Abstract
Modelling the complexity of urban freight transport requires large amounts of data related to supply chain management, delivery practices, tour configuration, time windows, etc., but when all this detailed data is not available local authorities still need models that represent this type of transport and its contribution to congestion and environmental impacts. We present here an improvement on other recent works, consisting of a demand model for B2B and home deliveries during the morning peak hour that uses only very limited data to estimate the number of delivery vehicles entering and leaving each zone of the city. We then calculate the trip distribution using an entropy maximisation approach, and solve the resulting model using simulated annealing. We apply this model to a case study in the city of Seville, in Spain, and compare its results to those produced by a gravity model, and with actual traffic counts.
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