基于改进马尔可夫链的航线预测算法

在交通领域，研究分析旅客的出行目的地会产生很多商业价值。针对旅客出行目的地的不确定性造成研究困难的问题，现有方法利用熵衡量移动的不确定性来描述个体的出行特性，并同时考虑个体轨迹的时空相关性，并不能达到理想的预测精度，因此，提出了基于改进马尔可夫链的航线预测算法来对旅客的出行目的地进行预测。首先对旅客历史出行的距离分布、地点分布和时间规律特性进行了分析；然后又分析了人类移动对历史行为和当前地点的依赖性；最后将旅客的常住地特性和新航线的探索概率加入到转移矩阵的计算中，提出并实现了改进的马尔可夫链航线预测算法，进而对旅客的下一次出行进行预测。实验结果显示，该模型可以达到66.4%的平均预测精度。研究成果可以应用在航空领域的用户出行分析中，使航空公司更好地了解和预测旅客的出行，提供个性化的出行服务。

Airline predicting algorithm based on improved Markov chain

In the transportation field, analyzing passengers' travel destinations brings a lot of commercial value. However, research on the passengers' travel destinations is difficult because of its uncertainty. In order to solve this problem, in existing studies, entropy is used to measure the uncertainty of human mobility to describe individuals' travel features, and the spatiotemporal correlation of individual trajectories is taken into account simultaneously, which can not achieve the desired accuracy. Therefore, an algorithm for airline prediction based on improved Markov chain was proposed to predict passengers' travel destinations. First, the distance distribution, site distribution and temporal regularity on history records of passengers' travels were analyzed. Then, the dependence of human mobility on historical behavior and current location was analyzed. Finally, the characteristics of passengers' permanent residence and the exploration probability of new airlines were added into the calculation transition matrix, and an algorithm based on improved Markov chain was proposed and realized to predict passengers' next travels. The experimental results show that the average prediction accuracy of the proposed model can reach 66.4%. Applying in the field of customer travel analysis, airline company can benefit from the research to predict passenger travel better and provide personalized travel services.