This paper presents an innovative methodological framework for determining urban freight flows using a multimodal origin–destination synthesis (ODS) model, which we ran with a novel combination of primary and secondary data. Primary data included classified traffic volume counts at limited locations, whereas secondary data, enriched with certain techniques, came from (a) map application programming interfaces used to extract real-time speeds and calibrate a disaggregated speed–volume relationship and (b) advanced techniques for estimating direct flow models (e.g. spatio-temporal Kriging and machine learning models). The paper also discusses the sensitivity of multimodal ODS models to variations in base–seed matrices.

本文提出了一种创新的方法框架，该框架使用多模式原点-目的地综合（ODS）模型确定城市货运流量，并结合了主要和次要数据的新颖组合。主要数据包括有限位置处的分类交通量计数，而富含某些技术的辅助数据来自（a）地图应用程序编程接口，该接口用于提取实时速度并校准分类的速度-体积关系，以及（b）先进技术用于估计直接流模型（例如，时空克里金模型和机器学习模型）。本文还讨论了多峰ODS模型对基础种子矩阵变化的敏感性。