Decision makers in freight transportation need to assess new distribution systems and the impacts of changes in the freight distribution environment on infrastructure needs and usage, logistical performance, emissions, and energy use. There is a need, therefore, for behavioral models that can predict goods flows and vehicle flows in both current and future situations. This research outlines a conceptual framework consisting of the markets, actors, and supply chain elements of freight movement. Supply chains are constructed by linking distribution channels (of different logistics characteristics) between different activity types, such as consumers, supermarkets, stores, offices, distribution centers, and factories. The framework outlined in this research was used to develop the GoodTrip model—a demand-driven, commodity-based freight movement model that incorporates supply chains. Starting with consumer demand, the model estimates goods flows and simulates vehicle tours. The open architecture of the model allows mixed use of empirical data, behavioral models, and scenario-type assumptions. The behavioral models will be developed in future research. In its first application, the GoodTrip model was used to compare the logistical performance and external impacts of three types of urban distribution systems: the traditional system and two concepts using urban distribution centers (one using vans, the other using automated underground vehicles). The results show considerable differences in the performance and effects of the alternatives, especially when they are applied to different types of distribution channels, such as food retail stores or bookstores.

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Shared Automated Vehicles (SAVs) are a new road-based means of transport, usually small in size and capacity, with a relatively low operating speed and no (regular) possibility for the user to engage in any of the driving tasks. Past research focused on the implication of fully Automated Vehicles (AVs) in the transport sector, especially automated cars, analysing travel behaviour, network design, costs and infrastructure development. Such an extensive research on SAVs cannot be found, and most results are based on predictions for AVs acceptance instead, next to simulation studies, assumptionbased models or stated choice experiments. In this paper we conduct a meta-analysis of existing literature, analysing the underlying factors that determine the adoption of SAVs. We identify the factors that have a positive effect, the ones that have a negative effect and the ones for which the effect is still unknown. Subsequently, we propose a conceptual scheme to illustrate the links between the public transport network components and the implementation of SAVs, defining a set of research questions that can help integrate SAVs in the public transport system.