<abstract xmlns="http://www.w3.org/1999/xhtml"><p>Recent advances in green and smart mobility aim to reduce congestion and foster greener, cheaper and with less delay transportation. The reduction of fuel consumption and CO<sub>2</sub> emissions have worked on light-duty vehicles. However, the reduction of emissions and consumables without sacrificing on emission standards is an important challenge for heavy-duty vehicles. The paper introduces a big data system architecture that provides 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 system was proven efficient during the performance evaluation phase, since the cumulative engine-out NOx has been decreased more than 10%.</p></abstract>

Recent advances in green and smart mobility aim to reduce congestion and foster greener, cheaper and with less delay transportation. The reduction of fuel consumption and CO2 emissions have worked on light-duty vehicles. However, the reduction of emissions and consumables without sacrificing on emission standards is an important challenge for heavy-duty vehicles. The paper introduces a big data system architecture that provides 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 system was proven efficient during the performance evaluation phase, since the cumulative engine-out NOx has been decreased more than 10%.

A Big Data Application for Low Emission Heavy Duty Vehicles

Centre for Research and Technology Hellas/Hellenic Institute of Transport Thessaloniki

Transport and Telecommunication Journal

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

{"article-title":"A Big Data Application for Low Emission Heavy Duty Vehicles"}

Recent advances in green and smart mobility aim to reduce congestion and foster greener, cheaper and with less delay transportation. The reduction of fuel...

A Big Data Application for Low Emission Heavy Duty Vehicles

Online veröffentlicht: 26. Nov. 2020

Seitenbereich: 265 - 274

DOI: https://doi.org/10.2478/ttj-2020-0021

Schlüsselwörter
Green vehicle, intelligent transport system, data warehouse, cloud computing, emissions

© 2020 Nikos Dimokas et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

A Big Data Application for Low Emission Heavy Duty Vehicles

Online veröffentlicht: 26. Nov. 2020

Seitenbereich: 265 - 274

DOI: https://doi.org/10.2478/ttj-2020-0021

SchlüsselwörterGreen vehicle, intelligent transport system, data warehouse, cloud computing, emissions

© 2020 Nikos Dimokas et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Schlüsselwörter
Green vehicle, intelligent transport system, data warehouse, cloud computing, emissions