Safety Risk Assessment of Low-Volume Road Segments on the Tibetan Plateau Using UAV LiDAR Data

Abstract

The intricate topography and numerous hazards of highland roads contribute to a significantly higher incidence of traffic accidents on these roads compared to those on the plains. Although precise road data can enhance the safety evaluation and management of these road segments, the cost of data acquisition in highland areas is prohibitively high. To tackle this issue, our paper proposes a system of assessment indices and extraction methods specifically designed for plateau regions, supplementing existing road safety audit techniques. We are pioneers in integrating a high-precision 3D point cloud model into the safety risk assessment of low-traffic plateau roads, utilizing unmanned aerial vehicle (UAV) LiDAR technology. This innovative approach enhances both the efficiency and accuracy of road mapping. Building on this, we amalgamated three categories of indices—road 3D alignment, geographical environment, and natural disasters—to formulate a comprehensive safety risk assessment model. Applying this model to seventeen representative road segments on the Tibetan Plateau, we found that road alignment significantly influences road safety risk. The segments with the highest risk ratings are predominantly those located in the southwestern part of the Tibetan region, such as Zanda and Gar. Road safety management should prioritize road alignment, particularly the role of the curve radius, without overlooking the impact of environmental factors and natural disasters.

Systematic review, evaluation and comparison of different approaches for the implementation of road network safety analysis

“…Zhang et al [ 61 ] propose a methodology based on the reconstruction of road geometry from LiDAR and assignment of rating based on geometrical parameters. The rating concurred to generate a risk index.…”

“…The paper from Zhang et al [ 61 ] does not provide any validation. The same for the paper from Shah et al [ 35 ], which in addition, includes in the required data the crash data, therefore the results cannot be compared with crash indices (like crash frequency or crash rate).…”

“…They took the segmentation from the work of Janssens et al [ 112 ], who segmented the network considering traffic analysis (not road safety analysis). The same for the work of Zhang et al [ 61 ], where they considered seventeen road segments without specifying the criteria the use for the selection.…”

Enhancing Road Safety Decision-Making through Analysis of Youth Survey Data: A Descriptive Statistical Approach

“…This approach views road safety issues as stemming from human fallibility, human tolerance, and potentially harsh road networks that require an accommodating transportation system [20]. Road safety risk assessment is crucial for reducing casualties and property damage [21].…”