Machine learning assist nyc subway navigation safer and faster

Autor:	Bao, Wencheng, Feng, Shi
Rok vydání:	2023
Předmět:	Computer Science - Social and Information Networks Computer Science - Computers and Society Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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. Comment: 7 pages, 3 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2310.02447 Zobrazit plný text záznamu View this record from Arxiv