A better understanding of Driving Patterns and their relationship with geographical driving areas could bring great benefits for smart cities, including the identification of good driving practices for saving fuel and reducing carbon emissions and accidents. The process of extracting driving patterns can be challenging due to issues such as the collection of valid data, clustering of population groups, and definition of similar behaviors. Naturalistic Driving methods provide a solution by allowing the collection of exhaustive datasets in quantitative and qualitative terms. However, exploiting and analyzing these datasets is complex and resource-intensive. Moreover, most of the previous studies, have constrained the great potential of naturalistic driving datasets to very specific situations, events, and/or road sections. In this paper, we propose a novel methodology for extracting driving patterns from naturalistic driving data, even from small population samples. We use Geographic Information Systems (GIS), so we can evaluate drivers’ behavior and reactions to certain events or road sections, and compare across situations using different spatial scales. To that end, we analyze some kinematic parameters such as speeds, acceleration, braking, and other forces that define a driving attitude. Our method favors an adequate mapping of complete datasets enabling us to achieve a comprehensive perspective of driving performance.

更好地了解驾驶方式及其与地理驾驶区域的关系可以为智慧城市带来巨大的好处，包括确定节约燃料，减少碳排放和事故的良好驾驶习惯。由于诸如有效数据的收集，人口群体的聚类以及类似行为的定义等问题，提取驾驶模式的过程可能具有挑战性。自然驾驶通过允许以定量和定性术语收集详尽的数据集，这些方法提供了解决方案。但是，开发和分析这些数据集非常复杂且占用大量资源。此外，大多数以前的研究已经将自然驾驶数据集的巨大潜力限制在非常特定的情况，事件和/或路段。在本文中，我们提出了一种从自然驾驶数据甚至是小样本中提取驾驶模式的新颖方法。我们使用地理信息系统（GIS），因此我们可以评估驾驶员对某些事件或路段的行为和反应，并使用不同的空间比例在各种情况下进行比较。为此，我们分析了一些运动学参数，例如速度，加速度，制动和其他定义驾驶姿势的力。