Campus trajectory forecast based on human activity cycle and Markov method

摘要

Traditional Markov method used in trajectory prediction fails to capture the property of the moving objects. In this paper, a zoning method was discussed to extract the most popular areas in the campus. We presented a prediction model based on the students' activity cycle in campus. Markov method was applied in a periodically way to forecast the campus trajectory. Our forecast result was obtained by the weighted integration of different sub-models. Experimental results show that the optimized prediction gives us a satisfying forecast result.