基于贝叶斯网络的停车行为分析

Bayesian network-based parking behavior analysis

建立贝叶斯网络模型进行停车行为分析.应用K2算法和贝叶斯参数估计方法,进行了贝叶斯网络的结构和参数学习,建立了停车行为分析贝叶斯网络,应用联合树推理引擎推断在出行目的、停车费率等因素的影响下停车行为的变化.结果表明,贝叶斯网络能够直观表现决策行为与其影响因素之间的交互作用机理,并方便进行决策行为的敏感性分析,同时模型具有较高的精确度.可以为政府和规划部门深入了解居民的停车选择行为和停车决策机理提供依据.

In this paper,a Bayesian network was developed to analyze the parking behavior.A Bayesian network for parking behavior analyzing was built by structure and parameter learning,using K2 method and Bayesian method respectively.The influences of some factors,such as the trip purpose and the parking fee,on the parking behavior were computed using the junction tree engine.The results indicate that the Bayesian network can express the coupling mechanism between the decision behavior and the influencing factors visually,and can analyze the sensitivity of the decision behavior conveniently.Moreover,the Bayesian network has a high accuracy degree.The results of this paper can provide basis for the government to study residents＇ parking behaviors and parking decision mechanism.