Data collectors and their technology embedded are exponentially increasing and the major interest of enterprises is focused on how this data can be analysed and put into real-time service for users and operators. The EU H2020 project Transforming Transport (TT) has leveraged Big Data technologies to demonstrate their impact on thirteen pilots working on seven specific transport areas such as highways, connected vehicles, rail, ports, airports, urban mobility and e-commerce. Throughout several KPI consciously selected, it has been possible to assess the benefits of the Big Data implementation on the transportation sector. This paper presents the results of this assessment, which reveals improvements around 40-60% regarding the operative cost, energy consumption, environmental quality and the enhancement of the predictive maintenance of assets, among others.

Assessing the impact of Big Data for improving transport efficiency: A cross-modal approach / Vázquez, Pablo; Monzón, Andrés; Boggio-Marzet, Alessandra; Corral, Víctor J.. - ELETTRONICO. - (2020). (Intervento presentato al convegno The 8th Transport Research Arena: Rethinking transport - towards clean and inclusive mobility tenutosi a Helsinki nel 27th-30th April 2020. Conference cancelled).

Assessing the impact of Big Data for improving transport efficiency: A cross-modal approach

Alessandra Boggio-Marzet;
2020

Abstract

Data collectors and their technology embedded are exponentially increasing and the major interest of enterprises is focused on how this data can be analysed and put into real-time service for users and operators. The EU H2020 project Transforming Transport (TT) has leveraged Big Data technologies to demonstrate their impact on thirteen pilots working on seven specific transport areas such as highways, connected vehicles, rail, ports, airports, urban mobility and e-commerce. Throughout several KPI consciously selected, it has been possible to assess the benefits of the Big Data implementation on the transportation sector. This paper presents the results of this assessment, which reveals improvements around 40-60% regarding the operative cost, energy consumption, environmental quality and the enhancement of the predictive maintenance of assets, among others.
2020
978-952-311-484-5
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2998209