A Markov Chain Position Prediction Model Based on Multidimensional Correction
User location prediction in location-based social networks can predict the density of people flow well in terms of intelligent transportation, which can make corresponding adjustments in time to make traffic smooth, reduce fuel consumption, reduce greenhouse gas emissions, and help build a green cyc...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2021/6677132 |
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| _version_ | 1832550542263451648 |
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| author | Sijia Chen Jian Zhang Fanwei Meng Dini Wang |
| author_facet | Sijia Chen Jian Zhang Fanwei Meng Dini Wang |
| author_sort | Sijia Chen |
| collection | DOAJ |
| description | User location prediction in location-based social networks can predict the density of people flow well in terms of intelligent transportation, which can make corresponding adjustments in time to make traffic smooth, reduce fuel consumption, reduce greenhouse gas emissions, and help build a green cycle low-carbon transportation green system. This paper proposes a Markov chain position prediction model based on multidimensional correction (MDC-MCM). Firstly, extract corresponding information from the user’s historical check-in position sequence as a position-position conversion map. Secondly, the influence of check-in period, space distance, and other factors on the position prediction is linearly weighted and merged with the position prediction of the n-order Markov chain to construct MDC-MCM. Finally, we conduct a comprehensive performance evaluation of MDC-MCM using the dataset collected from Brightkite. Experimental results show that compared with other advanced location prediction technologies, MDC-MCM achieves better location prediction results. |
| format | Article |
| id | doaj-art-2df2f81b89754091a8788617a66e8cde |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-2df2f81b89754091a8788617a66e8cde2025-02-03T06:06:31ZengWileyComplexity1076-27871099-05262021-01-01202110.1155/2021/66771326677132A Markov Chain Position Prediction Model Based on Multidimensional CorrectionSijia Chen0Jian Zhang1Fanwei Meng2Dini Wang3School of Control Engineering, Northeastern University at Qinhuangdao, Qinhuangdao 066004, ChinaSchool of Control Engineering, Northeastern University at Qinhuangdao, Qinhuangdao 066004, ChinaSchool of Control Engineering, Northeastern University at Qinhuangdao, Qinhuangdao 066004, ChinaSchool of Control Engineering, Northeastern University at Qinhuangdao, Qinhuangdao 066004, ChinaUser location prediction in location-based social networks can predict the density of people flow well in terms of intelligent transportation, which can make corresponding adjustments in time to make traffic smooth, reduce fuel consumption, reduce greenhouse gas emissions, and help build a green cycle low-carbon transportation green system. This paper proposes a Markov chain position prediction model based on multidimensional correction (MDC-MCM). Firstly, extract corresponding information from the user’s historical check-in position sequence as a position-position conversion map. Secondly, the influence of check-in period, space distance, and other factors on the position prediction is linearly weighted and merged with the position prediction of the n-order Markov chain to construct MDC-MCM. Finally, we conduct a comprehensive performance evaluation of MDC-MCM using the dataset collected from Brightkite. Experimental results show that compared with other advanced location prediction technologies, MDC-MCM achieves better location prediction results.http://dx.doi.org/10.1155/2021/6677132 |
| spellingShingle | Sijia Chen Jian Zhang Fanwei Meng Dini Wang A Markov Chain Position Prediction Model Based on Multidimensional Correction Complexity |
| title | A Markov Chain Position Prediction Model Based on Multidimensional Correction |
| title_full | A Markov Chain Position Prediction Model Based on Multidimensional Correction |
| title_fullStr | A Markov Chain Position Prediction Model Based on Multidimensional Correction |
| title_full_unstemmed | A Markov Chain Position Prediction Model Based on Multidimensional Correction |
| title_short | A Markov Chain Position Prediction Model Based on Multidimensional Correction |
| title_sort | markov chain position prediction model based on multidimensional correction |
| url | http://dx.doi.org/10.1155/2021/6677132 |
| work_keys_str_mv | AT sijiachen amarkovchainpositionpredictionmodelbasedonmultidimensionalcorrection AT jianzhang amarkovchainpositionpredictionmodelbasedonmultidimensionalcorrection AT fanweimeng amarkovchainpositionpredictionmodelbasedonmultidimensionalcorrection AT diniwang amarkovchainpositionpredictionmodelbasedonmultidimensionalcorrection AT sijiachen markovchainpositionpredictionmodelbasedonmultidimensionalcorrection AT jianzhang markovchainpositionpredictionmodelbasedonmultidimensionalcorrection AT fanweimeng markovchainpositionpredictionmodelbasedonmultidimensionalcorrection AT diniwang markovchainpositionpredictionmodelbasedonmultidimensionalcorrection |