Automated calculation of passing sight distance using global positioning system data.

Auteur(s)
Namala, S.R. & Rys, M.J.
Jaar
Samenvatting

Most of the rural highways in the United States of America are two-lane, two-way highways. In order to ensure smooth flow of traffic, maximum-passing opportunities must be provided on these highways, where the fast moving vehicles can overtake slow moving vehicles. However, due to the geometric characteristics of the highways and cost limitations, passing opportunities cannot be provided throughout the length of the entire highway. Hence there is a need to find the segments of the highway that are not safe for overtaking called "no passing zones." The accurate placement of no passing zones on two-lane highways is critical to ensure safety of the travellers and also to protect the departments of transportation of various states from lawsuits. Literature review shows that current methods used to mark the passing zones and no passing zones are very tedious and time consuming. The objective of this study was to develop a suitable model for measuring passing sight distance on two-lane, two-way highways using Global Positioning System (GPS) data and identify no passing zones. The model was converted into a computer algorithm and coded in Matlab version 6.5 and requires database toolbox. The algorithm has been tested on 10 highway segments and the results obtained are in agreement with the existing conditions. This model can be used to identify the no passing zones on any highways where the GPS data are available. It is accurate and cost effective when compared to the existing methods. The proposed model (procedure) has been automated, right from the importing the raw GPS data to the transferring of results into the final database.

Publicatie

Bibliotheeknummer
C 49554 [electronic version only] /21 / ITRD E839213
Uitgave

Topeka, KS, Kansas Department of Transportation (K-TRAN), 2006, V + 68 p., 24 ref.; K-TRAN: KSU-03-2

Onze collectie

Deze publicatie behoort tot de overige publicaties die we naast de SWOV-publicaties in onze collectie hebben.