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A Bayesian approach for modeling origin-destination matrices

机译：贝叶斯方法建模起点-目标矩阵

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摘要

The majority of origin destination (OD) matrix estimation methods focus on situations where weak or partial information, derived from sample travel surveys, is available. Information derived from travel census studies, in contrast, covers the entire population of a specific study area of interest. In such cases where reliable historical data exist, statistical methodology may serve as a flexible alternative to traditional travel demand models by incorporating estimation of trip-generation, trip-attraction and trip-distribution in one model. In this research, a statistical Bayesian approach on OD matrix estimation is presented, where modeling of OD flows derived from census data, is related only to a set of general explanatory variables. A Poisson and a negative binomial model are formulated in detail, while emphasis is placed on the hierarchical Poisson-gamma structure of the latter. Problems related to the absence of closed-form expressions are bypassed with the use of a Markov Chain Monte Carlo method known as the Metropolis-Hastings algorithm. The methodology is tested on a realistic application area concerning the Belgian region of Flanders on the level of municipalities. Model comparison indicates that negative binomial likelihood is a more suitable distributional assumption than Poisson likelihood, due to the great degree of overdispersion present in OD flows. Finally, several predictive good-ness-of-fit tests on the negative binomial model suggest a good overall fit to the data. In general, Bayesian methodology reduces the overall uncertainty of the estimates by delivering posterior distributions for the parameters of scientific interest as well as predictive distributions for future OD flows.
机译：大多数原始目的地（OD）矩阵估计方法着重于从样本旅行调查中获得的薄弱或部分信息可用的情况。相比之下，来自旅行普查研究的信息涵盖了特定研究领域的全部人口。在存在可靠历史数据的情况下，统计方法可以通过将旅行发生，旅行吸引力和旅行分布的估计合并到一个模型中，从而替代传统旅行需求模型。在这项研究中，提出了一种关于OD矩阵估计的统计贝叶斯方法，其中从人口普查数据得出的OD流量建模仅与一组一般性解释变量有关。详细阐述了泊松模型和负二项式模型，而重点放在后者的分层泊松伽玛结构上。使用称为Metropolis-Hastings算法的马尔可夫链蒙特卡罗方法绕过了与缺少闭合形式的表达式有关的问题。在市政一级，在涉及法兰德斯比利时地区的实际应用领域中对该方法进行了测试。模型比较表明，负二项式似然性比Poisson似然性更合适的分布假设，因为OD流中存在很大程度的过度分散。最后，对负二项式模型的一些预测拟合优度检验表明，该数据总体拟合良好。通常，贝叶斯方法通过提供科学兴趣参数的后验分布以及未来OD流量的预测分布来减少估计的总体不确定性。

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  • 来源
    《Transportation Research》 |2012年第1期|p.200-212|共13页
  • 作者

    Konstantinos Perrakis; Dimitris Karlis; Mario Cools; Davy Janssens; Koen Vanhoof; Geert Wets;

  • 作者单位

    Transportation Research Institute, Hasselt University, Wetenschapspark 5, Bus 6, BE-3590 Diepenbeek, Belgium;

    Department of Statistics, Athens University of Economics and Business, 76 Patision Str., 10434 Athens, Greece;

    Transportation Research Institute, Hasselt University, Wetenschapspark 5, Bus 6, BE-3590 Diepenbeek, Belgium,Research Foundation Flanders (FWO), Egmontstraat 5, BE-1000 Brussels, Belgium;

    Transportation Research Institute, Hasselt University, Wetenschapspark 5, Bus 6, BE-3590 Diepenbeek, Belgium;

    Transportation Research Institute, Hasselt University, Wetenschapspark 5, Bus 6, BE-3590 Diepenbeek, Belgium;

    Transportation Research Institute, Hasselt University, Wetenschapspark 5, Bus 6, BE-3590 Diepenbeek, Belgium;

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  • 原文格式 PDF
  • 正文语种 eng
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  • 关键词

    bayesian modeling; census data; metropolis-hastings algorithm; OD matrix; predictive inference;

    机译：贝叶斯建模人口普查数据;大都市-骚动算法;OD矩阵预测推理;

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