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A mixed Bayesian network for two-dimensional decision modeling of departure time and mode choice

机译：用于出发时间和模式选择的二维决策建模的混合贝叶斯网络

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摘要

The modeling of travel decision making has been a popular topic in transportation planning. Previous studies focused on random-utility discrete choice models and machine learning methods. This paper proposes a new modeling approach that utilizes a mixed Bayesian network (BN) for travel decision inference. The authors use a predetermined BN structure and calculate priori and posterior probability distributions of the decision alternatives based on the observed explanatory variables. As a "utility-free" decision inference method, the BN model releases the linear structure in the utility function but assumes the traffic level of service variables follow multivariate Gaussian distribution conditional on the choice variable. A real-world case study is conducted by using the regional travel survey data for a two-dimensional decision modeling of both departure time choice and travel mode choice. The results indicate that a two-dimensional mixed BN provides better accuracy than decision tree models and nested logit models. In addition, one can derive continuous elasticity with respect to each continuous explanatory variable for sensitivity analysis. This new approach addresses a research gap in probabilistic travel decision making modeling as well as two-dimensional travel decision modeling.
机译：出行决策建模已成为交通规划中的热门话题。先前的研究集中在随机效用离散选择模型和机器学习方法上。本文提出了一种新的建模方法，该方法利用混合贝叶斯网络（BN）进行旅行决策推理。作者使用预定的BN结构，并根据观察到的解释变量计算决策选择的先验概率和后验概率分布。作为一种“无效用”的决策推理方法，BN模型释放效用函数中的线性结构，但假设服务变量的流量水平以选择变量为条件遵循多元高斯分布。通过使用区域旅行调查数据对出发时间选择和旅行模式选择进行二维决策建模，进行了实际案例研究。结果表明，与决策树模型和嵌套logit模型相比，二维混合BN的准确性更高。此外，可以针对灵敏度分析的每个连续解释变量得出连续弹性。这种新方法解决了概率旅行决策建模以及二维旅行决策建模方面的研究空白。

著录项

  • 来源
    《Transportation》 |2018年第5期|1499-1522|共24页
  • 作者

    Zhu Zheng; Chen Xiqun; Xiong Chenfeng; Zhang Lei;

  • 作者单位

    Univ Maryland, Dept Civil & Environm Engn, 1173 Glenn Martin Hall, College Pk, MD 20742 USA;

    Zhejiang Univ, Coll Civil Engn & Architecture, Hangzhou 310058, Zhejiang, Peoples R China;

    Univ Maryland, Dept Civil & Environm Engn, 1173 Glenn Martin Hall, College Pk, MD 20742 USA;

    Univ Maryland, Dept Civil & Environm Engn, 1173 Glenn Martin Hall, College Pk, MD 20742 USA;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Bayesian network; Bayesian inference; Mode choice; Departure time choice;

    机译：贝叶斯网络;贝叶斯推理;模式选择;出发时间选择;

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