Innovative & Groundbreaking Sight Distance Study done at Michigan State University: Safety Evaluation of Available Stopping Sight Distance using High-Fidelity LiDAR Data

Researchers, Peter T. Savolainen, Ph.D., P.E., Megat-Usamah Megat-Johari, Ph.D., Nischal Gupta, M.Sc., and Timothy J. Gates, Ph.D., P.E. set out to create a new pathway to help roadway engineers build and redesign safer roads, releasing a sight distance study.


Stopping sight distance (SSD) has long been cited as a concern among design engineers, given difficulties in attaining minimum values in certain roadway contexts in consideration of cost constraints. For example, SSD is one of the ten controlling criteria for design and documentation of design exceptions. However, the extant research literature has generally not shown that locations with insufficient SSD experience higher crash risks as compared to locations that meet or exceed recommended design values. This study investigates the relationships between SSD and crash risk using high-fidelity LiDAR data. These data are used to examine crash trends on the roadway network maintained by the Utah Department of Transportation. LiDAR data are used to assess compliance with the current SSD design policy. These data are integrated with crash and roadway geometric information. A series of negative binomial regression models are estimated to assess the relationship between available sight distance and the frequency of crashes while controlling for other important variables of interest. The results show that roadways with limited sight distance tended to experience significantly more crashes as compared to other, similar segments where sight restrictions were not present.



Stopping sight distance is one of the most important criteria for highway design. However, the literature has shown limited quantitative evidence as to the nature of this relationship. As such, the results from this study provide an important contribution to the research literature.

The study demonstrated the value of high-fidelity LiDAR data, which allowed for a large-scale investigation of the relationship between crash risk and available sight distance on a diverse set of roadway facilities. Negative binomial regression models were estimated separately for freeway and non-freeway facilities while controlling for the effects of other important variables.

The results show similar relationships for both facility types, with crashes persistently increasing as the amount of available sight distance is reduced. The safety performance functions estimated as a part of this study provide an empirical basis for estimating the potential impacts of design scenarios where it may be impractical to satisfy the minimum recommendation distances from the AASHTO Green Book. The results can also help to inform agency practices and the development of projects to mitigate the sources of the sight distance limitations.


RDV’s Road Safety Audit – 3D Technology was the tool used in this study to match the LiDAR data to the 3D environment. For the first time in history, this predictive data enables engineers to have precise data to show where deficiencies are in the roadway. We no longer need to rely on roadway deaths for road safety audits.

Click Here to Read the Full Study Safety-Evaluation-of-Available-Stopping-Sight-Distance-using-High-Fidelity-LIDAR-Data

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