Crowdsourcing Logistics Pricing Optimization Model Based on DBSCAN Clustering Algorithm

From the perspective of platform economics, crowdsourcing is a very efficient business model, and the pricing of crowdsourcing tasks is a key factor for the sustainable development of the crowdsourcing model. In the logistics industry, crowdsourcing provides a new idea of sustainable development for...

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Vydané v:	IEEE access Ročník 8; s. 92615 - 92626
Hlavní autori:	Li, Zhichao, Li, Yilin, Lu, Wanchun, Huang, Jilin
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Piscataway IEEE 2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Algorithms Business Clustering Clustering algorithms Crowdsourcing Crowdsourcing logistics DBSCAN clustering algorithm Domains Economic models Logistics Mathematical analysis Optimization models Outliers (statistics) Polynomials Pricing pricing optimization model Regression analysis Regression models Sustainable development Task analysis Task complexity
ISSN:	2169-3536, 2169-3536
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Abstract	From the perspective of platform economics, crowdsourcing is a very efficient business model, and the pricing of crowdsourcing tasks is a key factor for the sustainable development of the crowdsourcing model. In the logistics industry, crowdsourcing provides a new idea of sustainable development for logistics enterprises, and reasonable distribution pricing is the key to achieving sustainable development. This paper innovatively adds dynamic and decentralized characteristics of logistics on the basis of a detailed analysis of pricing methods and uses this as a basis to build a pricing model. First, based on existing crowdsourced photography task pricing data, this paper establishes a project-centric domain and builds metrics into the attributes of each project based on the data in that domain. Then, a regression model is used to fit the completion rate of previous projects, and a multiple linear regression and optimal pricing mechanism are established. Finally, the DBSCAN algorithm is used to cluster areas with a high project density, and a pricing optimization model based on polynomial Logit (MNL) is established. We found through the model analysis that the optimized pricing strategy of crowdsourcing logistics services has a better packaging completion rate based on a combination of complex factors including bundling and outliers. In short, the main contributions of this paper are to build a complex mathematical model for crowdsourcing tasks, improve the algorithmic deficiencies of the previous crowdsourcing task pricing methods, and provide a reference for further research on crowdsourcing tasks.
AbstractList	From the perspective of platform economics, crowdsourcing is a very efficient business model, and the pricing of crowdsourcing tasks is a key factor for the sustainable development of the crowdsourcing model. In the logistics industry, crowdsourcing provides a new idea of sustainable development for logistics enterprises, and reasonable distribution pricing is the key to achieving sustainable development. This paper innovatively adds dynamic and decentralized characteristics of logistics on the basis of a detailed analysis of pricing methods and uses this as a basis to build a pricing model. First, based on existing crowdsourced photography task pricing data, this paper establishes a project-centric domain and builds metrics into the attributes of each project based on the data in that domain. Then, a regression model is used to fit the completion rate of previous projects, and a multiple linear regression and optimal pricing mechanism are established. Finally, the DBSCAN algorithm is used to cluster areas with a high project density, and a pricing optimization model based on polynomial Logit (MNL) is established. We found through the model analysis that the optimized pricing strategy of crowdsourcing logistics services has a better packaging completion rate based on a combination of complex factors including bundling and outliers. In short, the main contributions of this paper are to build a complex mathematical model for crowdsourcing tasks, improve the algorithmic deficiencies of the previous crowdsourcing task pricing methods, and provide a reference for further research on crowdsourcing tasks.
Author	Li, Zhichao Li, Yilin Lu, Wanchun Huang, Jilin
Author_xml	– sequence: 1 givenname: Zhichao surname: Li fullname: Li, Zhichao organization: School of Political Science and Public Administration, East China University of Political Science and Law, Shanghai, China – sequence: 2 givenname: Yilin orcidid: 0000-0002-0608-8846 surname: Li fullname: Li, Yilin organization: School of Political Science and Public Administration, East China University of Political Science and Law, Shanghai, China – sequence: 3 givenname: Wanchun surname: Lu fullname: Lu, Wanchun organization: School of Political Science and Public Administration, East China University of Political Science and Law, Shanghai, China – sequence: 4 givenname: Jilin surname: Huang fullname: Huang, Jilin email: jilinhuang@pku.edu.cn organization: Department of Environment Science, College of Environmental Sciences and Engineering, Peking University, Beijing, China
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SubjectTerms	Algorithms Business Clustering Clustering algorithms Crowdsourcing Crowdsourcing logistics DBSCAN clustering algorithm Domains Economic models Logistics Mathematical analysis Optimization models Outliers (statistics) Polynomials Pricing pricing optimization model Regression analysis Regression models Sustainable development Task analysis Task complexity
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Title	Crowdsourcing Logistics Pricing Optimization Model Based on DBSCAN Clustering Algorithm
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