TRAIN LOCALIZATION USING OPTIMIZED STA ALGORITHM WITHOUT GPS

ADAPTIVE TRAIN LOCALIZATION USING OPTIMIZED STA ALGORITHM WITHOUT GPS

G. Anand,
Email id- anandg.embedd@gmail.com

Mobile No- +91 9994879483

ABSTRACT

Nowadays a hectic problem around the world is about traffic densities. This is also common to railway sectors too. Recent years we often hearing the word train collision and it bags huge precious human life and time. Travel time information is a vital component of many intelligent transportation systems (ITS) applications. Large cities with fleet of vehicles require a system to determine location of movement of passenger vehicles at a given time. With great passion regarding this issue, this paper deals with the simple sensor based train location identification system using optimized street tracking algorithm. The method introduced in this paper utilizes GPS data only for training purpose and not for testing. So without using GPS train location can be identified in an efficient manner. This method is incorporated with the advanced LPC1768 ARM processor to improve the performance of the processor.

INTRODUCTION

Rail transport is a means of conveyance of passengers and goods by way of wheeled vehicles running on rail tracks. Railways are a safe land transport system when compared to other forms of transport. In contrast to road transport, where vehicles merely run on a prepared surface, rail vehicles are also directionally guided by the tracks they Run on. 

Railway transport is capable of high levels of passenger and cargo utilization and energy efficiency, but it is often less flexible and more capital intensive than highway transport is, when lower traffic levels are considered routes. This has inherent inefficiencies. For passengers most mass transit systems move people in groups overscheduled, time is wasted by waiting for the next arrival, indirect routes to their destination, stopping for passengers with other destinations, and often confusing or inconsistent schedules. Slowing and accelerating large weights can undermine public transport's benefit to the environment while slowing other traffic.

In recent years, the number of passengers travels in train & number of trains in India has increased tremendously. Due to the increase in number of trains the train times may be delayed and the passengers have to wait at railway stations. A desirable strategy to deal with such issues is to provide better service (comfort, convenience and so on) the notification of location. Vehicle tracking is one of the very important issues in this world in recent years. And even train tracking and monitoring is also an important crisis now a days. Because a train collision takes huge amount of human life and creating a massive loss to the railway sector in terms of money and time. So the system what we are 

Fig.1 overall data flow diagram

Proposing here is a real time wireless based, which will track trains through wireless communication, make the communication between each trains through wireless, share their location details between server. In this paper efficient sensor based train location identification is discussed. GPS data is not needed to find the location of a train. This leads to low cost and efficient location identification system. Optimized street tracking algorithm is used to predict the location.

IMPLEMENTATION

In this proposed method, there are 2 modules. They are mobile module (i.e.) android app and hardware module. First, the user needs to install the android app in mobile phone. Hardware module consists of LPC1768 processor and speed sensor. LPC1768 is an ARM Cortex-M3 based microcontroller for embedded applications featuring a high level of integration and low power consumption.

            The LPC1768/66/65/64 operates at CPU frequencies of up to 100 MHz the ARMCortex-M3 CPU incorporates a 3-stage pipeline and uses Harvard architecture with separate local instruction and data buses as well as a third bus for peripherals. The ARM Cortex-M3 CPU also includes an internal pre-fetch unit that supports speculative branching. The peripheral complement of the LPC1768/66/65/64 includes up to 512 kB of flash memory, up to 64 kB of data memory, Ethernet MAC, USB Device/Host/OTG interface, 8-channel general purpose DMA controller, 4 UARTs, 2 CAN channels, 2 SSP controllers, SPI interface, 3 I2C-bus interfaces, 2-input plus 2-output I2S-bus interface, 8-channel 12-bit ADC, 10-bit DAC, motor control PWM, Quadrature Encoder interface, 4 general purpose timers, 6-output general purpose PWM, ultra-low power Real-Time Clock (RTC) with separate battery supply, and up to 70 general purpose I/O pins. The LPC1768/66/65/64 are pin-compatible to the 100-pin LPC236x ARM7-based microcontroller series.           

 

Fig.2 Overall Architecture

RTOS of this processor is capable of identifying location of the train. It uses optimized street tracking algorithm to find the location of a train. Speed of a train is measured using speed sensor. The information about the train is sent to the RTOS processor. Information about the train includes Speed and ID of the train. RTOS processor also gets the two databases from the server. One database consists of train id, arrival time of a train, departure time of a train, starting place, ending place and intermediate stops. Another data base contains the data set which is trained with GPS and speed information. That is relation between speed and GPS position is stored in the database. Using these details RTOS processor calculates the location of a train.

  

                                              Fig.3 Processor side working

To identify the location of a train, first the details (ID, Speed) about the train is sent to the processor. Next, the direction of a train is identified with the help of train ID and time. These details are compared with the data base. By comparing train id and time with the database details staring point and ending point of a train can be retrieved. The location of a train is identified by comparing the speed, time, direction of a train will be compared against the database which is trained using GPS and speed information. This method can be applied for fixed path vehicles. For these vehicles the information about the route or location with speed is stored in the database. Thus we can compare the speed of a train which is enough to track the location of a train. Finally the location identified by the processor with the help of street tracking algorithm is sent to the server using GPRS technology.

Fig.4 Street tracking algorithm

User can get the location information of a train from the server using the app in installed in his/her mobile phone.

Fig.5 Illustration of Street Tracking Algorithm

Fig.6 Location of a train in map view

There are 2 options to get the information about the location. First option is to get location information through SMS and second option is to get information via map. Fig.6 shows the map view of a location a train. In addition to this, ticket booking and other services can be incorporated with the mobile app. This can be very helpful to user who wants to access the railway service.

CONCLUSION

Our proposed navigation system improves the estimation of position without using GPS signal. The STA works based on the GPS and speed information stored in the database. The performance of the proposed system has been verified with experimental data. This system can be applied in a wide range of transportations such as car, railway, etc. where fixed route is followed by the vehicle.

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FUSE NUMBERS

FUSE NUMBERS

Consider X, Y and Z is three variable, To Fuse the three variable into single variable and result does not match to other X, Y and Z combination

Solutions:

X=0:100

Y=0:100

Z=0:100

Normal Fuse method

O=X+Y+Z

This method

Consider X=1, Y=3, Z=3

O=1+3+3= 7

But this result match to

X=1, Y=2, Z=4,

O=1+2+4= 7

(1+3+3)==(1+2+4)

So this not unique for all combination

Another method is binary to decimal converter method

Ex:

0 0 0 = 0

0 0 1 = 1

0 1 0 = 2

0 1 1 = 3

1 1 0 = 4

1 0 1 = 5

1 1 0 = 6

1 1 1 = 7

How is it working?

Three combination X, Y, Z

Max binary counts =2 (0 or 1)

So,

O = 2^2*X + 2^1*Y + 2^0*Z

Finally integer fusion

O = M^2*X+M^1*Y+M^0*Z

Where M is a max number of (X, Y and Z) +1

Here M= 100+1

So, Output is

O = 101^2*X+101^1*Y+101^0*Z

Implemented by MATLAB Program

clc

clear all

close all

X=0:100;        % X is 0 to 100

Y=0:100;        % Y is 0 to 100

Z=0:100;        % Z is 0 to 100

figure(1)

subplot(2,2,1),plot(X,'g-'),title('X value');   % plot  X is linear

subplot(2,2,2),plot(Y,'b-'),title('Y value');   % plot Y is linear

subplot(2,2,3),plot(Z,'y-'),title('Z value');   % plot Z is linear

M=101;

k1=0;

O=zeros(length(X)*length(Y)*length(Z),1);

for i=1:length(X)

    for j=1:length(Y)

        for k=1:length(Z)

            k1=k1+1;

            O(k1)=M^2*X(i)+M^1*Y(j)+M^0*Z(k);       % Add different combination of X, Y and Z,

        end

    end

end

subplot(2,2,4),plot(O,'r-'),title('Output value');  % Output also linear

Result