Research on Taxi Operation Characteristics by Improved DBSCAN Density Clustering Algorithm and K-means Clustering Algorithm

Content Provider	Scilit
Author	Jian, Saisai Li, Dongyi Yu, Yaqi
Copyright Year	2021
Description	Journal: Journal of Physics: Conference Series With the development of urbanization, the problem of urban traffic congestion is becoming more and more serious. An improved k-means clustering algorithm was proposed to solve the problem that the traditional k-means clustering center could easily be affected by the clustering center and fall into the local optimal solution. Based on the big data of New York City taxis, the operational characteristics are analyzed. The experimental results show that the improved K-means clustering algorithm has a better clustering analysis effect in terms of hot demand for taxis.
Related Links	https://iopscience.iop.org/article/10.1088/1742-6596/1952/4/042103/pdf
ISSN	17426588
e-ISSN	17426596
DOI	10.1088/1742-6596/1952/4/042103
Journal	Journal of Physics: Conference Series
Issue Number	4
Volume Number	1952
Language	English
Publisher	IOP Publishing
Publisher Date	2021-06-01
Access Restriction	Open
Subject Keyword	Journal: Journal of Physics: Conference Series Means Clustering Algorithm Algorithm and K Means Clustering
Content Type	Text
Resource Type	Article
Subject	Physics and Astronomy