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Automating the Process of Identifying Line of Sight Obstructions

 

Abstract: Transportation agencies often use the two-vehicle method to locate segments along a route that should be marked as a no-passing zone. The two-vehicle method calls for two vehicles to be driven along a route at a distance determined by the design speed of a roadway. If at a location along the route the trailing vehicle is unable to see the leading vehicle downstream, the implication is that not enough sight distance exists at the location and that a no-passing zone is needed. While standard practice, the two-vehicle method can be time-consuming and labor-intensive.

Fortunately, technology, data analysis procedures, and available datasets have evolved to the point that datasets commonly owned by transportation agencies across the country can be used to identify locations along a road that have available sight distance values that are candidates for a no-passing zone given a design/posted speed. One of these available datasets is the path followed by data collection vans used for pavement and asset inventory. In fact, a by-product of the roadway asset inventory process is often a data file for each route inventoried that contains the cumulative distance, elevation, and geographic position (longitude and latitude) followed by the data collection vehicle. These files, which contain observations spaced at a fixed distance interval along a road, can be used to obtain an accurate model that describes the characteristics of the vertical and horizontal alignment that defines each route surveyed as part of the roadway asset inventory process. The accurate model is possible due to the use of a differentially-corrected GPS device, an inertial motion unit, and a distance measuring instrument.

Recently completed research established analytical procedures that take advantage of existing datasets owned or used by transportation agencies to automated the process of identifying line of sight obstructions. To simplify the process of implementing the procedures at a state-wide level, a software tool was created to process existing datasets and apply the analysis procedures. Results from the completed research can be used to expand the contents of crash reports by including detailed information about the available sight distance at the crash location thus improving the overall quality and content of the reports.


Kelvin Santiago - is an Assistant Researcher at the Traffic Operations and Safety Laboratory from the University of Wisconsin-Madison. Kelvin received his Ph.D. in Civil and Environmental Engineering from the University of Wisconsin-Madison and his Bachelor of Science in Civil Engineering from the University of Puerto Rico-Mayagüez. His research ranges from the use of technology to tackle practical transportation engineering problems to the use of driving simulators to help designers conduct virtual road safety audits before the construction of a project. Kelvin is also an Adjunct Instructor at Madison College where he teaches Python computer programming courses focused on data analysis.


 

 

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