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Traffic congestion is a huge problem in many countries. It affects not only the inner workings of cities but also the quality of life of the people that endure it. In Portugal, traffic congestion happens mainly on national/urban roads, and this phenomenon has increased since the introduction of the so called shadow-toll systems in highways that were free to use. This work proposes a toll charging system that relies on a novel dynamic congestion charging scheme, supported by state of the art Big Data technologies, in order to shift traffic from national/urban roads to tolled highways, taking into account not only the Quality of Service of the highways and national roads, but also the competitiveness of toll prices for users. This Intelligent Transportation System was tested and validated in a real-world scenario with one of the biggest freight logistics companies in Portugal and with the Portuguese public road infrastructure operator. Highlights A novel dynamic toll charging scheme applied to freight vehicles for Portugal's shadow-toll highways  A Big Data platform supporting the Dynamic Toll Charging system  Real-world test and validation of the Dynamic Toll Charging system, through the participation of a road operator and a freight logistics company

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One of the areas that can heavily benefit with Industry 4.0 is the logistics, namely with the association of sensing technologies and the application of techniques such as Big Data Analytic, Data Visualization, prediction algorithms, and especially 3D simulation. The association of real data, prediction techniques, and 3D models, allow the creation of realistic Digital Twins that emulate factory processes, making possible the experimentation and testing of new ideas and different scenarios by tweaking key variables, without stopping production. However, there are many challenges in order to handle and compute all fast-growing, multi dimension data generated, so that all this production related data can be quickly used for defect control, preventive maintenance, advanced analytics for production and resources management, or even later simulation. The work presented in this paper focus in this “in between” processing work, presenting an easily deployable and self-reconfigurable Big Data architecture, where different technologies can work together to extract, transform, load, apply analytics, and then feed a 3D Digital Simulation model. The work presented in this paper is funded by the EU project BOOST4.0 and focus in a specific logistic process of car manufacturing.

Urban and national road networks in many countries are severely congested, resulting in increased travel times, unexpected delays, greater travel costs, worsening air pollution and noise levels, and a greater number of traffic accidents. Expanding traffic network capacities by building more roads is both extremely costly and harmful to the environment. By far the best way to accommodate growing travel demand is to make more efficient use of existing networks. Portugal has a good but underused toll highway network that runs near to an urban/national road network that is free to use but congested. In choosing not to pay a toll, many Portuguese drivers are apparently accepting greater risk to their safety and longer travel times. As a result, the urban/national road network is used far more intensively than projections anticipated, which raises maintenance costs while increasing levels of risk and inconvenience. The main idea behind the work presented here, is to motivate a shift of traffic from the overused network to the underused network. To this end, a model for calculating variable toll fees needs to be developed. In order to support the model, there is the need to accurately predict the status of road networks for real-time, short and medium term horizons, by using machine learning algorithms. Such algorithms will be used to feed the dynamic toll pricing model, reflecting the present and future traffic situations on the network. Since traffic data quantity and quality are crucial to the prediction accuracy of road networks’ statuses, the real-time and predictive analytics methods will use a panoply of data sources. The approach presented here, is being developed under the scope of the H2020 OPTIMUM, a European R&D project on ITS.

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