Location deployment of depots and resource relocation for connected car-sharing systems through mobile edge computing
Mobile edge computing supports the connected cars to ensure real-time, interactive, secured, and distributed services for customers. Connected car-sharing systems, as the promising appliance of connected cars, provide a convenient transportation mode for citizens’ intra-urban commutes. Determining t...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2017-06-01
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| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1177/1550147717711621 |
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| _version_ | 1850219767343874048 |
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| author | Xiaolu Zhu Jinglin Li Zhihan Liu Fangchun Yang |
| author_facet | Xiaolu Zhu Jinglin Li Zhihan Liu Fangchun Yang |
| author_sort | Xiaolu Zhu |
| collection | DOAJ |
| description | Mobile edge computing supports the connected cars to ensure real-time, interactive, secured, and distributed services for customers. Connected car-sharing systems, as the promising appliance of connected cars, provide a convenient transportation mode for citizens’ intra-urban commutes. Determining the locations of depots is the primary job in connected car-sharing systems. Existing methods mainly use qualitative method and do not consider spatial–temporal dynamic travel demands. This article proposes a mobile edge computing–based connected car framework which uses normal taxis as connected cars to describe their Global Positioning System trajectory and perform the computing tasks in each mobile edge computing server independently. A spatial–temporal demand coverage approach is developed to optimize the location of depots. This article proposes a deep learning method to predict car-sharing demand constructed by a stacked auto-encoder model and a logistic regression layer. The stacked auto-encoder model is employed for learning the latent spatial and temporal correlation features of demand. A graph-based resource relocation model is proposed to minimize the cost of relocation considering spatio-temporal variation of car-sharing demand. Experiments performed on the large-scale real-world data sets illustrate that our proposed model has superior performance than existing methods. |
| format | Article |
| id | doaj-art-7a286d4584e54c9eb79b88e4b69b00b1 |
| institution | OA Journals |
| issn | 1550-1477 |
| language | English |
| publishDate | 2017-06-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj-art-7a286d4584e54c9eb79b88e4b69b00b12025-08-20T02:07:16ZengWileyInternational Journal of Distributed Sensor Networks1550-14772017-06-011310.1177/1550147717711621Location deployment of depots and resource relocation for connected car-sharing systems through mobile edge computingXiaolu ZhuJinglin LiZhihan LiuFangchun YangMobile edge computing supports the connected cars to ensure real-time, interactive, secured, and distributed services for customers. Connected car-sharing systems, as the promising appliance of connected cars, provide a convenient transportation mode for citizens’ intra-urban commutes. Determining the locations of depots is the primary job in connected car-sharing systems. Existing methods mainly use qualitative method and do not consider spatial–temporal dynamic travel demands. This article proposes a mobile edge computing–based connected car framework which uses normal taxis as connected cars to describe their Global Positioning System trajectory and perform the computing tasks in each mobile edge computing server independently. A spatial–temporal demand coverage approach is developed to optimize the location of depots. This article proposes a deep learning method to predict car-sharing demand constructed by a stacked auto-encoder model and a logistic regression layer. The stacked auto-encoder model is employed for learning the latent spatial and temporal correlation features of demand. A graph-based resource relocation model is proposed to minimize the cost of relocation considering spatio-temporal variation of car-sharing demand. Experiments performed on the large-scale real-world data sets illustrate that our proposed model has superior performance than existing methods.https://doi.org/10.1177/1550147717711621 |
| spellingShingle | Xiaolu Zhu Jinglin Li Zhihan Liu Fangchun Yang Location deployment of depots and resource relocation for connected car-sharing systems through mobile edge computing International Journal of Distributed Sensor Networks |
| title | Location deployment of depots and resource relocation for connected car-sharing systems through mobile edge computing |
| title_full | Location deployment of depots and resource relocation for connected car-sharing systems through mobile edge computing |
| title_fullStr | Location deployment of depots and resource relocation for connected car-sharing systems through mobile edge computing |
| title_full_unstemmed | Location deployment of depots and resource relocation for connected car-sharing systems through mobile edge computing |
| title_short | Location deployment of depots and resource relocation for connected car-sharing systems through mobile edge computing |
| title_sort | location deployment of depots and resource relocation for connected car sharing systems through mobile edge computing |
| url | https://doi.org/10.1177/1550147717711621 |
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