论文标题
贝叶斯推断运输起源矩阵：泊松式高斯和其他泊松混合物
Bayesian inference for transportation origin-destination matrices: the Poisson-inverse Gaussian and other Poisson mixtures
论文作者
论文摘要
在本文中，我们介绍了运输分析中的泊松混合物方法（OD）建模。我们介绍了基于协变量的模型，该模型结合了不同的传输模型阶段，还可以根据贝叶斯预测对链路流量进行直接概率推断。重点放在泊松式高斯式高斯，以替代常用的泊松仪和泊松式模型。我们提出了第一个完整的贝叶斯配方，并证明由于理想的边际和分层特性，poisson inspersegressian高斯特别适合OD分析。此外，集成的嵌套拉普拉斯近似（INLA）被视为马尔可夫链蒙特卡洛的替代方法，并且在特定的建模假设下进行了比较两种方法。该案例研究基于2001年比利时的人口普查数据，并重点介绍了一个大型，稀疏分布的OD矩阵，其中包含308个佛兰芒市政当局的旅行信息。
In this paper we present Poisson mixture approaches for origin-destination (OD) modeling in transportation analysis. We introduce covariate-based models which incorporate different transport modeling phases and also allow for direct probabilistic inference on link traffic based on Bayesian predictions. Emphasis is placed on the Poisson-inverse Gaussian as an alternative to the commonly-used Poisson-gamma and Poisson-lognormal models. We present a first full Bayesian formulation and demonstrate that the Poisson-inverse Gaussian is particularly suited for OD analysis due to desirable marginal and hierarchical properties. In addition, the integrated nested Laplace approximation (INLA) is considered as an alternative to Markov chain Monte Carlo and the two methodologies are compared under specific modeling assumptions. The case study is based on 2001 Belgian census data and focuses on a large, sparsely-distributed OD matrix containing trip information for 308 Flemish municipalities.
