Large scale activity-based simulation models inform a variety of transportation and planning policies using models that often rely on fixed or flexible workplace location in a synthetic population as input to work related activity, participation, and subsequent destination dependent travel decisions. Although discrete choice models can estimate workplace location with greater flexibility, disaggregate data available (e.g., travel surveys) are often too sparse to estimate workplace location at sufficient spatial detail. Alternatively, aggregated employment data are often readily available at higher spatial resolutions, but are typically only used in separately estimated ad hoc models, which introduces error if the estimations have divergent solutions. This paper’s primary contribution is to reduce error by integrating population synthesis and workplace assignment, yielding a synthetic population with home and work locations included as attributes. The two are integrated using additional variables shared between population and workplace assignment (i.e., industry sector), but this increased matrix size can render conventional multilevel person-household re-weighting methods computational intractable. A secondary contribution is to mitigate this scalability challenge using more efficient optimization-based re-weighting approaches, substantially reducing computation time. The proposed process is applied to the Greater Boston Area, generating a population of 4.6-million persons within 1.7-million households across 965 census tract zones. The integrated process is compared against conventional ad hoc location assignment process, using both classical and contemporary synthesis techniques of Iterative Proportional Fitting, Markov chain Monte Carlo simulation, and Bayesian Network simulation. The integrated approach yielded an improvement in workplace location assignment, with only modest impact on population accuracy.

中文翻译：

---

使用基于有效优化的人户匹配方法集成人口合成和工作场所分配

基于大型活动的模拟模型使用模型来告知各种交通和规划政策，这些模型通常依赖于合成人群中固定或灵活的工作场所位置，作为与工作相关的活动、参与和随后的目的地相关旅行决策的输入。虽然离散选择模型可以更灵活地估计工作场所位置，但可用的分解数据（例如，旅行调查）通常太稀少，无法在足够的空间细节上估计工作场所位置。或者，聚合就业数据通常在更高的空间分辨率下很容易获得，但通常仅用于单独估计的临时模型，如果估计有不同的解决方案，则会引入错误。本文的主要贡献是通过整合人口合成和工作场所分配来减少错误，产生一个包含家庭和工作地点作为属性的合成人口。这两者使用人口和工作场所分配（即行业部门）之间共享的附加变量进行整合，但这种增加的矩阵大小会使传统的多层次个人-家庭重新加权方法的计算变得棘手。第二个贡献是使用更有效的基于优化的重新加权方法来缓解这种可扩展性挑战，从而大大减少计算时间。拟议的过程适用于大波士顿地区，在 965 个人口普查区的 170 万个家庭中产生了 460 万人口。使用迭代比例拟合、马尔可夫链蒙特卡罗模拟和贝叶斯网络模拟的经典和现代综合技术，将集成过程与传统的临时位置分配过程进行比较。综合方法改善了工作场所的位置分配，对人口准确性的影响不大。