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A Markov Chain Position Prediction Model Based on Multidimensional Correction

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  • Sijia Chen
  • Jian Zhang
  • Fanwei Meng
  • Dini Wang
  • Wei Zhang

Abstract

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.

Suggested Citation

  • Sijia Chen & Jian Zhang & Fanwei Meng & Dini Wang & Wei Zhang, 2021. "A Markov Chain Position Prediction Model Based on Multidimensional Correction," Complexity, Hindawi, vol. 2021, pages 1-8, January.
    • Handle: RePEc:hin:complx:6677132
    DOI: 10.1155/2021/6677132
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    Cited by:

    1. Ruijuan Li & Onur Alp İlhan & Jalil Manafian & Khaled H. Mahmoud & Mostafa Abotaleb & Ammar Kadi, 2022. "A Mathematical Study of the (3+1)-D Variable Coefficients Generalized Shallow Water Wave Equation with Its Application in the Interaction between the Lump and Soliton Solutions," Mathematics, MDPI, vol. 10(17), pages 1-17, August.
    2. khabaz, Mohamad Khaje & Eftekhari, S. Ali & Toghraie, Davood, 2022. "Vibration and dynamic analysis of a cantilever sandwich microbeam integrated with piezoelectric layers based on strain gradient theory and surface effects," Applied Mathematics and Computation, Elsevier, vol. 419(C).
    3. Abdulaziz S. Alkabaa & Osman Taylan & Mustafa Tahsin Yilmaz & Ehsan Nazemi & El Mostafa Kalmoun, 2022. "An Investigation on Spiking Neural Networks Based on the Izhikevich Neuronal Model: Spiking Processing and Hardware Approach," Mathematics, MDPI, vol. 10(4), pages 1-21, February.

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