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.
Highlights
With the development of the world’s industrial economy, the rapid increase in population, and the unrestrained production and lifestyles, the world climate faces more serious problems
In a location-based social network, people can share their location and location information at any time through communication devices, known as sign-in. ese data can be used for user location prediction, friend relationship prediction, and personal behavior patterns [2,3,4]. e user’s location prediction is of great use in intelligent transportation
It can predict the density of people flow and make corresponding adjustments in time to make traffic smooth [5], reduce fuel consumption, reduce greenhouse gas emissions, and help build a green cycle lowcarbon transportation green system
Summary
With the development of the world’s industrial economy, the rapid increase in population, and the unrestrained production and lifestyles, the world climate faces more serious problems. Based on the high-order influence of norder weighted Markov chain, Zhang and Chow [9] combined time and space with friend relationship and popularity factors for location prediction. Markov chain position prediction model based on multidimensional correction (MDC-MCM) comprehensively considers the check-in time period, spatial distance, friend relationship, and check-in point popularity.
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