LOGISTICS NETWORKS
The technology developed in this project provides a real-time monitoring
system for any type of train movements, regardless of the different signalling
systems in service. This fact represents an important step in improving the
management and planning of supply chains, especially those that include
different modes. In short, it allows the design of optimized synchromodal
transport chains, with a high level of integration between the different modes
used and the agents involved in them0 - Any improvement in the management of railway services and its
integration with the other modes means an increase in its efficiency, and
therefore, renders it more attractive to potential users. In this context, it
boosts the use of this mode and its participation in freight transport.
Any improvement in the management of railway services and its
integration with the other modes means an increase in its efficiency, and
therefore, renders it more attractive to potential users. In this context, it
boosts the use of this mode and its participation in freight transport.
(Generation 0)
The development of this real-time traceability system will allow the
definition and establishment of communication protocols between the different
railway networks involved, as well as the participating agents, simplifying the
current systems, and increasing their effectiveness.
Simultaneously, this communication improvements can result in better
integration between the different modes (better coordination of modal exchange
operations), which lead the way to a higher degree of synchromodality. (Generation
1)
After implementing specific communication protocols (including the rules
for data sharing between different agents in a trusty and security framework),
a digital shipment system could be developed based on the information provided
and with the aim of dumbing services operation and management down. Hence, a
computer module could be implemented to plan and manage different transport and
logistics activities (e.g. load assignment to specifics services and
schedules). The result of all these activities would give rise to a definition
of network operational protocols in the different nodes. (Generation 2)
The evolution of the system implemented in the previous phase would
include the development of predictive models. In this context, the treatment of
available information, coming from different scenarios (incidents, peak &
low capacity periods, etc.) by using technologies such as Big Data and genetic
algorithms, would allow optimizing the network operation and services. This way
some contingency protocols could be developed and implemented. (Generation 3)
A step further, by using artificial intelligence or machine learning
technologies, the implemented system would allow the autonomous operation
design (synchromodal chain, dispatches, etc.) and the establishment and
automatic update of performance protocols in a borderless scenario. (Generation
4).
