DBSCAN Clustering Algorithms for Non-Uniform Density Data and Its Application in Urban Rail Passenger Aggregation Distribution

Xiaolu Li, Peng Zhang and Guangyu Zhu
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Xiaolu Li: School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China
Peng Zhang: Transport Planning and Research Institute, Ministry of Transport, Beijing 100028, China
Guangyu Zhu: School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China

Energies, 2019, vol. 12, issue 19, 1-22

Abstract: With the emergence of all kinds of location services applications, massive location data are collected in real time. A hierarchical fast density clustering algorithm, DBSCAN(density based spatial clustering of applications with noise) algorithm based on Gauss mixture model, is proposed to detect clusters and noises of arbitrary shape in location data. First, the gaussian mixture model is used to fit the probability distribution of the dataset to determine different density levels; then, based on the DBSCAN algorithm, the subdatasets with different density levels are locally clustered, and at the same time, the appropriate seeds are selected to complete the cluster expansion; finally, the subdatasets clustering results are merged. The method validates the clustering effect of the proposed algorithm in terms of clustering accuracy, different noise intensity and time efficiency on the test data of public data sets. The experimental results show that the clustering effect of the proposed algorithm is better than traditional DBSCAN. In addition, the passenger flow data of the night peak period of the actual site is used to identify the uneven distribution of passengers in the station. The result of passenger cluster identification is beneficial to the optimization of service facilities, passenger organization and guidance, abnormal passenger flow evacuation.

Keywords: clustering analysis; heterogeneity data; gaussian mixture model; location data; expectation-maximization (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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