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Table 2: Simulated Results of MATLB in co-
ordination with PCM Agent
Date &
Time
Road
Status
Locatio
n
Speed
Contro
l
(Y/N)
Dur
atio
n
(min
)
Action
28/06/20
12,
08:00:01
AM
Normal AR N n/a
28/06/20
12,
08:15:02
AM
Normal
(terminatio
n area)
LM Y Msg to HEBS
28/06/20
12,
08:20:10
AM
Normal
(activity
area)
PR Y Msg to HEBS
28/06/20
12,
08:30:17
AM
Congested OP Y Msg to HEBS
28/06/20
12,
08:32:19
AM
Normal OP N 2 Traffic cleared
28/06/20
12,
08:45:45
AM
Normal
(transition
area)
LT Y Msg to HEBS
28/06/20
12,
08:55:19
AM
Normal SL N n/a
28/06/20
12,
09:25:18
AM
Congested
(with
Emergency
Vehicle
PD Y(othe
r
vehicle
s),
N(eme
rgency
vehicle
Msg to HEBS &
RFID tag for lane
clearance
28/06/20
12,
09:27:28
AM
Congested PD Y Lane cleared for
emergency vehicle
28/06/20
12,
09:29:54
AM
Normal PD N 4 Traffic Cleared
0 0 0 0 0
2
0 0 0 0 0 0 0 0
4
00 0 0
5
0 0 0 0
7
0 0 0
11
0 0 00
1
2
3
4
5
8:00
:01
8:20
:10
8:30
:17
8:40
:45
8:52
:19
9:15
:18
9:26
:28
9:29
:54
T ime
Du
rati
on
of
Co
ng
es
tio
n
0
2
4
6
8
10
12
Duration of C onges tion with P C M(Min)
Duration of C onges tionwith MA TL B (Min)
Fig 3: Performance Evaluation of PCM with
MATLB
From the Analysis, it is found out that PCM
Agent in coordination with the MATLB provides
efficient congestion Management and instant
handling of High priority & emergency vehicles. In
case of congestion the PCM Agent consumes
minimum time duration for clearing the vehicle.[5]
7. Related Work
In this paper the MATLB agent that works in co-
ordination with the proposed Mobile Agent PCM is
an approach defined by us in the paper Mobile Agent
Approach for Traffic Load Balancing Using Sensors.
This MATLB agent can be combined to work
together with the PCM agent by providing appropriate road status information. Based on the
information provided by the MATLB agent the PCM
agent controls the speed of vehicles in case of
warning area, activity area, termination area and
interconnection area. The PCM agent also identifies
emergency or high priority vehicles and gives
clearance signal to these vehicles using RFID tags.
The MATLB agent along with SALSA
middleware counts the number of vehicles in each
type in a lane and compares it with a predetermined
threshold value.[3] If the threshold value is less than
the traffic intensity, then the traffic is considered
normal else congested. This agent hence capable of
determining the road status as normal or congested
and this status is provided to the PCM agent for
further processing [5].
According to Vikramaditya Dangi,Amol
N Sudha Bhuvaneswari et al ,Int.J.Computer Technology & Applications,Vol 3 (4), 1545-1549
IJCTA | July-August 2012 Available [email protected]
1548
ISSN:2229-6093
Parab,KshitijPawar & S.S.Rathod, the paper Image
processing Based Intelligent Traffic Controller
applies Image processing Egde detection methods to
implement real time Emergency vehicle detection
without considering the speed control factor.[9]
8. Conclusion
This paper highlights on functioning of Agent
Technology along with sensors, SALSA middleware
and RFID Technology considering real time traffic issues in metropolitan cities. This paper proposed
architecture with Mobile agent PCM in coordination
with MATLB [5] for traffic control and handling high
priority or emergency vehicles effectively. The PCM
agent implemented in this architecture is based on the
combination of two technologies (i) Hall Effect
Based Sensors located in the wheels of the vehicles
for high accuracy speed measurement.(ii)RFID
tagging of traffic signals for controlling the active
signal and clearing the lane for high priority or
emergency vehicles. The experimental study
described in this paper has been carried out using
traffic signals, MATLB agent, PCM Agent, RFID
technology and Sensors. The simulation results
showed that the proposed architecture with PCM
Mobile agent handles unexpected traffic
circumstances successfully by reducing congestion
and time period of congestion clearance thereby providing an effective way for dynamic transportation
services.
9. References
[1]Assaf M.H (2011), RFID for Optimization of Public Transportation System, ISSNIP-2011-The Seventh
International Conferenceon Intelligent Sensors, Sensor
Networks and Information Processing, Adelaide, Australia, [2] Bo Chen, Harry H. Cheng, (2010), A Review of the Applications of Agent Technology in Traffic and
Transportation Systems, IEEE Transaction on Intelligent
Transportation System, Volume 11, No.2.
[3] Cucchiara, A. Prati, R. Vezzani (2005), Ambient Intelligence for Security in Public Parks: the LAICA
Project, Proceedings of IEEE International Symposium on
Imaging for Crime Detection and Prevention, London, UK,
pp. 139-144. [4] Dejan Milojicic (1999), Mobile Agent Applications,
IEEE Concurrency.
[5] T.Karthikeyan, S.Sujatha, N.Sudha
Bhuvaneswari(2012), Mobile Agent Based Approach for Traffic Load Balancing using Sensors, International Journal
of Computer Applications, Volume 47, Number 6.
[6] National Spotlight on Maricopa County Test Site for
High-Tech Traffic Management Prototype,Department of Transportation Maricopa ,2011.
[7]P.Pongpaibool,,P.Tangamchit & K.Noodwong (2007),
Evaluation of Road Traffic Congestion Using Fuzzy
Techniques,Proceedings of IEEE, TENCON ,Taiperi,
Taiwan. [8]N.Sudha Bhuvaneswari, S.Sujatha (2010), Vibrant
Ambient Intelligent System for traffic congestion control in
Coimbatore city(VAISTC4), Proceedings of the 12th
International Conference on Networking, VLSI and Signal Processing, Pg.No. 288-293.
[9] Vikramaditya Dangi, Amol Parab, Kshitij Pawar, S.S
Rathod(2012), Image Processing Based Intelligent Traffic
Controller, Academic Research Journal, Volume 1, Issue 1. [10] Wiroon Sriborrirux,Sorakrai Kraipui,Nakorn Indra-
Payoong (2009), B-VIS Servic –Oriented Middleware for
RFID Sensor Network,World Academy of
Science,Engineering and Technology.
N Sudha Bhuvaneswari et al ,Int.J.Computer Technology & Applications,Vol 3 (4), 1545-1549
IJCTA | July-August 2012 Available [email protected]
1549
ISSN:2229-6093
