智能运输系统.使用游牧和移动设备提取用于估算CO2排放量的行程数据.第1部分:车队管理的燃油消耗测定

[中文适用范围]： 本文件规定了一种确定燃料消耗和由此产生的二氧化碳排放的方法，使车队管理者能够以可持续的方式降低燃料成本和温室气体（GHG）排放。 通过从游牧设备 (ND) 的全球导航卫星系统 (GNSS) 接收器中提取行程数据和速度曲线，通过移动通信将其发送到数据库服务器并计算机械能的偏差，来确定燃油消耗相对于给定的参考驾驶循环，a) 空气动力学、b) 滚动摩擦、c) 加速/制动、d) 坡度阻力和 e) 静止的贡献（以 [%] 为单位）。 由于参考循环的机械能耗是通过一组静态车辆配置参数进行测量而得知的，因此该方法使驾驶员、车队经理或物流服务提供商能够通过简单地收集行程数据来计算和分析每次行程的燃料消耗和二氧化碳排放量包含在移动车辆内的 ND 中的 GNSS 接收器。 除了在行程中和行程后监测能源消耗（燃料、二氧化碳）外，该解决方案还提供有关环保驾驶行为和道路状况的信息，以便更好地进行事前和事后行程规划。 因此，该解决方案还允许浮动汽车评估公共当局采取的特定交通管理行动的影响，以实现给定道路网络内温室气体减排。 ND 不了解车辆的特性。 ND 收集的动态数据与静态车辆配置参数之间的联系超出了本文件的范围。 此连接对于使用所描述的方法的软件或应用程序来说是依赖于实现的，该方法包括来自 ND 的静态车辆参数和每秒动态速度曲线。 对 ND 收集的数据的隐私和数据保护的考虑不在本文件的范围内，本文件仅描述基于此类数据的方法。 然而，使用该方法的软件和应用程序开发人员需要仔细考虑这些问题。 如今，大多数国家和公司在将新产品推向市场之前都必须遵守严格且透明的当地隐私法规，并拥有相应的批准委员会和认证法规。 [外文原描述]： This document specifies a method for the determination of fuel consumption and resulting CO 2 emissions to enable fleet managers to reduce fuel costs and greenhouse gas (GHG) emissions in a sustainable manner. The fuel consumption determination is achieved by extracting trip data and speed profiles from the global navigation satellite system (GNSS) receiver of a nomadic device (ND), by sending it via mobile communication to a database server and by calculating the deviation of the mechanical energy contributions of: a) aerodynamics, b) rolling friction, c) acceleration/braking, d) slope resistance, and e) standstill, relative to a given reference driving cycle in [%]. As the mechanical energy consumption of the reference cycle is known by measurement with a set of static vehicle configuration parameters, the methodology enables drivers, fleet managers or logistics service providers to calculate and analyse fuel consumption and CO 2 emissions per trip by simply collecting trip data with a GNSS receiver included in an ND inside a moving vehicle. In addition to the on-trip and post-trip monitoring of energy consumption (fuel, CO 2 ), the solution also provides information about eco-friendly driving behaviour and road conditions for better ex-ante and ex-post trip planning. Therefore, the solution also allows floating cars to evaluate the impact of specific traffic management actions taken by public authorities with the objective of achieving GHG reductions within a given road network. The ND is not aware of the characteristics of the vehicle. The connection between dynamic data collected by the ND and the static vehicle configuration parameters is out of scope of this document. This connection is implementation-dependent for a software or application using the described methodology which includes static vehicle parameters and dynamic speed profiles per second from the ND. Considerations of privacy and data protection of the data collected by a ND are not within the scope of this document, which only describes the methodology based on such data. However, software and application developers using the methodology need to carefully consider those issues. Nowadays, most countries and companies are required to be compliant with strict and transparent local regulations on privacy and to have the corresponding approval boards and certification regulations in force before bringing new products to the market.