基于贝叶斯网络的城市居民出行方式研究

为解决含有不确定信息的非期望产出效率评价问题,建立了一个非期望产出的随机DEA模型．该模型将非期望产出作为负期望产出进行处理,引入了期望效率值、显著性水平来刻画随机问题,并通过机会约束规划的相关知识将模型转化为确定形式．对模型的最优值的相关性质进行了探讨,说明最优值与期望效率值、显著性水平之间的关系．最后给出数值实验说明该模型的有效性．

Bayesian Network Modeling for Trip Mode Analysis of Urban Residents

The trip modes of residents in Xi''an city were taken as study subject, and the survey data of some regions in 2013 were collected. A Bayesian network for trip mode analysis was developed by structure and parameter learning, using correlation analysis, K2 algorithm and Bayesian method. Based on the Bayesian network, the influences of private car, gender, age and travel purpose on the choice of trip mode were analyzed. The results show that the Bayesian network can express the complicated relationship between the trip mode and the causes. Moreover, the Bayesian network has a high accuracy. The study can contribute to the development of the trip behaviors theory of residents in Xi''an city.