气候变化领域集成服务门户

Interest in energy cost saving and in global warming have persuaded transport companies to apply measures to reduce fuel consumption. Efficient driving is one of the most employed solutions as it allows savings in fuel consumption of around 10% with a minimal investment. The drawback is that efficient driving is a learning process, and it greatly depends on the drivers' behavior, which in turn is closely related to their motivation. If drivers are not really involved or after some time their interest decreases, efficiency improvements would disappear. Thus, an efficient driving program should make drivers' motivation one of the main targets. One option could be the implementation of reward programs. However, these should be based on a clear individual evaluation process, as an unfair system could lead to discomfort, complaints, and repudiation. In this paper, we propose an analytic system, based on the detection of efficient and inefficient behavioral patterns, to evaluate the individual driver's progression in efficient driving with the aim of being the basis of a reward program. The system receives relevant, driving related, vehicle information every 1.5 s, allowing a precise searching of patterns. It has been tested successfully in 16 bus companies, analyzing the performance of 880 professional drivers. To accurately illustrate the analytic process, three detailed driver analyses have been included as a case study. Results of this applied research on the eco-driving field show that the proposed system identifies efficient and inefficient actions that are used to fairly evaluate the drivers' performance.