Ensuring that the available sight distance (ASD) on highways meets the minimum requirements of geometric design standards is crucial for safe and efficient operation of highways. Current practices of ASD assessment using design software or through site visits are labor intensive, time consuming, and traffic disruptive. Thus, this paper introduces a fully automated algorithm that allows large-scale assessment of ASD in three-dimensional (3D) space on highways utilizing mobile light detection and ranging (LiDAR) data. The algorithm was tested on LiDAR data of highway segments in Alberta, Canada. The results showed that the algorithm was highly accurate in detecting sight distance limitations at the defined regions and, in all cases, the driver’s vision was restricted by the pavement surface on vertical crest curves. In the case of combined vertical and horizontal curves, the vertical crest curve was found to be the controlling element in sight distance deficiencies. In addition, the assessment of historical collision data revealed clusters along the regions defined with ASD limitations, indicating that restrictions in drivers’ vision could have contributed to the collision occurrence.

Producing as-built drawings is an important task in any road construction project. In fact, in an ideal situation, these drawings must be updated whenever major maintenance work takes place. Unfortunately, constantly updating those drawings is not always feasible due to the amount of manual work associated with the data collection in traditional surveying practice. The increase in computing power and the advancement in technology has led many transportation agencies to consider utilizing remote sensing techniques to extract roadway design features and prepare as-builts of roads. In this note, a procedure to generate as-built drawings of vertical profiles on highways using light detection and ranging (LiDAR) point cloud data are proposed. The procedure is a multistep procedure where the road centerline of each segment is first defined, after that a best fit alignment of points along the road’s centerline is generated. A digital surface model (DSM) of the LiDAR highway is created and the centerline is relayed onto the DSM before generating the road profile. The proposed method is tested using LiDAR data collected on two highways in the province of Alberta, Canada. The profiles extracted using the proposed method are compared against vertical profiles that were generated for the same segments using data collected in GPS surveys and as-built drawings developed in manual surveys. The results show the feasibility of accurately extracting road profiles from LiDAR data. The average difference in grades estimated using the proposed method and the GPS data ranged from 0.023% to 0.061%. In fact, the proposed method was able to capture details in the road profile that were not detected using GPS data, demonstrating the value of using LiDAR for road profile extraction.

The majority of vehicle collisions occur because of human error; in fact, studies have shown that approximately 95% of collisions are caused, at least in part, as a result of human mistakes. Therefore, it is important to study the main causes of human mistakes and develop mitigation strategies to reduce, if not eliminate, these errors from occurring. In this respect, designing highways that balance mental workload is a crucial task that ensures drivers have sufficient time to make appropriate decisions. However, the quantitative relationship between mental workload and collisions is not well documented in the safety literature. Enough evidence exists to support the reasonable conclusion that safety is affected by changes in workload, but there is no quantitative evidence of this effect. Consequently, this paper tries to investigate the relation between mental workload ratings and collisions on highways using data from Alberta, Canada. Horizontal and vertical curve parameters on two-lane, two-way highways were first extracted from LiDAR and GPS data using MATLAB algorithms, and the resulting features were summarized using Civil 3D. The mental workload ratings were assigned based on the presence of four major geometric features, namely, intersections, horizontal curves, vertical curves, and changes in cross-section. The results show that mental workload has a significant effect on safety for two-way, two-lane highways. Furthermore, the findings strongly indicated the need to integrate mental workload into the design process, to not only meet the operational needs of the highway, but also ensure that the geometric layout does not mentally overwhelm drivers.

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