A Markov Chain Position Prediction Model Based on Multidimensional Correction. (5th January 2021)

• Record Type: Journal Article
• Authors: Chen, Sijia; Zhang, Jian; Meng, Fanwei; Wang, Dini
• Other Names: Zhang Wei Academic Editor.
• Is Part Of: Complexity. Volume 2021(2021)
• Publication Date: 2021-01-05
• Subjects: Chaotic behavior in systems -- Periodicals; Complexity (Philosophy) -- Periodicals; 003
• DOI: 10.1155/2021/6677132
• Languages: English
• ISSNs: 1076-2787

Abstract

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.