A data-driven framework for incentivising fuel-efficient driving behaviour in heavy-duty vehicles

Fuel economy in freight transport is key in attaining economic and environmental efficiency and sustainability. Fuel consumption in Heavy-Duty Vehicle (HDV) is dependent on a set of driverbehaviour parameters and a set of external factors including terrain, loading, vehicle type, and goods etc. Therefore, this study aims at identifying the company-specific critical driving behaviour factors contributing to fuel consumption at HDV fleet using General Estimation Equation analysis. Further, a methodology is proposed to identify fuel-efficient and inefficient drivers using Data Envelopment Analysis and efficiency frontiers. As an illustrative example, a timber haulage company was analysed in which monthly driver behaviour performance data was collected using a telematics system. The results identified average speed, braking, and idling to be key factors influencing fuel consumption. Individual driver's performance in a month is marked as efficient or inefficient considering the set of critical driving-behaviour parameters, and inefficient drivers can improve their performance by following the proposed personalised incentive scheme.