A Geotemporal Clustering Model for COVID-19 Projection

2020 ◽  
Author(s):  
N. Bora Keskin ◽  
Xu Min ◽  
Jing-Sheng Jeannette Song
Keyword(s):  
Clustering Model

scholarly journals REMAXINT: a two-mode clustering-based method for statistical inference on two-way interaction

2021 ◽  
Author(s):  
Zaheer Ahmed ◽  
Alberto Cassese ◽  
Gerard van Breukelen ◽  
Jan Schepers
Keyword(s):  
Null Hypothesis ◽  
Type I Error ◽  
Type I ◽  
The Novel ◽  
Simulation Studies ◽  
Test Statistic ◽  
Clustering Model ◽  
Novel Method ◽  
Main Effects ◽  
Empirical Case Studies

AbstractWe present a novel method, REMAXINT, that captures the gist of two-way interaction in row by column (i.e., two-mode) data, with one observation per cell. REMAXINT is a probabilistic two-mode clustering model that yields two-mode partitions with maximal interaction between row and column clusters. For estimation of the parameters of REMAXINT, we maximize a conditional classification likelihood in which the random row (or column) main effects are conditioned out. For testing the null hypothesis of no interaction between row and column clusters, we propose a $$max-F$$ m a x - F test statistic and discuss its properties. We develop a Monte Carlo approach to obtain its sampling distribution under the null hypothesis. We evaluate the performance of the method through simulation studies. Specifically, for selected values of data size and (true) numbers of clusters, we obtain critical values of the $$max-F$$ m a x - F statistic, determine empirical Type I error rate of the proposed inferential procedure and study its power to reject the null hypothesis. Next, we show that the novel method is useful in a variety of applications by presenting two empirical case studies and end with some concluding remarks.

scholarly journals Improving penalized regression-based clustering model in big data

2021 ◽  
Vol 1897 (1) ◽  
pp. 012036
Author(s):  
Sarah Ghanim Mahmood Al-Kababchee ◽  
Omar Saber Qasim ◽  
Zakariya Yahya Algamal
Keyword(s):  
Big Data ◽  
Penalized Regression ◽  
Clustering Model

scholarly journals Achieving High Data Utility K-Anonymization Using Similarity-Based Clustering Model

2016 ◽  
Vol E99.D (8) ◽  
pp. 2069-2078 ◽  
Author(s):  
Mohammad Rasool SARRAFI AGHDAM ◽  
Noboru SONEHARA
Keyword(s):  
Data Utility ◽  
High Data ◽  
Clustering Model

scholarly journals Grey Clustering Evaluation for the Cooperation Efficiency of PPP Project: Taking Beijing Metro Line 4 as an Example

2019 ◽  
Vol 2019 ◽  
pp. 1-13 ◽  
Author(s):  
Hui Sun ◽  
Yingzi Liang ◽  
Yuning Wang
Keyword(s):  
Public Sector ◽  
Private Sector ◽  
Evaluation Model ◽  
Evaluation Index System ◽  
The Public ◽  
Clustering Model ◽  
Grey Clustering ◽  
Public Products ◽  
Infrastructure Project

PPP model is an important model which provides public products or services based on the coordination between the public sector and private sector. The implementation of PPP model is helpful for relieving the stress of insufficient funding for public sector and improving the efficiency of resource allocation. Comparing with traditional infrastructure project, PPP project involves many stakeholders, and the cooperation efficiency during the different stakeholders impacts the results of the project directly. Thus, it is important to explore the cooperation efficiency of PPP project. Based on grey clustering model, this paper evaluates the cooperation efficiency of PPP project. An evaluation index system including 36 indexes is established based on the aims and objectives of three stakeholders (public sector, private sector, and passengers). A case study of Beijing Metro Line 4 PPP project is implemented to verify the validity and applicability of the evaluation model. And the results showed that the cooperation efficiency of Beijing Metro Line 4 PPP project is relatively high. The model also provided insights into the shortage of the cooperation efficiency of Beijing Metro Line 4 PPP project. As such, the results can assist all stakeholders in adjusting the cooperation efficiency.

Truck Parking Usage Patterns by Facility Amenity Availability

2020 ◽  
Vol 2674 (10) ◽  
pp. 749-763
Author(s):  
Sharif Mahmud ◽  
Taslima Akter ◽  
Sarah Hernandez
Keyword(s):  
Stated Preference ◽  
Critical Issue ◽  
Time Of Day ◽  
Geographic Region ◽  
Clustering Model ◽  
Global Positioning ◽  
Usage Patterns ◽  
Transportation Research ◽  
Parking Facilities ◽  
Cluster 2

Truck parking is currently ranked by the American Transportation Research Institute (ATRI) as the fifth most critical issue for the trucking industry and, more importantly, as the second most important issue for truck drivers. Part of the problem can be attributed to inadequate supply of parking and federal hours of service (HOS) regulations. Recent truck driver stated-preference surveys reveal that amenities including restrooms, fuel, and showers are important considerations while seeking available parking. A link between parking usage patterns and facility amenity bundles can guide transportation agency investments in relation to the design and type of parking facilities with high potential to mitigate overcrowding issues, and can be used for predictive modeling in real-time parking availability algorithms and information systems. This paper used historical, anonymous truck global positioning system (GPS) data to determine the extent to which hourly parking usage patterns, that is, average parking duration, percentage of parked trucks, and parking usage ratio, vary by amenity availability. A K-means clustering model grouped parking facilities by time of day parking usage patterns, season, and geographic region. Each cluster, represented by parking usage patterns, was then tied to unique amenity bundles. Three usage pattern clusters were identified: overnight usage with long parking durations ( Cluster 1), off-peak usage with long parking durations, ( Cluster 2), and off-peak usage with short parking durations ( Cluster 3). In general, overnight and longer duration parking was associated with facilities that had fewer amenities, notably without showers, while peak and off-peak hours and shorter duration parking was associated with full-service facilities.

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