Investigation of horizontal alignment data extraction methodologies in terms of cost and time Available to Purchase

Lack of coordination between driver and road geometry results in driver errors and crashes. Road geometric data constitute one of the primary requirements for safety analysis and improvement projects. A systematic literature review is presented here to identify existing methodologies for road geometric data extraction. Methodologies based on Global Positioning System (GPS), geographic information system (GIS) maps, AutoCAD digital maps, satellite imagery, inertial measurement unit, light detection and ranging (LiDAR) and vision technology were found employing manual, semi-automatic and automatic algorithms for extraction of horizontal alignment features of roads. A multi-criteria analysis in terms of device/software cost, data treatment cost and time acquisition was performed for the methodologies using an expert survey. Survey responses were analysed to rank the methodologies for minimum cost and time using an analytical hierarchical process. GPS- and GIS maps-based methodologies were the most economical, whereas the LiDAR-based methodology was the least economical. Overall, the findings provide valuable insights into components of existing methodologies. These findings could benefit practitioners, policymakers, enforcement authorities, vehicle manufacturers and researchers in the judicious selection of an appropriate methodology. An inventory of road geometric data could also be established to develop an intelligent transportation system by combining vehicle, road and driver in effective communication through information technologies.