Traffic signal timing optimisation based on genetic algorithm approach, including drivers' routing
ABSTRACT The genetic algorithm approach to solve traffic signal control and traffic assignment problem is used to tackle the optimisation of signal timings with stochastic user equilibrium link flows. Signal timing is defined by the common network cycle time, the green time for each signal stage, and the offsets between the junctions. The system performance index is defined as the sum of a weighted linear combination of delay and number of stops per unit time for all traffic streams, which is evaluated by the traffic model of TRANSYT [User guide to TRANSYT, version 8, TRRL Report LR888, Transport and Road Research Laboratory, Crowthorne, 1980]. Stochastic user equilibrium assignment is formulated as an equivalent minimisation problem and solved by way of the Path Flow Estimator (PFE). The objective function adopted is the network performance index (PI) and its use for the Genetic Algorithm (GA) is the inversion of the network PI, called the fitness function. By integrating the genetic algorithms, traffic assignment and traffic control, the GATRANSPFE (Genetic Algorithm, TRANSYT and the PFE), solves the equilibrium network design problem. The performance of the GATRANSPFE is illustrated and compared with mutually consistent (MC) solution using numerical example. The computation results show that the GA approach is efficient and much simpler than previous heuristic algorithm. Furthermore, results from the test road network have shown that the values of the performance index were significantly improved relative to the MC.

Conference Paper: GPU based Nondominated Sorting Genetic AlgorithmII for multiobjective traffic light signaling optimization with agent based modeling
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ABSTRACT: Microsimulation becomes more and more important in the Intelligent Transportation Systems (ITS) research, because it can provide detailed descriptions of the system. For a multiagent systems (MAS) modeling of an ITS, the computation burden is large, as it involves the computation of the state changing of all the agents. And, there are many multiobjective optimization problems in the ITS research. In this paper, we solve the traffic light signaling optimization problem and we take the average delay time and the average stop times as two objectives. We use a famous method of Nondominated Sorting Genetic Algorithm II (NSGAII). As NSGAII can be viewed as an intelligent way of running a number of microsimulations, usually the computation burden is huge. Graphics Processing Units (GPUs) have been a popular tool for parallel computing. The real transportation system runs in parallel and we think that a parallel tool is more suitable for the simulation and optimization of the system. We test GPU based NSGAII method on a 4 intersection lattice road network, and on the 18 intersection road network of the Zhongguancun area of Beijing. Compared with the CPU version, the GPU version implementation achieves a speedup factor of 21.46 and 27.64 respectively.2013 16th International IEEE Conference on Intelligent Transportation Systems  (ITSC 2013); 10/2013  Procedia  Social and Behavioral Sciences. 06/2013; 80:729752.

Conference Paper: Adaptive traffic lights based on hybrid of neural network and genetic algorithm for reduced traffic congestion
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ABSTRACT: Traffic congestion is a challenging problem in the present scenario where we are enjoying the conveniences of automobiles every day and want faster transportation. This problem is increasing exponentially day by day so to deal with this problem we devise an adaptive traffic signal controller (TSC) as traditional traffic signal controllers are inefficient in dealing with increasing demands of growing traffic. This controller uses neural network (NN) and Genetic Algorithm (GA) to adapt the traffic signal timings according to the congestion. NN takes signal timings as input and gives the queue length as output. GA is further applied to get the optimized green signal timing at its output, which is capable of reducing the queue length and overall delay. The performance of proposed model is also compared with fixed time TSC and an already existing adaptive TSC and a significant improvement were observed.2014 Recent Advances in Engineering and Computational Sciences (RAECS); 03/2014
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Traffic signal timing optimisation based on genetic
algorithm approach, including drivers? routing
Halim Ceylana,*, Michael G.H. Bellb
aDepartment of Civil Engineering, Engineering Faculty, Pamukkale University, Denizli 20070, Turkey
bDepartment of Civil and Environmental Engineering, Imperial College, Exhibition Road, SW7 2BU London, UK
Received 3 July 2002; received in revised form 6 December 2002; accepted 13 February 2003
Abstract
The genetic algorithm approach to solve traffic signal control and traffic assignment problem is used to
tackle the optimisation of signal timings with stochastic user equilibrium link flows. Signal timing is defined
by the common network cycle time, the green time for each signal stage, and the offsets between the
junctions. The system performance index is defined as the sum of a weighted linear combination of delay
and number of stops per unit time for all traffic streams, which is evaluated by the traffic model of
TRANSYT [User guide to TRANSYT, version 8, TRRL Report LR888, Transport and Road Research
Laboratory, Crowthorne, 1980]. Stochastic user equilibrium assignment is formulated as an equivalent
minimisation problem and solved by way of the Path Flow Estimator (PFE). The objective function
adopted is the network performance index (PI) and its use for the Genetic Algorithm (GA) is the inversion
of the network PI, called the fitness function. By integrating the genetic algorithms, traffic assignment and
traffic control, the GATRANSPFE (Genetic Algorithm, TRANSYT and the PFE), solves the equilibrium
network design problem. The performance of the GATRANSPFE is illustrated and compared with mu
tually consistent (MC) solution using numerical example. The computation results show that the GA
approach is efficient and much simpler than previous heuristic algorithm. Furthermore, results from the test
road network have shown that the values of the performance index were significantly improved relative to
the MC.
? 2003 Elsevier Ltd. All rights reserved.
Transportation Research Part B 38 (2004) 329–342
www.elsevier.com/locate/trb
*Corresponding author. Tel.: +902582134030; fax: +902582125548.
Email address: halimc@pamukkale.edu.tr (H. Ceylan).
01912615/$  see front matter ? 2003 Elsevier Ltd. All rights reserved.
doi:10.1016/S01912615(03)000158
Page 2
1. Introduction
In an urban road network controlled by fixedtime signals, there is an interaction between the
signal timings and the routes chosen by individual road users. From the transportation engi
neering perspective, network flow patterns are commonly assumed fixed during a short period and
Nomenclature
L
N
M
m
c
cmin
cmax
h
/
Ii
/min
set of links on a road network, 8a 2 L
set of nodes, 8n 2 N
numbers of signal stages at a signalised road network
numbers of signal stages for a particular signalised junction, 8m 2 M
a road network common cycle time
minimum specified cycle time,
maximum specified cycle time
vector of feasible range of offset variables
vector of duration of green times
intergreen time between signal stages
minimum acceptable duration of the green indication for signal stage w ¼ ðc;h;/Þ
whole vector of feasible set of signal timings
vector of feasible region for signal timings
vector of the average flow qaon link a
vector of stochastic user equilibrium link flows
set of origin–destination pairs
set of paths each origin–destination pair w, 8w 2 W
vector of origin–destination flows
vector of all path flows
linkpath incidence matrix
ODpath incidence matrix
vector of expected minimum origin–destination cost
gðq;wÞ vector of path travel times
c0
vector of freeflow link travel times
cðq;wÞ vector of all link travel times
dU
a
uniform delay at a signalcontrolled junction
dro
a
random plus over saturation delay at a signalised junction
Kmatrix of link choice probabilities
k
the number of signal timing variables on a whole road network, the dimension of the
problem is k ¼PN
NN population size
l
total number of binary bits in the string (i.e., chromosome)
ttgeneration number
X0
q
q?ðwÞ
W
Pw
t
h
d
K
y
i¼1miþ N
Xtt
potential solution matrix of dimension ½NN ? l? for the GA random search space
330
H. Ceylan, M.G.H. Bell / Transportation Research Part B 38 (2004) 329–342
Page 3
control parameters are optimised in order to improve some performance index. On the other
hand, from the transportation planning perspective, traffic assignment models are used to forecast
network flow patterns, generally assuming that capacities decided by network supply parameters,
such as signal settings, are fixed during a short period. The mutual interaction of these two
processes can be explicitly considered, producing the socalled combined control and assignment
problem.
The TRANSYT model proposed by Robertson (1969) has been widely recognised as one of the
most useful tools in studying the optimisation of area traffic control. On the other hand, many
traffic assignment models have been developed in order to find the link flows and path flows given
origin–destination trip rates in an urban road network. One of these assignment models is the
Path Flow Estimator (PFE). It has been developed by TORG (Transport Operation Research
Group), in Newcastle University, to find link and path flows based on stochastic user equilibrium
routing. However, it has been noted (Allsop, 1974; Gartner, 1974) that a full optimisation process
needs to be applied where both problems are relevant; the area traffic control optimisation and
user routing. The combined optimisation problem can be regarded as an Equilibrium Network
Design Problem (ENDP) (Marcotte, 1983). Genetic Algorithms (GAs), first introduced by
Goldberg (1989), have been applied to solve the ENDP (Lee and Machemehl, 1998; Cree et al.,
1999; Yin, 2000).
A number of solution methods to this ENDP have been discussed and good results have been
reported in a medium sized networks. Allsop and Charlesworth (1977) found mutually consistent
traffic signal settings and traffic assignment for a medium size road network. In their study, the
signal settings and link flows were calculated alternatively by solving the signal setting problem
for assumed link flows and by carrying out the user equilibrium assignment for the resulting signal
settings until convergence was achieved. The link performance function is estimated by evaluating
delay for different values of flow and then fitting a polynomial function to these points. The
resulting mutually consistent signal settings and equilibrium link flows, will, however, in general
be nonoptimal as has been discussed by Gershwin and Tan (1979) and Dickson (1981).
The first appearance of the GA for traffic signal optimisation was due to Foy et al. (1992), in
which the green timings and common cycle time were the explicit decisional variables and the
offset variables were the implicit decisional variable in a fourjunction network when flows remain
fixed. In the optimisation process, a simple microscopic simulation model was used to evaluate
alternative solutions based on minimising delay. The results showed an improvement in the
system performance when the GA was used and suggested that the GA has the potential to
optimise signal timing. The results, however, were not compared with what could be achieved
using existing optimisation tools. It was also concluded that the GA model may be able to solve
more difficult problems than traditional control strategies and search methods in terms of con
vergence and that good convergence were reported in that study.
In this paper, for the purpose of solving the problem, a bilevel approach has been used. The
upper level problem is signal setting while the lower level problem is finding equilibrium link flows
based on the stochastic effects of drivers? routing. It is, however, known (see Sheffi and Powell,
1983) that there are local optima. It is not certain that the local solution obtained is also the global
optimum because equilibrium signal setting is generally a nonconvex optimisation problem.
Hence, the GA approach is used to globally optimise signal setting at the upper level by calling
TRANSYT (Vincent et al., 1980) traffic model to evaluate the objective function.
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331
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2. Formulation
The network performance index (PI) is a function of signal setting variables w ¼ ðc;h;/Þ and
equilibrium link flows q?ðwÞ. The objective function is therefore to minimise PI with respect to
equilibrium link flows q?ðwÞ subject to signal setting constraints. This gives the ENDP problem as
the following minimisation problem:
Minimise
w2X0
PIðw;q?ðwÞÞ ¼
X
8
>
>
a2L
ðWwaDaðw;q?ðwÞÞ þ KkaSaðw;q?ðwÞÞ
cmin6c6cmax
06h6c
/min6/6/max
Pm
ð1Þ
subject to
wðc;h;/Þ 2 X0;
cycle time constraints
values of offset constraints
green time constraints
8m 2 M;8n 2 N
i¼1ð/iþ IiÞ ¼ c
>
>
>
>
>
>
<
:
where q?ðwÞ is implicitly defined by
Minimise
q
Zðw;qÞ
t ¼ Kh;
ð2Þ
subject toq ¼ dh;
hP0
Then the fitness function (i.e., objective function for ENDP) becomes
Maximise FðxÞ ¼
1
PIðw;q?ðwÞÞ
ð3Þ
where PIðw;q?ðwÞÞ is the value of the performance index of the network which is a function of
equilibrium flow pattern q?ðwÞ and signal settings w. All control variables are expressed in integer
seconds, and x is a set of chromosomes that represents w 2 X0, and F is a fitness function for the
GA, to be maximised.
2.1. GA formulation for the upperlevel problem
Suppose the fitness function ðFÞ takes a set of w signal timing variables, w ¼ ðc;h1;/1;...;
hn;/nÞ: Rk! R. Suppose further that each decision variable w can take values from a domain
X0¼ ½wmin;wmax? ? R for all w 2 X0. In order to optimise the objective function, we need to code
the decision variables with some precision. The coding process is illustrated as follows:
Decision variables
Mapping
ChromosomeðstringÞ x ¼ j01010101jj01010111;...;10101011jj10101010;...;01010010j
Then, the mapping from a binary string ðbl1bl0...b0Þ representation of variables into a real
numbers w from the range ½wmin;wmax? is carried out in following way:
w ¼
#
jcj
#
jh1;h2;...;hnj
#
j/1;/2;...;/nj
#
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H. Ceylan, M.G.H. Bell / Transportation Research Part B 38 (2004) 329–342
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(a) Convert the binary string ðbl1bl0...b0Þ from base 2 to base 10:
(b)
ðbl1bl0...b0Þ2¼
X
li
j¼li?1
bj2j
!
10
¼ Ui
i ¼ 1;2;3;...;k
ð4Þ
(c) Find a corresponding real number for each decision variable for a particular signal timing:
wi¼ wi;minþ Ui
wi;max? wi;min
2li? 1
i ¼ 1;2;3;...;k
ð5Þ
where Uiis the integer resulting from (4). The decoding process from binary bit string to the real
numbers is carried out by means of (5).
The following transformations are carried out for each signal timing variable for use in signal
timing optimisation and traffic assignment purposes.
2.1.1. For common network cycle time
c ¼ cminþ Uiðcmax? cminÞ
2li? 1
i ¼ 1
ð6Þ
2.1.2. For offsets
hi¼ Ui
c
2li? 1
i ¼ 2;3;...;N
ð7Þ
Mapping the vector of offset values to a corresponding signal stage change time at every junction
is carried out as follows:
hi¼ Si;j
where Si;jis the signal stage change time at every junction.
i ¼ 1;2;...;N; j ¼ 1;2;...;m
2.1.3. For stage green timings
Let p1;p2;...;pibe the numbers representing by the genetic strings for m stages of a particular
junction, and I1;I2;...;Imbe the length of the intergreen times between the stages.
The binary bit strings (i.e., p1;p2;...;pi) can be encoded as follows first;
pi¼ pminþ Uiðpmax? pminÞ
2li? 1
i ¼ 1;2;...;m
where pminand pmaxare set as cminand cmax, respectively.
Then, using the following relation the green timings can be distributed to the all signal stages in
a road network as follows second:
/i¼ /min;iþ
pi
k¼1pi
Pm
c
?
X
m
k¼1
Ik?
X
m
k¼1
/min;k
!
i ¼ 1;2;...;m
ð8Þ
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333
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2.2. The formulation for the lowerlevel problem
2.2.1. The (PFE) as a stochastic user equilibrium assignment (SUE)
The underlying theory of the PFE (see for details Bell et al., 1997) is the logit SUE model based
on the notion that perceived cost determines driver route choice. The basic idea is to find the path
flows and hence links flows, which satisfy an equilibrium condition where all travellers perceive
the shortest path (allowing for delays due to congestion) according to their own perception of
travel time.
The logit model assumes a particular distribution, the Gumbel distribution, for perceived travel
times, which has the great advantage of allowing the formulation of a convex mathematical
program whose solution is unique in the path flows. The equilibrium path flows are found by
solving an equivalent minimisation problem. The attraction of the logit model is that it allows
SUE flows and costs to be calculated by solving the convex mathematical program. The following
minimisation problem
minimise
ZðhÞ ¼ hTðlnðhÞ ? 1Þ þ a
X
a2L
ZqaðhÞ
0
caðxÞdx
ð9Þ
subject tot ¼ Kh;
hP0
where all the notation is as previously stated, due to Fisk (1980), leads to a logit path choice
model. Provided that the link cost functions are monotonically increasing with flows and as
suming separable link cost functions, then ZðhÞ is strictly convex and, since the constraints are
also convex, it can be proved that there exists one unique solution to the program. The Kuhn–
Karush–Tucker optimality conditions are
rZðhÞ þ KTu?P0;
Since all paths are used
h?P0 and
ðrZðhÞ þ KTu?ÞTh?¼ 0
rZðhÞ ¼ ln h þ ag
ln h?¼ ?ag?? KTu?
It can be shown that this implies the following logit path choice model
expð?ag?
P
where twis the demand for origin destination pair w in W, u is the dual variable and its definition
can be obtained in Bell and Iida (1997). u may be associated with equilibrium link delays when it is
divided by a, the dispersion parameter.
At the optimum
As h?> 0
h?
p¼ tw
pÞ
p2Pwexpð?ag?
pÞ
ð10Þ
rZðh?Þ þ KTu?¼ 0
where rZðh?Þ ¼ ln h?þ ag?
At optimality
ln h?þ ag?þ KTu?¼ 0
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H. Ceylan, M.G.H. Bell / Transportation Research Part B 38 (2004) 329–342
Page 7
implying
hp¼ expð?agp? uwÞ:
From this logit model
expð?uwÞ ¼
tw
P
p2Pwexpð?agpÞ
so
?uw¼ lntw? ln
X
p2Pw
expð?agpÞ
Hence
?u ¼ ln t þ ay
PFE consists of two loops; an outer loop, which generates paths, and an inner loop, which assigns
flows to paths according to logit path choice model (10). The SUE path flows are found by solving
an equivalent optimisation problem (9) iteratively. An outer loop generates paths and an inner
loop assigns flows to paths according to a logit path choice model.
2.3. The GATRANSPFE solution of the ENDP
A decoded genetic string is required to translate into the form of TRANSYT and PFE inputs,
where TRANSYT model accepts the green times as stage start times, hence offsets between signal
controlled junctions, and the PFE requires the cycle time and duration of stage greens for that
stages. The assignment of the decoded genetic strings to the signal timings is carried out using the
following relations in the GATRANSPFE.
1. For road network common cycle time
c pði;jÞ
i ¼ 1; j ¼ 1;2;3;...;NN
where p represents the corresponding decoded parent chromosome, j represents the population
index, and i represents the first individual in the chromosome set.
2. For offset variables
hnði;jÞ pði;jÞ;
Since there is no closedform mapping for offset variables, it is common to map these values to the
interval ð0;cÞ, hence offset values are mapped using (7). The decoded offset values are in some
cases higher than the network cycle time due to the coding process in the GA. In this case, the
remainder of a division between pði;jÞ and the c (i.e., modulo division) is assigned as a stage
change time as follows:
hnði;jÞ MODðpði;jÞ;cÞ; i ¼ 2;3;4;...;N j ¼ 1;2;3;...;NN
3. For green timing distribution to signal stages as a stage change time is
hn;mði;jÞ ¼ hn;m?1ði;jÞ þ ððI þ /Þn;mði;jÞÞ6c;
i ¼ 2;3;4;...;N; j ¼ 1;2;3;...;NN;
8n 2 N; 8m 2 M; i ¼ 1;2;3;...;M
H. Ceylan, M.G.H. Bell / Transportation Research Part B 38 (2004) 329–342
335
Page 8
The solution steps for the GATRANSPFE is:
Step 0. Initialisation. Set the userspecified GA parameters; represent the decision variables w as
binary strings to form a chromosome x by giving the minimum wminand maximum wmax
specified lengths for decision variables.
Step 1. Generate the initial random population of signal timings Xtt; set tt ¼ 1.
Step 2. Decode all signal timing parameters of Xttby using (6)–(8) to map the chromosomes to
the corresponding real numbers.
Step 3. Solve the lower level problem by way of the PFE. This gives a SUE link flows for each
link a in L.
At Step 3, the link travel time function adapted for the PFE is the sum of freeflow travel time
under prevailing traffic conditions (i.e., c0
a) and average delay to a vehicle at the stopline at a
signalcontrolled junction by simplifying the offset expressions for the PFE link travel time
function, where the appropriate expressions for the delay components can be obtained in Ceylan
(2002), as follows:
caðqa;wÞ ¼ c0
aþ dU
aþ dro
a
Step 4. Get the network performance index for resulting signal timing at Step 1 and the corre
sponding equilibrium link flows resulting in Step 3 by running TRANSYT.
Step 5. Calculate the fitness functions for each chromosome xjusing the expression (3).
Step 6. Reproduce the population Xttaccording to the distribution of the fitness function values.
Step 7. Carry out the crossover operator by a random choice with probability Pc.
Step 8. Carry out the mutation operator by a random choice with probability Pm, then we have a
new population Xttþ1.
Step 9. If the difference between the population average fitness and population best fitness index
is less than 5%, restart population and go to the Step 1. Else go to Step 10.
Step 10. If tt¼maximal generation number, the chromosome with the highest fitness is adopted
as the optimal solution of the problem. Else set tt ¼ tt þ 1 and return to Step 2.
3. Numerical application
A test network is chosen based upon the one used by Allsop and Charlesworth (1977) and
Chiou (1998). Basic layouts of the network and stage configurations for GATRANSPFE are
given in Fig. 1a and b, where Fig. 1a is adapted from Chiou (1998) and Fig. 1b adopted from
Charlesworth (1977). Travel demands for each origin and destination are those used by
Charlesworth (1977) and also given in Table 1. This numerical test includes 20 origin–destination
pairs, and 21 signal setting variables at six signalcontrolled junction.
The GATRANSPFE is performed with the following userspecified parameters:
Population size is 40.
Reproduction operator is binary tournament selection.
336
H. Ceylan, M.G.H. Bell / Transportation Research Part B 38 (2004) 329–342
Page 9
1
A
C
G
F
E
D
B
26
3
5
4
6
14
5
11
12
13
10 17
21
8
9
18
20
154
23
22
19
7
16
3
1 2
Legend
OriginDestination
Junction
N
Junction
Stage 1Stage 2 Stage 3
1
16
1
2
19
3
15
23
20
4
14
5
11
12
12
10
5
6
13
8
9
8
17
21
7
18
22
1
2
3
4
5
6
(a)
(b)
Fig. 1. (a) Layout for Allsop and Charlesworth?s test network. (b) Stage configurations for the test network.
H. Ceylan, M.G.H. Bell / Transportation Research Part B 38 (2004) 329–342
337
Page 10
Crossover operator is uniform crossover, and the probability is 0.5.
Mutation operator is creep mutation operator, and the probability is 0.02.
The maximal number of generation is 100.
The signal timing constraints are given as follows:
3.1. GATRANSPFE solution for Allsop and Charlesworth’s network
Although the bilevel problem (1) is nonconvex and only a local optimum is expected to be
obtained, in this numerical test, the GATRANSPFE model is able to avoid being trapped in a bad
local optimum. The reason for this is that the model starts with a large base of solutions, each of
which is pushed to converge to the optimum. If there is no more improvement on the population
best fitness and population average fitness for the current generation, the GATRANSPFE
restarts the population. This has the effect of jumping from the current hill to different hills.
The method applied is not dependent on the initial assignments of the signal setting variables.
The random number seed given controls the initial sets of solutions within the population size.
Unlike the GATRANSPFE solution of the network, the MC solution requires the initial as
signment.
The application of the GATRANSPFE model to Allsop and Charlesworth?s network can be
seen in Fig. 2, where the convergence of the algorithm and improvement on the network per
formance index and hence the signal timings can be seen. The model calculates the fitness of each
individual chromosome xjin the population. The maximum fitness value found in the current
generation is noted, then for each population pool, the selection, crossover and mutation oper
ators are applied. When the differences between the population average fitness and population
best fitness of the current generation is less than 5% then the algorithm restarts with new ran
domly generated parents, whilst keeping the best fit chromosome from the previous population.
The reason for this is to improve the speed of the model towards the optimum.
cmin;cmax¼ 36;120 s
hmin;hmax¼ 0;120 s
/min¼ 7 s
I1?2;I2?1¼ 5 s
Common network cycle time
Offset values
Minimum green time for signal stages
Intergreen time between the stages
Table 1
Travel demand for Allsop and Charlesworth?s network in vehicles/h
Origin/destinationABDEFOrigin totals
A
C
D
E
G
Destination totals
–250
20
250
130
450
1100
700
200
30
130
50a
200
900
100
20
20
1240
1180
1290
800
480
1250
5000
40
400
300
550
1290
–
30a
170
1100
–
60
270
aWhere the travel demand between O–D pair D and E are not included in this numerical test which can be allocated
directly via links 12 and 13.
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H. Ceylan, M.G.H. Bell / Transportation Research Part B 38 (2004) 329–342
Page 11
In Fig. 2, there are no improvements on the best fitness value on the first few generations. The
reason for this is that in the first iterations, the algorithm finds a chromosome with very good
fitness value which is better than average fitness of the population. The algorithm keeps the best
fitness then starts to improve population average fitness to the best chromosome while improving
the best chromosome to optimum or near optimum. The considerable improvement on the ob
jective function usually takes place in the first few iteration because the GA start with randomly
generated chromosomes in a large population pool. After that, small improvements to the ob
jective function takes place since the average fitness of the whole population will push forward the
population best fitness by way of genetic operators, such as mutation and crossover.
Model analysis is carried out for the 75th generation, where the difference between the popu
lation average fitness and population best fitness is less than 5%, and network performance index
obtained for that generation is 712.5 £/h. The model convergence can be seen in Fig. 3. The re
start process began after the 75th generation and there was not much improvement to the pop
ulation best fitness previously found as can be seen in Fig. 2.
Table 2 shows the signal timings and the final value of the performance index in terms of £/h
and vehh/h. The common network cycle time resulting from the GATRANSPFE application is
77 s and the start of greens for every stage in the signalised junctions are presented in Table 2.
300.0
500.0
700.0
900.0
1100.0
1300.0
1500.0
1700.0
1900.0
2100.0
2300.0
1917253341
Generation Number
495765738189 97 105
Performance index (£/h)
Avg (£/h)
Best(£/h)
Fig. 2. The application of GATRANSPFE model to the test network.
960.0
980.0
1000.0
1020.0
1040.0
1060.0
1080.0
1100.0
1120.0
1140.0
1160.0
147 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58
Iteration number
Performance Index (£/h)
PI(£/h)
Fig. 3. The convergence behaviour of MC calculation.
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3.2. MC solution for Allsop and Charlesworth’s network
The MC calculations were carried out with the initial set of signal timings given in Table 3,
where the signal timing are equally distributed to the signal stages. For this initial set of signal
timings, along with equilibrium link flows resulting from the PFE, the initial performance index
and its corresponding value of vehh/h is given in Table 3.
As can be seen in Fig. 3, for the first iteration after performing a full TRANSYT run with the
corresponding equilibrium flows, the value of the performance index increased from 1024 £/h to
1100 £/h, an increase of 7%. In the second iteration the MC soluton also increases the system
performance index. Thereafter, alternately carrying out the two separate procedures of traffic
assignment and TRANSYT optimisation of the signal timings, the final value of the performance
index is 1075 £/h. This shows that the MC solution increases the system performance index by 5%
when it is compared to the initial value of 1024 £/h. Fluctuations of the value of the performance
index from iteration to iteration is obvious, which shows the nonoptimal characteristic of the
mutually consistent signal settings and equilibrium flows for the solution of the bilevel problem.
The total number of iterations in performing the MC calculations in Fig. 3 is 60. The maximum
degree of saturation is 0.97.
Table 4 shows the final values of the start of green timings for each signalised junction and
performance index resulting from the MC solution. The network common cycle time is 82 s.
As for the solution of the MC calculation, Fig. 3 showed that the MC calculation increases
the system performance index. In terms of the convergence, the MC is dependent on the ini
Table 2
The final values of signal timings derived from the GATRANSPFE model
Cycle time c
(s)
£/hvehh/h
Performance indexJunction
number i
Start of green in seconds
Stage 1 Si;1
Stage 2 Si;2
Stage 3 Si;3
712.5 75.4771
2
3
4
5
6
032
25
60
72
5
6
–
–
–
59
13
44
64
47
20
30
–
Table 3
Initial signal timing assignment for use in the MC
Performance indexCycle time c
(s)
Junction
number i
Start of green in seconds
Stage 1 Si;1
£/hvehh/h
Stage 2 Si;2
Stage 3 Si;3
1024.0 110.0701
2
3
4
5
6
0
0
0
0
0
0
35
35
35
23
23
35
–
–
–
46
46
–
340
H. Ceylan, M.G.H. Bell / Transportation Research Part B 38 (2004) 329–342
Page 13
tial assignment. Various sets of initial signal timings were used as a starting point for the MC
(see Ceylan, 2002). Only one set of the initial solutions converged to the predetermined threshold
value that is presented in Fig. 3.
4. Conclusions
1. Allsop and Charlesworth?s example network was used as an illustrative example for showing
the performance of the GATRANSPFE method in terms of resulting values of performance
index for the whole network and the degree of saturation on links. The performance of the pro
posed method in solving the nonconvex bilevel problem showed in that the differences be
tween resulting values of performance index in all cases were negligible at the 75th
generation. Furthermore, none of the degree of saturation, resulting from the GATRANSPFE
model, were over 90%. The GATRANSPFE model showed good improvement over the MC
calculation in terms of the final values of performance index, with a 34% improvement over
the MC solution of the problem, at the 75th generation, and an improvement in terms of con
vergence in all cases.
2. The MC solution of the problem was dependent on the initial set of signal timings and its so
lution was sensitive to the initial assignment. Depending on the initial signal timings, the con
vergence of the MC solution was not guaranteed. Note that applying the MC solution to Allsop
and Charlesworth?s road network caused the network performance index to increase compared
with the initial performance index, whilst the GATRANSPFE model converged to the optimal
solution (i.e., at the 75th generation) irrespective of the initial signal timings.
3. As for the computation efforts for the GATRANSPFE model, performed on PC 166 Toshiba
machine, each iteration for this numerical example was less than 16.5 s of CPU time in Fortran
90. The total computation efforts for complete run of the GATRANPFE model run was 18.4 h.
On the other hand, the computation effort for the MC solution on the same machine was per
formed for each iteration in less than 20 s of CPU time and the complete run did not exceed 1 h
on that machine.
4. In this work, the effect of the stage ordering to a network performance index is not taken into
account due to the coding procedure of the GATRANSPFE. Future work should take into
account the effect of the stage orders by appropriately representing the stage sequences as a
suitable GA code.
Table 4
The final values of signal timings resulting from the MC solution
Performance indexCycle time c
(s)
Junction
number i
Start of green in seconds
Stage 1 Si;1
£/hvehh/h
Stage 2 Si;2
Stage 3 Si;3
1075.0116.0821
2
3
4
5
6
32
15
52
2
27
80
72
66
20
32
62
46
–
–
–
59
5
–
H. Ceylan, M.G.H. Bell / Transportation Research Part B 38 (2004) 329–342
341
Page 14
Acknowledgements
The author would like to thank to David Carroll who has provided me some part of the source
code of the genetic algorithm. The work reported here was sponsored by the scholarship of
Pamukkale University, Turkey. We thank the anonymous referees for their constructive and
useful comments on the paper.
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