Analytics improves transportation projects for citizens and environment

NC DOT finds a better way to build roads, avoid polluting natural water sources

How do you build a road faster, at less cost and with minimal disruption to the environment? In North Carolina you do it by first building computer models using advanced data sources that help narrow the choices of possible road corridors without resorting to costly land surveys. The process can save as much as $500,000 per road project and shave up to 20 percent off the time it takes to select and plan for a road. SAS® Analytics provide the engine for this innovative project that is gaining national attention and won the state an Environmental Excellence Award from the Federal Highway Administration.

To avoid polluting natural water sources, transportation planners need to carefully assess the impacts a transportation project will have on the surrounding environment and come up with ways to avoid, minimize and mitigate the impacts. Road builders typically rely on geographical surveys and verify that information by sending surveyors and water quality experts into the field to document all the streams and wetlands.

Every time we impact a wetland, we have to restore a wetland. Every time we impact a stream, we have to restore a stream. So getting good data is important not only for environmental, but for cost-saving reasons.

Morgan Weatherford
Environmental Program Consultant

This approach is costly because environmental issues must be identified before the transportation department can even narrow the choices for a new roadway to two or three “corridors.” On a large building project – such as a bypass – the state might need to survey thousands of acres of land. “(With a big project) you might end up with hundreds of possible combinations for one road,’’ explains Morgan Weatherford, Environmental Program Consultant with the North Carolina Department of Transportation’s (NCDOT) Natural Environmental Unit. “One of the biggest challenges that (NCDOT) faces right now is complying with all the federal and state environmental regulation, and doing it in a manner that is beneficial not only for the environment but for the taxpayer as well.”

Taking advantage of LIDAR

In the last 15 years, a new data source emerged that had the potential to eliminate some costly field work, but road builders and water quality experts weren't certain how to use it. The data comes from LIDAR, which stands for light detection and ranging. LIDAR uses laser pulses to record the distance between two points and is particularly good for charting land elevation – a key clue to the existence of wetlands and streams. LIDAR data is used extensively to update flood maps and is considered more detailed than any existing geological survey information. But LIDAR produces large volumes of data. For the area of a proposed transportation project, there might be upward of 30 million records with 20 to 30 different attributes per record. And the data had never been used specifically to delineate streams and wetlands for construction planning purposes.

In North Carolina, Environmental Specialist Susan Gale and Environmental Senior Specialist Periann Russell, both with the Division of Water Quality (DWQ), set out to build multiple logistical regression models that could predict headwater streams – and then test the model’s accuracy with field surveys. “The accuracy of the models range from 85 to 95 percent depending on the application terrain,’’ explains Russell. After the DWQ developed the logistic regression for streams, NCDOT was able to apply the same process to wetland predictions.

The work convinced an initially skeptical Weatherford, so much so that the state is using the process on a much larger project that includes predicting stream and wetland locations using LIDAR data for an entire county in Eastern North Carolina. The data will not only be used to help transportation planners choose a corridor for a 20-mile bypass, it will also be used on several bridge modernization projects and be available to private developers who need stream and wetland information for their proposed developments.

Choosing SAS for the work

Russell had previous experience writing programs in SAS and thought it was the best solution for this project because SAS can handle large data files and works well with Esri geographic information software. Russell is also able to export her models to JMP® programs, which help provide a visual representation of the models. Russell said it took about a week to build the model used in the pilot program.

The pilot work has been met with enthusiasm from both state and federal agencies. Weatherford is getting positive feedback from groups like the US Army Corps of Engineers and the US Environmental Protection Agency, and questions from state and local agencies responsible for planning transportation projects. Weatherford emphasizes that the model doesn’t end field work. It simply allows the state to do less field work because the model has helped narrow the corridor choices to a handful. “Every time we impact a wetland, we have to restore a wetland. Every time we impact a stream, we have to restore a stream. So getting good data is important not only for environmental, but for cost-saving reasons,’’ Weatherford says.


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Shaves up to $500,000 off project costs and 20% off planning time.

Los resultados que se ilustran en este artículo son específicos a las situaciones, modelos de negocios, datos aportados y entornos de cómputo en particular que se describen aquí. Cada experiencia del cliente de SAS es única basada en variables de negocios y técnicas y todas las declaraciones se deben considerar no típicas. Los ahorros, resultados y características de desempeño reales variarán dependiendo de las configuraciones y condiciones de los clientes individuales. SAS no garantiza ni augura que todos los clientes lograrán resultados similares. Las únicas garantías aplicables a los productos y servicios de SAS son aquellas que se estipulan en las declaraciones de garantía explícitas en el contrato por escrito relativo a dichos productos y servicios. No se debe considerar que nada de lo aquí mencionado constituye una garantía adicional. Los clientes han compartido sus éxitos con SAS como parte de un intercambio contractual convenido o resumen de éxito de proyectos tras una implementación exitosa de software de SAS. Los nombres de marcas y productos son marcas comerciales de sus respectivas compañías.

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