Affiliation:
1. Rzeszow University of Technology
Abstract
Abstract
One of the increasingly common methods to counteract the increased fuel consumption of vehicles is start-stop technology. This paper introduces a methodology which presents the process of measuring and creating a computational model of CO2 emissions using artificial intelligence techniques for a vehicle equipped with start-stop technology. The method requires only measurement data of velocity, acceleration of vehicle and gradient of road to predict the emission of CO2. In this paper, 3 methods of machine learning techniques were analyzed, while the best prediction results are shown by the gradient boosting method. For the developed models, the results were validated using the coefficient of determination, the mean squared error, and based on visual evaluation of residual and instantaneous emission plots and CO2 emission maps. The developed models present a novel methodology and can be used for microscale environmental analysis.
Publisher
Research Square Platform LLC
Reference90 articles.
1. Assessing the En-vironmental Performances of Urban Roundabouts Using the VSP Methodology and AIMSUN;Acuto F;Energies,2022
2. Anagnostopoulos, A., & Kehagia, F. (2018, October). Turbo-roundabouts as an alternative to roundabouts in terms of traffic safety, capacity and pollutant emissions. In Proceedings of the 7th Pan-Hellenic Road Safety Conference, Larissa, Greece (pp. 11–12).
3. Particulate matter-attributable mortality and relationships with carbon dioxide in 250 urban areas worldwide;Anenberg SC;Scientific reports,2019
4. Modeling of CO emissions from traffic vehicles using artificial neural networks;Azeez OS;Applied Sciences,2019
5. A hybrid intrusion detection model using ega-pso and improved random forest method;Balyan AK;Sensors,2022