International Journal On Engineering Technology and Sciences – IJETS™
ISSN (P): 2349-3968, ISSN (O): 2349-3976
Volume 2 Issue 4, April -2015
Mobile Phone based Roadway Transport System
using Participatory Sensor
K.SEDHURAMAN
PG Scholar,
Department of CSE,
Velalar College of Engineering
And Technology,
Thindal,Erode-638052,India.
Mail id:sedhuraman29@gmail.com
MRS .R.KAVITHA M.SC., M.PHIL., M.E.,
Assistant Professor [Sr.Gr],
Department of CSE,
Velalar College of Engineering
And Technology,
Thindal,Erode-638052,India.
Mail id:rkavi_baskar@yahoo.co.in
ABSTRACT--The roadway transport system is the primary information to most travelers. Terribly
long waiting time at bus stop often discourage the customers and makes them hesitate to take
buses. This paper introduces a new approach called a roadway transport prediction system based
on bus passengers participatory sensing. By using smart phones, we can collect the passenger’s
information to estimate the roadway travelling routes and predict the transport arrival time at
various bus stops. The proposed system is mainly based on the endeavor of the participating users
and is self-determining from the bus operating system so that we can survey by own, without
requesting support from particular roadway operating companies. As a replacement for referring to
GPS enabled information, by help of energy efficient sensing resources, including cell tower
signals, movement status which bring less trouble to the participatory party and encourage the
customers. By using the participatory sensing technique which suggests the easy operation of our
system. At the similar time, the future result is more generally available and energy content.
Index Terms—Participatory Sensing, Cell Tower Signals, Smarts Phone, GPS.
1.
INTRODUCTION
Roadway transport system, especially the bus
transport has well grown in many parts of the
world. When the customer is travelling in bus,
they usually want to know the exact time and
route of the bus. For such services, however
customer usually requires the bus operating
companies to install some tracking devices. This
System uses a method called crowd-participatory
sensing based on the bus arrival time and routing
system. Most of the customers really want to track
the arrival time of the buses in short time So, that
create a design “crowd participated service” who
want to know about the bus arrival time and
routing system(querying users).To reach the goal,
the bus customers themselves cooperatively sense
the bus route information using android phones.
The roadway transport system consists of three
major components:
lightweight cellular signals to storage area. At
first, its starts to collect a chain of nearby cell
tower IDs. The collected data is transmitted to the
server via cellular networks. Since the distribute
customer may travel with different means of
transport, the mobile phone needs to first detect
whether the current user is on a bus or not. the
android mobile phone occasionally samples the
neighboring environment and extracts identifiable
features of transit buses. Once the android phone
confirms it is on the bus, it starts sampling the cell
tower sequences and sends the sequences to the
storage area . Ideally, the mobile phone of the
distribute customer automatically performs the
data collection and transmission without the
manual input from the distribute customer.
Distribute customer: using android phones as well
as build-in-sensors to sense and report the
Query user: querying for the bus arrival time and
route for particular bus with the mobile phones.
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International Journal On Engineering Technology and Sciences – IJETS™
ISSN (P): 2349-3968, ISSN (O): 2349-3976
Volume 2 Issue 4, April -2015
Querying user queries the bus arrival time by
sending the request to the Storage area.
Bus identification: since the distribute customer
may travel with the means of transport, first their
mobile phones accurately detect or not, whether
the current user is on the bus then it will
automatically the useful information.
Bus classification: In bus classification need to
carefully classify the bus route information from
the mixed reports of participatory customers.
Information Assembling: Single distribute
customer may not stay on single bus to collect
plenty time period of information. inadequate
amount of uploaded information may result in
inaccuracy in predicting the bus route and timing.
An valuable information assembling strategy is
required to solve the jigsaw puzzle of combining
pieces of incomplete information from multiple
users to intact the bus route status.
II.
present bus location, number of association,
number of intersection, customer stipulate at each
stop and traffic position of the urban network, etc.
The rule is built to check the practicability and
efficiency of the approach, by the support of
history of bus arrival time predicting system.
Scalable Sound Sensing For People-Centric
Applications on Android Phones
Smart phones include a number of
focused (e.g., accelerometer, GPS) and general
purpose sensors (e.g., microphone, camera) which
enable new crowd-centric sensing applications.
Possibly unexploited sensor on mobile phones is
the microphone a great sensor which building
comfortable inferences about human movement,
location, and social actions from sound. This
Sound Sense, a scalable structure for sound
actions on smart phones. Sound Sense is
implemented on the Apple iPhone and particularly
considered to work on resource limited phones.
The scalability and SoundSense uses a together of
supervised and unsupervised learning techniques
specially creates architecture and algorithms to
classify both general sound types (e.g., music,
voice) and separate sound events specific to
individual users. The structure runs exclusively on
the mobile phone with no back-end interactions.
By the implementation and estimate of two proof
of concept crowd- centric sensing applications,
SoundSense is capable of recognizing meaningful
sound events that occur in customers’ everyday
lives.
RELATED WORK:
Mobile Phones
Matching
Using
Cell-Id
Sequence
CAPS is a Cell-ID Aided positioning
System. CAPS gives the record of a customer to
achieve considerably enhanced accuracy than the
cell tower based system, while keeping power
simplicity low. CAPS is considered based on the
imminent that customers show uniformity in
routes travelled. By the cell-ID transition points
that the customer frequently travelled in that
route, which helps to identify the place uniquely.
CAPS uses a cell-ID sequence method to predict
the present position based on the record of cell-ID
and GPS position sequences that similar to the
present cell-ID sequence. CAPS should be in the
Android based smart phones. The outcome of the
CAPS can accumulate more than 90% of the
energy spent by the positioning system.
Rate-Adaptive GPS-Based Positioning For
Android Phones
Now-a-days
emerging
Smartphone
applications require position information to
provide location-based aware services. GPS is
often preferred because it gives the accurate
results when compared to GSM/Wi-Fi based
positioning system, but the drawback GPS is
extremely power hungry and GPS provides less
accuracy in urban areas. A key requirement is
needed for the positioning system that provides
accurate position information with minimal
energy for that we need a new approach called
Prediction Based On Traffic Information
Management System
By the systematic approach, we can
easily predict the public bus arrival time system
based on the traffic information system. This
approach consider number of causes that makes
bus travelling time difficult such as leaving time,
69
International Journal On Engineering Technology and Sciences – IJETS™
ISSN (P): 2349-3968, ISSN (O): 2349-3976
Volume 2 Issue 4, April -2015
RAPS, rate-adaptive positioning system for smart
phone applications. RAPS uses a group of
techniques to smartly determine when to turn on
GPS. With the help of record of the user is to
evaluate the user velocity and adaptively turn on
GPS and also powerfully evaluate user
movement using a duty-cycled accelerometer, and
preferred the Bluetooth communication to reduce
position ambiguity among neighboring devices.
Finally evaluate RAPS through real-world
experiments using a prototype implementation on
a modern smart phone and show that it can
increase the lifetime of mobile phones than the
system using GPS.
are short, while by concatenating the two cell
tower sequences the backend server may obtain an
adequately long cell tower sequence which can be
used for more accurate bus classification. A
simple way of concatenating the cell tower
sequences is to let the mobile phones of
distributing passengers locally communicate with
each other (e.g., over Bluetooth) [20]. This
approach, however, mandates location exposure
among distribute passengers and might raise
privacy concerns. We thereby move such a job to
the storage area.
III. IMPLEMENTATION
Implementing the participatory sensing for that:
Bus identification: since the distribute customer
may travel with the means of transport,first their
mobile phones accurately detect or not,whether
the current user is on the bus then it will
automatically the useful information.
Bus classification:In bus classification need to
carefully classify the bus route information from
the mixed reports of participatory customers.
Fig.3.1 Time Predicting System
Recall that the mobile phone needs to collect
audio signals for bus detection. Here, we reuse
such information to detect whether the sharing
passengers are on the same bus for cell tower
sequence concatenation.
Information Assembling: Single distribute
customer may not stay on single bus to collect
plenty time period of information. inadequate
amount of uploaded information may result in
inaccuracy in predicting the bus route and timing.
An valuable information assembling strategy is
required to solve the jigsaw puzzle of combining
pieces of incomplete information from multiple
users to intact the bus route status.
Arrival Time Prediction
After the cell tower sequence matching, the
backend server classifies the uploaded
information according to different bus routes.
When receiving the request from querying
users the backend server looks up the latest bus
route status, and calculates the arrival time at the
particular bus stop. The server needs to estimate
the time for the bus to travel from its current
location to the queried bus stop. Suppose that the
sharing user on the bus is in the coverage of cell
tower 2, the backend server estimates its arrival
time at the bus stop according to both historical
data as well as the latest bus route status. The
Cell Tower Sequence Concatenation: Solving
Jigsaw Puzzles
In many practical scenarios, the length of
the cell tower sequence obtained by a single
distribute customer , however, may be lacking for
exact bus route classification. An sensitive idea is
that we can concatenate several cell tower
sequences of different distribute customer on the
same bus to form a longer cell tower
sequence,both cell tower sequences of A and B
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International Journal On Engineering Technology and Sciences – IJETS™
ISSN (P): 2349-3968, ISSN (O): 2349-3976
Volume 2 Issue 4, April -2015
[4]
server first computes the dwelling time of the bus
at the current cell (i.e.,cell 2 in this example)
denoted as t2. The server also computes the
traveling time of the bus in the cell that the bus
stop is located denoted as tbs. The historical
dwelling time of the bus at cell 3 is denoted as T3.
The arrival time of the bus at the queried stop is
then estimated as follows,
T = T2 − t2 + T3 + tbs.
Without loss of generality, we denote the dwelling
time in cell i as Ti, 1 ≤ i ≤ n, the bus’s current cell
number ask, and the queried bus stop’s cell
number as q. The server can estimate the arrival
time of the bus as follows,
T =q−1_i=k
Ti − tk + tq.
The server periodically updates the prediction
time according to the latest route report from the
sharing users and responds to querying users. The
querying users may indicate desired updating
rates and the numbers of successive
bus runs to receive the timely updates
[5]
[6]
[7]
[8]
[9]
[10]
[11]
[12]
IV. CONCLUSION
This approach mainly based on
a
participatory sensing technique called
“Roadway transport bus arrival time and route
prediction system. Finally the proposed
system provides cost-efficient solutions to the
problem. In this approach evaluate the timing
and route through an Android prototype
system. Being self-determining of any support
from tracking agencies and location services,
the proposed scheme provides a flexible
framework for participatory contribution of
the community.
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