Aveneu Park, Starling, Australia

Multi the tracks. The structure and performance of transportation

Multi
Modal System For Safety On Railways

Dheebika.
V, Computer Science and Engineering, S.A. Engineering College

We Will Write a Custom Essay Specifically
For You For Only $13.90/page!


order now

Lokeswari.R,
Computer Science and Engineering, S.A. Engineering College

Dr
R. Prasanna Kumar, Professor, S.A. Engineering College

Chennai,
India

 

 

 

Abstract

Transportation
networks are one of the most important aspect for economic development of a
country.
Accidents in railways leads to loss of lives and financial loss for the
government.   Modern railway transport
systems are designed under the principles of safety and reliability, and the
development of high-speed railway lines is based on such premises. This
projects are designed based on railway safety. Here we propose a system
which consisting of ultrasonic sensor, camera, GPS, and GSM.

This project describes a camera with MATLAB software which is used in
integrating visuals and programs. This also gives a graphical user interface
for this model. This helps us to detect an object  on the track , thus giving us the image of
the hindrance on the track.GPS are used here to get the location and GSM are used
here as a communication channel to transmit GPS coordinates, like geography
location.

 

 

           I. INTRODUCTION

Railway transportation is known as the
backbone of Indian economy. Safety on railway networks has to be maintained for
the security of the people is guaranteed. Several monitoring system such
as stereo visions, thermal scanners, and vision metric etc., are used in
monitoring platforms. But they could not achieve the goal by detecting the
obstacle on the tracks.

 

The
structure and performance of transportation network reflects the ease of
travelling and transferring goods among the different parts of a country            thus affecting trade and other
aspects of country’s economy.

 

               II.ACCIDENTS

 

According
to the statistics, signal system failures, track failures, vehicle breakdown
are some of the causes of train accidents. Obstacle on the train tracks is the
most important reason. The obstacle may be any vehicle, animals and humans
crossing the track and also in some cases any cracks on the rails.

Around
15,000 lives are lost rail accidents. The unmanned crossings are responsible
for maximum number of train accidents

 

 

           III.EXISTING SYSTEM

 

In all transport systems safety and reliability are
highly considered, particularly in railways. In Railway System all the control
are done through man power. In this present condition we have faced the
following problems wastage of time,
wastage of energy and difficulty for a manual operator. Because of the
constant need to improve rail safety, the existence of the objects on tracks
are considered, particularly the grade crossings.

 

DISADVANTAGES:

 

·        
This
system needs human being to detect the obstacles.

·        
It
take time to detect the obstacle.

 

    

 

     IV.PROPOSED
SYSTEM

In
proposed method we develop a safety system for Indian railway and human beings.The
system consist of microcontroller which is interfaced with GPS module, GSM
modem, Buzzer, Ultrasonic Sensor, and LCD display.The ultrasonic sensor senses
the obstacle in front of the train and send information to the centralized
server using UART and the display unit of the train. The camera along with the
MATLAB capture the detected obstacle image in front of the train and check what
type of obstacles are detected and send the information of the detected
obstacle image to the centralized server using UART. If any obstacles are
detected in front of the train the GPS are used here to find the location of
the obstacles detected train information, and GSM are used to send the location
of the obstacle detected location information to the nearby railway station by
using UART.
Here MATLAB are used to check what type of obstacle are detected. Buzzer are also used here to
produce alarm if any obstacles are detected infront of the train.

 

 

ADVANTAGES:

Avoid
accidents

Identify
the obstacles detected train location

Reduce
time to find the obstacles infront of the train.

 

BLOCK
DIAGRAM

 
 
 
 
   ARDUINO

Camera

MATLAB

UART

GPS
 

 UART

Ultrasonic       Sensor

ADC
 

UART

GSM

        LCD DISPLAY

   Buzzer

                                   

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

HARDWARE REQUIREMENTS:

 

·        
Arduino

·        
GSM

·        
GPS

·        
LCD
Display

·        
UART

·        
Power
supply module

·        
Ultrasonic
sensor

·        
Camera

·        
Buzzer

 

 

 

SOFTWARE REQUIREMENTS:

 

·        
MATLAB

·        
EMBEDDED
C

 

     

           V.
CONCLUSION

Thus
the proposed system helps in preventing accidents due to humans, vehicles and
vehicles crossing the tracks by using simple mechanism of obstacle detection.

 

 BASE PAPER

Juan
Jesús García, Álvaro Hernández, Jesús Ureña, Enrique García,
“FPGA-Based Architecture for a Multisensory Barrier to Enhance Railway Safety” IEEE TRANSACTIONS ON INSTRUMENTATION AND
MEASUREMENT, VOL. 65, NO. 6, JUNE 2016 PP.

                

 REFERENCES

 

1
J. Jesús García, Manuel Mazo, Ana Jiménez, “Efficient Multisensory Barrier for
Obstacle

Detection
on Railways” IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, VOL. 11,
NO. 3, SEPTEMBER 2010. Pp 702-713

2
Houssam Salmane, Louahdi Khoudour, and Yassine Ruichek, “A Video-Analysis-Based
Railway–Road Safety System for Detecting Hazard Situations at Level Crossings”
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, VOL. 16, NO. 2, APRIL
2015 pp 596-609

3
Qianru Wang, Bin Guo, Leye Wang, Tong Xin, He Du, Huihui Chen, and Zhiwen Yu
“CrowdWatch: Dynamic Sidewalk Obstacle Detection Using Mobile Crowd Sensing”,
IEEE INTERNET OF THINGS JOURNAL, VOL. 4, NO. 6, DECEMBER 2017  pp 2159-2171

4
Nils Gageik, Paul Benz and Sergio Montenegro, “Obstacle detection and collision
avoidance for UAV with complementary low cost sensors”, Received April 14,
2015, accepted May 5, 2015, date of publication May 12, 2015, date of current
version June 1, 2015. Digital Object Identifier 10.1109/ACCESS.2015.2432455 pp 599-609

5
Néstor Morales, Jonay Toledo, Leopoldo Acosta, Javier Sánchez-Medina, “A
Combined Voxel and Particle Filter-Based Approach for Fast Obstacle Detection
and Tracking in Automotive Applications”, IEEE TRANSACTIONS ON INTELLIGENT
TRANSPORTATION SYSTEMS, VOL. 18, NO. 7, JULY 2017, pp 1824-1834

 

6
Vinh Dinh Nguyen, Hau Van Nguyen, Dinh Thi Tran, Sang Jun Lee, and Jae Wook
Jeon, “Learning Framework for Robust Obstacle Detection, Recognition, and
Tracking”, IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, VOL. 18,
NO. 6, JUNE 2017, pp 1633-1646

 

7
Matej Kristan, Vildana Suli´c Kenk, Stanislav Kova?ci?, Janez Perš, “Fast
Image-Based Obstacle Detection From Unmanned Surface Vehicles” IEEE
TRANSACTIONS ON CYBERNETICS, VOL. 46, NO. 3, MARCH 2016 pp 641-654

 

8
Javier Hernández-Aceituno, Rafael Arnay, Jonay Toledo, and Leopoldo Acosta,
“Using Kinect on an Autonomous Vehicle for Outdoors Obstacle Detection”, IEEE
SENSORS JOURNAL, VOL. 16, NO. 10, MAY 15, 2016, 
pp. 3603-3610

 

9
K. Guan, Z. Zhong, and B. Ai, “Assessment of LTE-R using high speed railway
channel model,” in IEEE Proc. 3rd Int. Conf. Communications Mobile Computing,
2011, pp. 461–464

 

10
Saikat Ray, David Starobinski, Ari Trachtenberg, and Rachanee Ungrangsi,
“Robust Location Detection With Sensor Networks”, IEEE JOURNAL ON SELECTED
AREAS IN COMMUNICATIONS, VOL. 22, NO. 6, AUGUST 2004, pp 1016-1025

 

11
H.-R. Trankler and O. Kanoun, “Improvement of sensory information using
multi-sensor and model-based sensor systems,” in Proc. IEEE Instrum. Meas.
Technol. Conf. (IMTC), vol. 3. May 2005, pp. 2259–2263

 

12
K. Chakrabarty, S. S. Iyengar, H. Qi, and E. Cho, “Grid coverage for
surveillance and target location in distributed sensor networks,” IEEE Trans.
Comput., vol. 51, pp. 1448–1453, Dec. 2002.

13
S. Meguerdichian, S. Slijepcevic, V. Karayan, and M. Potkonjak, “Localized
algorithms in wireless ad-hoc networks: Location discovery and sensor
exposure,” in Proc. ACM MOBICOM, Long Beach, CA, Oct. 2001, pp. 106–116.

 

14
K. Celik, S.-J. Chung, M. Clausman, and A. K. Somani, “Monocular vision SLAM
for indoor aerial vehicles,” in Proc. IEEE/RSJ Int. Conf. Intell. Robots
Syst., Oct. 2009, pp. 1566_1573.

 

15
M. C. Kang, S. H. Chae, J. Y. Sun, J. W. Yoo, and S. J. Ko, “A novel
obstacle detection method based on deformable grid for the visually
impaired,” Consumer Electronics, IEEE Transactions on, vol. 61, pp.
376-383, 2015

 

16
A. D. Zayas, C. A. G. Perez, and P. M. Gomez, “Third-generation partnership
project standards: For delivery of critical communications for railways,” IEEE
Veh. Technol. Mag., vol. 9, no. 2, pp. 58–68, 2014

 

17
A. Ess, B. Leibe, K. Schindler, and L. van Gool, “A mobile vision system for
robust multi-person tracking,” in Proc. IEEE Conf. Comput. Vis.
Pattern Recognit. (CVPR), Jun. 2008, pp. 1–8.

 

18
Subhan Khan, Mujtaba Hussain Jaffery, Athar Hanif, and Muhammad Rizwan Asif,
“Teaching Tool for a Control Systems Laboratory Using a Quadrotor as a Plant in
MATLAB” IEEE TRANSACTIONS ON EDUCATION, VOL. 60, NO. 4, NOVEMBER 2017, pp
249-256

 

19
J. L. Guzman, S. Dormido, and M. Berenguel, “Interactivity in education: An
experience in the automatic control field,” Comput. Appl. Eng. Educ.,
vol. 21, no. 2, pp. 360–371, 2013.

 

20
P. Bahl and V. N. Padmanabhan, “RADAR: An in-building RF-based user location
and tracking system,” in Proc. IEEE INFOCOM, Tel-Aviv, Israel, Mar.
2000, pp. 775–784.

 

 

 

x

Hi!
I'm Edward!

Would you like to get a custom essay? How about receiving a customized one?

Check it out