The ASIGNAT project aims to design and develop tools that facilitate and enable better decision-making in the operation on freight road transport in port supply chains, establishing the following general objectives:
– Specify and develop a system for calculating and updating the ETA (Estimated Time of Arrival) in real time, with quality information obtained from transport information systems such as on-board communications platforms, which will have the capacity to continuously send information from sensors to a central infrastructure in the cloud, with an open architecture.
– Design and develop intelligent tools based on machine learning for the optimal planning and allocation of multimodal routes, based on the collection and analysis of historical information collected from previous journeys. The incorporation of these tools will allow for better decision support systems.
– Implement a digital twin model continuously connected to transport vehicle fleets that allow to update data in real-time, reflect and represent changes in the variables of different sensors of interest. This data will be used by machine learning algorithms for ETA prediction or assignment planning.
– To increase the degree of digitalisation of existing processes in road transport companies, in turn obtaining optimum performance in transport operations, allowing the visualisation of information that helps decision-making, such as forecasting the arrival of vehicles at loading/unloading points, information on service times at loading/unloading points (queues at terminals, waiting at loaders…) or the updating of forecast information en route (traffic status, incidents…). To this end, all the tools and algorithms will be integrated into a service architecture model that facilitates interconnection with the different data sources, as well as their sharing in a secure environment.