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

Saisai Jian*, Dongyi Li, Yaqi Yu

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

11 Citations (Scopus)

Abstract

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.

Original languageEnglish
Article number042103
JournalJournal of Physics: Conference Series
Volume1952
Issue number4
DOIs
Publication statusPublished - 29 Jun 2021
Externally publishedYes
Event2021 Asia-Pacific Conference on Image Processing, Electronics and Computers, IPEC 2021 - Dalian, Virtual, China
Duration: 14 Apr 202116 Apr 2021

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