Automated Parameter Determination for Horizontal Curves for the Purposes of Road Safety Models with the Use of the Global Positioning System

Abstract

This paper presents the results of research conducted to develop an automated system capable of determining parameters for horizontal curves. The system presented in this article could calculate the actual course of a road by means of a two-stage positioning of recorded points along the road. In the first stage, measurements were taken with a Real-Time Network (RTN) receiver installed in a research vehicle. In the second stage, pictures from three cameras, also installed in the vehicle, were analyzed in order to correct the accuracy of the location of the measurement points along the road. The RTN messages and the pictures from the cameras were sent to a mobile workstation which integrated the received signals in an ArcGIS (Esri) environment. The system provides a way to quickly accumulate highly accurate data on the actual geometric parameters of a road. The computer scripts developed by the authors on the basis of the acquired data could automatically determine the parameters of the horizontal curves. The solution was tested in the field and some comments on its advantages and disadvantages are presented in this paper. The automation of data acquisition with regards to the run of a road provides effective data input for mathematical models that include the effect of horizontal curve parameters on road safety. These could be used to implement more effective ways of improving road safety.

Automatic Identification of Roadway Horizontal Alignment Information Using Geographic Information System Data: CurvS Tool

“…Mobile asset vehicles (MAVs) are often used to extract roadway alignment data for such purposes. These vehicles are equipped with a variety of technologies including high-resolution video cameras, scanners, Ball Bank Indicator (BBI), GPS systems, LiDAR technology, and distance measurement instruments (5)(6)(7)(8)(9)(10)(11)(12). However, in addition to being time consuming, labor intensive, and expensive, requiring comprehensive post-processing of collected data, this method has its shortcomings as discussed in Findley et al (8), in which curvature data collected by three different commercial vendors utilizing MAV are found to have contrasting results.…”