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

Saisai Jian; Dongyi Li; Yaqi Yu

doi:10.1088/1742-6596/1952/4/042103

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 journal › Conference article › peer-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 language	English
Article number	042103
Journal	Journal of Physics: Conference Series
Volume	1952
Issue number	4
DOIs	https://doi.org/10.1088/1742-6596/1952/4/042103
Publication status	Published - 29 Jun 2021
Externally published	Yes
Event	2021 Asia-Pacific Conference on Image Processing, Electronics and Computers, IPEC 2021 - Dalian, Virtual, China Duration: 14 Apr 2021 → 16 Apr 2021

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 9 Industry, Innovation, and Infrastructure
SDG 11 Sustainable Cities and Communities

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10.1088/1742-6596/1952/4/042103

Cite this

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title = "Research on Taxi Operation Characteristics by Improved DBSCAN Density Clustering Algorithm and K-means Clustering Algorithm",

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.",

author = "Saisai Jian and Dongyi Li and Yaqi Yu",

note = "Publisher Copyright: {\textcopyright} Published under licence by IOP Publishing Ltd.; 2021 Asia-Pacific Conference on Image Processing, Electronics and Computers, IPEC 2021 ; Conference date: 14-04-2021 Through 16-04-2021",

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AU - Jian, Saisai

AU - Li, Dongyi

AU - Yu, Yaqi

N1 - Publisher Copyright: © Published under licence by IOP Publishing Ltd.

PY - 2021/6/29

Y1 - 2021/6/29

N2 - 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.

AB - 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.

UR - https://www.scopus.com/pages/publications/85109194054

U2 - 10.1088/1742-6596/1952/4/042103

DO - 10.1088/1742-6596/1952/4/042103

M3 - Conference article

AN - SCOPUS:85109194054

SN - 1742-6588

VL - 1952

JO - Journal of Physics: Conference Series

JF - Journal of Physics: Conference Series

IS - 4

M1 - 042103

T2 - 2021 Asia-Pacific Conference on Image Processing, Electronics and Computers, IPEC 2021

Y2 - 14 April 2021 through 16 April 2021

ER -

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

Abstract

UN SDGs

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