The transportation sector is a major contributor to greenhouse gas (GHG) emissions, accounting for 14% of global emissions in 2010 according to the United States Environmental Protection Agency. In Quebec, this share amounts to 43%, of which 80% is caused by road transport according to the MinistÉre de l’Environnement et de la Lutte contre les changements climatiques of QuÅbec. It is therefore essential to support the actions taken to reduce GHGs emissions from this sector and to quantify the impact of these actions. To do so, accurate and reliable emission models are needed. Driving cycles are defined as speed profiles over time and they are a key element of emission models. They represent driving behaviors specific to various road types in each region. The most widely used method to construct driving cycles is based on Markov chains and consists of concatenating small sections of speed profiles, called microtrips, following a transition matrix. Two of the main steps involved in the development of driving cycles are microtrip segmentation and microtrip classification. In this study, several combinations of segmentation and clustering methods are compared to generate the most reliable driving cycle. Results show that segmentation of microtrips with a fixed distance of 250 m and clustering of the microtrips by applying a principal component analysis on many key parameters related to their speed and acceleration provide the most accurate driving cycles.

根据美国环境保护署的数据，交通运输部门是温室气体排放的主要贡献者，占2010年全球排放的14％。根据魁北克省环境与气候变化研究部的数据，在魁北克，这一比例达到了43％，其中80％是由公路运输引起的。因此，必须支持为减少该部门温室气体排放而采取的行动，并量化这些行动的影响。为此，需要准确可靠的排放模型。行驶周期定义为随时间变化的速度曲线，它们是排放模型的关键要素。它们代表每个区域中各种道路类型特有的驾驶行为。构造行驶周期的最广泛使用的方法是基于马尔可夫链，并且由过渡矩阵将速度曲线的小部分（称为微行程）串联起来组成。行驶周期发展中涉及的两个主要步骤是微程分段和微程分类。在这项研究中，比较了分割和聚类方法的几种组合，以生成最可靠的驾驶周期。结果表明，通过对与速度和加速度相关的许多关键参数进行主成分分析，可以对固定距离为250 m的微车进行分段和将微车聚类，从而提供最准确的行驶周期。行驶周期发展中涉及的两个主要步骤是微程分段和微程分类。在这项研究中，比较了分割和聚类方法的几种组合，以生成最可靠的驾驶周期。结果表明，通过对与速度和加速度相关的许多关键参数进行主成分分析，可以对固定距离为250 m的微车进行分段和将微车聚类，从而提供最准确的行驶周期。行驶周期发展中涉及的两个主要步骤是微程分段和微程分类。在这项研究中，比较了分割和聚类方法的几种组合，以生成最可靠的驾驶周期。结果表明，通过对与速度和加速度相关的许多关键参数进行主成分分析，可以对固定距离为250 m的微行程进行分割和将微行程聚类，从而提供最准确的行驶周期。