Quantifying modelingon risk of travel demand and measure to sustaining road safety gis and pca enabled approach

IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308
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Volume: 04 Issue: 10 | Oct-2015, Available @ http://www.ijret.org 23
QUANTIFYING MODELINGON RISK OF TRAVEL DEMAND AND
MEASURE TO SUSTAINING ROAD SAFETY- GIS AND PCA ENABLED
APPROACH
Madhubabu Dudipalli1
1
Department of Civil Engineering, Urban Transportation Engineering, JNTU, Hyderabad.
Abstract
In these days urbanization of road transportation facilities are more complexity to developing in the form of to improve road
safety. With the increased usage of Vehicles has enhanced the need for developing the infrastructure where these motor vehicles
can move safely. By developing safe roads which connect destinations and cities is a key foundation to infrastructural
development in a safe connectivity of road transportation. Mainly in this study can approach the road safety by using principal
component analysis(PCA) by using MAT LAB and geographical information system (GIS) Arc-GIS software to develop base maps
and accident causing zones identify in the study area. In this study an attempt has been made to study the existing road network
for Ongole, Pernamitta village road (Kurnool road state highway) area and propose the necessary improvements to be done. And
this model presented in this paper discussing with a multi set of variables under the one dimensionality set to identifying and
deriving the new data set for risk identify zones with raking by this analysis of principal component analysis. The safety audit is
defined as the place or location which causes number of accidents. It may be curve or faulty infrastructure. Such accidents are
taken as input from Ongole taluka Police Station at Ongole for further study. These accidents are registered from First
Information Report (FIR) informed by people. The study areas taken into consideration are Ongole to Pernamitta village Road
(Ongole to Kurnool UN divided two way line state highway). The aim of this study is to minimize the accidents and find out the
risk identify zones on the particular road network.
Key words: road safety,faulty infrastructure, PCA analysis, Arc-GIS,FIR and urbanization.
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1. INTRODUCTION
In this modern days all of the national and international wise
growth will considering like rural areas and urban areas, by
this modernizations mainly comes on the these areas to
came for improving road transportation facilities in form of
major connectivity’s and all rural road network to urban
area road network connectivity should facilitate. But in these
urban transportation developments a lot of major conflicts to
improve on the road by considering road users on urban
areas as well as rural connectivity areas also. Majorly in this
roadtransportationTakes lot of problems in urban areas like
in the form of congestion, connectivity, parking problems,
accidents, management and enforcement problems and
safety problems by increasing industrialization,
modernization and the sociological process of
rationalization. And the RapidUrbanization causes
haphazard and unplanned growth of urban centers which
becomes more complicated with the fact that it must take
place within the built up area.by all these consideration’s
majorly take road user safety point view on semi urban
context way.
This study is attempted with reference to the static and
dynamic features of the study area, to identify major
stretches with traffic characteristics as independent
characteristics of risk generation as basis to identify
stretches causing more accidents. Andgiving rank to that
particular stretch in road network of study area.
1.1 Model Interface
There are different mathematical models which are used to
find the crash rate, severity index, accident rate prediction,
risk prioritize identification etc.
In this particular analysis I have used Principal Component
Analysis (PCA)
1.2 Principal Component Analysis
Principal Component Analysis is a well-established
technique for dimensionality reduction and multivariate
analysis. Examples of its applications include data
compression, image processing, visualization, exploratory
data analysis, pattern recognition, and time series prediction.
And the principal component analysis is a multivariate
statistical method, the study of how to through the original
variable of the few linear combination to explain the original
variable most information. Correlation between accidents
involving many variables, there must be a co-factors of play
a dominant role, According to this, the original variable
correlation matrix or the covariance matrix of the internal
structure of the relationship, use of a linear combination of
the original variables to form several indicators (principal
components), keep the original variable in the main
information under the premise of dimensionality reduction
and simplify the problem effect, makes it easier to grasp the

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principal contradiction in the study of a traffic accident
problem. Generally speaking, the principal components and
the original variables using principal component analysis
follows the basic relationship between:
 The number of principal components is far less than
the number of the original variables
 The principal components retain the vast majority
information of variable
 Each principal component is the linear combination of
the original variables
 Each principal component is irrelevant.
Through the principal component analysis of road traffic
accident influence factor, we could find some main
compositions from complicated relationship between the
variables, which can quantitative analyses effectively with
lots of statistical data, reveal the inner relationship between
variables and get on deep inspiration between traffic
accident characteristics and the law of development, lead the
research work further.
The flowchart showing the step by step procedure of
conducting Principal component analysis is presented in Fig:
Data acquisition
Standardization of input values
Collection of covariance matrix
Calculation of the Eigen vector
Choosing components and forming a feature
vector
Deriving new data set
Figure 1: Flowchart showing the step by step procedure of
Principal component analysis.
2. Study area
The study area selected in Ongole municipality, which is
located in Prakasam district, Andhra Pradesh. Ongole to
Pernamitta village road (Ongole to Kurnool state highway)
for analyzing the accident prone stretches in that road
network. The geometric characteristics such as width of the
road, elevations of the stretches, presence of traffic features
also taken into the study. The elevation values are taken
from advance Google maps which give the elevation values
with respect to the mean sea level.
The following figure shows total road network Ongole
municipality
Figure 2: the total road network of the Ongole municipality.
The risk study taken for major road corridors of road
network
Figure 3: the major road corridors of road network
3. Data collection and Processing
In the data collection and processing’s mainly some of field
surveys are carried out to collect the information or data in
the above mentioned study area.it has been
governed/specified into two major categories.
1. Geometric characterization studies.
2. Traffic characterization studies.
In these two specified categories have some of the attributes
for each criterion those are as follows, like,
Geometric (Static Features):
Geometric characterization represent the geometric features
of the roadway affecting
The static characteristics of the road infrastructure.

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They are:
1. Number of curves (CU)
2. Road width (RW)
3. Lane width (LW)
4. Elevation difference (ED)
5. Distance (D)
6. Length (KM)
Traffic (Dynamic Features):
In this traffic characterization or dynamic features are the
following parameters
1. Speed (S)
2. Fatal accidents (Deaths)
3. Non-fatal accidents (Injuries)
4. Non-fatal accidents (NoInjuries)
5. Total accidents (TA)
6. Light motor vehicles(LMV)
7. Light commercial vehicles (LCV)
8. Heavy motor vehicles(HMV)
9. Non-motor vehicles(NMV)
10. Buses (B)
4. METHODOLOGY
Step-1: Data collected from the location is tabulated in
matrix form. From the data table, maximum values are
extracted in the next step.
Step-2: The maximum values are used for the factoring the
Data table for the process of standardizing the table.
Step-3: From the factored matrix, mean values are extracted
and the extracted mean value is subtracted from the matrix
to obtain a standardized matrix.
Step-4: The standardized matrix is considered for the
principle componentanalysis (PCA) to be carried out in Mat
lab.
Step-5: In Mat lab, from the standardized matrix the
covariance of matrix, Eigen vectors & Eigen values are
calculated for PCA analysis.
Step-6: PCA is carried out and final data set is obtained for
which the mean value is added and the maximum value is
multiplied to obtain the final output which can be considered
for the differentiation with the collected data set.
Data Collection and Analysis:
Table.1: Actual data collection.
Table.2: Factoring With Maximum Value.
Table.3: Final output after PCA analysis.
Table.4:With Difference to Analysis Total Ranking Priority
with Max Value Given For This Analysis
5. FINDINGS AND CONCLUSIONS
A) FINDINGS
 Maximum scope of accidents are in segment 3 and
segment 7.
 Accidents due to high speeds are possible at segments
9 and 10.
 The heavy vehicles influence is more in segments 3, 4
and 7 to the existing traffic.
 The influence of other traffic on accidents can be
found at segments1, 3, 4, 5 and 8.
 In all the stretches 3-wheeler autos and 2-wheelers
were more and major causes of accidents.
 Detail maps of identify location reflecting existing
conditions and specific suggestions for improvement
are presented in the study.
The best way to reduce severity and fatality of road
accidents shall be the adoption of the engineering,
education, enforcement and encouragement measures.
B) CONCLUSIONS
 As per the observations the maximum accidents are
recorded at segments 5, 8 and 9 considering all types
of accidents. So proper signal can be adopted based on
IRC Warrant-4.
 The non-fatal non-injured or injured accidents involve
more of LMV’s and LCV’s and the analysis shows
these accidents are more susceptible at segments 3, 4, 5
and 8. So allocation of separate lane for Light
Commercial Vehicles and Light Motorized Vehicles.
 Based on above results the stretch from 2.0 km to
4.5km is most dangerous in context of all factors. Due
to the presence of sudden curve there is a discomfort
and accidental criteria is heavy so Transition curve can
be adopted at that particular location.
 The complete road segment is two lane two way
without median. So proper median with specified
dimensions can be adopted.
 As there is more scope of accidents in stretch 2.0km to
4.5km there is a need of road widening with provision
of median.
 In case of heavy traffic intersections a controlled
intersection can be provided i.e. at segment 8 –
segment 9 (near Pernamitta bus stop). Based on traffic
volume Warrant- 1, we can adopt signalized
intersection at that particular location.
 Some of the bus stops are not properly located in this
stretch. So people are rushing towards the road. Proper
infrastructure can be adopted that consists of Bus bays.
 Segment 1, 5,4,7,8 are the areas were the educational
zone (schools, colleges) and Industrial zones are
present. At these places there is no facility for crossing
the road is provided. So proper facility like zebra
crossing, Push button system

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Data Collection And Analysis Tables:
Table.1: Actual Data Collection
LOCATION
DISTANCE
LENGTH(KM)
ROADWIDTH
FATAL
ACCIDENTS(DEATHS)
NONFATAL
ACCIDENTS(INJURIES)
NONFATALACCIDENT
(NOINJURIES)
TOTALACCIDENTS
CURVES
SPEED
ELEVATION
DIFFERENCE
LMV
LCV
HV
BUS
NMV
NH5(ONG) 0.5 0.5 9 2 8 12 22 0 50 1.70 5 1.4 4 2.2 0
1.0 0.5 7 0 0 3 3 0 50 0.85 4 1.4 4 0.0 0
1.5 0.5 7 0 0 0 0 0 60 0.00 5 2.8 0 0.0 0
2.0 0.5 7 0 0 0 0 0 60 0.35 3 1.4 0 0.0 0
2.5 0.5 7 0 4 15 19 0 65 0.70 4 1.4 4 0.0 0
3.0 0.5 7 0 3 4 7 0 65 2.25 9 1.4 4 2.2 0.4
3.5 0.5 7 0 0 2 2 0 55 3.80 8 2.8 0 2.2 0.4
4.0 0.5 7 3 10 14 27 1 55 6.35 8 4.2 0 0.0 0.8
4.5 0.5 7 3 12 15 30 1 45 8.90 7 1.4 4 0.0 0.4
PERNAMITTA 5.0 0.5 7 0 5 11 16 0 45 9.7 6 4.2 4 0.0 0.4
MAX VALUE 0.5 9 3 12 15 30 1 65 9.7 9 4.2 4 2.2 0.8
Table .2: Factoring With Maximum Value.
FACTORING WTH MAX
LOC ATION D L RW FA-D NFA-INJ NFA-NO INJ TA CURVES SPEED ELE DIFF LMV LCV HV BUS NMV
NH5(ONG) 0.5 1 1 0.67 0.67 0.8 0.73 0 0.77 0.18 0.56 0.33 1 1 0
1.0 1 0.78 0 0 0.2 0.1 0 0.77 0.09 0.44 0.33 1 0 0
1.5 1 0.78 0 0 0 0 0 0.92 0 0.56 0.67 0 0 0
2.0 1 0.78 0 0 0 0 0 0.92 0.04 0.33 0.33 0 0 0
2.5 1 0.78 0 0.33 1 0.63 0 1 0.07 0.44 0.33 1 0 0
3.0 1 0.78 0 0.25 0.27 0.23 0 1 0.23 1 0.33 1 1 0.5
3.5 1 0.78 0 0 0.13 0.07 0 0.85 0.39 0.89 0.67 0 1 0.5
4.0 1 0.78 1 0.83 0.93 0.9 1 0.85 0.65 0.89 1 0 0 1
4.5 1 0.78 1 1 1 1 1 0.69 0.92 0.78 0.33 1 0 0.5
(PERNAMITTA) 5.0 1 0.78 0 0.42 0.73 0.53 0 0.69 1 0.67 1 1 0 0.5
MEAN 1 0.802 0.267 0.35 0.506 0.419 0.2 0.846 0.357 0.656 0.532 0.6 0.3 0.3

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Table.3: Final output after pca analysis.
LOCATION D L RW FA-
D
NFA-
INJ
NFA-
NOINJ
TA CURVES SPEED ELE
DIFF
LMV LCV HV BUS NMV
NH5 (ONG) 0.5 9.91 3.17 -1.55 -0.12 20.88 0.46 49.88 3.47 6.06 2.23 2.40 0.66 0.24
1.0 1.17 1.91 9.79 8.21 3.67 0.22 44.90 4.59 5.67 2.23 2.40 0.66 0.24
1.5 -1.23 -
0.94
7.90 5.57 13.99 0.25 47.98 2.30 5.26 2.23 2.40 0.66 0.24
2.0 -1.58 -
0.60
8.91 3.14 10.60 0.29 63.84 3.52 6.88 2.23 2.40 0.66 0.24
2.5 6.89 2.72 10.70 6.36 22.18 -0.13 62.19 3.55 5.63 2.23 2.40 0.66 0.24
3.0 3.72 2.16 -4.83 10.12 4.45 -0.06 51.84 2.83 6.35 2.23 2.40 0.66 0.24
3.5 1.08 -
0.72
-5.72 9.84 14.32 0.25 64.16 4.15 5.28 2.23 2.40 0.66 0.24
4.0 20.05 -
2.21
3.29 6.07 16.66 0.03 46.93 3.87 6.19 2.23 2.40 0.66 0.24
4.5 21.76 1.24 6.59 5.24 1.21 0.30 63.47 3.11 5.53 2.23 2.40 0.66 0.24
PERNAMITTA 5.0 10.41 1.28 6.93 21.48 17.72 0.39 54.71 3.26 6.18 2.23 2.40 0.66 0.24
Table.4: With Difference to Analysis Total Ranking Priority with Max Value Given For This Analysis.
LOCATION DISTANCE TA RANK SPEED RANK LMV RANK LCV RANK HV RANK BUS RANK NMV RANK
SEGMENT
1
0.5 -1.12 -0.12 1.06 0.83 1 -1.60 -1.54 0.24 2
SEGMENT
2
1.0 0.67 -5.10 1.67 2 0.83 -1.60 0.66 0.24 2
SEGMENT
3
1.5 13.99 1 -12.02 0.26 -
0.57
2.40 1 0.66 0.24 1
SEGMENT
4
2.0 10.60 3.84 3.88 1 0.83 2.40 2 0.66 0.24
SEGMENT
5
2.5 3.18 -2.81 1.63 0.83 1 -1.60 0.66 0.24
SEGMENT
6
3.0 -2.55 -13.16 -2.65 0.83 -1.60 -1.54 -0.16
SEGMENT
7
3.5 12.32 2 9.16 -2.72 -
0.57
2.40 2 -1.54 -0.16
SEGMENT
8
4.0 -
10.34
-8.07 -1.81 -
1.97
2.40 0.66 1 -0.56
SEGMENT
9
4.5 -
28.79
18.47 1 -1.47 0.83 -1.60 0.66 -0.16
SEGMENT
10
5.0 1.72 9.71 2 0.18 -
1.97
-1.60 0.66 2 -0.16
And proper sign boards can be adopted.
Side connectivity of the local streets are more in this
stretch.so at that connectivity places provide sign boards are
necessary.
Segment 3,4,8,9 are segment were the highest number of
accidents are occurred due to absence of proper cautionary
measures. So speed limits sign boards are mounted like
40kmph, 20kmph like etc… should provide on these
stretches.
NOTE: In this above using PCA methodology is an
already used model. By this model only taken input as
reference to analyze these risk priority zones to give rank on
this analysis.For that using references are given below.
ACKNOWLEDGEMENTS
I am very thankful to all transportation department
professors of Jawaharlal Nehru technological university,
Hyderabad. All of my friends and family members for those
guidance and support till end.
6. REFERENCES
[1] The relationship between traffic congestion and road
accidents: an econometric approach using GIS.
[2] Kadiyali L.R. “Traffic Engineering and Transportation
Planning” Khanna Publication, New Delhi 1983.
[3] “Accident Analysis and Prediction of Model on
National Highways” by Rakesh Kumar
Singh&S.K.SumanDepartment of Civil Engineering,

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National Institute of Technology Patna, Patna, Bihar,
India.
[4] “Multivariate analyses for infrastructure – based crash
predictions model for rural highways” by Haneen
Farah, AbishaiPolus and Moshe A.cohen.
[5] “Risk priority estimation for major corridors by using
PCA applications of traffic mobility in urban area: by
Awari Mahesh Babu, K.M.Lakshmana Rao and
ArtiKhaparde.
[6] AcekMalczewski, 1998, “GIS and Multicriteria
Decision Analysis”, Edition 1.
[7] Bruno Santos, AntónioAntunes, Eric Miller, February
24, 2009, “A MultiobjectiveApproach to Long-term
Interurban Multi-level Road Network Planning”,
Journal of Transportation Engineering.
[8] Jolliffe I.T, “Principal Component analysis”, second
edition.
[9] BhargabMaitra, P.K.Sikdar, and S.L.Dhingra, Nov/Dec
1999 “Modelingcongestion on urban roads and
assessing Level of service”, ASCE journal
ofTransportation Engineering, volume 125.
[10]Antony Stathopoulos and Mattew G. Karlaftis,
Nov/Dec 2002,”Modellingduration of urban traffic
congestion”, ASCE journal of
TransportationEngineering, volume 128.
[11]A.Garib, A.E.Radwan and H.Al-Deek,”Estimating
magnitude and Duration of indent delays”.SCE.

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