A Big Data Application for Low Emission Heavy Duty Vehicles

• Main Authors: Dimokas Nikos, Margaritis Dimitris, Gaetani Manuel, Koprubasi Kerem, Bekiaris Evangelos
• Format: Article
• Language: English
• Published: Sciendo 2020-12-01
• Series: Transport and Telecommunication
• Subjects: green vehicle; intelligent transport system; data warehouse; cloud computing; emissions
• Online Access: https://doi.org/10.2478/ttj-2020-0021

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%.