An increasing amount of research is dedicated to the consideration of tour formation in freight transportation demand models. While empirical tour formation models so far have been starting from limiting assumptions about the resulting trips, we develop a generalized shipment-based model. We formulate a random utility model embedded in an iterative algorithm to construct tours through the incremental allocation of shipments. It considers different objectives and constraints and acknowledges the difference between commodity, vehicle and location types. Parameters are estimated on a large and comprehensive shipment database. The model reproduces observed tour statistics well for the given set of shipments.

越来越多的研究致力于在货运需求模型中考虑旅行组成。尽管到目前为止，经验性旅行编队模型是从对结果旅行的局限性假设开始的，但我们还是开发了基于发货的广义模型。我们制定了嵌入迭代算法中的随机效用模型，以通过货运的增量分配来构造行程。它考虑了不同的目标和约束条件，并确认了商品，车辆和位置类型之间的差异。在庞大而全面的货运数据库中估算参数。该模型很好地重现了给定装运量的观察到的旅行统计数据。