Impact of circulation speed on fuel consumption in the Metropolitan District of Quito
DOI:
https://doi.org/10.37431/conectividad.v7i1.369Keywords:
Machine learning, Fuel consumption, Speed optimization, Random Forest, QuitoAbstract
This study investigates the relationship between vehicle speed and fuel consumption in the Metropolitan District of Quito through machine learning techniques using a 2019 Hyundai Accent vehicle with a 1.6L engine as a case study. Data from representative driving cycles obtained via GPS were used, processing 3,020 speed-time records. A Random Forest model was developed that predicts fuel consumption with high accuracy (R² = 0.68), identifying the optimal speed for energy efficiency between 65-75 km/h. The results show that city consumption is significantly higher (11.29 L/100 km) compared to the combined cycle (9.77 L/100 km) and highway cycle (7.52 L/100 km). The optimization model reveals potential fuel savings of 22.3% through speed management strategies.
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