Proceedings of "The Second World Congress on Intelligent Transport Systems '95", Vol. IV, pp.1977-1982

The Model Validation of Traffic Simulation Systemfor Urban Road Networks: 'AVENUE'
Ryota Horiguchi, Masao Kuwahara and Isao Nishikawa
Institute of Industrial Science, University of Tokyo
7-22-1, Roppongi, Minato-ku, Tokyo 106, JAPAN
Phone: +81-3-3401-0522 / Fax: +81-3-3401-6286 / E-Mail: poepoe@nishi.iis.u-tokyo.ac.jp

ABSTRACT
2. TRAFFIC MODEL OF AVENUE
The first version of AVENUE (an Advanced & Visual
Evaluator for road Networks in Urban arEas) including thebasic functions concerning to the traffic flow reproduction
and the driver's route choice behaviors has been developed.
It is required that the traffic model used in AVENUE isvalidated to establish its reliability. This paper examines
the applicability of AVENUE with observed traffic data
including bus traffic for the system validation. Based on
the field application, the problems specific to thesimulation of urban traffic are discussed.

1. INTRODUCTION
As traffic congestion has caused serious social andeconomic problems in urban areas, there is a great demand
for traffic simulation that can evaluate impacts ofimprovements on intersection geometry, trafficregulations, signal control, etc. Such a simulation system
can also be an efficient tool for traffic assessment at
planning stages of large events or urban developments.
We have developed the traffic simulation system for
urban road networks named AVENUE (an Advanced &Visual Evaluator for road Networks in Urban arEas) Ver.1
and have worked with it to improve its utility for thepractical traffic assessment[1][2]. The scope of theapplication of AVENUE is to reproduce the detailed traffic
condition within an area which contains dozens ofintersections. For this purpose, not only the traffic flow
and the route choice behavior but also some microscopic
phenomena, such as the lane changing behavior and thespill over from turn pockets, should be incorporated into
the system.
In the following chapters, the framework of the traffic
flow and the route choice model are described. Afterward,
the model is validated using observed traffic data in a real
network, which is characterized by the complicated lane
usage containing bus lanes and bus bays.

2.1. Hybrid Block Density MethodAVENUE employs the Hybrid Block Density Method as
its traffic flow model. The flow calculation of thismethod has the basis on the Block Density Method[3][4],which treats traffic flow as continuum. In the Block
Density Method, each link is divided into several blocksof which length is equal to the distance that a vehicle runs
at the free flow speed of the link during a scanninginterval. The in/out-flow and the density of each block arerevised at every scanning interval based on the flowconservation law and the Q-K relationships shown in
Figure 1. This method can reasonably reproduce bothundersaturated and oversaturated traffic conditions
including propagation of shock waves.

11110101
0000
0101
01
Q (flow)

Qclinkcapacity

free speed

Vf0Kc
critical density

Kj
jam density

K (density)

Figure 1: The flow-density relationship used in
the Block Density Method
Figure 2 illustrates the variables used in the flow
calculation between the two neighboring block with
suffix i and i+1. At every scan, the allowable out-flow
and the allowable in-flow of each block are calculated
according to the Q-K relationship, then the flow between
the neighboring blocks is determined by taking theminimum of the allowable out-flow of the upstream
block and the allowable in-flow of the downstream block.

Proceedings of "The Second World Congress on Intelligent Transport Systems '95", Vol. IV, pp.1977-1982

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discrete vehicle images

Figure 2: The flow calculation between
two neighboring blocks

Figure 3: The movements of discrete vehicles
in the Hybrid Block Density Method

The densities at the next scan are revised using the inflow and out-flow of each block. The following equations
show the flow calculation of each block with suffix i:

choice criteria for the convenience of modelling thevehicle behavior. Figure 3 illustrates the movements of
discrete vehicles in this method. The number of vehicles
which move from block i+1 to i is proportional to theflow from i+1 to i. The difference between the number ofvehicles and the continuum flow remains as underflow
from i+1 to i and will be treated at the next scan intervalso that both cumulative values will be equal throughout
the simulation period.

Aiout(t) = min Kci, Ki(t)

dL
dt

(1)

Aiin =:dL=min Kci, Kji -Ki(t),Ki(t)#Kci==dt=;=Kci Kji -Ki(t) dL==,Kci < Ki(t)#Kji=Kji -Kcidt<(2)
Qi +1,i(t) = min Aiout(t), Aiin(t)+1(3)

Ki(t+1) dL = Ki(t) dL-Q i, i -1(t) dt+Qi +1,i(t) dt(4)
where, Aouti(t): allowable out-flow of i,Aini(t): allowable in-flow of i,
Ki(t): density of i,
Qi+1,i(t): flow from i+1 to i,
Kci: critical density of i,Kji: jam density of i,t: time,dL: length of block,
dt: scanning interval.
Since the fluid approximation of traffic has somedifficulties in handling individual vehicle informationsuch as its destination, the Block Density Method wasextended to the Hybrid Block Density Method, i.e. theblocks have not only the continuum traffic density but
also the discrete vehicle images containing the various
attributes such as vehicle types, destinations and route2.2. Route and Lane Choice Behaviors
AVENUE incorporates drivers' route choice behaviors tomeet the recent requirements for the evaluation of theinfluence of ITS technologies.
In the simulation procedure, time dependent traffic
demand of each origin-destination pair is input to themodel at first. The vehicles generated in proportion to thetraffic demand have the information of their destinations
and the route choice criteria such as being conservative orsensitive to the route information.
On the other hand, each link contains the route
guidance attribute which contains the route informationfor each destination. The route information shows thecandidate of the routes for each destination, the travel timeof each route, the traffic regulations along each route, etc.
When a vehicle enters a link, it chooses the appropriate
route by using the route information of the linkaccording to its destination and its choice criteria. The
contents of the route guidance attributes are dynamically
updated by the route information module during
simulation.
At the same time when a vehicle chooses a route, thevehicle decides the turn movement at the link end andenters an appropriate lane on the next link. Figure 4
illustrates a mechanism of the lane choice in the Hybrid

Proceedings of "The Second World Congress on Intelligent Transport Systems '95", Vol. IV, pp.1977-1982

3.1. Input Datavehicle with right-turn at Link k
Link kLink jblock with turnmovement permission
Figure 4: The modelling of the lane choice behavior in theHybrid Block Density Method
Block Density Method. A link is divided into small
blocks of which widths cover one lane. Each block has
the attribute of the permissions such as traffic regulations
for each of turning movements and vehicle types. Avehicle can move into a downstream block according tothe flow calculated by the Hybrid Block Density Method
only if the block grants permission to the vehicle. In thisway, various traffic regulations on lanes including bus
lanes can be easily described in the traffic model.

3 . APPLICATION TO A REAL NETWORKFOR THE MODEL VALIDATION

Figure 5 illustrates the network geometry used for themodel validation. The network contains 5 intersections
systematically controlled by the signals of which cycles
are 140 seconds (A, B, C, D and F) and one intersection
without signals (E) along the main street lying east andwest with the span of about 500 meters. In the center ofthe network, there are a taxi pool and a bus terminal. Twobus lanes and bus bays are facilitated beside the mainstreet.
The traffic volume passing through the network
during 7:50 - 9:10 was observed by license platematching at every network ends numbered from 0 to 10.
The data was aggregated by each O-D pair for every 10minute. Main sources and sinks are 0, 5, 6 and 9.
3.2. Traffic Condition on the Network
During the morning peak period, the traffic on thenetwork stays in over-saturated condition. Since thelength of the links are mostly less than 100 meters,vehicle queues headed with red signals easily reach theupstream ends of the links and hamper vehicles on theupstream links.
The number of commuting buses coming from thenetwork ends 0, 1 and 6 is more than 100 during the peak
period, and they impede the smooth traffic flow. Thesebuses once stop at either bus bay G or H for 50 secondsi
3

1

bus terminal

taxi pool

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2bus laneexit

bus bay GNNW0CDEEAS

4ESEWWW

B
Nentrancebus bay H

E7bus routes
NFW

8106
about 500 m
Figure 4: The road network used for the model validation

5NES

9

Proceedings of "The Second World Congress on Intelligent Transport Systems '95", Vol. IV, pp.1977-1982

Figure 5: The displayed image of the network for the model validationn average, then they start moving toward the bus
terminal; however they often spill over from the bus bays
and even from the bus lanes sometimes.
Furthermore, since the buses exiting from the bus
terminal through C-N are lot during the peak period, theright-turn vehicles at C-S is interfered by these buses andconsequently they spill over from the right-turn pocket of
C-S (C-N means approach N at Intersection C in Figure4).
3.3. Comparison with Simulation
Figure 5 shows the displayed image of the network. Fromvisual inspection, the simulation model seems to well
reproduce the over-saturated condition upon the mainstreet and the spilling over from the right turn pocket of
C-S. In order to discuss the validity of the simulation
model, let us compare the observed data with thesimulated values in regard to the throughput traffic
volumes, the queue lengths and the travel times.
The observed throughput volumes at the sections
listed in Table 1 during 8:00 - 9:00 are compared witheach of the simulated ones. Since the saturation flow rate
at each section is fitted to the reasonable value in a range

from 1600 to 1800 [veh/hr/lane], most of the simulated
values are close to the observed ones and the appropriate
number of vehicles flowing into the network.
Table 1Comparison of the observed throughput volume withthe simulated one at each section during 8:00 - 9:00
SectionA-N A-W C-N C-S F-E F-S
Observed
672 1086 148 511 605 978
Simulated
679 1095 150 510 609 974

Figure 6 illustrates the maximum queue length ofboth observed and simulated values at each intersection
during 8:00 - 9:00. Although the definitions of bothqueue length are different (i.e. the observed ones were
measured with the eye by the observer, on the other handthe simulated ones are defined as the total length of theblocks where the spatial speed are less than 10 km/hr),
the result may say that the simulation model has atendency to reproduce the over-saturated traffic condition
reasonably. However, the differences between the bothqueue lengths in some peripheral links flowinf into thenetwork are large. This is because the vehicle arrival rate

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Proceedings of "The Second World Congress on Intelligent Transport Systems '95", Vol. IV, pp.1977-1982

48/40

56/90

84/70 B

35/90

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56/70

A49/40D56/40C 49/40

56/40

98/70

84/14
observation0

280/90F 105/90simulation
100 / 100 observation / simulation (meter)
Figure 6: The comparisons of the queue lengths

at the upstream end of the link flowing into the network
is designated by the observed throughput volume at thedownstream stop line of such link, therefore the vehicle
arrival patterns in the simulation are different from thereal ones.
Furthermore, the travel time along the main street
between A and F are compared as shown in Figure 7. The
observed data is collected by several test runs of floating
vehicles, on the other hand, the simulated data represents
the sum of each link average travel time along the mainstreet in every 5 minutes.
Figure 7a illustrates the comparison within theTravel Time (min.)04:00

section from A to F. In this direction, the simulated datawell agrees with the observed one. Since the flow of thisdirection is relatively smooth comparing with theopposite direction, the numbers of stops in every trips
from A to F are almost same. Consequently, thesimulated data is close to the average travel time of all
floating runs.
Figure 7b shows for the opposite direction. In thiscase, the fluctuation of each observed travel time are verylarge. This means the number of stop by the red signal in
the trip greatly varies depending on the departure time ofeach floating run. However, though the number of thefloating run is still insufficient, the average of theobserved travel time seems close to the simulated one.
3.4. Problem in Lane Changing ModelIn this application, it is not necessary to incorporate lanechange behaviors within a link because the lengths ofevery link are quite short so that there are few chance tochange lanes. The lane choice model is employed insteadsuch that each vehicle chooses an appropriate lane when it
enters a link according to its turning movement at the endof the link. In a real case, a vehicle often changes lanes
considering the turning movement at further downstream
Simulation (average by every 5minutes)
Observation (by floating car)
Observation (average over 1 hour)

03:00
02:00
01:00
Departure Time
00:00
8:00

8:10

Travel Time (min.)
04:00

8:20

8:30a) Travel time from A to F

8:40

8:50

9:00

Simulation (average by every 5minutes)
Observation (by floating car)
Observation (average over 1 hour)

03:00
02:00
01:00
Departure Time
00:00
8:00

8:10

8:20
8:30b) Travel time from F to A

8:40

Figure 7: The comparison of travel times

8:50

9:00

Proceedings of "The Second World Congress on Intelligent Transport Systems '95", Vol. IV, pp.1977-1982

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11Link kLink jLink iright turn vehicles at Link i
straight vehicles at Link ia) Real traffic situation

Link kLink jhampering

Link istraight vehicles at Link iright turn vehicles at Link ib) Hampering in the simulation

Figure 8: The problem in the lane choice model in the simulationintersection. Figure 8a illustrates a typical situation suchthat a vehicle which turns at the downstream end of Linki changes the appropriate lane on Link j beforehand, then
the traffic on the other lane keeps smooth. This kind ofsituation easily happens when the traffic is congested andthe lengths of the links are relatively short.
In the simulation, a vehicle cannot find theappropriate lanes until it reaches the link where thevehicle turns at the downstream end. Therefore, as shownin Figure 8b, a vehicle sometimes hampers the upstream
traffic at the downstream end of the Link j, then thethroughput volume at the downstream intersection
decreases. This kind of hampering sometimes detectedaround Intersection C and F in the simulation.
We are now revising the lane choice model toincorporate this kind of lane changing as well as the lanechanging in the middle of a link.

4 . SUMMARY
In this paper, the Hybrid Block Density Method and theconcept of the route and lane choice model used in the
traffic model of AVENUE were briefly described. The
traffic model was applied to a real network for the modelvalidation. Although the availability of the observed data
used in the comparison with the result of the simulation
is limited, we may conclude that the traffic model has theability to reproduce over-saturated condition of urban
traffic which involves complicated lane usages.
For the future issues, the lane choice model should be
improved to incorporate the lane changing in the previous
links to the turning intersection as well as the lanechanging in the middle of a link.
Furthermore, the route choice model will be improved
so as to be applied to more complicated and larger
networks. The current route choice model takes the waythat some vehicles always select pre-fixed paths and theothers always select the minimum time paths revised inevery certain minutes, but this way is too restricted to beapplied to more complicated network than the one used inthis paper. The later route choice behavior will bechanged to the stocastic one to fit the recent demand for
traffic simulations in the next version.

REFERENCES[1] Horiguchi, R. et al., "A Development of a Traffic
Simulator for Urban Road Networks: AVENUE", VehicleNavigation & Information Systems Conference
Proceedings, Yokohama, 1994, pp.245-250[2] Horiguchi, R. et al., "The Case Studies of the Traffic
Assessment Using AVENUE", IATSS Research, Vol.18,
No.2, 1994, pp.76-79[3] Ozaki, H., "A Dynamic Traffic Simulator to Evaluate
Signal Control (DESC)", Traffic Engineering, Vol.24,
No.6, pp.31-37, Japan Society of Traffic Engineers, 1989
(In Japanese)[4] Ueda, I. et al., "Development of a Traffic Simulation
Model Incorporating Route Choice", Proceedings ofInfrastructure Planning , No.14-1, pp.279-286, 1991 (InJapanese)