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

III：媒介：研究交通科学的时空信息学

• 批准号: 1901099
• 负责人: Shashi Shekhar
• 金额: $ 120万
• 依托单位: University of Minnesota-Twin Cities
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-08-01 至 2024-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1901099&HistoricalAwards=false
• 关键词: III; Medium; Investigating; Spatial; Temporal

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.

交通运输占美国能源使用和温室气体的四分之一以上，每年还有数十万人因有毒排放（如一氧化二氮）而过早死亡。因此，减少有害车辆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实际驾驶过程中排放和能耗的了解有限。该项目研究了新兴车辆大数据的潜力，以进一步了解实际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尚未充分利用车辆大数据，这些数据以高频率和空间分辨率详细描述了排放和能源使用情况。它具有丰富的信息，有助于识别不可接受的高排放或能源使用模式以及相关的车辆属性或道路特征。这些模式将用于改善对实际驾驶过程中排放和能源使用的预测。在此过程中，该研究将改进车辆设计和操作实践，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挽救生命。该项目还将通过创造性的生态驾驶挑战来改善教育，以在驾驶模拟器环境中最大限度地增加固定能源（或排放）预算的行驶距离。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。具体目标是探索一套概念并开发一套时空信息学工具，以便：（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者感兴趣的常见模式，（c）从车辆大数据中有效地挖掘新颖、有用和有趣的时空模式，（d）利用挖掘出的模式来改进真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纳入教育。该项目将以多种方式增进对科学、技术和创新的认识和理解。例如，它将探索新的算法来检测高排放或能源效率低下的重要线性热点，即使这些热点不是通过考虑运输网络中的简单路径来沿着最短路径。此外，它将设计新的策略，以有效地挖掘时空共现模式，即使这些模式在整个道路网络中并不突出。该项目将扩大科学、技术和创新的重点，从简单的全球定位系统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。它还将通过提高对现实世界能源使用、排放和用于预测这些因素的物理科学模型的理解，丰富当前以实验室和试验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知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

What is special about spatial data science and Geo-AI?

空间数据科学和地理人工智能有何特别之处？

• DOI: 10.1145/3468791.3472263
• 发表时间: 2021
• 期刊: SSDBM 2021: 33rd International Conference on Scientific and Statistical Database Management
• 作者: Shekhar, Shashi

Uncertainty-aware Energy Management of Extended Range Electric Delivery Vehicles with Bayesian Ensemble

• DOI: 10.1109/iv47402.2020.9304826
• 发表时间: 2020-10
• 期刊: 2020 IEEE Intelligent Vehicles Symposium (IV)
• 作者: Pengyue Wang;Yan Li;S. Shekhar;W. Northrop

GeoAI – Accelerating a Virtuous Cycle between AI and Geo

GeoAI — 加速人工智能与地理之间的良性循环

• DOI: 10.1145/3474124.3474179
• 发表时间: 2021
• 期刊: IC3 '21: 2021 Thirteenth International Conference on Contemporary Computing (IC3-2021
• 作者: P. S. Chauhan, Lokendra;Shekhar, Shashi
• 通讯作者: Shekhar, Shashi

Significant DBSCAN+: Statistically Robust Density-based Clustering

• DOI: 10.1145/3474842
• 发表时间: 2021-10
• 期刊: ACM Transactions on Intelligent Systems and Technology (TIST)
• 作者: Yiqun Xie;X. Jia;S. Shekhar;Han Bao;Xun Zhou

Towards geographically robust statistically significant regional colocation pattern detection

• DOI: 10.1145/3557989.3566158
• 发表时间: 2022-11
• 期刊: Proceedings of the 5th ACM SIGSPATIAL International Workshop on GeoSpatial Simulation
• 作者: Subhankar Ghosh;Jayant Gupta;Arun Sharma;Shuai An;S. Shekhar