Pick-up point recommendation strategy based on user incentive mechanism
In recent years, with the development of spatial crowdsourcing technology, online car-hailing, as a typical spatiotemporal crowdsourcing task application scenario, has attracted widespread attention. Existing researches on spatial crowdsourcing are mainly based on the coordinate positions of user an...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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PeerJ Inc.
2023-11-01
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| Series: | PeerJ Computer Science |
| Subjects: | |
| Online Access: | https://peerj.com/articles/cs-1692.pdf |
| _version_ | 1797517338208108544 |
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| author | Jing Zhang Biao Li Xiucai Ye Yi Chen |
| author_facet | Jing Zhang Biao Li Xiucai Ye Yi Chen |
| author_sort | Jing Zhang |
| collection | DOAJ |
| description | In recent years, with the development of spatial crowdsourcing technology, online car-hailing, as a typical spatiotemporal crowdsourcing task application scenario, has attracted widespread attention. Existing researches on spatial crowdsourcing are mainly based on the coordinate positions of user and worker roles to achieve task allocation with the goal of maximum matching number or lowest cost. However, they ignores the problem of the selection of the pick-up point which needs to be solved in the actual scene of online car booking. This problem needs to take into account the four-dimensional coordinate positions of users, workers, pick-up point and destination. Based on this, this study designs a pick-up point recommendation strategy based on user incentive mechanism. Firstly, a new four-dimensional crowdsourcing model is established, which is closer to the practical application of crowdsourcing problem. Secondly, taking cost optimization as the index, a user incentive mechanism is designed to encourage users to walk to the appropriate pick-up point within a certain distance. Thirdly, a concept of forward rate is proposed to reduce the computation time. Some key factors, such as the maximum walking distance limit of users and task cost, are considered as the recommendation index for measuring the pick-up point. Then, an effective pick-up point recommendation strategy is designed based on this index. Experiments show that the strategy proposed in this article can achieve reasonable recommendation for pick-up points and improve the efficiency of drivers and reduce the total trip cost of orders to the greatest extent. |
| first_indexed | 2024-03-10T07:15:16Z |
| format | Article |
| id | doaj.art-42b478db8acb47759db5d2a6c477fa7d |
| institution | Directory Open Access Journal |
| issn | 2376-5992 |
| language | English |
| last_indexed | 2024-03-10T07:15:16Z |
| publishDate | 2023-11-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ Computer Science |
| spelling | doaj.art-42b478db8acb47759db5d2a6c477fa7d2023-11-22T15:05:14ZengPeerJ Inc.PeerJ Computer Science2376-59922023-11-019e169210.7717/peerj-cs.1692Pick-up point recommendation strategy based on user incentive mechanismJing Zhang0Biao Li1Xiucai Ye2Yi Chen3School of Computer Science and Mathematics, Fujian Provincial Key Laboratory of Big Data Mining and Applications, Fujian University of Technology, Fu Zhou, Fu Jian, ChinaSchool of Computer Science and Mathematics, Fujian Provincial Key Laboratory of Big Data Mining and Applications, Fujian University of Technology, Fu Zhou, Fu Jian, ChinaDepartment of Computer Science, University of Tsukuba, Ibaraki, Tsukuba, JapanDepartment of Computer and Information Security Management, Fujian Police College, Fu Zhou, Fu Jian, ChinaIn recent years, with the development of spatial crowdsourcing technology, online car-hailing, as a typical spatiotemporal crowdsourcing task application scenario, has attracted widespread attention. Existing researches on spatial crowdsourcing are mainly based on the coordinate positions of user and worker roles to achieve task allocation with the goal of maximum matching number or lowest cost. However, they ignores the problem of the selection of the pick-up point which needs to be solved in the actual scene of online car booking. This problem needs to take into account the four-dimensional coordinate positions of users, workers, pick-up point and destination. Based on this, this study designs a pick-up point recommendation strategy based on user incentive mechanism. Firstly, a new four-dimensional crowdsourcing model is established, which is closer to the practical application of crowdsourcing problem. Secondly, taking cost optimization as the index, a user incentive mechanism is designed to encourage users to walk to the appropriate pick-up point within a certain distance. Thirdly, a concept of forward rate is proposed to reduce the computation time. Some key factors, such as the maximum walking distance limit of users and task cost, are considered as the recommendation index for measuring the pick-up point. Then, an effective pick-up point recommendation strategy is designed based on this index. Experiments show that the strategy proposed in this article can achieve reasonable recommendation for pick-up points and improve the efficiency of drivers and reduce the total trip cost of orders to the greatest extent.https://peerj.com/articles/cs-1692.pdfSpatial crowdsourcingOnline car hailingPick-up point recommendationUser incentive |
| spellingShingle | Jing Zhang Biao Li Xiucai Ye Yi Chen Pick-up point recommendation strategy based on user incentive mechanism PeerJ Computer Science Spatial crowdsourcing Online car hailing Pick-up point recommendation User incentive |
| title | Pick-up point recommendation strategy based on user incentive mechanism |
| title_full | Pick-up point recommendation strategy based on user incentive mechanism |
| title_fullStr | Pick-up point recommendation strategy based on user incentive mechanism |
| title_full_unstemmed | Pick-up point recommendation strategy based on user incentive mechanism |
| title_short | Pick-up point recommendation strategy based on user incentive mechanism |
| title_sort | pick up point recommendation strategy based on user incentive mechanism |
| topic | Spatial crowdsourcing Online car hailing Pick-up point recommendation User incentive |
| url | https://peerj.com/articles/cs-1692.pdf |
| work_keys_str_mv | AT jingzhang pickuppointrecommendationstrategybasedonuserincentivemechanism AT biaoli pickuppointrecommendationstrategybasedonuserincentivemechanism AT xiucaiye pickuppointrecommendationstrategybasedonuserincentivemechanism AT yichen pickuppointrecommendationstrategybasedonuserincentivemechanism |