Investigation of methodologies for extraction of horizontal alignment features of roads in terms of cost and time

Abstract

The lack of coordination between the driver and geometry results in driver errors and crashes. Road geometric data is one of the primary requirements for safety analysis and improvement projects. This paper presents a systematic literature review to identify existing methodologies for road geometric data extraction. Methodologies based on GPS, GIS maps, AutoCAD digital maps, satellite imagery, IMU, 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 Analytical Hierarchal Process (AHP). GPS and GIS maps-based methodologies were the most economical, while the LiDAR-based methodology was the most uneconomical. Overall, the findings provide valuable insights into components of existing methodologies. The findings would benefit practitioners, policymakers, enforcement authorities, vehicle manufacturers, and researchers in choosing an appropriate methodology judiciously. An inventory of road geometric data could also be established to develop an intelligent transportation system (ITS) by combining vehicle, road, and driver in effective communication through information technologies.