# Time Series Clustering Model based on DTW for Classifying Car Parks
## 4 Conclusions  
The data of 27 car parks has the characteristics of the time series, so the DTW distance is employed as the distance measurement for classifying the car parks in this paper. Meanwhile, a new framework for classifying car parks, which contains four steps, including preprocessing, distance measurement, clustering, and evaluation. Due to the randomness of existing clustering algorithms, a new clustering algorithm, namely DTW+ DBPAM, is proposed. In order to evaluate the performance of the proposed method, nine public UCR datasets are employed and results show that the proposed method has obviously higher purity than clustering algorithms based on ED and traditional clustering algorithms based on DTW. Subsequently, these 27 car parks are divided into three clusters according to managers' demand. Considering the periodic characteristics of occupancy rates of car parks and the impact of holidays on occupancy rates of car parks, two datasets are selected from data of these car parks, one is regular one week data from Sunday to Saturday, the other is one week data from Sunday to Saturday containing Thanksgiving Day. The results of these two datasets can be summarized as follows:  
(1) Different locations of the car parks may result in different occupancy rates of the car parks.  
(2) The occupancy rates of car parks near workplaces are susceptible to holidays.  
(3) The occupancy rates of car parks on weekend and that on weekday are significantly different.  
There are many similarities in the change of occupancy rate of car parks in each cluster. The daily traffic situation of car parks and their peaks and low peaks can be observed by the occupancy rate of the car parks. Then operators and managers of these car parks can learn some experience from other car parks and then guide people's parking when needed for easing the parking difficulties and congestion.  
In this paper, only one feature, occupancy rate, is used for classifying the car parks, and other features, geographic location, weather, etc., are not considered. In future, we will consider more related features for more effective clustering.  
Conceptualization, T.L. and J.Z.; methodology, T.L. and X.W.; validation, X.W.; formal analysis, T.L.; investigation, X.W.; resources, X.W.; data curation, J.Z.; writing--original draft preparation, T.L. and X.W.; writing--review and editing, T.L. and J.Z.; visualization, X.W.; supervision, T.L.; project administration, T.L.; funding acquisition, T.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant number 71271034, 51939001 and 61976033, the National Social Science Foundation of China, grant number 15 CGL031, the Liaoning Revitalization Talents Program, grant number XLYC1907084, the Natural Science Foundation of Liaoning Province, grant number 20180550307, the Science & Technology Innovation Funds of Dalian, grant number 2018J11 CY022, the Fundamental Research Funds for the Central Universities, grant number 3132019353 and 3132019345.  
The authors declare no conflict of interest.