A Cloud-Based Big Data Architecture for an Intelligent Green Truck

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A Cloud-Based Big Data Architecture for an Intelligent Green Truck

Dimokas, Nikos / Margaritis, Dimitris / Gaetani, Manuel / Favenza, Alfredo

The rapid growth of cloud computing in combination with the advances in the communication boosted the green and smart mobility. Smart mobility aims to reduce congestion and foster greener, cheaper and with less delay transportation. Many of the current options to reduce fuel consumption and CO₂ emissions have worked on light-duty vehicles but are not feasible for heavy duty vehicles. Reduction of emissions and consumables (e.g. urea) without sacrificing on emission standards is an important challenge for heavy-duty vehicles. The paper introduces a cloud-based system architecture exploiting the potentials of big data analytics to deliver an on-demand route optimization service reducing NOx emissions of heavy-duty vehicles. The system utilizes the information provided by the navigation systems, big data analytics such as predictive traffic and weather conditions, road topography and road network and information about vehicle payload, vehicle configuration and transport mission to develop a strategy for the best route and the best velocity profile. The performance evaluation attested the efficiency and effectiveness of the system, since the cumulative engine-out NOx has been decreased more than 13%.

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Document information

Title :

A Cloud-Based Big Data Architecture for an Intelligent Green Truck

Additional title:

Advs in Intelligent Syst., Computing

Contributors:

Nathanail, Eftihia G. (editor) / Adamos, Giannis (editor) / Karakikes, Ioannis (editor) / Dimokas, Nikos (author) / Margaritis, Dimitris (author) / Gaetani, Manuel (author) / Favenza, Alfredo (author)

Conference:

Conference on Sustainable Urban Mobility ; 2020 ; Skiathos Island, Greece June 17, 2020 - June 19, 2020

Published in:

Advances in Mobility-as-a-Service Systems ; Chapter : 103 ; 1076-1085

Advances in Intelligent Systems and Computing ; 1278

Publication date :

2020-11-04

Size :

10 pages

ISBN :

978-3-030-61075-3 , 978-3-030-61074-6

ISSN :