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Making Big Data Real in Upcoming Future: The Dynamic Toll Prices in the Portuguese Highways

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Data Analytics: Paving the Way to Sustainable Urban Mobility (CSUM 2018)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 879))

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Abstract

In the present context, it is frequent for urban and national road networks to be highly congested, resulting in increased travel times and delays. At the same time, due to toll charging, some high-quality highway networks, particularly in Portugal, are underused. Dynamic charging on highway networks can contribute towards the optimization of the network performance and understanding the behavior of logistic operators as one of the main users of the network is of great importance for the development of any dynamic charging toll model. Within this context, one of the pilot cases of the H2020 project OPTIMUM includes the development of such a forecasting and dynamic (toll) charging model with the key objective of transferring heavy traffic from the urban and national roads to highways. The first run of pilot occurred between March 09th, 2017 and April 07th, 2017, performance indicators (KPI) were highly positive in terms of freight traffic shift from the national roads to the highways and, consequently, an increase in toll revenues and freight operations efficiency was registered.

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Acknowledgments

The authors acknowledge the European Commission for its support and funding and the partners of the research project: H2020–636160 OPTIMUM.

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Correspondence to André Ramos .

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Ramos, A. et al. (2019). Making Big Data Real in Upcoming Future: The Dynamic Toll Prices in the Portuguese Highways. In: Nathanail, E., Karakikes, I. (eds) Data Analytics: Paving the Way to Sustainable Urban Mobility. CSUM 2018. Advances in Intelligent Systems and Computing, vol 879. Springer, Cham. https://doi.org/10.1007/978-3-030-02305-8_81

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  • DOI: https://doi.org/10.1007/978-3-030-02305-8_81

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-02304-1

  • Online ISBN: 978-3-030-02305-8

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