  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.
