A methodology to evaluate driving efficiency for professional drivers based on a maturity model

Over the last decade, transport companies have tried to reduce fuel consumption using efficient driving programs. In them, motorists have to apply different specific techniques while driving. Thus, to succeed in this learning process there are two key elements: the knowledge of efficient driving techniques and the drivers’ motivation. The latter is a human factor which companies usually bring about by using reward systems. In this case, having a fair evaluation mechanism is the keystone to determine goal fulfilment. This paper presents a complete methodology to evaluate driving efficiency of drivers in professional fleets. The evaluation methodology is based on a continuous process which determines the maturity of the motorist in different aspects, such as the efficiency during the start of the vehicle movement, during motion or in stop events. In addition, the evaluation methodology includes an early-classification method to establish the initial efficiency level of the individual drivers which permits an adaptation of the learning process from the beginning. A dashboard has also been developed to support the evaluation methodology. 880 professional drivers have been evaluated with this methodology. Results show that the evaluation methodology identifies drivers’ weaknesses, to be improved in successive iterations of the learning process.     

Strategic planning support for road safety measures based on accident data mining

When actions and measures to increase road safety are to be planned by the police and local authorities, it is necessary to consider the specific accident circumstances as well as their historical, current, and predicted course. In particular, combinations of accident circumstances not contained in existing police statistics are often neglected, but may nevertheless be relevant, e. g., due to an increasing frequency. In order to identify these undiscovered interesting combinations, we propose a framework to support strategic planning of road safety measures based on several consecutive data mining stages. The scope, type, and location of road safety measures must be planned at a strategic level several months in advance to be fully effective. Therefore, it is essential to investigate and predict the accident circumstances and the temporal changes in their frequency comprehensively. Only with the knowledge, e. g., about the temporal pattern, locations, conditions of roads or speeds, meaningful actions can be derived. The embedded data mining approaches, i. e., frequent itemset mining, time series clustering, time series classification, forecasting, and scoring, are carefully selected, coordinated, and aligned. As a result, the framework provides police users with information about circumstances of accidents that are of interest in the future and presents their previous temporal and local patterns in a dashboard. In this study, the framework is applied in four different geographical regions. Thereby, default parameter settings for all approaches are found that are particularly suitable for the framework to investigate novel geographic regions.     

基于通用设计的老年人辅助出行产品开发研究

针对老年人下肢行动功能衰退的问题,提出通用设计理念与SAEA模块化功能设计方法的研究思路,及辅助型轮椅的创新设计成果。利用STS生物力学实验法分析老年人使用轮椅过程中的问题,采用调研法分析下肢行动不便的老年人对辅助出行产品的需求重点,作为辅助型轮椅开发研究的切入点;通过SAEA模块化设计矩阵形式逐级解决辅助出行产品开发对用户需求及服务功能的方案,以通用设计思维模式对SAEA模块化设计矩阵分析成果进行方案优化;用计算机辅助设计看板模型整合最终方案,完成多功能辅助型轮椅设计成果。结论表明以通用设计理念对老年人辅助型轮椅开发进行STS实验和SAEA模块化研究所获得数据成果,可以为老年人辅助出行产品的研发提供技术参考及理论依据。