Interest in using Light Detection and Ranging (LiDAR) technology in Transportation Engineering has grown over the past decade. The high accuracy of LiDAR datasets and the efficiency by which they can be collected has led many transportation agencies to consider mobile LiDAR as an alternative to conventional tools when surveying roadway infrastructure. Nonetheless, extracting semantic information from LiDAR datasets can be extremely challenging. Although extracting roadway features from LiDAR has been considered in previous research, the extraction of some features has received more attention than others. In fact, for some roadway design elements, attempts to extract those elements from LiDAR have been extremely scarce. To document the research that has been done in this area, this paper conducts a thorough review of existing studies while also highlighting areas where more research is required. Unlike previous research, this paper includes a thorough review of the previous attempts at data extraction from LiDAR while summarizing the detailed steps of the extraction procedure proposed in each study. Moreover, the paper also identifies common tools and techniques used to extract information from LiDAR for transportation applications. The paper also highlights common limitations in existing algorithms that could be improved in future research. This paper represents a valuable resource for researchers and practitioners interested in knowing the current state of research on the applications of LiDAR in the field of Transportation Engineering while also understanding the opportunities and challenges that lie ahead.

Geometric design guides provide minimum standards that must be met to ensure the safe and efficient operation of highways. One of the most important elements when designing a highway is the sight distance available to road users. Designing safe highways involves ensuring that this distance exceeds the minimum distance required by drivers negotiating different segments of a highway. If the sight distance available is less than that required, the likelihood of a driver safely completing a certain maneuver decreases. Measuring the available sight distance along a highway and ensuring that minimum design standards are met often requires field visits or graphic analysis of as-built drawings. Although these techniques are effective, they require significant time and effort. As a means of reducing the burden associated with sight distance assessment and in an attempt to help proactively address sight distance limitations on roads, this paper proposes an process by which sight distance along a highway can be automatically assessed on Light Detection and Ranging (LiDAR) point cloud data collected on test highways. The data was first used to create a digital surface model; “observer” and “target” points are then defined along the highway’s travel lanes. After that, the line of sight assessment is conducted to determine the existence of obstructions between the pairs of observer and target points. An algorithm to calculate the available sight distance based on the outputs of the Line of Sight assessment is then developed. The algorithm was tested on two different highway segments in Alberta, Canada, and was successful in determining the available sight distance in both cases. The extracted information was then compared to theoretical sight distance values, and it was found that the minimum stopping sight distance (SSD) was met in all but a small region of one of the segments. Moreover, a passing sight distance (PSD) assessment also revealed that minimum requirements were not met at one of the passing zones in one of the segments. Finally, collision records for locations where sight distance was limited were examined, and it was found that sight distance limitation could have been a factor in collision occurrence.

Road cross sections are designed to ensure safe operation of highways. Tangent segments are typically designed with cross slopes to ensure efficient drainage of water off the road's surface, likewise, on horizontal curves the cross section is superelevated (tilted) to help vehicles counteract centrifugal forces. In both cases, ineffective slopes that do not meet design requirements, put vehicles at risk of overturning and skidding. Similarly, if deficiencies exist in side slopes, the chance of recovery for vehicles that run-of-the-road decreases substantially. Thus, transportation agencies must constantly assess elements of a road's cross section to ensure that they meet current design standards throughout their service life. The microscopic nature of cross sectional elements makes measuring such information time consuming, highly disruptive to traffic, and resource intensive. To facilitate more efficient assessments of such features, this paper proposes a novel algorithm to extract road cross sections from light detection and ranging data. The algorithm involves estimating vectors which intersect the road's axis, whereby points within proximity to the vectors are retained and extracted. Slope information is then measured off the retained points. The proposed algorithm is fully automated and employs multivariate adaptive regression splines to identify locations of change in slope. The algorithm was tested on two highway segments in Alberta. The high efficiency and precise manner in which the slope data was extracted, demonstrates the value of using the proposed algorithm in performing network-level assessment of road cross sections.