A general multi-step model for urban freight movements

Freight demand models are one of the key components of the transport plans at the strategic, tactical and operative level. Local authorities need to predict future transport requirements both for passengers and freight so as to plan the development of infrastructures and related human resources. The private sector requires models to predict transport service demand in order to evaluate future needs. This applies both to transport service managers, producers of consumer goods and firms using transport services, as well as manufacturers of commercial vehicles. One of the greatest difficulties in analysing freight mobility is the identification of decision-makers involved in the process. In the case of freight, there is no sole decision-maker who chooses trip characteristics, but rather a complex set of decision-makers responsible for production, distribution and marketing who, in turn, operate in different field as producers, who have an economic function and dealwith the production of goods, or as consumers, who are freight consumers and become producers of semi-finished goods, destined for the markets and hence towards end-consumers. In this paper a general multi-step model to simulate urban freight transport is proposed. Then the specification, calibration and validation of the attraction model is reported, performed by means of two different sample and corrected by means of traffic counts. The proposed freight transport models, for a medium-size city, with a disaggregated approach, can be segmented in different steps some of which have been specificated and calibrated: * Quantity attraction and distribution models. From the general population data (residents, number of employed, stores) the quantity for each category is calculated that reaches each traffic zone o in one day arriving from each zone d; in this case it is assumed that the decision-maker is the end consumer; * Acquisition Model (or large scale distribution). From the data on the location of logistic bases, general stores, freight village, etc. it is possible to calculate for a general retailer in zone d the d-w probability of purchasing, in the generic zone w, the goods that are on sale in his/her store; * Models for the choice of service and vehicle type. The type of vehicle used for each goods class, with the quantity that it transports and the type of service effected by it (one-to-one, one-to-many, many-to-many, many-to-one) is obtained by means of a logistic model; * Path choice model. For each type of service the probability of each path is evaluated.