基于自适应多阶Markov模型的位置预测

准确预测用户的地理位置可以有效地改善基于位置服务的质量.针对标准Markov模型预测能力不足，以及多阶Markov模型阶数难以确定的问题，提出了一种基于自适应多阶Markov模型的位置预测方法.该方法采用一种基于规则图形的方式对原始位置信息进行抽象化处理，并使用一种基于训练数据的启发式方式自动确定用于预测的模型阶数.最后，基于真实的位置数据，对自适应多阶Markov模型的预测性能进行了评测.结果表明自适应多阶Markov模型的预测精度和预测长度始终高于多阶Markov模型，平均预测精度提高将近20%，平均预测长度提高将近10个单位区域，且不易受训练数据质量影响.

Position Prediction Based on Adaptive Multi-Order Markov Model

Accurately predicting the geographic position of users can efficiently improve the quality of location-based service. To solve the problem of low prediction power of the basic Markov model and the problem of being hard to determine the order of higher order Markov model in practice, the authors present a position prediction approach based on the novel adaptive multi-order Markov model. The approach first processes the raw location information of a user based on regular shape abstraction, and automatically determines the order of the model in a heuristic way in which the heuristic is obtained from the users training data represented by a tree structure. Then, it predicts the future position of the user by using the Markov model with the most appropriate order. Finally, the performance of the adaptive multi-order Markov model is evaluated based on real location data. The results show that the prediction accuracy and prediction length of adaptive multi-order Markov model is always higher than multi-order Markov model. The average prediction accuracy is improved by nearly 20% and the average prediction length is improved by nearly 10 unit regions. Moreover, the performance of the adaptive multi-order Markov model is not apt to be influenced by the quality of training data.