scholarly journals Location and Expansion of Electric Bus Charging Stations Based on Gridded Affinity Propagation Clustering and a Sequential Expansion Rule

2021 ◽  
Vol 13 (16) ◽  
pp. 8957
Author(s):  
Yajun Zhang ◽  
Jie Deng ◽  
Kangkang Zhu ◽  
Yongqiang Tao ◽  
Xiaolin Liu ◽  
...  
Keyword(s):  
Capital Investment ◽  
Clustering Algorithm ◽  
Empirical Investigation ◽  
Affinity Propagation ◽  
Guizhou Province ◽  
Electric Bus ◽  
The Government ◽  
Charging Stations ◽  
Ap Clustering

With the escalating contradiction between the growing demand for electric buses and limited supporting resources of cities to deploy electric charging infrastructure, it is a great challenge for decision-makers to synthetically plan the location and decide on the expansion sequence of electric charging stations. In light of the location decisions of electric charging stations having long-term impacts on the deployment of electric buses and the layout of city traffic networks, a comprehensive framework for planning the locations and deciding on the expansion of electric bus charging stations should be developed simultaneously. In practice, construction or renovation of a new charging station is limited by various factors, such as land resources, capital investment, and power grid load. Thus, it is necessary to develop an evaluation structure that combines these factors to provide integrated decision support for the location of bus charging stations. Under this background, this paper develops a gridded affinity propagation (AP) clustering algorithm that combines the superiorities of the AP clustering algorithm and the map gridding rule to find the optimal candidate locations for electric bus charging stations by considering multiple impacting factors such as land cost, traffic conditions, and so on. Based on the location results of the candidate stations, the expansion sequence of these candidate stations is proposed. In particular, a sequential expansion rule for planning the charging stations is proposed that considers the development trends of the charging demand. To verify the performance of the gridded AP clustering and the effectiveness of the proposed sequential expansion rule, an empirical investigation of Guiyang City, the capital of Guizhou province in China, is conducted. The results of the empirical investigation demonstrate that the proposed framework that helps find optimal locations for electric bus charging stations and the expansion sequence of these locations are decided with less capital investment pressure. This research shows that the combination of gridded AP clustering and the proposed sequential expansion rule can systematically solve the problem of finding the optimal locations and deciding on the best expansion sequence for electric bus charging stations, which denotes that the proposed structure is pretty pragmatic and would benefit the government for long-term investment in electric bus station deployment.

scholarly journals PENGARUH EKSPOR TERHADAP PERTUMBUHAN EKONOMI INDONESIA

2018 ◽  
Vol 5 (2) ◽  
pp. 10-31
Author(s):  
Dara Resmi Asbiantari ◽  
Manuntun Parulian Hutagaol ◽  
Alla Asmara
Keyword(s):  
Economic Growth ◽  
Significant Influence ◽  
Capital Investment ◽  
Industrial Sector ◽  
Ordinary Least Squares ◽  
Short Term ◽  
Capital Goods ◽  
Agricultural Export ◽  
The Government

Economic growth is a matter of the economy in the long term and is influenced by various factors. This study aimed to analyze the effect of the agricultural export, industrial export, mining export, import of capital goods, government spending and gross fixed capital formation to economic growth of Indonesia. The analytical method used was Ordinary Least Squares (OLS) with Cochrane-Orcutt method. This study uses secondary data quarterly time series from 2000 Q1 to 2016 Q1 which is obtained from the Ministry of Trade, the Central Bureau of Statistics, Bank Indonesia and the Capital Investment Coordinating Board. The results showed that on the first model to see the effect of the aggregate exports on economic growth show that imports of capital goods have a significant influence in the short term to economic growth. While in the long term, the variables that have a significant impact on economic growth is GFCF. While the second model to see the role of exports by sector to economic growth getting results that exports in the industrial sector has a significant influence both in the short-term and long-term to economic growth. It concluded that outward looking policies has an effective impact to be applied in Indonesia if the Government to develop exports in the industrial sector.

Education, Divorce, and Household Income Inequality in Hong Kong

2017 ◽  
Author(s):  
Yue Chim Richard Wong
Keyword(s):  
Human Capital ◽  
Early Childhood ◽  
Income Inequality ◽  
Government Policy ◽  
Capital Investment ◽  
Divorce Rates ◽  
The Government ◽  
Income Dispersion ◽  
Early Childhood Students

Education is the most important determinant of income dispersion among individuals and, indirectly, among households. Government policy should place human capital investment at the center of its strategy to reduce poverty and enhance inter- generational mobility. Rising divorce rates should be given far more attention as a growing source of poverty that impedes intergenerational mobility. Investing in the children of poor and broken families is the best policy to reduce long-term income inequality. Society should provide additional subsidies and support to students from these families as an investment in their human capital, especially during their early childhood. Students with ability should be offered scholarships to study in the best schools. The government does not have to fund everything, but it should take the lead to encourage private contributions for this purpose.

scholarly journals PENGARUH EKSPOR TERHADAP PERTUMBUHAN EKONOMI INDONESIA

2018 ◽  
Vol 5 (2) ◽  
pp. 10-31
Author(s):  
Dara Resmi Asbiantari ◽  
Manuntun Parulian Hutagaol ◽  
Alla Asmara
Keyword(s):  
Economic Growth ◽  
Significant Influence ◽  
Capital Investment ◽  
Industrial Sector ◽  
Ordinary Least Squares ◽  
Short Term ◽  
Capital Goods ◽  
Agricultural Export ◽  
The Government

Economic growth is a matter of the economy in the long term and is influenced by various factors. This study aimed to analyze the effect of the agricultural export, industrial export, mining export, import of capital goods, government spending and gross fixed capital formation to economic growth of Indonesia. The analytical method used was Ordinary Least Squares (OLS) with Cochrane-Orcutt method. This study uses secondary data quarterly time series from 2000 Q1 to 2016 Q1 which is obtained from the Ministry of Trade, the Central Bureau of Statistics, Bank Indonesia and the Capital Investment Coordinating Board. The results showed that on the first model to see the effect of the aggregate exports on economic growth show that imports of capital goods have a significant influence in the short term to economic growth. While in the long term, the variables that have a significant impact on economic growth is GFCF. While the second model to see the role of exports by sector to economic growth getting results that exports in the industrial sector has a significant influence both in the short-term and long-term to economic growth. It concluded that outward looking policies has an effective impact to be applied in Indonesia if the Government to develop exports in the industrial sector.

scholarly journals Identification and Analysis of Barriers against Electric Vehicle Use

2020 ◽  
Vol 12 (12) ◽  
pp. 4850 ◽  
Author(s):  
Madhusudhan Adhikari ◽  
Laxman Prasad Ghimire ◽  
Yeonbae Kim ◽  
Prakash Aryal ◽  
Sundar Bahadur Khadka
Keyword(s):  
Analytical Hierarchical Process ◽  
Social Barriers ◽  
Transportation Sector ◽  
Infrastructure Policy ◽  
Comparative Survey ◽  
Economic Infrastructure ◽  
The Government ◽  
Charging Stations ◽  
Relevant Factors

Electric vehicles (EVs) can be considered an alternative technology to reduce greenhouse gas emissions in the transportation sector. However, numerous barriers need to be overcome in this regard. This study is aimed at presenting the framework for the identification and analysis of barriers against the use of EVs. Then, the framework was applied to identify the challenges and rank them in order of importance against the diffusion of EVs in Nepal. Seventeen barriers were identified from previous studies, reports, policy documents, and interactions with experts. The identified barriers were classified into five categories: technical, policy, economic, infrastructure, and social. Then, a comparative survey was performed to obtain experts’ opinions on the identified barriers and the analytical hierarchical process was used to analyze and rank them. The results revealed that infrastructure, policy, economic, and technical barriers pose more pressing concerns than social barriers. The lack of charging stations, relatively higher purchase price of EVs compared to internal combustion vehicles, and poor long-term planning and goal setting on the part of the government were ranked as the top three barriers against EV uptake in Nepal. This framework can be applied to replicate the study in other countries, taking their inherent relevant factors into account.

scholarly journals Deployment Strategy for Car-Sharing Depots by Clustering Urban Traffic Big Data Based on Affinity Propagation

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Zhihan Liu ◽  
Yi Jia ◽  
Xiaolu Zhu
Keyword(s):  
Big Data ◽  
Clustering Algorithm ◽  
Optimization Method ◽  
Urban Traffic ◽  
Affinity Propagation ◽  
Superior Performance ◽  
Trajectory Data ◽  
Car Sharing ◽  
Gps Trajectory Data ◽  
Ap Clustering

Car sharing is a type of car rental service, by which consumers rent cars for short periods of time, often charged by hours. The analysis of urban traffic big data is full of importance and significance to determine locations of depots for car-sharing system. Taxi OD (Origin-Destination) is a typical dataset of urban traffic. The volume of the data is extremely large so that traditional data processing applications do not work well. In this paper, an optimization method to determine the depot locations by clustering taxi OD points with AP (Affinity Propagation) clustering algorithm has been presented. By analyzing the characteristics of AP clustering algorithm, AP clustering has been optimized hierarchically based on administrative region segmentation. Considering sparse similarity matrix of taxi OD points, the input parameters of AP clustering have been adapted. In the case study, we choose the OD pairs information from Beijing’s taxi GPS trajectory data. The number and locations of depots are determined by clustering the OD points based on the optimization AP clustering. We describe experimental results of our approach and compare it with standard K-means method using quantitative and stationarity index. Experiments on the real datasets show that the proposed method for determining car-sharing depots has a superior performance.

scholarly journals The Effects of Munsell Neutral Grey Backgrounds on the Colour of Chrysoprase and the Application of AP Clustering to Chrysoprase Colour Grading

Minerals ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 1092
Author(s):  
Yuansheng Jiang ◽  
Ying Guo ◽  
Yufei Zhou ◽  
Xiang Li ◽  
Simin Liu
Keyword(s):  
Clustering Algorithm ◽  
Affinity Propagation ◽  
Fluorescent Lamp ◽  
Colour Difference ◽  
Green Colour ◽  
Difference Formula ◽  
First Time ◽  
Ap Clustering ◽  
Scientific Classification

Chrysoprase is a popular gemstone with consumers because of its charming apple green colour but a scientific classification of its colour has not yet been achieved. In this research, we determined the most effective background of the Munsell Chart for chrysoprase colour grading under a 6504 K fluorescent lamp and applied an affinity propagation (AP) clustering algorithm to the colour grading of coloured gems for the first time. Forty gem-quality chrysoprase samples from Australia were studied using a UV-VIS spectrophotometer and Munsell neutral grey backgrounds. The results determined the effects of a Munsell neutral grey background on the observed colour. It was found that the Munsell N9.5 background was the most effective for colour grading in this case. The observed chrysoprase colours were classified into five groups: Fancy Light, Fancy, Fancy Intense, Fancy Deep and Fancy Dark. The feasibility of the colour grading scheme was verified using the colour difference formula DE2000.

Image Clustering Using Active-Constraint Semi-Supervised Affinity Propagation

2016 ◽  
Vol 20 (7) ◽  
pp. 1035-1043 ◽  
Author(s):  
Qi Lei ◽  
◽  
Jun Liu ◽  
Min Wu ◽  
Jie Wang ◽  
...  
Keyword(s):  
Active Learning ◽  
Clustering Algorithm ◽  
Color Space ◽  
Image Data ◽  
Affinity Propagation ◽  
Image Feature ◽  
Image Clustering ◽  
Active Constraint ◽  
Pairwise Constraints ◽  
Ap Clustering

Image clustering is an effective way to discover and analyze large quantities of image data. The HSV color space is particularly advantageous in image feature extraction because of its relatively prominent feature vector. The objective of this study is to develop an image clustering method using the active-constraint semi-supervised affinity propagation (ACSSAP) algorithm. The algorithm adds supervision to the affinity propagation (AP) clustering algorithm with pairwise constraints and uses active learning to guide the AP clustering algorithm. Active learning of pairwise constraints leads to an adjustment of the similarity matrix in AP at each iteration. In the experiments, the advantage of HSV space is analyzed and the ACSSAP algorithm is evaluated for data sets of different sizes in comparison with other algorithms. The result demonstrates that the ACSSAP has better performance.

scholarly journals K-affinity propagation clustering algorithm for the classification of part-time workers using the internet

2021 ◽  
Vol 24 (1) ◽  
pp. 464
Author(s):  
Novendri Isra Asriny ◽  
Muhammad Muhajir ◽  
Devi Andrian
Keyword(s):  
Clustering Algorithm ◽  
Optimal Number ◽  
Affinity Propagation ◽  
The Internet ◽  
Affinity Propagation Clustering ◽  
Part Time ◽  
Second Period ◽  
Ap Clustering ◽  
Cluster Points

There has been a significant increase in the number of part-time workers in the last 3 years. Data collected from sakernas BPS showed that the number of part-time workers was 125,443,748 in the second period of 2016. This number rapidly increased in 2017, 2018 and 2019 in the same period, by 128,062,746, 131,005,641, and 133,560,880 workers. Based on the increase in the last 3 years, East Java province has the highest number of part-time workers that use the internet. This research aims to determine the number of part-time workers that use the internet by using the k-affinity propagation (K-AP) clustering. This method is used to produce the optimal number of cluster points (exemplar) is the affinity propagation (AP). Three clusters were used to determine the sum of the smallest value ratio. The result showed that clusters 1, 2, and 3 have 3, 23, and 5 members in Bondowoso, Jombang, and Surabaya districts.

scholarly journals An Extended Affinity Propagation Clustering Method Based on Different Data Density Types

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
XiuLi Zhao ◽  
WeiXiang Xu
Keyword(s):  
Clustering Algorithm ◽  
Affinity Propagation ◽  
Similar Degree ◽  
Clustering Method ◽  
Data Set ◽  
Affinity Propagation Clustering ◽  
Initial Cluster ◽  
Data Density ◽  
Data Points ◽  
Ap Clustering

Affinity propagation (AP) algorithm, as a novel clustering method, does not require the users to specify the initial cluster centers in advance, which regards all data points as potential exemplars (cluster centers) equally and groups the clusters totally by the similar degree among the data points. But in many cases there exist some different intensive areas within the same data set, which means that the data set does not distribute homogeneously. In such situation the AP algorithm cannot group the data points into ideal clusters. In this paper, we proposed an extended AP clustering algorithm to deal with such a problem. There are two steps in our method: firstly the data set is partitioned into several data density types according to the nearest distances of each data point; and then the AP clustering method is, respectively, used to group the data points into clusters in each data density type. Two experiments are carried out to evaluate the performance of our algorithm: one utilizes an artificial data set and the other uses a real seismic data set. The experiment results show that groups are obtained more accurately by our algorithm than OPTICS and AP clustering algorithm itself.

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