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

目前的移动排放估计系统通常针对一支车队和一个大的地理区域的预测平均排放量。然而，在道路上，测量到的车辆排放是高度间歇性的，持续几秒钟的短期事件会产生高排放率，并对总出行排放有很大贡献。排放受到现行交通控制措施(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的影响。因此，迫切需要制定一套严格的技术基础，以便在考虑所有这些关键因素的情况下预测个别车辆的排放量。这个项目的目标是：(1)评价为个别车辆开发微型能源和排放模型的可行性，包括在模型构建中未使用的条件下对模型进行验证；(2)测试IVEEIS原型，该原型接受来自车辆、交通基础设施(例如道路等级)和其他外部来源(例如环境条件)的输入，以产生对特定车辆的微尺度能源和排放预测；以及(3)确定大规模实施IVEEIS的途径，以设计和评估各种交通设计和控制措施的有效性以及未来的政策制定。本项目将采用的方法包括以下任务：(1)设计道路现场试验；(2)收集较长时间内(例如，70小时一秒一秒的数据)和覆盖能源和排放预期变异性全范围的若干车辆的现场数据；(3)利用工程和统计方法开发IVEEIS模型；(4)IVEEIS的验证和应用；以及(5)规范IVEEIS的应用路径，以改进交通设计、控制和政策，包括改进目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和更有力地了解现实世界中影响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基础，例如在司机、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.