Cluster Analysis on Sustainable Transportation: The Case of New York City Open Data

Artificial Intelligence (AI) provides the opportunity to analyze complex transportation domains from various perspectives. Sustainability is one of the important transportation factors vital for a robust, fair, and efficient living environment and the livability of a city. This article leverages different feature engineering techniques on the New York City mobility dataset to identify the significant sustainability factors and employ the k-means clustering technique to cluster the commuters based on their transportation modes and demographics. Cluster analysis is performed based on the specified features and sustainable mode of transportation. Our cluster analysis of commuters on the New York City dataset shows that demographic information such as gender or race does not influence the sustainable mode of transportation, while the "start location" of travellers and their car access are influencing factors on sustainability.