A Parallel Road Traffic Simulator Core – Dwi Handoko, Wahju Sediono, Made Gunawan
ISSN 1858-1633 2005 ICTS 103
work is based on the serial traffic simulator developed in this work.
As described previously, the simulation method in the developed traffic simulator is based on lane-by-
lane simulation. This method made easy of porting of the simulator to parallel execution.
In this simulation strategy, the output of the lane with smaller distance number is the input of the lane
with larger distance number. Therefore, lanes with larger distance number have to be simulated first
before the lane with smaller distance number.
However lanes with the same distance number are not affected each other, since they don’t have a direct
connection. Hence, lanes with same distance number can be simulated in parallel.
The simulation steps of parallel execution are as follow:
1. Vehicle generation 2. Took a lane with distance number = DMAX
3. Simulate every body related to the lane, simulated parallel for all lanes with the same
number. 4. If all lanes with same distance number has
been simulated, decrease the distance number, go to step 3
5. If all lanes have been simulated, increase the simulation time, go to step 2.
These steps are shown in Fig.5. The parallel execution steps are in line with steps shown in Fig. 4
except that lanes with the same number are simulated parallel. This characteristic shows also an easy porting
to parallel execution of the simulator program.
Figure 3 only shows one source for vehicle generation. In this case it is easy to determine the
distance number of the lane. However in real problem, there exist several sources of vehicle generator. One
lane may have a distance number of x from one source of vehicle, but it also has a distance number of y from
another source of vehicle. There should a rule for lane number decision. The rule for lane number decision in
a simulation model with several sources for vehicle generation is as follow:
1. Choose one vehicle source 2. Set distance number of lanes based on the
vehicle source 3. If the existing distance number of lane is
larger than the new one, then the existing distance number is used.
4. Choose another vehicle source 5. Go to step 2
Figure 6 shows one example of distance number calculation with two vehicle sources according to the
prescribed steps.
4. EXPERIMENTS AND DISCUSSION
Time components of one time of the proposed parallel scheme execution are as follows:
1. Data distribution time 2. Parallel execution time
3. Data synchronization time
Veh. Source
Distance = 1 Distance = 2
Distance = 2 Distance = 2
Distance = 4 Distance = 3
Distance = 5 Veh.
Source Distance = 2
Fig. 6. Distance number of lanes with two vehicle sources.
D=D-1
Search Lane
Is Distance =D ? D = MAX Distance
Simulation
Fig. 5 Parallel execution of steps
Information and Communication Technology Seminar, Vol. 1 No. 1, August 2005
ISSN 1858-1633 2005 ICTS 104
Data distribution time is the time required to distribute data to each processor, while the data
synchronization time is the time required to gather data and synchronized the data. Vehicle moving
from one lane to another lane is done in this period. So its clear that exist additional time of simulation
in parallel. Therefore, in such a heavy traffic condition that number of vehicles is much more
than number of lanes, this parallel scheme will show its merit, since there much time can be saved
in the parallel execution time compare to additional data distribution and data synchronization time.
However, there still exist place that should be optimized for parallel execution. If there are many
of lanes, the lane searching process may require a considerable of process time. Therefore in order to
more optimize the speed of parallel execution, lane search method should also be parallelized. Several
parallel search algorithms exists as candidates such parallel tree search etc.
A computer program for the proposed serial algorithm has been developed. The core of the
simulator has a text output. The graphical view is developed separately from the simulator core, and
is used to verify the simulation. In the simulation result shown in Fig. 7, here, a proper traffic
simulation based on the proposed algorithm can be verified.
5. CONCLUSION
A traffic simulator that able to be performed in parallel is described. It works based on lane-by-lane
execution. The developed parallel simulator is useful to simulate such heavy traffic condition with
vehicles distributed to all of the lanes. The developed parallelization strategy is directly
adapted from serial one, therefore it is easy to port the serial to parallel. A serial computer program based on the
proposed algorithm has been developed and its proper performance can be verified.
We are currently developing the parallel computer program. In the near future we will also add function of
calculating the lane throughput and other traffic analysis functions to the core.
REFERENCE
[1] D. Handoko, “Desain Simulator Kendaraan”, Proc. KOMMIT, B-16 – B20, 2002
[2] D. Handoko, “Pertimbangan Perilaku dalam Pemodelan Pengikutan Kendaraan untuk
Simulator Trafik Kendaraan”, Proc. Semiloka Teknologi Simulasi dan Komputasi serta Aplikasi
2003, pp. 43-46, 2003.
[3] W.Sediono, D. Handoko, Pemodelan dan Simulasi Antrian Kendaraan di Gerbang Tol, Proc.
Semiloka Teknologi Simulasi dan Komputasi serta Aplikasi 2004, pp. 11-14, Nov.,2004.
[4] Staffan Algers, Eric Bernauer, Marco Boero, et all, “Review of Micro-Simulation Models”, Smartest
Project, 8 August 1997. http:www.its.leeds.ac.ukprojectssmartest
[5] Agathoeles G et al, “Using Clusters for Traffic Simulation”,
http:www.lpds.sztaki.hupgradepgrade_downloa dopatija_2003.pdf, June, 2005.
[6] Schaerf A., Shoham Y., Tennenholtz M., “Adaptive Loadbalancing: A study in Multi-Agent
Learning”, Journal of Artificial Intelligence Research , May 1995
Information and Communication Technology Seminar, Vol. 1 No. 1, August 2005
ISSN 1858-1633 2005 ICTS 105
CI PERFORMANCE COMPARISON OF AN ADAPTIVE AND SWITCHED BEAM IN THE GSM SYSTEMS EMPLOYING SMART ANTENNA
Tito Yuwono
1
, Mahamod Ismail
2
, Zuraidah bt Zainuddin
2
1
Department of Electrical Engineering Universitas Islam Indonesia UII Jogjakarta INDONESIA
2
Department of Electrical, Electronics and System Engineering Universiti Kebangsaan Malaysia, 43600 UKM Bangi Selangor, MALAYSIA
Email:titoyuwonoyahoo.com ,mahamod.ukm..my,
zuraidahyahoo.com
ABSTRACT
The continuous increment of mobile communication subscribers, require a higher capacity
and spectrum efficiency of the cellular system especially in an urban area where the traffic density is
high. In order to accommodate the increasing number of subscribers, smart antenna technology is used to
improve system’s performance, which could increase the number of call and data transmission as well as
simplifying the networking management. The basic principle of a smart antenna is to control and reduce
the effect of interference to provide the user with the highest quality of uplink and downlink signal. This
can be achieved by using narrow beams with signal processing capability at the base sites and to optimize
its radiation pattern, which automatically updated according to MS movement or as signals conditions
change. In this paper, two types of smart antenna were considered, which are the switched beam systems and
adaptive antenna systems. The significant difference between these two systems is from the beamforming
point of view. The key part of this study is to develop a simulator for adaptive antenna systems AAS and
switched beam systems SBS based on GSM 900 in order to determine the factors that could affect
network’s quality and capacity. Simulation results of different techniques are compared and the
performance evaluations of CI are discussed. Results show that the AAS gives a better performance
compared to SBS, with average improvement of CI around 5 to 10 dB.’
Keywords : CI, Smart Antenna, Adaptive, Swithched beam, GSM
1. INTRODUCTION
