TY - JOUR
T1 - Research on Taxi Operation Characteristics by Improved DBSCAN Density Clustering Algorithm and K-means Clustering Algorithm
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 - http://www.scopus.com/inward/record.url?scp=85109194054&partnerID=8YFLogxK
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 -
