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A Method for Making a Fair Evaluation of Driving Styles in Different Scenarios With Recommendations for Their Improvement

机译:一种在不同情景中进行公平评估的方法,提出改进的建议

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摘要

Evaluating driving from the point of view of efficiency is a complex task. The environment is continuously changing and there is no direct relationship between all the factors that affect fuel consumption. In this paper, we propose a method to evaluate driving that can fit any scenario. It is based on Data Envelopment Analysis (DEA). This technique allows us to measure the efficiency of the drivers and to ascertain which behaviors they should improve. There are many solutions to evaluate driving in the literature. However, these solutions have difficulties adapting to changes in the driving environment and the result of the evaluation could be inaccurate. A driver could be classified as less efficient when in fact the driving could be affected by factors beyond his or her control. In addition, they do not provide the sources of inefficiency. With our method, we can analyze and quantify the cause of inefficiency. This proposal has been used to evaluate drivers of a bus fleet in Spain. The results show that drivers who were rated as efficient achieve lower fuel consumption. They can save up to 9.21% in comparison with the least efficient drivers. It is also observed that anticipation is a skill that saves a large amount of fuel and that it is an aspect that most bus drivers have a lot of room for improvement.
机译:从效率的角度来看驾驶是一个复杂的任务。环境不断变化,所有影响燃料消耗的因素之间没有直接关系。在本文中,我们提出了一种评估驾驶的方法,可以适应任何情况。它基于数据包络分析(DEA)。这种技术使我们能够衡量驱动程序的效率并确定他们应该改善哪种行为。有许多解决方案可以在文献中评估驾驶。然而,这些解决方案难以适应驾驶环境的变化,并且评估结果可能是不准确的。当实际上驾驶可能会受到监控之外的因素的影响时,驾驶员可以归类为效率。此外,他们没有提供低效率的来源。通过我们的方法,我们可以分析和量化效率低下的原因。该提案已被用来评估西班牙公交车队的司机。结果表明,驾驶员被评为高效实现较低的燃料消耗。与最低效率的司机相比,它们可以节省高达9.21%。还观察到,预期是一种节省大量燃料的技能,并且这是大多数公共汽车司机有很多改进空间的一个方面。

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