The document proposes an Automated Vigilant Transportation System to minimize road accidents. It uses an Expert Speed Authority System (ESAS) that combines input from a vehicle's speedometer, GPS for speed limits, and a Geo Obstacle Detection System (GODS) to detect nearby obstacles. ESAS includes an advisory system to warn drivers when they exceed the speed limit and an intervention system to control the vehicle if warnings are ignored. It also uses an emergency notification system (ENS) to alert local authorities if rules are violated after intervention. The proposed system was simulated and showed a reduction in speed violations and accidents compared to real traffic data from accident-prone areas.

1. Automated Vigilant Transportation System for Minimizing the Road
Accidents
Chaitra V.R. Thota, Lavanya K. Galla, Ramya Narisetty, Uttam Mande, Member, IEEE
Abstract— Roads are one of the most important
infrastructures in any country. The major problem on road
based transportation networks is accident. The need of the
hour is to implement an expert system which helps in
preventing the occurrence of Road accidents. The proposed
model reduces the percentage of road accidents by
implementing the Vigilant Transport System which exploits the
use of “Expert Speed Authority System”. The model makes use
of the hybrid adaptive approach while controlling the vehicle.
This module is connected with GODS (Geo Obstacle Detection
System) that detects the nearby obstacles. And also controls the
vehicle depending on the geographic location and the
surrounding objects like schools, Hospitals. And a novel EVNS
(Emergency Vehicle Notification System) is used that gives e-
call to the nearby local authorities. Thereby, informing them
about the accidents.
Keywords: Road Accidents, GODS (Geo Obstacle Detection
System), ENS (Emergency Notification System), HAA (Hybrid
Adaptive Approach), ESAS (Expert Speed Authority System).
I. INTRODUCTION
Road based transportation networks are the major means of
transport for both passengers and goods movement in India.
This heavy movement of vehicles leads to accidents which is
the most risky part of road transportation. Lack of road sense
and poor management and maintenance of roads are some of
the causes. It is high time to realize that there is no single
cause and a solution to the road accidents. One of the
advisory methods to prevent accidents is, to be attentive and
watchful while driving so that it would be more risk free.
According to the survey of “Accidental deaths in India”, a
total of 3, 99,482 deaths is reported during 2012 showing an
increase of 1.0%. There are several causes of Road
Accidents. One of them includes improper road
maintenance and management. The Intelligent Road
Accident System is that which deals with the analysis and
management of road accidents. It aims at identifying the
accident prone areas and the accident patterns are analyzed
in such a way that the most appropriate way could be
selected for each location [18].
Sometimes, the environment conditions and roadway
designs become the major factors of the accident. Meysam
Effati1in 2012 used a fuzzy reasoning technique for
detecting these road hazardous segments [17] vulnerable to
accidents. If we could identify these, accidents could be
reduced to far extent. A Geo-spatial information system
measures the road geometry to identify the locations those
are prone to accidents [19]. A Bayesian road model system
is designed of for approximating the geometry of the lane
and locating the obstacles position irrespective to any kind
of road by implementing an algorithm. Instead of the usual
camera and radar sensors, we use a fusion, sensor system
consisting of a camera and radar. This model does not
experiment on sloppy roads or with sharp edges, but only on
straight lane [1].
However, most of the accidents do not happen just because
of improper road geometry or maintenance but have another
important cause - “speed”. It is rightly said that “Speed
Thrills But Kills”. High speed driving is the major cause of
accidents these days. It is the peak hour to realize that speed
and rash driving takes off many lives. So, the need of the
hour is to design an expert system that makes the driver
cautious about the speed at which he is travelling and helps
him in taking necessary steps to reduce the speed.
In this paper, an Expert Speed Authority System is proposed
which takes several inputs from different modules. These
inputs include the speed of the vehicle, the speed limit of the
location which is obtained by GPS and the input from the
GODS( Geo Obstacle Detetction System) which gives the
data of movable and immovable obstacles nearby. The
model compares the speed of the vehicle with speed limit of
the location and checks for the obstacle around. When the
driver goes beyond the speed limit, advisory system warns
the driver and suggests few actions that could be performed
to mitigate the accident. However, when these are ignored,
vehicle control actions are taken.
Fig 1
In the above diagram, Fig 1, depicts the model that
exploits the use of Expert Speed Authority System. GPS
(Global Positioning System) determines the speed limit of a
particular location. It collates with the Speed of the vehicle
and detection of obstacles are done by GODS
simultaneously. Further ESAS includes EAS and EIS
(Expert Advisory System and Expert Intervention System)
that take the responsibilities of warning the driver and
interfering in the driving respectively when there is a
detection of accident occurrence. Further, E-call is used to
GPS
Speed
GODS
ESAS
EAS
EIS
E-call

2. send the information to the nearby local authorities and
hospitals.
II. EXPERT SPEED AUTHORITY SYSTEM
The need of the intelligent systems in the vehicle on the road
will make substantial role in the decrease of road accidents.
A lot of work has been done for many years to conquer the
problem of the uncontrollability of vehicles. B. Kaerthikeyan
in 2010 proposed an elementary model depicting the
position of a vehicle using Global Positioning system by grid
lines which yields the speed constraint on that particular
location deriving from go databases and present scenarios.
From this, the system suggests drivers, local authorities
about the traffic condition of the accident prone area. When
the system reaches the position, the controller informs the
driver about the speed limit and also limits the speed of the
vehicle to the speed limit levels. Here, the system checks for
the checkpoints and depending on the checkpoints the
system would adjust its speed limit [16].
The system is connected to the advisory module that gives
the alert signal on exceeding the speed limit. It also has an
intervention system that interferes if the driver violates the
rule. GODS- The module which is connected to ESAS looks
for the nearby objects and determines the road
geometry.Figure 2. Shows the overall module structure of
the expert system.
A. Advisory System
It is a passive expert speed authority system. On
identifying that the driver is crossing the speed limit, it gives
a visual or an auditory alert to slow down. The driver
advisory system includes a vehicle speed sensor, an inbuilt
processor and a communication unit. The sensor monitors the
speed of the driver. The sensor provides an output
quantifying the speed of the driver of the vehicle. The
processor receives the output provided by the sensor. This
calculates a risk factor as a function of the output provided by
the sensor and provides an output signal having information
concerning the speed of the driver of the vehicle in response
to the risk factor exceeding a predetermined threshold value.
The communication unit receives the output signal from the
processor and transmits the information to the driver through
a warning signal.
B. Intervention System
It is the active expert speed authority system. The
intervention system includes a sensor, a processor and a
reaction unit. The sensor monitors whether the driver is
following the warning or not. The processor receives the
output provided by the sensor. This calculates a risk factor as
a function of the output provided by the sensor and provides
an output signal having information about what action to be
performed. The reaction unit receives the output signal from
the processor and performs the necessary functions. The
function involved here is that this takes an immediate action
like applying brakes, or cut down on the fuel respectively.
Then the control is passed again to the advisory system to
warn the driver and to give him the actions that are to be
performed. However, if the driver does not act upon the
specified actions, then an automatic e-call is made to the
nearby local authorities.
C. Geo Obstacle Detection System
This expert system detects for nearby vehicles and it takes
actions depending on the situation. The situation could be the
distance between two vehicles and objects, road geometry,
accident prone areas, etc. This study involves the information
about the high accident prone areas and less safety measures
locations using the spatial analysis irrespective to type of
vehicle or the geographical locations and determines the type
of accident or the patterns involving the accidents. With GIS,
we consume less time and also facile which is rather tedious.
The study using maps, police records can yield
recommendations to the local authorities [10].
Figure 2.
D. Emergency Notification System
It takes the input from the expert system if the person
violates the rules even after the system intervened. It gives
information to the nearby local authorities through an e-call.
This mitigates the after effects of the accident.
III. IMPLEMENTATION
To make the driver aware about the speed violation and take
necessary actions if driver object is not responding.

3. The inputs Speed Signal of Vehicle(SP_SIG_V), Speed
Limit Signal from GPS SL_SIG_GPS, Obstacle signal
GODS_SIG are given to the expert systems and the outputs
achieved are an Alert signal, involuntary methods, an
emergency call from model. Some features like Obtain
SP_SIG_V, Get SL_SIG_GPS, Get GODS_SIG are continuously
processed irrespective of the location of the frequently
moving of vehicles.
Figure 3
The algorithm (Fig 4) explains about the process of how the
proposed system works and tells the sequence of actions
performed.
The proposed system is simulated where the inputs are
given manually. Figure 3 and 5 depicts the input from ESAS
and GODS respectively.
Figure 5
Fig 6
Fig 6
Fig 6
Fig 7
Fig 8
Fig 9
Fig 6 depicts the colors that an experts system shows on
voilating the speeds.
Fig 7-9 depicts that “GREEN” color glows when the the
speed difference between the vehicle and the speed limit is
10, indicating a warning signal and advises the driver to
slow down. “YELLOW” color glows when the speed
difference between the vehicle and the speed limit is 20,
indicating that intervention actions are taking place in order
to reduce the speed. “RED” and “YELLOW” colors glow
when the speed difference between the vehicle and the speed
limit is 30, indicating that along with the intervention
actions, an e-call is made to alert the authorities about the
occurence of the accident.
IV. MAIN OBJECTIVE OF THE SYSTEM
The study team developed two key hypotheses based on the
goals for the system. The hypotheses and associated
measures and data sources for testing each are shown in
Table 5.1 below.
Start
Step 1: CG=0, CY=0
If ((SP_SIG_V>SL_SIG_GPS) || (GODS_SIG==1))
If ((SP_SIG_V-SL_SIG_GPS)<10)
If CG>2
GOTO yellow()
Else GOTO green ()
Else
CG=0; CY=0;
If ((SP_SIG_V-SL_SIG_GPS) <20)
GOTO yellow ()
Else
CG=0; CY=0;
If ((SP_SIG_V-SL_SIG_GPS) >30)
GOTO red ()
Step 2: Green () {
An alert signal to the driver;
CG++;
GOTO Step1; }
Yellow () {
Get GODS_SIG
Apply brakes and make an emergency call to nearby vehicles
GOTO Step 2 }
Red () {
Call yellow () and make e-call to nearby vehicles and as well a
rescue centers }
End
Fig 4.

4. Table 5.1
The main objectives of the system are to:
Reduce the number of road accidents through an
expert system ESAS which compares the speed of
the vehicle and the speed limit when the driver
exceeds the speed.
Alerts the driver when the difference in speed is
minimal and interferes the driving when the
difference exceeds.
Provide information to emergency centers about the
accident through an e-call.
V. ANALYSIS AND RESULTS
A. Hypothesis and measures for evaluation
The main objective of this evaluation is to determine the
effect of the system on road accidents and quantify the
benefits of the system.
B. Comprision of Model
The traffic data of twelve hours is taken in the 100m range
at accident prone area and the same data is submitted to the
simulated model (Table 5.2) which shows that the model
minimizes the speed violation and road accidents as shown
in Graph 5.1
Graph 5.1
Table 5.2
VI. CONCLUSION
This paper solves the problem of road accident occurances
because of speed neglegency by implementing Expert
Speed Authority System which takes the imput from the
vehicle and GPS and takes the appropriate actions.The
proposed system takes the decision by considering the
features like the position of the surrounding vehicles and
obstacles using GODS. In addition to this, it alerts the
surrounding vehicles about the actions performed by
intervention system. The simulated model highly reduced
the speed violation and also mitigated the occurrence of
accidents in the test scenario conducted.
The developed system excludes the situations involving
high priority vehicles like ambulances or emergency
services. Sometimes depending upon the real time traffic
constraints, it is inevitable for the driver to overtake the
vehicles and this may be possible only by crossing the speed
limit.
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Hypothesis Measures of
effectiveness
Data Sources
The use of ESAS ]
helps the travelers to
slow down the speed
when they cross the
speed limit
Number of road
accidents,
Decrease in the
speed violation.
GPS,
Speedometer,
GODS sensors
The use of GODS
in ESAS helps to
detect the obstacle
and take the action
Missed acceptance
rate of vehicles,
missed detection
rate of vehicles,
response time.
ESAS, sensors

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Chaitra V.R. Thota is a final year student of
Computer Science and Engineering, GITAM
Institute of Technology, GITAM University,
Visakhapatnam, India. Her interests lie in Big
Data, Expert Systems, Cloud computing and AI.
She is currently working on an intelligent
transportation system for mitigating Road
accidents based on speed analysis in Expert
Systems, AI and aims to work on ways for
reducing the number of accidents using
advanced technologies.
Lavanya K. Galla is currently pursuing her
final year in Computer Science and Engineering
at GITAM Institute of Technology, GITAM
University, and Visakhapatnam, India. She is
passionate about the areas related to Web
Designing, Core Java application and Expert
Systems. At present, she is working on an
intelligent transportation system to lower the
rate of road accidents based on location analysis
in Expert systems, AI and intends to lessen the
number of lives lost in accidents applying leading concepts.
Ramya Narisetty is in her final year of
Bachelors degree in Computer Science and
Engineering at GITAM Institute of Technology,
GITAM University, and Visakhapatnam, India.
She is enamored with the concepts of web
designing, Adv. Java, Data mining technology.
Her endeavor in intelligent transportation
system to scale down the rate of accidents
based on the obstacle detection analysis
implementing progressive mechanism.
Dr.Uttam Mande received the Bachelor of
Computer Applications from Andhra
University, Visakhapatnam and proceeded to do
his Master of Science in Information Science
and Master’s in technology of Computer
Science and Technology department in Andhra
University. He has received his PhD degree in
Computer Science and Engineering from CSE
department of JNT University, Kakinada, India
for his work in the field of. Expert Crime
Investigation Systems. He is a member of IEEE and IEEE CS and has
organized many workshops and was involved in Research Projects. He has
also published several international journals on the subject of Expert
Crime Investigation Systems. He is currently working as Assistant
Professor, Department of CSE at GITAM University, Visakhapatnam and
his main field of research includes Data Mining and Rule-based
Reasoning.
.