Freight transport demand is a demand derived from all the activities needed to move goods between locations of production to locations of consumption, including trade, logistics and transportation. A good representation of logistics in freight transport demand models allows us to predict the effects of changes in logistics systems on future transport flows. As such it provides better estimations of the costs of interaction and allows to predict changes in spatial patterns of freight transport flows more accurately. In recent years, the attention for freight modelling has been growing and new research work has appeared aimed at incorporating logistics in freight models. In this paper we review the state of the art in the representation of logistics considerations in freight transport demand models. Our focus is on the service and cost drivers of changes in logistics networks and how these affect freight transport. Our review proceeds along a conceptual framework for modelling that goes beyond the conventional 4-step modelling approach. We identify promising areas for freight modelling that have an integrative function within this framework, such as spatial computable general equilibrium models, supply chain choice models and hypernetwork models.

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This paper presents an extension of the classical four-step freight modeling framework with a logistics chain model. Modeling logistics at the regional level establishes a link between trade flow and transport flow, allows the warehouse and distribution center locations and throughput volumes to be determined, and permits more detailed and accurate policy decision support systems. This paper describes a two-stage logistics model that estimates the volume of regional warehouse throughput. The first stage estimates interregional trade flows by means of a gravity model application and starts from regional production and consumption volumes. The second stage, the logistics chain model, splits the production–consumption flow between direct shipments and shipments that go through warehouse facilities. An aggregate multinomial logit discrete choice model is used to determine the flow volumes for each of the possible logistics chains. Consistency is achieved between the gravity and logistics chain models by a joint estimation of unknown parameters. A new data set from a transport flow survey produced by Statistics Netherlands is used; the data set includes information on the types of loading and unloading location. This data set enables model calibration with respect to regional warehouse throughput. The proposed logistics chain model produces accurate estimates of regional warehouse throughput and plausible parameter values. The paper presents the specification of the new model, the data set used, and the results of the estimation.

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