THE COMPUTERIZATION OF TRANSPORTATION: Sophisticated Systems Incorporating IT in the Mobility of People and GoodsEVALUATION OF A DYNAMIC SIGNAL OPTIMISATIONCONTROL MODEL USING TRAFFIC SIMULATIONSuphasawas NIGARNJANAGOOL

Hussein DIAIntelligent Transport Systems Research Laboratory
The University of Queensland
Brisbane, Australia

Intelligent Transport Systems Research Laboratory
The University of Queensland
Brisbane, Australia(Received December 15, 2004)
The objective of this paper is to demonstrate the feasibility of implementing a traffic signal optimisation model to improve real-time operationsof traffic control systems. Advanced computer algorithms and traffic optimisation techniques can provide benefits over existing systems by reducing
delays, improving travel times and reducing environmental emissions. The feasibility of the proposed approach is demonstrated by interfacing thetraffic signal optimisation model to a microscopic traffic simulation tool, which enabled the evaluation of the benefits of the algorithm using computers
in a controlled environment without disrupting traffic conditions. The main advantage of the proposed algorithm is its ability to detect dynamic changes
in traffic flow conditions by using short-term historical demand data obtained from upstream vehicle loop detectors. The experimental results for under-saturated traffic conditions showed that the algorithm’s performance was superior to optimal fixed time control. The results also confirmed that as
traffic volumes reach saturated conditions, the performance of the algorithm decreased but remained better than what can be achieved by fixed time
control systems.
Key Words: Traffic signal control and optimisation, Microscopic traffic simulation, Adaptive Traffic Management

1. INTRODUCTION
There is sufficient evidence in the literature to suggest that the application of advanced technologies including computers, electronics and communications cancontribute to improving traffic conditions, enhancing environmental quality and increasing economic productivity. Collectively known as Intelligent Transport Systems,
these technologies are rapidly being accepted by road andtransport authorities around the world as a viable alternative to reliance on building more roads to reduce congestion. Advanced Traffic Management Systems (ATMS),
in particular, have been shown in many experiments
around the world to reduce travel times, improve travel
time reliability for public transport systems, improve network speeds, reduce environmental emissions and congestion. The benefits of these systems have also beenfound to be a function of the accuracy and robustness of
the underlying computer algorithms and optimisation
techniques which provide various levels of intelligenceto traffic signal control and operations.
Traffic signal control is generally defined as“power-operated traffic devices which alternatively directtraffic to stop and to proceed”. More specifically, traffic
signals are used to control the assignment of right-of-way
at locations where conflicts exist or where passive de-22 • IATSS RESEARCH Vol.29 No.1, 2005

vices, such as signs and markings, do not provide the necessary flexibility of control to properly move traffic in asafe and efficient manner. Traffic signal control is a key
determinant for efficient operation of the urban street network and as such is considered an essential element ofIntelligent Transport Systems.
Many cities around the world still implement fixedtime control traffic signal systems. These systems operate on a number of fixed or predetermined plans which
are put in operation at different times of the day e.g., one
set of plans would operate during morning peak, anotherduring off peak and a third set of plans during evening
peak hours. The disadvantage of these systems is that they
do respond to changes in traffic demands and assume that
traffic conditions during each of these periods will notchange. If an accident happens between any two set ofintersections, the traffic signal does not have the capacity to detect these changes or respond to such events.
Advances in computer technologies and communications systems have now allowed for the introduction ofvarious levels of intelligence in these systems by enablingthem to collect traffic data about flows, speeds and travel
times to enable the system to respond to incidents, accidents, road works and other events that may reduce thecapacity of the road system and contribute to congestion.
Examples of such technologies include commerciallyavailable adaptive traffic management systems such as

EVALUATION OF A DYNAMIC SIGNAL OPTIMISATION CONTROL MODEL USING TRAFFIC SIMULATION

2. REVIEW OF SIGNAL OPTIMISATION MODELSOver the years, traffic engineers have used several
methods for designing pre-timed isolated signals. For example, Homburger and Kell’s method utilised traffic volumes as the basis for allocating times to approaches withthe constraint of keeping off-peak cycles as short as possible (40 to 60 seconds)7. The highway capacity method,on the other hand, determines the traffic signal cycle
length based on the capacity of lane groups8.
The well known method which influenced the Australian and U.K. signal design practice is Webster’s
method3, which he introduced to obtain an optimum cycle
time that produces minimum delays to vehicles. He proposed the use of a “lost time” parameter (which represents the time lost before vehicles start to move) and thesaturation flow (which is the maximum rate of discharge).
The Webster method has since become a traditional technique to design signal timings for isolated intersections
both in Australia and overseas4,5,9.
In 1984, Akcelik4 introduced several changes to theoriginal formulation. The main modification included

changing the core concept of ‘phase-related’ methods to‘movement-related’ techniques. Consequently, an important aspect of this change was the use of ‘movement losttime’ instead of ‘phase lost time’ which led to a definition of the intersection’s lost time as ‘the sum of criticalmovement lost times’ rather than ‘the sum of phase lost
times’. This new approach also facilitated a clearer understanding of the relationships between movement andsignal phasing characteristics.

3. MOVEMENT-BASED SIGNAL TIMINGOPTIMISATION MODEL
This study applies the movement-related method
proposed by Akcelik to determine traffic signal cycles.
A brief explanation of the approach, as implemented inthis study, is presented next. Unfortunately, a full discussion of the technique is outside the scope of this paper
but the reader is referred to Akcelik4 for a detailed explanation of the method.
3.1

Movement characteristics
The basic movement characteristics are illustrated
in Figure 1. The main movement parameters are saturation flow, effective green time and lost time. The model
assumes that when the signal changes to green, the flow
across the stop line increases rapidly to saturation flowwhich remains constant until either the queue is exhausted
or the green period ends. As indicated by the dotted line
in Figure 1, the model replaces the actual departure flow
curve by a rectangle of equal area whose height is saturation flow (s) and width is the effective green time (g).
Rate of discharge queue
in a fully saturated green period

SCATS and SCOOT in addition to other technologies
being developed in research institutions such as Agentbased traffic management systems being developed in the
ITS Research Laboratory at the University of Queensland1
and the Traffic Responsive Urban Control (TRUC) system being developed at the Dynamic Systems and Simulation Laboratory, Technical University of Crete in Greece2.
The objective of this paper is to contribute to such researchand development efforts by demonstrating the applicationof a signal optimisation model, which is generally used
to design pre-timed traffic signal timings, to real-time
network-wide optimisation of traffic conditions. Interestingly, the origins of this signal optimisation model arefound in the work by Webster in 19583. However, themodel was initially developed for operation in under-saturated conditions4. A number of researchers introduced
several changes to the model over the years and a variant of the model (described below) is currently implemented in the well-known aaSIDRA4,5 software.
To demonstrate the applicability and potential of thetechnique, this study evaluated the algorithm using a microscopic traffic simulation model, AIMSUN6, on a realworld network in Brisbane comprising 28 signalised intersections. A brief description of the AIMSUN modelis provided in Section 4.

S. NIGARNJANAGOOL, H. DIA

Start lag, a

Effective green time, gSaturation flow, s
End gain = end lag = b
Start loss

Time

Displayed green time, GInter-green, I

Phase A
Phase B

Fig. 1 Basic movement characteristics3-5

The start and end times of the effective green period for a movement are best defined with reference to
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THE COMPUTERIZATION OF TRANSPORTATION: Sophisticated Systems Incorporating IT in the Mobility of People and Goods

phase change times. Start lag (a) is defined as the sumof the movement inter-green time (I) and start loss, andend lag (b) is defined simply as the end gain. The difference between the start and end lag time is defined as
movement lost time (l). It should be noted that the movement inter-green time is the inter-green time of the starting phase of the movement.
l = a – b ...................................................................... (1)From Figure 1, the relationship between the displayed green time (G) and the effective green time (g) is
g + l = G + I ............................................................... (2)The sum of all phase inter-green and green times
is the cycle time (c):c = Σ (I + G) ............................................................... (3)From Equation 2 and Equation 3, the similar relationship holds for movement parameters is:c = Σ (g +l) ................................................................. (4)where the summation is for critical movements.
The movements which determine the capacity andtiming requirements of the intersection are called critical movements. Sufficient time must be allocated to eachcritical movement to meet its capacity requirements inorder to give all movements sufficient capacity. The technique to identify the critical movements is explained in
Section 3.2.
The time allocated to a movement is the sum of effective green time (g) and lost time (l) and is given by:
t = g + l = I + G ......................................................... (5)The required movement times can be calculated from:t = 100 u + l ............................................................... (6)100 is the first estimate of cycle time and u is therequired green time ratio which is calculated to achieve
maximum acceptable (practical) degree of saturation (xp),and is given by:
u = y / xp ..................................................................... (7)Movement flow ratio (y) is the ratio of arrival flow(q) to saturation flow (s) is given by:
y = q / s ....................................................................... (8)It must be noted that the movement time calculated
from Equation 6 must satisfy the sum of fixed minimum
effective green time and lost time as:t = gm + l .................................................................... (9)The minimum displayed green time (Gm), therefore,
can be calculated from the relationship:
gm + l = Gm + I ......................................................... (10)
3.2

Critical movement identification
The identification method is based on the comparison of the required movement time (t) values. If all movements were non-overlap movements, there would be one

24 • IATSS RESEARCH Vol.29 No.1, 2005

critical movement per phase. This would be the movement which requires the longest movement time in thephase. For the overlap movements, their movement time
includes the green and inter-green times of all phases during which it has right of way. The overlap movement is
critical if its t value is longer.
This method requires a phase-movement matrix asshown in Table 1, and a critical movement search diagram as illustrated in Figure 2. In the diagram, the nodes
correspond to phase change events, and the links to movements.
Table 1 Phase-movement matrix
Movement

Starting
Phase

Terminating
Phase

1

A

C

2

A

B

3

B

C

4

C

A

1

A

B

2

C

3

A

4

Fig. 2 Critical movement search diagram

Then, intersection lost time (L), intersection green
time ratio (U) and intersection flow rate (Y) which are
the summation of respective critical movement parameters are calculated.
3.3

Cycle and phase timing determination
The optimum cycle time, which minimises a performance measure that is a function of total delays andnumber of stops for all critical movements at an isolated
intersection, is calculated from the following formula:co = (1.4+k) L + 6 / (1 – Y) ...................................... (11)where k = K/100 is the stop penalty parameter. This
study selected a value of k which is commonly used tominimise cost (k = 0.2). The practical cycle time which
ensures that the degrees of saturation of all movements
are below specified maximum acceptable degree of saturation, x < xp, is calculated from the following formula:cp = L / (1 – U) .......................................................... (12)This paper selected a default value of xp = 0.9.

EVALUATION OF A DYNAMIC SIGNAL OPTIMISATION CONTROL MODEL USING TRAFFIC SIMULATION

For a given cycle time (c) and total available green
time (c – L), the available green time can be distributed
to critical movements according to the formula:g = u (c – L) / U ........................................................ (13)For non-overlap movements, where the overlap
movement is critical, the green time for non-overlap
movements can be calculated by treating the criticalmovement time as a sub-cycle time, c* = gc + lc, wheregc and lc are the critical movement green and lost times.
Then available green time is (c* – L*), where L* is thesum of non-overlap movement lost times. The green time
distributed to non-overlap movement is as follows:g = u (c* – L*) / U* .................................................. (14)By modifying Equation 5, the displayed green time
for a phase can be calculated from:G = (g + l) – I ............................................................ (15)where (g + l ) is the time allocated to a movement
which receives right of way during the phase only, and I
is the inter-green time of that phase.
Having introduced the reader to the theoreticalbackground of the algorithm, the next section of this paper presents the application of this algorithm within microscopic traffic simulation to evaluate its performance
under a variety of traffic conditions and scenarios.

4. APPLICATION DEVELOPMENT
A number of commercially available traffic simulation models provide facilities to interface the simulator to an external algorithm (such as the one being tested
in this study) to enable a full evaluation of its performance
under controlled conditions. For example, PARAMICS10
provides a Programmer module and AIMSUN6 provides
GETRAM Extensions for developing a plug-in module
in C++ code. This study employed the AIMSUN for developing, testing and evaluating the application. This section presents the model development, data requirements
and traffic modelling tasks needed to set up the simulator and interface it to the signal optimisation model.
4.1

Model development and data requirements
This study used the Brisbane Western Corridor
Traffic Simulation Model as a test-bed for evaluating theperformance of the signal optimisation model. The modelled traffic network, shown in Figure 3, was developed
based on the year 2002 traffic conditions. The study area
was about 3.1km (East-West) and 2.1km (North-South)
or approximately 6.5 square kilometres. The lengths of

S. NIGARNJANAGOOL, H. DIA

the major routes in the study area were 3.3 kilometres forCoronation Drive and 3.2 kilometres for Milton Road.
Hale Street
WESTERN CORRIDOR MODEL
Riverside Expressway
Milton Road
Western Freeway
Coronation DriveNorth

Moggill Road

Fig. 3 Schematic of the Brisbane Western Corridor
Model

Microscopic simulation is characterised by a highlevel of modelling detail. The accuracy of the model will
depend on the availability and quality of the input data.
The following data were collected and used for model development in this study:
Network Layout – Digitised maps (DXF format) and raster images showing locations of both signalised and unsignalised intersections, possible turning movements foreach intersection, recommended turning speeds for eachmovement, visibility distances at junctions, presence ofStop or Yield signs and detector positioning were required. For each section, the road centrelines, number oflanes in each section of road, width of each lane, reserved
lanes (where entry is allowed only for certain vehicle
types), restrictions on lane changes (solid line markings),
maximum speed for each section (or each lane if necessary), capacity (vehicles per hour for use in cost functions
for the calculation of shortest paths), visibility distances,length of each section, slope and lane changing distanceswere also required. A schematic of the network is shownin Figure 3.
Traffic Demand Data – In AIMSUN, traffic demand data
can be defined either by the combination of traffic flows
at input sections and turning proportions at intersections
or by Origin-Destination (O-D) matrices. For this study,
the traffic demand on the network was represented by OD matrices related to centroids. The number of trips between each O-D pair were required for each vehicle typein time-sliced intervals (e.g., 15 minutes). The O-D maIATSS RESEARCH Vol.29 No.1, 2005 •25

THE COMPUTERIZATION OF TRANSPORTATION: Sophisticated Systems Incorporating IT in the Mobility of People and Goods

trices gave the number of trips from each origin centroid
to each destination centroid, for each time slice (e.g. 15
minutes) and for each vehicle type.
As was discussed previously, basic movement characteristics including inter-green (I), minimum displayed
green time (Gm), saturation flow (s) and practical degreeof saturation (xp) are all required as inputs to the model.
Default values were used for these inputs4. A value of
0.9 was selected as practical degree of saturation for all
movements of all intersections.
The application also required the input of traffic
volumes for the network. The real-time traffic volumes
were obtained by vehicle loop detectors located at thestop-line of every signalised intersection. However, inorder to optimise signal timings for the next cycle, thepredicted traffic volume is also required. The application
used the average traffic volumes from three previous
cycles to capture the change in traffic condition.
The Origin-Destination (O-D) matrix used in thisstudy was for the morning peak starting from 6:45 A.M.
to 8:45 A.M.
Simulator settings
The use of traffic simulation software requires thatseveral simulation parameters are specified. These include
important parameters related to car following and lanechanging algorithms and other global parameters that control the conduct of the experiments. Some of the important parameters used in the study included reaction time,
simulation step per second and reaction time at stop. Allof these parameters were assigned a value of 1 second.
For route choice, a fixed distance was selected as the default for all experiments. This meant that route choice was
not an option and that all vehicles between origins anddestinations followed the same route. The simulation was
run for five replications to ensure statistical reliability.
The simulation time was two hours with a 30-minutewarm-up period to populate the network with vehicles
and allow traffic conditions to stabilise before collectionof statistics on performance.

ate its performance. The signal optimisation algorithm
was evaluated in two different scenarios. First, the algorithm was used to control the isolated intersections assuming that the effects of upstream and downstream
traffic from adjacent intersections are negligible. Second,
the algorithm was applied to control the 17 signalised intersections on Coronation Drive (which is the main routeconnecting the western suburbs to the CBD as was shownin Figure 3).
5.1

Scenario 1: Isolated junction without effects
from adjacent intersections
The Toowong junction in Figure 4 was selected
since it had a relatively large traffic volume and one of
its approaches is a short link (shown as approach number 1 in Figure 4). In the morning peak, there is large traffic volume moving from Approach 1 and Approach 3
through this intersection to city.

4.2

5. EVALUATION OF THE DYNAMIC SIGNAL
OPTIMISATION ALGORITHM USING
TRAFFIC SIMULATION
Having developed, calibrated and validated the traffic simulation model, the next task was to interface thesignal optimisation model to the traffic simulator to evalu-

26 • IATSS RESEARCH Vol.29 No.1, 2005

To Brisbane City
To Indooroopily
2
1

Toowong Junction

3
To University of Queensland

Fig. 4 Toowong junction in Brisbane

The performance of the dynamic signal optimisation
model was compared to an optimum fixed cycle time setting obtained by aaSIDRA10. The comparative results areshown in Figures 5 and 6 where the dotted and solid lines
represent dynamic and optimum fixed time control, respectively. The simulations were conducted for two hours
simulation time with v/c = 1.0 (i.e the volume was set equal
to capacity) and the majority of traffic was moving fromapproach 1 to the city (left turn traffic from 1 to 2).
The plots shown in Figure 5 clearly show how the
dynamic cycle control algorithm increased the throughput across the intersection, especially during medium toheavy conditions, when compared to fixed time control.
This demonstrates how simple improvements to the underlying core computer algorithms which control the operation of traffic signal systems can produce substantial

130

S. NIGARNJANAGOOL, H. DIA

Average delay (seconds)

Number of vehicles

140

Approach 1

Approach 2

Time of Day

120

a. Comparison of average delay time (seconds)
Average speed (Km/h)

110
100
90
80
260
240

Approach 3

220
200
180

Time of Day

160

b. Comparison of average speed (km/h)

140
6:50

7:00

7:10

7:20

7:30

7:40

7:50

8:00

8:10

8:20

8:30

8:40

Time of Day
Dynamic Cycle Time Control
Optimum Fixed Time Control (aaSIDRA)

Fig. 5 Comparative results of dynamic and optimum
fixed cycle time by approach

Average number of stops

Number of vehicles

180
170
160
150
140
130
120
110
100

Number of vehicles

EVALUATION OF A DYNAMIC SIGNAL OPTIMISATION CONTROL MODEL USING TRAFFIC SIMULATIONTime of Day

benefits without the need to spend large amounts of funds
to build or construct new roads to improve capacity.
Figures 5 and 6 also show that dynamic cycle time
control was superior to optimum fixed time control as
traffic volumes started to increase (e.g., around 7:45
A.M.). In Figure 6(a), the dynamic cycle time control reduced delays by approximately 20 percent and produced
higher average speeds as shown in Figure 6(b). In the beginning of simulation, there were few vehicles entering
the junction. Under fixed time control most of the vehicles were able to pass through the junction without
queuing. Since the dynamic cycle time control attempts
to optimise cycle time based on predicted traffic volumes,
the cycle times during the beginning periods resulted ina larger number of stops as shown in Figure 6(c).
The dynamic cycle time control initially attempted
to provide sufficient time for the major movements topass through the junction. This clearly resulted in queues
forming at the other approaches with less demand, wheresome of the vehicles on the minor approaches waited formore than a cycle to clear the intersection. The numberof stops under dynamic cycle time control was reduced
to less than two.

c. Comparison of number of stops
Dynamic Cycle Time Control
Optimum Fixed Time Control (aaSIDRA)

Fig. 6 Comparative results of dynamic and optimum
fixed cycle time control

In fact, it was observed that the controller was reducing the queue length by a small amount in every cycleby adjusting the cycle time and phase proportions to suitthe new traffic demands for the next cycle. In otherwords, while fixed time control was operating the intersection using the same cycle and phase time proportions,the dynamic cycle time control was adjusting the time andproportions to suit the current demand for all movements
as shown in Figures 7 and 8.
Figure 9 presents sensitivity analysis results for thetwo control strategies when the degree of saturation wasvaried from light traffic (v/c= 0.4) to over saturated traffic conditions (v/c > 1.0). These figures show that dynamic cycle control which uses real-time traffic volumes
to determine signal timings produced less delay in lighttraffic conditions (v/c = 0.4 to 0.7). However, both control strategies performed in a similar manner when traffic volumes approached capacity.
IATSS RESEARCH Vol.29 No.1, 2005 •

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136
134

Average delay (seconds)

Cycle time (seconds)

THE COMPUTERIZATION OF TRANSPORTATION: Sophisticated Systems Incorporating IT in the Mobility of People and Goods

132
130
128

6:50

7:20

7:50

8:20

Time of Day
Dynamic Cycle Time Control
Optimum Fixed Time Control (aaSIDRA)

Fig. 7 Variability in cycle times between 6:45 A.M. to
8:45 A.M.
Degree of saturation
100%

a. Average delay for variable degrees of saturation

80%
70%

Phase A

45

50%

Phase B

40%

19

30%
20%

Phase A

10%

36

Number of stops

60%

0%

Dynamic Cycle TimeaaSIDRA

Fig. 8 Phase time proportions
Degree of saturation

b. Average number of stops for varied degrees of saturation

5.2

Scenario 2: Commuting corridor with effects
from adjacent intersections
In this scenario, the dynamic signal control wasused to operate 17 out of the 28 signalised intersectionsin the Brisbane Western Corridor traffic network, as illustrated in Figure 10. More than 5,000 vehicles per hour
use this route during the morning peak. The simulation
was run for two hours with morning peak O-D matrix.
In order to allow the same traffic volume for both control strategies, the route choice model option in AIMSUN
was set as a fixed distance. This meant that route choice
was disabled to allow the effects of the control strategy
to be evaluated.
In this scenario, the algorithm was evaluated at intersections where there are effects from upstream anddownstream intersections, which is more realistic andcommon than Scenario 1. This meant that the queue formations from downstream intersections could create spillback or lane blocking conditions, which may result inunused green time and unnecessary delays. The dynamic
cycle time control addressed this by considering measurements from loop detectors which helped detect whether

28 • IATSS RESEARCH Vol.29 No.1, 2005

Fig. 9 Results of sensitivity analysis

To Brisbane city

Brisbane Western
Corridor traffic network

Fig. 10 Commuting corridor in Brisbane

traffic was passing through the intersection. The algorithm then assigned the minimum green time for that particular movement until conditions changed. It should be
noted here that phase termination was not considered as

EVALUATION OF A DYNAMIC SIGNAL OPTIMISATION CONTROL MODEL USING TRAFFIC SIMULATION

Flows (veh/h)

an option is this study.
The comparative results of both control strategies
are shown in Figure 11 where the dotted and solid lines
represent dynamic and fixed time control, respectively.
Five replications were run for statistical reliability and theperiodic statistical results were recorded every 15 minutes. The simulation time comprised three periods (light,
medium and heavy traffic conditions).

Light

MediumHeavy

Time of Day

Average speed (km/h)

a. Comparison of network flows (veh/h)LightMediumHeavy

Time of Day

Delay time (seconds)

b. Comparison of average speed (km/h)
LightMediumHeavy

Time of Day

Number of stops

c. Comparison of average delay time (seconds)
Light

Medium

S. NIGARNJANAGOOL, H. DIA

During light traffic conditions (before 7:15 A.M.),
the dynamic cycle time control was aiming to provide
sufficient time for all movements which resulted in delays on the approaches with less demand. The dynamic
cycle control allocated green times to suit current traffic
demands while fixed time control provided a constantgreen time. During medium traffic conditions (after 7:15
A.M.), the dynamic cycle time control performed better
than fixed time plans. The results presented in Figure 11
also indicate that, on average, speeds, delays and number of stops for the dynamic control scenario were superior to fixed time control (e.g. delays were reduced bymore than 10 percent). It should be noted here that the
improvements were not better than the first scenario dueto the effects from upstream and downstream intersections.
Under heavy traffic conditions (after 8:15 A.M.),
the network was full of vehicles and the volume exceeded
capacity. The performance of the dynamic control strategy decreased but remained superior to fixed time control. It should be noted here that this is not a uniquecharacteristic to this optimisation algorithm. Most adaptive traffic management systems (e.g., SCATS and
SCOOT) also demonstrate similar patterns where the control strategy’s benefits during over saturated conditions
are essentially limited and are similar to what can beachieved by fixed time systems.
It is also noted here that the main objective of thisstudy was to demonstrate the benefits of applying the dynamic cycle time optimisation algorithm to individual intersections. Although not considered in this study, it isexpected that implementation of a coordination strategy
using offset optimisation can further improve the results
for the dynamic cycle time algorithm. Offset optimisation
techniques will be considered in future studies and extensions of this work.

Heavy

6. CONCLUSIONS AND FUTURE RESEARCHDIRECTIONSTime of Day

d. Comparison of number of stops
Dynamic Cycle Time Control
Optimum Fixed Time Control (aaSIDRA)

Fig. 11 Comparative results of dynamic and fixed time
control

The dynamic cycle time control presented in thispaper is based on existing well established fixed cycle
time optimisation techniques6. The contribution of thiswork was to evaluate the performance of the algorithm
and its potential for optimising network-wide traffic conditions based on dynamic demand data provided by loop
detectors. The dynamic control strategy was evaluatedusing the AIMSUN traffic simulator. The optimisation
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THE COMPUTERIZATION OF TRANSPORTATION: Sophisticated Systems Incorporating IT in the Mobility of People and Goods

logic of the control strategy was based on the calculationof optimal signal timing and green time allocation foreach movement at the end of every cycle by using predicted demands calculated based on average volumes
from previous cycles. The application was evaluated in
two different scenarios. In the first scenario, an isolated
intersection which operated without influences fromdownstream intersections was explored. In the second
scenario, the algorithm was tested on 17 signalised intersections along the Corridor. The results for both scenarios showed that the performance of the dynamic
control system was superior to fixed time control and provided better throughput across the intersections. The performance of the dynamic control strategy started todecrease under very heavy traffic conditions but remained
superior to fixed time control. This finding is consistent
with results for most adaptive traffic management systems
which show that their performance under saturated conditions is limited and similar to what can be achieved by
fixed time control strategies. Further research is being
conducted at the University of Queensland’s ITS Research Laboratory to improve the performance of dynamic traffic signal control strategies using intelligent
agents theories1.

REFERNECES
1. Nigarnjanagool, S. Development and Evaluation of an Agent-Based
Adaptive Traffic Signal Control System. Proceedings of 25th Conference of Australian Institutes of Transport Research (CAITR), The
University of South Australia, Adelaide. (2003).
2. Diakaki, C., Dinopoulou, V., Aboudolas, K., Papageorgiou, M., BenShabat, E., Seider, E. and Leibov, A. Extensions and New Applicationsof the Traffic Signal Control Strategy TUC. Proceedings of the 82nd
Transportation Research Board Meeting, Washington DC. (2003).
3. Webster, F. V. & Cobee, B. M. Traffic signals. Road research technicalpaper 56. Her Majesty’s Stationary office, London. (1962).
4. Akcelik, R. Traffic signal: capacity and timing analysis. Australian Road
Research Report No.123. Australia. (1984).
5. aaTraffic. Introducing aaSIDRA 2.1. Web: http://www.aatraffic.com/
downloads/Introducing_aaSIDRA.pdf. (2004).
6. Transport Simulation Systems. GETRAM/AIMSUN. Web:www.aimsun.com. (2004).
7. Homburger, W. S. Fundamentals of traffic engineering. University ofCalifornia, Berkeley. (1996).
8. Garber, N. J. & Hoel, L. A. Traffic and highway engineering. Books/
Cole Thomson learning, USA. (2001).
9. Kristy, C. J. & Lall, B. L. Transportation engineering: an introduction.
Prentice Hall, New Jersey. (2003).
10. Quanstone. Paramics. Web: www.paramics-online.com. (2004).

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