线性约束广义线性模型及其在起讫点估计中的应用
Linearly Constrained Generalized Linear Models with Applications in Origin-Destination Estimation
- Boston University（波士顿大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本研究提出线性约束广义线性模型，通过Fisher得分过程在单一框架内处理OD估计中的删失数据、不匹配约束、预测因子及先验信息，并在合成与案例研究中验证其有效性。
AI中文摘要：
我们证明，受线性约束删失的数据可以在广义线性模型（GLM）框架内进行拟合，通过一次Fisher得分过程即可恢复期望的潜在计数及其不确定性。我们的应用动机来自交通研究中的起讫点（OD）估计，其中潜在数据是OD出行需求，约束条件要么是各区域的起点和终点边际计数（如OD矩阵估计），要么是观测到的路段计数（如网络断层扫描）。将该问题表述为线性约束GLM，使我们能够在同一个模型中处理实践中经常出现的三个特征：不匹配的约束、出行预测因子（如成本）以及先验信息（如对角占优和种子计数）。我们在合成研究和小规模研究以及基于BO4Mob研究模拟数据的更大规模案例研究中展示了该方法。
英文摘要：
We show that data censored by linear constraints can be fit within the generalized linear model (GLM) framework, recovering the expected latent counts together with their uncertainty in a single Fisher-scoring procedure. Our motivating application is origin-destination (OD) estimation in transportation studies, where the latent data are the OD trip demands and constraints are either origin and destination marginal counts per zone, as in OD matrix estimation, or observed link counts, as in network tomography. Casting the problem as a linearly constrained GLM lets us treat, within one model, three features that arise routinely in practice: unmatched constraints, trip predictors such as costs, and prior information such as diagonal dominance and seed counts. We demonstrate the methodology in synthetic and small scale studies and in a larger case study based on simulated data from the BO4Mob study.
