Journal of Transportation Technologies, 2012, 2, 32-40 Published Online January 2012 (
Developing a Novel Method for Road Hazardous Segment
Identification Based on Fuzzy Reasoning and GIS
Meysam Effati1, Mohammad Ali Rajabi1, Farhad Samadzadegan1, J. A. Rod Blais2
1Department of Geomatics Engin eer ing, University of Tehran, Tehran, Iran
2Department of Geomatics Engineering, University of Calgary, Calgary, Canada
Received October 29, 2011; revised December 1, 2011; accepted December 15, 2011
Roads are one of the most important infrastructures in an y countr y. One prob lem on road based transportation networks
is accident. Current methods to identify of high potential segments of roads for accidents are based on statistical ap-
proaches 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 iden tification (RHSI) is introdu ced 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 metho ds 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.
Keywords: Fuzzy Inference Systems (FIS); Geospatial Information System (GIS); Road Hazardous Segment
Identification (RHSI)
1. Introduction
The road based transportation networks have become the
most important part of the infrastructure in all countries.
Roads are not only important as the physical structure of
the society, but also as the foundation for social and eco-
nomic developments. An increased demand for suburban
mobility also increases the problems caused by transpor-
tation networks. One of these problems is accident oc-
currence. Several factors such as human factors, vehicle
factors, environmental factors and roadway design usu-
ally play a role in traffic accident occurrence [1]. At pre-
sent in Iran, accident data obtained from the “Analysis
Form for Traffic Accidents” are used to identify the road
segments with high potential for accident. This form is
filled out by a police officer for each traffic accident with
casualties on a public road in Iran. Based on the informa-
tion in these forms this method picks some segments
with high potential for accident and then the danger re-
lated to these segments is estimated using statistical ap-
proaches. Since there is no statistical information for the
newly-built route available, this method cannot be used
for transportation networks that have been recently built.
This research introduces a new and general method for
identification of road segments with high potential for
accident in transportation networks. Although driver mis-
takes often contribute greatly to the occurrence of any
particular accident event, spatial analysis of road haz-
ardous segments help to explain why accidents are more
frequent in some segments than in others. The study area
is Kohin-Loshan transit road that connects Tehran to the
North of Iran and is located in a mountainous region that
has most factors for accident occurrences. Since the
study area is an old one and adequate spatial data were
not available, among several factors that usually play a
role in traffic accident occurrence, in this research only
environmental factors and roadway design are considered.
Moreover, integrated use of GIS and fuzzy reasoning is
used for identification of roads hazardous segments. Geo-
spatial Information System (GIS) is a technology which
when incorporated in the analysis of road hazards, can
facilitate a quick way of data retrieval, in addition to fa-
cilitating a means of making precise remedial engineer-
ing designs to improve road sections which are prone to
road traffic accidents [2]. The most straightforward use
of GIS for accidents analysis is the examination of spatial
characteristics of accident locations [3]. Road hazardous
opyright © 2012 SciRes. JTTs
segment identification can benefit from the data man-
agement, representation and spatial analytical functions
offered by a GIS. This research shows how integrated
use of GIS and fuzzy reasoning can be properly applied
in modeling uncertainty of road hazardous segment iden-
tification. The terminology of fuzzy logic for spatial in-
formation management and modeling localities is intro-
duced in Section 2.1. In the following related researches
and proposed method is introduced. Section 3 presents
implementation process and the first successful applica-
tion of this new approach. Evalu ating of result points out
in Section 4.
In [4], Jha and McCall explored the applications of GIS
based computer visualization techniques in highway pro-
jects. In this project they found that GIS serves as a re-
pository of geographic information and enables spatial
manipulations and database management. Implementa-
tion of this project in a real highway project from Mary-
land indicated that integration of GIS and computer visu-
alization greatly enhances the highway development
process. Another research project conducted by Carreker
and Bachman demonstrated that by applying GIS, the
accuracy and efficiency of locating crashes could be im-
proved [5]. In [6], Fu ller et al. used GIS and remote sen-
sing data and analyzed several geometric road risk fac-
tors in the U.S. Southwest. This research used four road
geometry factors and geology-based criteria and did not
consider weather condition and road proximity land use
effect on road hazardous location identification. They
also did not use expert knowledge for determination of
fuzzy membership functions. In 2003, a so-called novel
adaptive neuro-fuzzy logic model was developed by
Adeli and Jiang to estimate freeway work zone capacity.
The model combined fuzzy logic with neuro-computing
concepts and was used for the nonlinear mapping of 17
Copyright © 2012 SciRes. JTTs
Table 4. Description of primary data layers.
Data Layers Source Resolution Comments
layers, a database of all data and layers is generated and
road geometry and environmental attributes are as-signed
to considered points. Each of variables in Table 2 is
treated as a risk factor in analysis of road hazardous
segment identification and critical standard boundaries
for each criterion (observed indicators) are determined.
Since classes or groups of data with boundaries are not
sharply defined, their indicators and relationships have
uncertain definition. Therefore some uncertainties are
lying in this method. Fuzzy set theory is a useful tool for
solving the uncertainty with linguistic variables. It also
facilitates subsequent integration of data layers in the
generation of composite risk maps. Prior to fuzzy process
the membership functions of each factor have to be
specified using expert knowledge. The membership fun-
ctions are depicted in Figure 5.
Topographic National
Carto nter1 1: 50,000 digital
Map graphic Ce
Digital Elevation Carto ter 10 meter digital
ric Road Ministry - attribute
ns Meteorological - weather
Highway Police -
ck Road Ministry - for evalua-
graphic Cen
Weather Statio
Crash Data
Organization stations
Excising Bla
Segments tion and test
Slope (Percent)
Low = [0, 0, 1, 4], Appropriate = [3, 5, 7, 9],
High = [8, 10, 100, 100]
Radius (m)
Very Small = [0, 0, 200, 400], Small = [300, 450, 550, 700]
Appropriate = [500, 650, 750, 900 ], = [700,High 800, 100000, 100000]
Visibility (m)
Appropriate = [0, 0, 100, 250],
Inappropriate 1000, 10 00] = [150, 300,
Dist. from Intersection (m)
Very Near = [0, 0, 50, 150], Near = [50, 125, 175, 250
Far ],
= [175, 250, 100000, 100000]
Dist. from Population Centers (m)
Near = [0, 0, 200, 500], Moderate= [400, 600, 900, 1100
Far ],
= [900, 1000, 100000, 100000]
Road Width (m)
Very Narrow = [0, 0, 5, 15], Narrow = [10, 15, 20, 25]
Appropriate = [15, 22.5,Width = [25, 32.5, 100,
100] 27.5, 35],
Rain Value (mm)
Very Low= [0, 0, 160, 175], Low = [165, 175, 185, 195
High =[185, 195, 205, 215], Very High= [205, 215, 300, 300]
Dist. From the Starting Cities (m)
Very Near = [0, 0, 4000, 5500], Near = [4500, 6000, 9000, 10500 ],
Far = [9500, 11000, 100000, 100000]
Figure 5. Membership functions.
The form
occurrence and hazard values, complexity of each factor
and the experience of experts. Therefore, selection of
appropriate rules for road hazardous segment identifica-
tion is a sensitive and an important subject. According to
samples of rules in Table 3 more ru les are con sider ed for
very dangerous and dangerous output classes.
gh po-
rea. In this figure, x and
ad, respectively, and
placement including:
ulation act con-
of impacidents
of the fuzzy rules requires ex
ct of each descriptor on the ac
4. Result and Evaluation
fter defining the input and output of fuzzy inference
system and its membership functions and rules, value of
danger for each point is determined. Danger values of
proposed fuzzy inference system are classified in the
range of 0 (safe) to 250 (very dangerous). Now each ha-
zard point should be assigned to one of these classes:
absolutely safe, safe, danger prone, dangerous or very
dangerous. Figure 6 shows final results of fuzzy reason-
ing process for identification of segments with hi
tential for accident in the study a
y axes show x and y coordinate of ro
each danger class have been shown with a special symbol
and color. Figure 7 shows the final results of proposed
approach for identification of hazardous segments in GIS
In this figure red and blue points indicate very dan-
gerous and dangerous segments respectively. This comp-
osite risk map depicts good correlation between existing
accident segments (yellow dots that have been taken
from statistical analysis of accident records) and seg-
ments with high potential for accident (red and blue
b la ck do t s) . However, in some instan ces accident location s
are somewhat displaced from the segments of highest risk
and in few sites hazardous segments are not determined
using traditional statistical methods. Several factors may
explain this dis
Figure 6. Results of fuzzy reasoning process for iden
tion of hazardous segments. tifica-
Figure 7. Final risk map of hazardous segments (Red: Very
dangerous, Blue: Dangerous, Yellow: Excising accident se-
Error associated with the accident data;
Approximate determination of existing accident points
by police officers;
Error in geometry and environmental data;
ctors that are unaccounted in
this analysis because of the lack of proper data.
5. Conclusion and Future Work
This research introduced a novel method for determina-
tion of hazardous segments in transportation network
under uncertainty, specially f or recently built transit roads
where there are not statistical data of accident occur-
rences. The analysis in this research has shown that al-
though driver error often contributes greatly to the oc-
currence of any particular accident event in suburban
roads, consideration of environmental and road geometry
factors help to explain why crashes are more frequent in
some segments than in others. Consequently, in this re-
search GIS was employed to obtain a new approach for
creating maps of the hazardous segments of roads based
on the theory of fuzzy logic. The study supports the pro
ould have been truncated in a crisp
ad hazardous
Temporary obstructions in the roadway;
Other parameters and fa
per application of fuzzy set theory to spatial concepts,
such as road hazardous locations and provides a mecha-
nism to address various kinds of uncertainty by preserv-
g the detail that win
set. Consideration of more criteria for ro
segment identification is the other issue that can be con-
sidered in the future researches.
6. Acknowledgements
This research is done using the data provided by National
Copyright © 2012 SciRes. JTTs
Cartographic Center (NCC), National Geographic Or-
ganization and Iran Ministry of Road and Urban Devel-
opment Transportation Research Institute.
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