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Developing a Novel Method for Road Hazardous Segment Identification Based on Fuzzy Reasoning and GIS

Author(s): Farhad Samadzadegan | J. A. Rod Blais | Meysam Effati | Mohammad Ali Rajabi

Journal: Advances in Molecular Imaging
ISSN 2161-6728

Volume: 02;
Issue: 01;
Start page: 32;
Date: 2012;
Original page

Keywords: Fuzzy Inference Systems (FIS) | Geospatial Information System (GIS) | Road Hazardous Segment Identification (RHSI)

Roads are one of the most important infrastructures in any country. One problem on road based transportation networks is accident. Current methods to identify of high potential segments of roads for accidents are based on statistical approaches that need statistical data of accident occurrences over an extended period of time so this cannot be applied to newly-built roads. In this research a new approach for road hazardous segment identification (RHSI) is introduced using Geospatial Information System (GIS) and fuzzy reasoning. In this research among all factors that usually play critical roles in the occurrence of traffic accidents, environmental factors and roadway design are considered. Using incomplete data the consideration of uncertainty is herein investigated using fuzzy reasoning. This method is performed in part of Iran's transit roads (Kohin-Loshan) for less expensive means of analyzing the risks and road safety in Iran. Comparing the results of this approach with existing statistical methods shows advantages when data are uncertain and incomplete, specially for recently built transportation roadways where statistical data are limited. Results show in some instances accident locations are somewhat displaced from the segments of highest risk and in few sites hazardous segments are not determined using traditional statistical methods.
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