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 terms intelligent transportation, which make corresponding adjustments time to traffic smooth, reduce fuel consumption, greenhouse gas emissions, and help build a green cycle low-carbon transportation system. This paper proposes Markov chain position model based on multidimensional correction (MDC-MCM). Firstly, extract information from user’s historical check-in sequence as position-position conversion map. Secondly, influence period, space distance, other factors is linearly weighted merged with n-order construct MDC-MCM. Finally, we conduct comprehensive performance evaluation MDC-MCM using dataset collected Brightkite. Experimental results show that compared advanced technologies, achieves better results.