ADAPTIVE TRAFFIC SIGNAL CONTROLUSING REAL-TIME DELAY MEASUREMENT- Consideration of Stochastic Delay Miho ASANO
Ph.D. student, Institute of Industrial Science, University of Tokyo
Cw-504, 4-6-1 Komaba, Meguro-ku, Tokyo 153-8505, JAPANTEL: +81-3-5452-6419, FAX: +81-3-5452-6420, E-mail: asano@iis.u-tokyo.ac.jp
Ryota HORIGUCHIi-Transport Lab. Co., Ltd.
2-12-404 Ageba-cho, Shinjuku-ku, Tokyo 162-0824, JAPANTEL/FAX: +81-3-5261-3077, E-mail: horiguchi@i-transportlab.jp
Masao KUWAHARA
Professor, Institute of Industrial Science, University of TokyoCw-504, 4-6-1 Komaba, Meguro-ku, Tokyo 153-8505, JAPANTEL: +81-3-5452-6419, FAX: +81-3-5452-6420, E-mail: kuwahara@iis.u-tokyo.ac.jp

ABSTRACTThis research developed an adaptive traffic signal control system named as CARREN(Control Algorithm Retuning paRameters with self performance EvaluatioN) that generates
signal parameters automatically to reduce network delay which is directly measured by ITS
technologies. In the latest improvement of this system, stochastic demand fluctuation isconsidered for delay evaluation and it made the algorithm possible to work more reliably innearly saturated condition. CARREN has been tested by simulation and field experiment.
Results of both experiment show that the system reduced delay compared with theconventional selection control.

INTRODUCTION
Traffic congestion in urban area is still an important issue to be solved. One of the reasonsof congestion in urban area is imperfect operation of traffic signal control. Conventionaltraffic signal control used in Japan, called as Program Selection Control, selects optimal
signal parameters from pre-determined sets to adapt the current traffic conditions. Thiscontrol has some problems. First, since the number of pre-determined sets of signal
parameters is limited, the system can adapt to limited conditions. That means it can notachieve good performance in unusual condition or after change of traffic condition in the longrun. Moreover, design of signal parameter sets based on degree of saturation does notguarantee minimization of congestion cost (= delay). Though adaptive controls, such as
SCOOT, SCATS, MODERATO, and so on, are developed and implemented practically, it
does not directly evaluate the delay itself, either.
The algorithm proposed in this study can solve these problems. This algorithm (named asCARREN) updates the signal parameters in every cycle based on delay evaluation. Delaysof individual vehicles are directly measured by recently developed sensors though it isestimated in existing control. Most of the other required data such as free flow travel time or1

saturation flow rate can also be obtained from sensors. This means CARREN works wellwithout periodic calibration and even in unusual condition.
In the previous study(1), CARREN could only evaluate deterministic delay with theassumption that measured arrival and departure patterns were the same as in the next cycle.
Actually, these patterns may fluctuate cycle by cycle. In this study, this stochastic
fluctuation of traffic pattern is considered to evaluate delay.
The structure of this paper is as follows: Section 2 shows the overview of CARREN.
Section 3 explains how CARREN evaluates delay especially in the case of stochastic delay inrelation to signal parameters. Results of simulation and field experiment are shown as inSection 4. The final section contains conclusions.

STRUCTURE OF SIGNAL CONTROL SYSTEM
The structure of CARREN is shown as in Figure 1. The required data are departure time atsubjective intersections of all vehicles and travel time of sample vehicles passing through theinflow links. AVI (Automatic Vehicle Identification) sensors and ultrasonic detectors are set
as in Figure 2. Since the departure time of all the vehicles is required, ultrasonic detectors
are used. Travel time of each vehicle can be measured by matching the plate numbers from
AVI sensors between upstream and downstream of subjective intersection.
CARREN represents current traffic conditions as cumulative flow diagrams (Figure 2). At
first cumulative departure curve (at point D) is drawn as while dots by using the flow profileof ultrasonic detectors. Second, passing time at point A can be obtained as black dots. The
distances between white and black dots show the travel time measured by AVI sensors.
Then, by shifting these black dots as much as free flow travel time between A and D andsmoothing, arrival curve can be drawn. The area surrounded by arrival and departure curveshows the delay per cycle in this subjective stream. Free flow travel time is calculated basedon histogram of travel time data.
If signal parameters change, the shape of cumulative curve and delay also change.
CARREN evaluates the delay when signal parameters change and searches the best
combinations of parameters from neighborhood of current parameters so that the total delayof subjective network is minimized.
Departing time at intersection(from detectors)

Travel time of subjective
link (from AVI sensors)

Drawing of cumulative flow diagram

Deterministic delay evaluation byusing cumulative flow diagram

Stochastic delay evaluation byusing cumulative flow diagram

Update of signal parameters

Figure 1

Structure of CARREN
2

Cumulative number ofvehicles

Free flow travel time from A to D
Passing time at A
Arrival curve
Departure curve

Delay

Passing time at Dtime
Travel time from A to D

UltrasonicDetector

AVI Sensor

DA

Figure 2

Cumulative Flow Diagram

DELAY EVALUATION FOR SIGNAL CONTROLEVALUATION OF DETERMINISTIC DELAY
The delay to be evaluated can be divided into deterministic delay and stochastic delay. The
deterministic delay is the delay when arrival and departure patterns are stationary. Figure 3shows the example case that split of subjective stream is increased by ∆G and offset and cycle
time are constant. The current cumulative curve is shown as solid line. The start time of
red phase becomes later by ∆G when split is increased. Departure curve in this case will beas upper dotted line and the delay in this case can be estimated. The change of delay in caseof offset and cycle time changing can be calculated in the same way. See the paper (1) for
detail explanation of deterministic delay evaluation.
example: when only split changes
Split = G + ∆G
Split = G - ∆G
In the presentSplit = G∆G∆GFigure 3

Change in Delay when Split changes

EVALUATION OF STOCHASTIC DELAY
In this paper, evaluation of stochastic delay is added to CARREN. We assumed thestationary demand for deterministic delay, which means the shape of the cumulative flow
curve in the next cycle is the same as that of measured curve. In fact, the shape ofcumulative curve is not exactly the same in every cycle, even if the expected demand rate is3

constant. This is because arrival and departure patterns are stochastically fluctuates cycle bycycle. The delay due to fluctuation of traffic pattern is called as stochastic delay.
If streams are in undersaturated and have sufficient splits, the stochastic delay is comparably
less than deterministic delay. However, if the stream is in nearly saturated condition, thestream may sometimes overflow. Once the stream is oversaturated, some vehicles have towait for another cycle to pass through and the delay of this stream suddenly gets large. Herelet’s consider the stochastic delay due to probabilistically overflow vehicles. This delay canbe represented as product of expected number of overflow vehicles and cycle length.
Newell(2) proposed expected delay per vehicle due to vehicles which can not pass intersection
in one cycle as follows.
E ( ws ) =IH  G q −2s  C s −1(1)

where C is cycle time, G is efficient green length and q and s are expected values of arrival
rate and saturation flow rate. I can be defined as below;（Variance of queued vehicles when green phase ends）
Var{qC − sG}＝
I=（Expectation value of queued vehicles when green phase ends） E{qC − sG}

where q and s are arrival and departure rates of each cycle.
function of I, and is between 0 and 1.

(2)

H, the collection factor, is theSuppose the cycle time is fixed and split of substitute stream is increased by ∆G.
stochastic delay of 1 cycle, dG, can be represented from (1) as following equation.

The−2dG =∂E ( ws )IHq  G q ∆G ⋅ qC = −−∆G∂G2s  C s (3)Change of the stochastic delay when cycle time change, dC, is also obtained from equation (1).
Equation (4) shows the change of stochastic delay when cycle time is increased by ∆C.
∂E ( ws )IH ∂G   G q =G −C−
2 ∂C2 sC ∂C   C s −2(4)We assume that green length of each stream change in proportion to C-L.
∂GG=
∂C C − L(5)Equation (4) and (5) gives the change of stochastic delay per unit time.
−2∂E ( ws )IHq G C  G q dC =∆C ⋅ q =−∆C
1 −
∂C2sC 2  C − L   C s (6)Evaluation of change in stochastic delay makes performance of CARREN more reliable andpossible to apply not only in undersaturated but also nearly saturated conditions.

TOTAL CHANGE OF DELAY
Sum of the change of deterministic delay and stochastic delay are regarded as the total changeof delay for choosing the signal parameters in the next cycle. For example in the case of4

deciding split, we suppose 3 cases: keeping current split, increasing by ∆s or decreasing by ∆s.
Change of deterministic delay is estimated in each case by shifting the cumulative curve ofeach stream. Then, stochastic delay is calculated by Equation (3). The best case is chosen
to minimize sums of the change of delay in each stream belongs to the subjective intersection.
Cycle time is also chosen in the same way as split.

SIMULATION AND FIELD EXPERIMENTSTUDY AREA
The performance of the algorithm is tested both by simulation and by field experiment. For
the simulation experiment, traffic simulation model AVENUE(3) is used. The study area inboth tests is a part of Nagoya-Nagakute Line as shown in Figure 4.
The common settings of simulation and field experiment are as follows. 4 intersections(Intersection 2 – 5 in Figure 5) are under control of CARREN. Other “uncontrolled”
intersections in Figure 5 have same cycle time as that of the 4 intersections operated byCARREN. Split and offset of “uncontrolled” intersections keep initial settings of program
selection control. 27 AVI sensors and 25 ultrasonic detectors are used for the control.
NTo place ofAichi Expo

This algorithm isimplemented hereHongo

Irigaike
Kosenjyo

Uchikoshi

To center ofNagoya

Hoshigaoka

Takenoyama

Higashiyama
Kohko Mae
2km

Figure 4

Iwasaki-Takenoyama

Study Area of Field Experiment

200m

2.9km
(1)
140m

200m

580m

(2)450m(3)255m

3
2300m

1390m

280m

150m

400m

5Hoshigaoka

Uchikoshi

46

78

10470m

610m

911：
AVI：
Ultrasonic detector

Figure 5

Study Area and Arrangement of Sensors

Simulation test has been done in the condition of morning peak (7:00-10:00). The field5

experiment was done during 7:00 – 16:00 from 11 th to 14th of May 2004 for Program
Selection Control and from 25th to 28th of May 2004 for CARREN.

RESULTS OF EXPERIMENTS

Total delay in the network[veh*h]

Delay in the whole network in simulation test is shown as in Figure 6. Compared to current
program selection control, the total delay in the peak time and length of congestion period
decreased with CARREN.
454035Program Selection Control

30252015

CARREN105Figure 6

10:00

9:4
0

9:2
0

9:0
0

8:4
0

8:2
0

8:0
0

7:4
0

7:2
0

7:0
0

0

Total Delay in the Study Network (Simulation Test)

For the field experiment, Table 1 shows the results of average travel time measured by AVI
sensors. Directions are shown in Figure 7. Because the periods of experiment are different
between simulation and field test, we have to note that results of simulation and field test can
not be compared directly. And although the average travel time of simulation is only in peak
hours, the travel times tend to be smaller than field test. This is because simulation is ideal
situation which does not have obstacles such as parking vehicles, pedestrians, inflow vehicles
from roadsides, and so on.
In the simulation test, the average travel time of major road is improved but that of minor road(to north) get worse with CARREN. The reason is that CARREN gave split to major
direction which has more delay, and so the travel time of minor roads increased. Total delayin this case is decreased as in Figure 6.
Table 1

Average Travel Time in the Field Test
(unit: sec)Direction
PSC*)
To EastE1 -> E5
E1 -> S1
E1 -> N4
E1 -> S3
To WestW2 -> W6
W2 -> S1To North
N2 -> E5
N2 -> W5Simulation (peak 3 hours)
CARREN
Ratio ofImprovement (%)

PSC

Field test (one day)
CARREN
Ratio ofImprovement (%)

179.7
99.6
182.8
182.5

163.9
96.0
166.9
166.4

8.8
3.6
8.7
8.8

157.1
96.9
218.0
178.3

125.9
97.8
190.5
164.2

19.9
-0.9
12.6
7.9

227.1
195.1

222.3
191.2

2.1
2.0

204.8
234.0

194.8
251.3

4.9
-7.4

258.6
98.5

269.6
103.1

-4.3
-4.7

318.2
100.5

300.0
89.5

5.6
11.0

*) PSC: Program Selection Control
6

N4E1
1W6

Uchikoshi

Hoshigaoka
2E53456W2W5S3910S1N2Figure 7Arrangement of AVI Sensors for Table 1In the field test, average travel times of most of the directions are decreased 5 – 20 % withCARREN. The reason for the increase in travel time of the route W2 -> S1 may be thatsightseeing bus has stopped at Hoshigaoka intersection during the test of CARREN and it
prevented the flow.
Signal parameters of Hoshigaoka intersection operated by CARREN are in Figure 8.
Compared to the result in simulation test, cycle time of the field test tends to be larger thanthat of simulation test. That is because of the settings of free flow travel time. In the field,
the shapes of cumulative flow curve are affected by inflow vehicles from small roads or fromfacilities at roadside. That is why larger cycle time is required in field test than in the case ofsimulation, where these factors are not considered.
Figure 9 represents the offset of link (1), (2) and (3) in Figure 5. Direction to east is written
as positive value. The offset of link (3) in the field test oscillates because of the timing ofcommunication between signal controller and operation center. CARREN sends new signal
parameters cycle by cycle. The calculation timing is synchronized at the end of the cycle ofa major intersection pre-determined by CARREN. If the cycle starting time of certain
intersection is just before the starting time of base intersection, signal parameters decided at
this time might not operated in the intersection. If this situation happens, offsets of theseintersections may not be the expected ones and CARREN correct the offsets in the next cycle.
In the link (3) of field test this situation occurs frequently. In the simulation test, this casedoes not occur because of calculation time and time to send signal parameters are less than theactual one.
Hoshigaoka Intersection (Field Test)

Hoshigaoka Intersection (Simulation Test)

145

80%
60%

140
40%

135
130

20%

80%

150
145

60%

140
40%

135
130

125
120

100%

155

cycle time
Split of major road

125

0%

20%
0%

120

1 6 11 16 21 26 31 36 41 46 51 56 61 66 71 76 81 86

1# of Cycles

265176101 126# of cycles

151

176

Figure 8 Cycle Time and Split of Hoshigaoka Intersection(Left Side is in Simulation Test and Right Side is in Field Test)

7201

Split

150

Cycle Time
Split of Major Road

Cycle time (sec)Cycle time (sec)

155

160

100%

Split

160

Offset in Simulation Test

70
60

60

Offset (sec.)

50

40
30
20

40

20-10

79

67
73

61

1

49
55

037
43

0
25
31

10-10

Link (1)
Link (2)
Link (3)

30

10

7
13
19

Offset (sec.)

50

Offset in Field Test

70

Link (1)
Link (2)
Link (3)

12651

76

# of Cycles

Figure 9101

126

151

176

201

# of Cycles

Offset (Left Side is in Simulation Test and Right Side is in Field Test)

CONCLUSIONSThis study improved a traffic signal control CARREN that reduces the network delay
measured by ITS technologies by taking into account the stochastic delay. Both simulationand field experiment show that CARREN has good performance to reduce the delay incomparison with the conventional program selection control.
The field test gives us valuable information about future studies. At first, calculation of freeflow travel time needs to be improved. Although CARREN can automatically calculate this,it sometimes does not work well because of the effect of other factors, such as inflow vehicles
from roadside or pedestrians. Since how to deal with free flow travel time is directly
affected to the parameter decision, practical study is needed for this problem.
Sensing errors should be also taken into account for the control. During the field test, thesensors sometimes could not send data. The current system is under assumption that sensors
always give sufficient data to CARREN. The reliability in case of data lacking is importantfor practical use. More discussion about robustness of the control is required.

ACKNOWLEDGEMENTS“Committee of ITS traffic signal control experiment in Nagoya, 2004” in UTMS (UniversalTraffic Management Society of Japan) supported this research and field experiment. Theauthors would like to thank Prof. M. Koshi, the National Police Agency and Aichi Prefectural
Police and to express special gratitude to Matsushita Electric Industrial Co., Ltd. for field
implementation and giving the data to the authors.

REFERENCES(1) Asano, M., Nakajima, A., Horiguchi, R., Oneyama, H. and Kuwahara, M. “A traffic signal control
based on a queueing model using ITS sensing technologies”, Proc. of 10th ITS World Congress,Madrid, 2003 (CD-ROM).
(2) Newell, G.F. “Approximation methods for queues with application to the fixed-cycle traffic light”,
SIAM Rev. No.2, pp. 223-240, 1965.
(3) Horiguchi, R. et al.; “A Network Simulation Model for Impact Studies of Traffic Management‘AVENUE Ver.2’ ”, Proceedings of the Third Annual World Congress on Intelligent Transport Systems,
ITS America, 1996.

8