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

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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.