III: Medium: Investigating Spatial-Temporal Informatics for Transportation Science

交通运输占美国能源消耗和温室气体的四分之一以上，每年有数十万人因一氧化二氮等有毒排放而过早死亡。因此，降低有害机动车排放和能源消耗是我们社会和交通科学的重要目标。一个关键的挑战是对现实世界驾驶过程中的排放和能源消耗的有限理解。该项目调查了新兴车辆大数据的潜力，以进一步了解现实世界驾驶过程中的排放和能源消耗。目前，汽车制造商和监管机构利用不足，车辆大数据以高频和空间分辨率详细说明排放和能源使用情况。它拥有丰富的信息，有助于识别高得令人无法接受的排放或能源使用模式，以及相关的车辆特性或道路特征。这些模式将被用来改进对真实驾驶过程中排放和能源使用的预测。通过这样做，这项研究将导致改进车辆设计和运营实践，以减少未来的排放和能源使用，通过改善空气质量和抑制气候变化来拯救生命。它还将通过创造性的生态驾驶挑战来改善教育，在驾驶模拟器环境中最大化在固定能源(或排放)预算下行驶的距离。该项目的目标是构建下一代时空信息学(STI)工具，以分析新兴的车辆大数据，如车载诊断数据，以进一步了解真实世界的排放和能源消耗。其具体目的是探索一套概念并开发一套时空信息学工具，以：(A)提供交通科学中的概念与当前信息学方法之间的映射；(B)方便地表示交通科学家和从业人员感兴趣的共同模式；(C)有效地从车辆大数据中挖掘新颖、有用和有趣的时空模式；(D)利用挖掘的模式来改进现实世界车辆排放和能源使用的物理科学模型；以及(E)通过生态驾驶活动将研究成果整合到教育中。该项目将以多种方式促进科技创新知识和理解。例如，它将探索新的算法，通过考虑交通网络中的简单路径，检测统计上显著的高排放或能源低效的线性热点，即使这些不是沿着最短路径。此外，它还将设计新的策略来高效地挖掘时空共现模式，即使这些模式在整个公路网上并不是全球突出的。该项目将把STI的重点从简单的GPS轨迹数据扩大到多属性轨迹数据，如具有数百个物理变量和约束的车辆车载诊断数据。它还将通过提高对用于预测这些因素的真实世界能源使用、排放和物理科学模型的理解，丰富当前以实验室和测试轨道为重点的交通科学。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Transportation accounts for over a quarter of U.S. energy use and greenhouse gases as well as hundreds of thousands of premature deaths annually from toxic emissions such as Nitrous oxides. Therefore, reducing harmful vehicle emissions and energy consumption are important goals for our society and transportation science. A key challenge is the limited understanding of emissions and energy-consumption during real-world driving. This project investigates the potential of emerging vehicle big data to further the understanding of emissions and energy consumption during real-world driving. Currently underutilized by vehicle manufacturers and regulatory agencies, vehicle big data details emissions and energy use at high frequency and spatial resolution. It has rich information to help identify patterns of unacceptably high emissions or energy use as well as associated vehicle properties or road features. Such patterns will be used to improve prediction of emissions and energy use during real-world driving. In doing so, the research will lead to improved vehicle design and operation practices to reduce future emissions and energy use to save lives by improving air-quality and dampening climate change. It will also improve education through a creative eco-driving challenge to maximize distance travelled for a fixed energy (or emission) budget in a driving simulator environment.The goal of this project is to build next-generation spatio-temporal informatics (STI) tools to analyze emerging vehicle big data such as on-board diagnostics data to further the understanding of real-world emissions and energy consumption. The specific aims are to explore a set of concepts and develop a set of spatio-temporal informatics tools to: (a) provide a mapping between the concepts in transportation science and current informatics methods, (b) conveniently represent common patterns of interest to transportation scientists and practitioners, (c) efficiently mine novel, useful and interesting spatio-temporal patterns from vehicle big data, (d) use mined patterns to improve the physical science models of real-world vehicle emissions and energy use, and (e) integrate research results in education via eco-driving activities. The project will advance STI knowledge and understanding in multiple ways. For example, it will probe new algorithms to detect statistically-significant linear hotspots of high emissions or energy inefficiency even if these are not along shortest paths by considering simple paths in a transportation network. Furthermore, it will design new strategies to efficiently mine spatio-temporal co-occurrence patterns even when those are not prominent globally over the entire road network. The project will broaden STI's focus from simple GPS-trajectory data to multi-attributed trajectory data such as vehicle on-board diagnostics data with hundreds of physical variables and constraints. It will also enrich current laboratory and test-track focused transportation science by improving understanding of real-world energy-use, emissions, and physical science models used to predict these factors.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.