CRII: OAC: Scalable and Integrated Data Collection Platforms for Connected Vehicle Data

到2030年，美国将有近1.46亿辆联网汽车投入运营，每辆车每小时将产生25 GB的数据;然而，传统的数据分析系统无法处理大量的数据。这将需要开发新的计算和科学工具，以塑造我们未来的联网汽车网络基础设施。该项目旨在创建一个嵌入在车辆级别的新数据挖掘引擎，以高效和可扩展的方式收集和处理数据流。利用新颖的数据科学工具，该项目将解决如何有效地决定收集什么信息，过滤什么以及在车辆内处理什么的重要基本问题。反过来，拥有一个高效的车载数据收集引擎将提高我们为互联车辆构建高影响力网络基础设施的能力。这种网络基础设施将开辟新的研究能力，并在智能交通和智慧城市领域提供社会效益。这些数据将告诉我们如何改善交通拥堵，如何创造更安全的街道，以及如何更好地设计我们的城市，从而改善交通，改善经济，拯救生命。该项目包括教育和劳动力发展，通过创建跨学科学习环境，课程和研究密集型计划来教育我们未来的数据科学家和工程师。该项目还将广泛促进本科生研究，K-12教育推广活动和代表性不足的群体的STEM教育。该项目将研究新的方法来理解连接车辆数据，包括时空轨迹和非空间传感器数据，并开发科学工具，可以压缩，估算，分区和总结连接车辆数据，同时解决与大规模数据库相关的重要数据挑战和可扩展性问题。首先，数据收集平台将通过基于邻近度的数据采样机制，最大限度地减少车辆级别的数据冗余。其次，该平台将通过捕获地图一致的轨迹点来提高联网车辆数据的可集成性。第三，它将通过利用新轨迹数据格式的特殊性，将分散的联网车辆数据与地图数据链接起来。这项科学调查的核心是矩阵分解、优化和谱聚类技术，包括全市范围的车辆传感器部署，以验证基于真实世界和大规模联网车辆数据的拟议工作。该项目的结果将挑战轨迹数据分析和矩阵分解技术之间的科学差距。最后，本项目将推进以下方面的科学知识：1）基于非空间传感器数据的轨迹数据压缩，2）用于连接车辆数据的位置推断的矩阵分解技术，3）轨迹和非空间传感器数据的谱图划分方法，（4）大-该奖项反映了NSF的法定使命，并通过使用基金会的智力价值进行评估而被认为值得支持和更广泛的影响审查标准。

By 2030, nearly 146 million connected vehicles will be in operation in the U.S. Each vehicle will generate 25 gigabytes of data per hour; however, traditional data analytics systems are not capable of handling the high amount of data. This will require the development of novel computational and scientific tools to shape our future connected vehicle cyberinfrastructure. The project aims to create a new data mining engine embedded at the vehicle level to collect and process the streams of data in a highly efficient and scalable way. Harnessing novel data science tools, this project will address important fundamental questions of how to efficiently decide what information to collect, what to filter, and what to process within a vehicle. In turn, having an efficient in-vehicle data collection engine will improve our capabilities to build a high-impact cyberinfrastructure for connected vehicles. This cyberinfrastructure will open up new research capabilities and provide social benefits in the fields of intelligent transportation and smart cities. The data will tell us how to improve traffic congestion, how to create safer streets, and how to better design our cities, resulting in improved mobility, economic improvements, and lives being saved. This project embraces education and workforce development to educate our future data scientists and engineers through the creation of interdisciplinary learning environments, courses, and research-intensive programs. This project will also broadly promote undergraduate research, K-12 educational outreach activities, and STEM education in underrepresented groups. This project will investigate novel approaches to understand connected vehicle data, comprised of spatio-temporal trajectories and non-spatial sensor data, and develop scientific tools that can compress, impute, partition, and summarize connected vehicle data, while addressing important data challenges and scalability issues associated with the large scale of the database. First, the data collection platform will minimize data redundancy at a vehicle level through a proximity-based data sampling mechanism. Second, the platform will improve the integrability of connected vehicle data by capturing map-consistent trajectory points. Third, it will link dispersed connected vehicle data with map data by harnessing the special characteristics of new trajectory data formats. This scientific investigation centers on matrix decomposition, optimization, and spectral clustering techniques and includes city-wide vehicle sensor deployment to validate the proposed effort based on real-world and large-scale connected vehicle data. Results of the project will challenge the scientific gap between trajectory data analytics and the matrix decomposition techniques. Finally, this project will advance scientific knowledge in 1) trajectory data compression based on non-spatial sensor data, 2) the matrix decomposition techniques for the location inference of connected vehicle data, 3) the spectral graph partitioning methods for trajectories and non-spatial sensor data, and 4) large-scale trajectory data mining.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.