01658370

Component

Roadway grade significantly affects onroad speed and acceleration, vehicle fuel consumption, vehicle emissions, driver behavior, traffic safety, roadway capacity, and congestion. However, it has always been challenging to obtain grade data at sufficient spatial resolution to use grade as an explanatory variable in transportation models. This paper aims to address this problem by proposing a method to obtain high-accuracy roadway grade data from the Digital Elevation Model (DEM), a nation-wide open data source from the U.S. Geological Survey (USGS). Although DEM data cover most of the nation, data resolution and the presence of roadway cut and fill sections affects spatial grade accuracy and requires a solid strategy to remove or refill these segments. Cubic smoothing spline is applied to minimize the impact of noisy data, and improve grade estimation accuracy. The selection of the key parameter λ in the spline method is also discussed to balance between smoothing out noisy elevation data, and retaining vertical fluctuations along the road. In general, λ is recommended 100~1,000 for local roads, and 1000~10,000 for highways to maintain small average estimation error. The relationship between optimum λ that minimizes Root Mean Square Error (RMSE) and road fluctuations is also explored, which can be used for λ selection. Using real-world measurements as ground truth, the grade results generated from the DEM achieve an average estimation error of 0.5-0.58% on local roads, and 0.21%-0.23% on highways, depending on the resolution of the DEM data used. The results demonstrated the validity and applicability of DEM in generating high-accuracy roadway grade data.

This paper was sponsored by TRB committee AFB80 Standing Committee on Geospatial Data Acquisition Technologies. Alternate title: Development of Road Grade Data Using the U.S. Geological Survey Digital Elevation Model.

18-03535

English

10p

2018

Transportation Research Board 97th Annual Meeting

Digital/other

Figures; References

Accuracy; Data collection; Highway grades; Splines

Digital Elevation Model; United States Geological Survey

Data and Information Technology; Highways

Transportation Research Board Annual Meeting 2018 Paper #18-03535

TRIS, TRB, ATRI

Jan 8 2018 10:52AM