NSF/USDOT: In-Vehicle Energy and Emissions Information System (EEIS)

当前的移动的排放估计系统通常针对车队和大的地理区域的预测平均排放。 然而，在道路上，测量的车辆排放是高度偶发性的，与持续时间的几秒钟的短期事件产生高排放率，并大大有助于总行程排放。排放受现行交通控制措施（TCM）、交通基础设施（例如，道路设计）、车辆特性、驾驶员行为、交通流量和环境条件。 因此，迫切需要制定一个严格的技术基础，以预测单个车辆的排放量，并考虑到所有这些关键因素。 该项目的目标是：（1）评估为单个车辆开发微尺度能量和排放模型的可行性，包括在模型构建中未使用的条件下验证模型;（2）测试原型IVEEIS，其接受来自车辆、交通基础设施（例如，道路等级），以及其它外部源（例如，环境条件），以产生微尺度的能源和排放预测的特定车辆;和（3）定义的路径，大规模实施的IVEEIS设计和评估各种交通设计和控制措施的有效性，并为未来的政策发展。 本项目将采用的方法包括以下任务：（1）设计道路实地试验;（2）长期收集实地数据（例如，70小时的逐秒数据），并涵盖了能源和排放的预期可变性的全部范围内的几种车辆;（3）IVEEIS模型开发使用工程和统计方法;（4）IVEEIS验证和应用;和（5）IVEEIS应用路径的规范，以改善交通设计，控制和政策，包括改善当前微观交通模拟模型中的排放预测。 这项研究的更广泛影响是使人们能够更深入、更有力地了解影响真实的世界中高排放和能源使用事件的因素，这反过来将为解决紧迫的社会问题提供强有力的科学依据，例如在驱动程序、网络和区域层面寻求有效的空气质量和能源使用管理战略。

Current mobile emissions estimation systems are typically aimed at predicted average emissions for a fleet of vehicles and for a large geographic area. However, on-road, measured vehicle emissions are highly episodic, with short-term events of a few seconds in duration producing high emission rates and substantially contributing to total trip emissions. Emissions are influenced by prevailing traffic control measures (TCMs), transportation infrastructure (e.g., roadway design), vehicle characteristics, driver behavior, traffic flow, and ambient conditions. Therefore, there is a critical need to develop a rigorous technical basis for predicting emissions for individual vehicles taking into account all these key factors. The goals of this project are to: (1) evaluate the feasibility of developing micro-scale energy and emissions models for individual vehicles, including validation of the model under conditions not used in model construction; (2) test a prototype IVEEIS that accepts inputs from the vehicle, transportation infrastructure (e.g., road grade), and other external sources (e.g., ambient conditions) to produce microscale energy and emissions predictions for that particular vehicle; and (3) define a path for large-scale implementation of IVEEIS for designing and evaluating the effectiveness of various transportation design and control measures and for future policy development. The approach that will be used in this project includes the following tasks: (1) design of on-road field experiments; (2) field data collection, for extended periods (e.g., 70 hours of second-by-second data) and for several vehicles covering the full range of expected variability in energy and emissions; (3) IVEEIS model development using engineering and statistical methods; (4) IVEEIS validation and application; and (5) specification of IVEEIS application paths to improve transportation design, control, and policy, including improved emission predictions in current microscopic traffic simulation models. The broader impact of this research is to enable a deeper and more robust understanding of the factors affecting high emissions and energy use episodes in the real world, which in turn will provide a strong scientific basis for addressing pressing societal issues such as seeking effective air quality and energy use management strategies at the driver, network and regional levels.