Clustered embedding using deep learning to analyze urban mobility based on complex transportation data.

ABSTRACT: Urban mobility is a vital aspect of any city and often influences its physical shape as well as its level of economic and social development. A thorough analysis of mobility patterns in urban areas can provide various benefits, such as the prediction of traffic flow and public transportation usage. In particular, based on its exceptional ability to extract patterns from complex large-scale data, embedding based on deep learning is a promising method for analyzing the mobility patterns of urban residents. However, as urban mobility becomes increasingly complex, it becomes difficult to embed patterns into a single vector because of its limited capacity. In this paper, we propose a novel method for analyzing urban mobility based on deep learning. The proposed method involves clustering mobili

SUBMITTER: Cho SB 

PROVIDER: S-EPMC8057591 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature