The use of real-time data in logistics is an important topic. Every day, logistics produces a large quantity of data, which is mainly produced by monitoring and controlling the enormous flow of goods. The last-mile delivery market is expanding at a rapid pace through large-and small-scale consumer platforms, but the economic drivers to create more sustainable systems are weak. Therefore, cities are facing the potential downside of this "Uberisation" of logistics. Urban and city planners, city administrators, and business stakeholders need a new adaptive approach such as the usage of digital twinning solutions of urban logistics. This to help in the interpretation of the dynamics of logistics networks in the city and the consequences of the introduction of particular innovations. The challenge is to predict the most likely developments for the coming years and propose feasible policies on that basis. This research work aims to advance research in the field of digital twins applied to city logistics, by proposing a framework enabling new applications for designing and assessing targeted urban logistics policies and to develop a range of logistics solutions for shared, connected, and low-emission logistics operations, empowered by an adaptive modeling approach.
There are some examples where freight choices may be of a multiple discrete nature, especially the ones at more tactical levels of planning. Nevertheless, this has not been investigated in the literature, although several discrete-continuous models for mode/vehicle type and shipment size choice have been developed in freight transport. In this work, we propose that the decision of port and mode of the grain consolidators in Argentina is of a discrete-continuous nature, where they can choose more than one alternative and how much of their production to send by each mode. The Multiple Discrete Extreme Value Model (MDCEV) framework was applied to a stated preference data set with a response variable that allowed this multiple-discreteness. To our knowledge, this is the only application of the MDCEV in regional freight context. Free alongside ship price, freight transport cost, lead-time and travel time were included in the utility function and observed and random heterogeneity was captured by the interaction with the consolidator’s characteristics and random coefficients. In addition, different discrete choice models were used to compare the forecasting performance, willingness to pay measures and structure of the utility function against.
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