Machine learning assist nyc subway navigation safer and faster

TL;DR

This paper proposes a machine learning-enhanced integer programming approach to optimize NYC subway routes by balancing safety and efficiency, utilizing safety coefficients derived from various models and evaluating shortest-path algorithms.

Contribution

It introduces a novel framework combining machine learning and integer programming to prioritize safety in subway navigation, a feature lacking in mainstream apps.

Findings

Safety coefficients estimated with ML models show high accuracy (low RMSE).

The integrated approach improves route safety without significantly increasing travel time.

Evaluation of shortest-path algorithms identifies the most suitable for safety-aware navigation.

Abstract

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.