Abstract:
Fuel consumption of a vehicle depends on several internal factors such as distance, load, vehicle characteristics, and driver behavior, as well as external factors such as road conditions, traffic, and weather. Moreover, not all of these factors are easily obtainable for the fuel consumption analysis. Therefore, fuel-fraud is relatively easier to conceal; thus, considered a significant threat to the fleet industry by managers. This research model and evaluate the fuel consumption of fleet vehicles based on vehicular data and suggest suitable process improvement actions to improve the fuel economy. We first model and predict the fuel consumption to identify possible frauds. We considered a case where only a subset of the factors mentioned above is available as a multivariate time series from a long-distance public bus. An evaluation of several machine learning techniques revealed that Random Forest could predict fuel consumption with 95.9% accuracy. To verify the detected cases of possible fuel fraud, we propose to use different indicators such as speed profile, the frequency of harsh events, total idle time, and day of the week. Further, we propose a solution to promote fuel-efficient driving through real-time monitoring and driver feedback. A classification model, derived from historical data, identifies fuel inefficient driving behaviors in real-time. The model considers both the driver-dependent and environmental parameters such as traffic, road topography, and weather in determining driving efficiency. If an inefficient driving event is detected, a fuzzy logic inference system is used to determine what the driver should do to maintain fuel-efficient driving behavior. The decided action is conveyed to the driver via a smartphone in a nonintrusive manner. We demonstrate that the proposed classification model yields an accuracy of 85.2% while increasing the fuel efficiency up to 16.4%.
