Cluster Analysis of Car Parking Data, and Development of their Web Applications

Kubota, Takafumi; Hayashi, Takayuki; Tarumi, Tomoyuki

doi:10.5351/ckss.2011.18.4.549

Cited by 1 publication

(2 citation statements)

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“…Therefore, it becomes urgent to solve this problem and some studies found that the main reasons of parking difficulty can be summarized as independent operation of car parks, unshared data and inefficient operation management [2]. Kubota et al [3] reported that the reasonable planning and classifying management of car parks can improve the problem of inefficient operation management. It is essential to classify car parks and analyze the similarity of car parks for guiding people to park and balance the occupancy rate of car parks, and help operators of car parks to improve the efficiency and ability for relieving the problem of parking difficulty.…”

Section: Introductionmentioning

confidence: 99%

“…In order to improve the availability of parking resources, Cheng et al [6] proposed a programming model for designing a time limited-parking management plan. Kubota et al [3] classified the characteristics of small areas through Okayama parking data and visualized the results of clustering analysis. Effective parking management can not only facilitate people's travel, improve the working efficiency of operation management of car parks, but also reduce their operating costs.…”

Section: Introductionmentioning

confidence: 99%

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Time Series Clustering Model based on DTW for Classifying Car Parks

Zhang

2020

Algorithms

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An increasing number of automobiles have led to a serious shortage of parking spaces and a serious imbalance of parking supply and demand. The best way to solve these problems is to achieve the reasonable planning and classify management of car parks, guide the intelligent parking, and then promote its marketization and industrialization. Therefore, we aim to adopt clustering method to classify car parks. Owing to the time series characteristics of car park data, a time series clustering framework, including preprocessing, distance measurement, clustering and evaluation, is first developed for classifying car parks. Then, in view of the randomness of existing clustering models, a new time series clustering model based on dynamic time warping (DTW) is proposed, which contains distance radius calculation, obtaining density of the neighbor area, k centers initialization, and clustering. Finally, some UCR datasets and data of 27 car parks are employed to evaluate the performance of the models and results show that the proposed model performs obviously better results than those clustering models based on Euclidean distance (ED) and traditional clustering models based on DTW.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%