Automatic network-level identification of sight distance values from existing datasets

Abstract

Many local, state, and national highways were built years before computer-based tools like geographic information systems (GISs) and computer aided design were available. Therefore, keeping track of existing roadways design parameters is a difficult task since the characteristics of those design are not easily available through modern day GIS and computer aided design-based datasets. Those technologies have become the standard method of information management in agencies and have replaced old paper-based methodologies. As a result, for some of the existing infrastructure, the only information available to decision makers is the location of the highway centerline, along with other asset management information such as shoulder presence, pavement type, and roadside features. The lack of information in GIS datasets about basic design characteristics, such as radius and centerline elevation, means that field surveys are required as the only available method to determine if a highway meets the latest design standards and guidelines. Frequently, field procedures necessary to collect existing geometric data can be not only labor-intensive, but also cost-prohibitive, especially in times of economic constraints. This paper focuses on the use of existing photographic logs commonly created and managed by transportation agencies to automate the process of computing the sight distance available, along an entire route. While the analysis presented is focused on identifying road segments in need of a no-passing zone, results from the methodology discussed can also be used to identify segments where advisory speeds need to be established, as well as those segments where posted speeds should be increased/decreased in order to improve safety. Through the application of the methods presented in this paper, the authors demonstrate how value can be added to existing datasets that were originally collected for completely different purposes such as the creation of a sign inventory.

Keywords:

• GPS
• photographic log
• sight distance
• study automation
• traffic control devices
• vertical curves