Evaluates energy inefficiency in diesel vehicles, suggesting significant interventions for emission reductions.
To effectively reduce fuel consumption and emissions in personal transport, it is essential to understand how energy transfer inefficiencies arise under real-world driving conditions. This study investigates the behavioral and spatial determinants of energy transfer inefficiency in personal diesel vehicles using high-resolution vehicle telematics data. The research proposes a composite Energy Inefficiency Index (EII) derived from real-world indicators of driving behavior, including acceleration, braking, idling, speed variability, and trip structure. These indicators are normalized and weighted using principal component analysis to quantify inefficiency at trip and spatial levels. Geospatial analysis, including Global Moran’s I and heatmap visualization, is employed to identify spatial clustering of energy inefficiency across urban and extra-urban environments. The results reveal a moderate average level of energy inefficiency across the analyzed vehicle fleet, with braking frequency, acceleration frequency, trip duration, and idling time emerging as the primary behavioral drivers of inefficient energy transfer. A statistically significant positive spatial autocorrelation indicates pronounced clustering of inefficiency in dense urban areas characterized by congestion and stop–start traffic dynamics. Furthermore, this study evaluates potential fuel, cost, and CO2 emission reductions achievable through improved driving behavior and compares these gains with those associated with vehicle electrification. The findings demonstrate that targeted behavioral interventions—such as eco-driving and idling reduction—can yield substantial efficiency improvements and emission reductions, complementing the benefits of electrification. Overall, this research provides a data-driven framework for managing energy transfer inefficiency in personal diesel vehicles by integrating behavioral analysis, spatial assessment, and telematics-based monitoring, offering practical insights for policymakers, transport planners, and vehicle technology developers.
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FLOREA et al. (2026) studied this question.
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