Automatic Horizontal Curve Identification for Large Areas from Geographic Information System Roadway Centerlines

Abstract

The geography of horizontal roadway curves is critical to various disciplines, especially to transportation safety, because of their strong correlation with traffic crashes. Remarkably, conventional geographic information system (GIS) roadway centerlines, while fundamental and ubiquitous in current geospatial databases, do not include curve inventories. This study presents an improved method for automatic horizontal curve identification using GIS roadway centerline networks as the data source. Analyzing each vertex of the network geometry, this method identifies the curves by detecting deflections from straight lines using a vertex deflection angle threshold. Different from literature that uses a static threshold, this method develops and applies a dynamic threshold by considering two variables—the roadway speed and the centerline vertex density. The method is capable of self-adjusting using k-means clustering to compensate for uneven centerline digitization. The method can also detect spiral transitions and can handle the complexities of street networks represented using dual centerlines. The testing and validation of the method were performed on a large dataset by applying a combination of goodness of fit metric and visual inspection. The results show that this method improves curve identification accuracy and can provide broader applicability for curve identification using GIS centerlines of various representations and digitization quality in large geographic areas.