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首页> 外文期刊>Transportation Research Record >Improving Performance of Multiagent Rule-Based Model for Activity Pattern Decisions with Bayesian Networks
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Improving Performance of Multiagent Rule-Based Model for Activity Pattern Decisions with Bayesian Networks

机译：贝叶斯网络提高基于多主体规则的活动模式决策模型的性能

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Several activity-based models are now becoming operational and are entering the stage of application in transport planning. Some of these models use a set of decision trees to support decision making instead of using principles of utility maximization. However, it is believed that the structure of decision trees can sometimes be very unstable and sensitive to highly correlated predictors. Therefore, this study examines whether decision trees constitute the best representational form to capture the behavioral mechanisms and principles that individuals and households use to organize their activities. Findings are reported from experiments conducted by means of Bayesian networks to gain a better understanding of the predictive performance of Albatross, a sequential rule-based model of activity-scheduling behavior. The performances of Bayesian networks and decision trees are compared and results are evaluated by means of detailed quantitative and qualitative analyses. The results showed that Bayesian networks outperformed the decision-tree-based approach for all decision agents of the Albatross model. Given this excellent performance, it is believed that the research community may potentially consider the use of Bayesian networks in developing activity-based transportation models.
机译：几种基于活动的模型现已开始运作，并已进入运输计划的应用阶段。其中一些模型使用一组决策树来支持决策，而不是使用效用最大化的原理。但是，人们认为决策树的结构有时可能非常不稳定，并且对高度相关的预测变量敏感。因此，本研究检验了决策树是否构成了最佳的表示形式，以捕获个人和家庭用于组织活动的行为机制和原则。通过贝叶斯网络进行的实验报告了发现，以更好地了解信天翁的预测性能，信天​​翁是一种基于规则的活动计划行为模型。比较贝叶斯网络和决策树的性能，并通过详细的定量和定性分析对结果进行评估。结果表明，对于Albatross模型的所有决策代理，贝叶斯网络的性能均优于基于决策树的方法。鉴于这种出色的性能，相信研究界可能会考虑在开发基于活动的运输模型时使用贝叶斯网络。

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