Integration of Remote Sensing Big Data into the Management and Design of Highway Infrastructures

该发现基金（DG）的重点是开发有效、准确地处理大型激光雷达数据集的方法，最终目标是帮助基础设施管理和高速公路设计过程更加明智和数据驱动。光探测与测距（LiDAR）技术就是这样一种遥感技术，它有可能改变公路工程中的测量过程。在一次勘测过程中，移动激光雷达设备可以以数百万个已知位置坐标的点组成的高密度云的形式，创建高速公路基础设施的精确三维表示。数据集的毫米级精度使不同特征的测量具有很高的精度。大数据集是复杂的，使用现有的工具和技术处理和提取信息是一个挑战。然而，从大型激光雷达数据中提取的丰富信息有可能彻底改变交通工程的决策过程。因此，此DG分为多个阶段。在第一阶段，开发高速公路几何特征的提取和评估算法。第二阶段的重点是使用提取的数据，以便大规模地适应基于性能的设计方法。这包括评估每个高速公路路段的几何性能，以确定它们满足设计标准的程度，或者不符合设计标准。进行基于性能的评估的目的是了解几何完整性需求与安全性能之间的潜在联系。最后，最后一个阶段探讨开发的算法如何通知基础设施规划和管理过程。***预期的研究将在公路工程领域产生重大影响，同时在基础设施管理和规划以及公路设计领域创造研究机会。通过使用提出的方法处理大型激光雷达数据集，通常难以使用传统测量工具获得的信息，以前所未有的规模和速度随时可用。这有助于运输机构通过重要道路特征和设计元素的详细信息丰富其数据库，从而实现更高效、更经济的基础设施资产管理流程。本研究提供的额外信息也将有助于研究人员更好地理解不同公路构件的几何完整性与安全性能之间的关系。本研究的最终贡献在于促进了交通基础设施管理和公路设计过程的转变，使其更具包容性、信息性和循证性。

The focus of this Discovery Grant (DG) is to develop methods to process big LiDAR data sets efficiently and accurately with the ultimate goal of helping make infrastructure management and highway design processes much more informed and data-driven. Light Detection and Ranging (LiDAR) technology is one such remote sensing technique that has the potential to transform the surveying process in highway engineering. During a single survey pass, mobile LiDAR equipment can create an accurate three-dimensional representation of highway infrastructure in the form of a highly dense cloud of millions of points with known positional coordinates. The millimeter-level precision of the dataset enables the measurement of different features at a high degree of accuracy. Big data sets are complex, making processing and extracting information a challenge using existing tools and techniques. However, the rich amounts of information that can be extracted from big LiDAR data have the potential to revolutionize the decision-making process in transportation engineering.***Consequently, this DG is divided into multiple phases. In the first phase, algorithms are developed for the extraction and assessment of geometric features of highways. Phase two focuses on using the extracted data to allow for a large-scale adaptation of a performance-based design approach. This involves assessing the geometric performance of each highway segment to identify how well they satisfy design standards or alternately fail to meet design standards. The performance-based assessment is conducted with the aim of understanding the underlying links between the demand for geometric integrity and safety performance. Finally, the last phase explores how the developed algorithms inform infrastructure planning and management processes. ***The anticipated research is expected to have significant impacts in the fields of highway engineering while creating research opportunities in the areas of infrastructure management and planning as well as highway design. By processing big LiDAR datasets using the proposed methods, information, often challenging to obtain using conventional surveying tools, is made readily available at an unprecedented scale and speed. This helps transportation agencies enrich their databases with detailed information about important roadway features and design elements, which, in turn, result in a more efficient and economic infrastructure asset management process. The additional information made available due to this research will also help researchers better understand the relationship between geometric integrity and safety performance of different highway elements. The ultimate contribution of this research is facilitating the means by which the process of transportation infrastructure management and highway design could be transformed into one that is more inclusive, informative and evidence-based.