，根据时间复杂度和真实数据评估其在兼顾安全性与时间效率方面的适用性。