An Evaluation of Effects of Dynamic Route Guidanceon an Urban Expressway Network
Toshio Yoshii 1)
Hirokazu Akahane 2)
Masao Kuwahara 1)
1)

Institute of Industrial Science, University of Tokyo
7-22-1 Roppongi, Minato-ku Tokyo 106, Japan
Tel 03-3402-6231, Fax 03-3401-6286, e-mail yoshii@ozak.iis.u-tokyo.ac.jp
2)
Department of Civil Engineering, Chiba Institute of Technology
2-17-1 Tsudanuma, Narashino, Chiba 275, Japan

been validated to
ABSTRACTThis study evaluates impacts of dynamic route guidance
by applying our traffic simulation model, SOUND, to
the Tokyo Metropolitan Expressway network. The
focus is placed on how the information quality (present
or predicted information) and a share of equipped
vehicles of navigation systems influence on traffic
condition. Proposing several scenarios with different
compositions of driver types and with/without anaccident, we examine the impacts qualitatively. In
most cases of this study, the predicted information
improves traffic condition more than the present one,
especially in a case with an accident unexpectedly
occurred.

1. INTRODUCTION
The recent developments in Intelligent Transport
Systems are expected to bring the environment in which
the dynamic traffic information and route guidance are
frequently supplied to users. On the other hand, since
there has been a negative opinion for the system [1].
For instance, due to providing traffic information, traffic
condition might sometimes get worse than one without
the information, since everyone would try to use routes
recommended by the current information. We should,
therefore, investigate how and to what extent route
guidance systems (or more generally traffic information
systems) can mitigate traffic congestion.
In this study, we focus on how the information quality(present or predicted information) and a share ofequipped vehicles of navigation systems influence on
traffic condition.
We use the dynamic traffic
simulation model, SOUND (a Simulation model On
Urban Networks with Dynamic route choice), which has

reproduce various traffic conditions reasonably well [2].
The model is here applied to the Tokyo Metropolitan
Expressway network to examine impacts of traffic
information.
Several studies have analysed effects of dynamic route
guidance on traffic. Moritsu et al. have analysed theimpacts of information simulating behavior of two typesof drivers: guided and unguided drivers [3]. A guided
driver chooses a route of the shortest travel time basedon the current traffic condition, whereas an unguided
driver chooses a fixed route. They report that thenegative effect appears, when over 70% of drivers are
guided and that the positive impact may also be reducedby the communication delay.
Mahmassani et al. have developed a simulation model toinvestigate effects on the performance of a congested
urban traffic under real-time in-vehicle information [4].
The study reports that the optimal condition is achieved
when a driver switches his current path only if the
improvement in remaining travel time exceeds somethreshold level of about 20% of the remaining trip time.
These previous studies mostly analyse impacts of traffic
information based upon present conditions. On theother hand, we here analyse both present as well as
predicted traffic information, since we expect to provide
traffic information which tells predicted future traffic
condition. Another unique feature of this study is thatthe model is applied to a real network with observed
time-dependent OD volumes so that the results derived
from the study could practically more appeal.

2. SIMULATION MODEL2.1. The model Framework
In this study, there are two types of models for thepresent information and the predicted information. The
present travel time of a route means the sum of present
instantaneous link travel times along the route, while thepredicted travel time is considered to approximate theactually experienced travel time and is obtained based on
the future traffic condition estimated by the prediction
logic mentioned later.

routes based on the estimated future traffic condition
above, and execute the computation for ∆T time interval.
This process is repeated at every ∆T. Therefore, forpredicted information, the simulation bypasses to themodel for present information every ∆T interval so as toestimate future condition.
Simulation
Time[min]0

Route Choice Module
Determine route choice probability(Dial's LOGIT assignment)
the probability ofchoosing a route

Travel timeof each link

Vehicle Simulation Module

20 30 40

10

50 60

70

Prediction of future traffic

ΔT

Prediction of future traffic

5

Prediction of future traffic

10
15
・
・
・

(1) The model for present information
Fig.1 shows the model for the present travel time thatconsists of two modules: the vehicle simulation and theroute choice modules. The vehicle simulation module
moves discrete vehicles forward at every scanninginterval of ∆t (=3 seconds) based on a simple
car-following model along routes determined by theroute choice module, and updates present link travel
times whenever vehicles leave links. On the other hand,
the route choice module evaluates every driver's route ata regular interval of ∆T (= 5 minutes) based on present
link travel times estimated by the vehicle simulation
module.
These two modules are repeatedly
implemented to reproduce dynamic evolution of traffic
flow on a network. The detailed description of themodel is written in reference [2].

0

Fig.2

Prediction of future traffic・・・The Model Structure for Predicted Information

2.2. Travel Time Information
In the simulation model for predicted information, at
every ∆T, future traffic condition is produced up to one
hour in advance.
And the average link travel time ofeach link is updated for every 5 minutes during the
one-hour time period. Then, predicted travel times ofroutes are calculated along the routes as the sum of
predicted link travel times at times when a vehicle willenter the links. For example, suppose the simple
network shown in Fig.3. Table 1 shows the average
travel time of each link for every 5 minutes. In thiscase, a vehicle at node A will arrive at node B 8 minutes
later because current travel time of link a is 8 minutes.
And it will arrive at node C 12 minutes later as travel
time of link b at time the vehicle arriving at node B (8minutes later than current time) is 4 minutes. In thisway, the predicted travel time is evaluated along a route.
On the other hand, the present travel time of a route is
obtained by simply summing up present link travel times
on the route.

(Car-following model)

b

a
Fig.1

The Model for Present Information

B

D

c

A

(2) The model for predicted information
Although, for predicted information, the general model
structure is the same as above, the difference is foundonly in provision of the predicted travel times. As shownin Fig.2, when the simulation starts at time 0, we first
implement "Prediction of future traffic" to estimates
future traffic condition up to one hour in advance.
"Prediction of future traffic" means the computation of
the model for present information; that is, everyone isassumed to choose a route based on the present travel
times in this prediction. Then we determine driver's

C

Fig.3
Table 1[min.]
15~20
10~15
5~10
0~5

Simple Network

Link Travel Time of Each Link for Every
5min.
Link a108
7
8Link b8
5
4
3Link c121088

2.3. Route Choice Behavior
We consider the following three different types ofdrivers.
(1) Fixed-Route Group
Drivers in this group do not change routes regardless oftravel times, and the fixed probabilities are calculated by
the Logit assignment based on free flow link travel times
at speed of 60 [km/h]. They will choose a next link to
go at diverging point abiding by the diverging ratio
determined by the assignment.
(2) VMS Group (Route Choice Group based on Variable
Message Signs)
Drivers in this group are respondent to traffic
information but they do not have vehicle navigationequipment. They are assumed to have experiences todrive on a network and have knowledge of average
travel times of all links under regular condition (with noaccident). They can get information covering only alimited area through VMS (Variable Message Signs).
The information updates a part of average travel times atregular condition. They are assumed to choose thetravel time minimum routes to their destinations,
therefore, based on the above partially update travel
times.
The rule of determining a section which VMS displays isin accordance with the logic employed by theMetropolitan Expressway Public Corporation. Roughly
speaking, the section is selected depending upon the
degree of congestion and the share of the traffic volume
related to the congestion.
In case of providing
predicted information, the selection is based on predictedtraffic condition at 30 minutes later than current time.

vehicle equipment. When the present information isprovided, only traffic condition on the horizontal axis
are updated by it. But, VMS Group can get the
information until the head of the queue on the routeaccording to the logic how to select the displayed section
mentioned above. On the other hand, when thepredicted information is provided, since Navigation
Group can get all the information up to one hour inadvance, they renew the all of the time-space plane of
the link travel times shown in Fig.4. While VMS
Group can update a part of the plane that is up to a headof the nearest queue at 30 minutes later time and hence
displayed on VMS.
[min]
60

[min]
60

50

50

40

40

30

30

20

20

10

10

Current
place

Destination
Navigation Group

Current
place

Destination
VMS Group

Present Information
[min]
60

[min]
60

50

50

40

40

30

30

20

20

10

10

Current
place

Destination
Navigation Group

Current
place

Destination
VMS Group

Predicted Information
No Information
Information by Predicted Traffic condition
Expected Travel Time by Regular Traffic condition
Congested Link

Fig.4

Covered Areas by Information(3) Navigation Group (Route Choice Group using
Vehicle Navigation Systems)

3. APPLICATIONSTOMETROPOLITAN EXPRESSWAY

Drivers in this group are respondent to traffic
information from control center through vehicle
equipment, which means they can access traffic
condition of an entire network at any time. And they
are similarly assumed to choose the shortest routes totheir destinations.

3.1. Network and Traffic DemandFig.4 shows the difference in covered areas by the
information between VMS and Navigation Groups.
The horizontal axis shows a route and each section
represents a link, while the vertical axis shows the future
traffic condition accessible either through VMS or

TOKYO

Table 2 and Fig.5 show the outline of the Tokyo
Metropolitan Expressway network used in this study.
The network is approximately 240 km in length with
about 800 links and 800 nodes, and a star mark in the
figure denotes the location of an accident we created.
During the simulation period from 4 am. to 11 am.,about 350 thousands vehicle trips are generated.
Table 2

Network and Traffic Demand in this study

Length in km
No. of Links
No. of Nodes
No. of Trips ( 4 am. - 11 am.)

240
800
800
350,000

all the scenarios, traffic capacity at the accident site is
reduced for one hour period from 7 to 8 am., and the
reduced capacity value is assumed to be exactly known
in the simulation.
3.3. Application Results(1) Improvement of Traffic Condition by Present andPredicted Information

★

5 km

Fig.5

★Accident Point
Tokyo Metropolitan Expressway Network

Fig. 6 shows the average travel time per trip by the shareof Navigation Group in scenario 1. Since the average
travel time per trip gets extremely large when the shareis less than 50%, the figure describes only a portion withgreater than 50% share of Navigation Group. From thefigure, traffic condition seems to be improved more by
predicted information than present information
regardless of the share of Navigation Group.
Especially, when traffic condition changes drastically
due to the accident, predicted information largely
improve the condition compared to present information.

The applications are made according to three scenarios
below.
Scenario 1
In order to simplify the situation, the VMS Group iseliminated. Then, we change the share of Fixed
Route Group and Navigation group to evaluate effects of
information at various different components of drivers
responding traffic information.
Scenario 2
It is reported by another research survey that about a halfof drivers traveling the expressway are respondent totraffic information and the remaining do not change theirroutes[5]. Thus, the share of Fixed Route Group is set
at 50% and effects of information is evaluated at various
shares of other two groups of VMS and Navigation
Group. In this scenario, impacts can be evaluated due
to diffusion of vehicle navigation systems under thecurrent situation of 50% Fixed Route Group.
Scenario 3
In the near future, it is expected that information will be
more accurate and easily acquired by drivers, then theshare of drivers responding information would increase
and that of Fixed Route Group would decrease. For
this reason, the share of Fixed Route Group is set at 20%
and effects of various shares of other two groups are
examined.
In each scenario, two situations of with and without anaccident at the location marked in Fig.5 are simulated.
Since many OD pairs have several alternative routes,
substantial amount of vehicles are expected to switch
routes due to congestion caused by the accident. For

Average Travel Time3.2. Scenarios

[sec]
2400

Present
Predicted

2000

Accident
No Accident

1600
50

60

70

80

90

100

The Ratio of Navigation Group [%]Fig.6

Changes in Traffic Condition depending on
the Share of Navigation Group (scenario 1)(2) Traffic Condition depending on Share of Navigation
Group
Figure 6 says that, with the share of Navigation Group of
more than 80%, traffic condition gets worse with higher
share even if the predicted information is provided.
When the present information is given, this worse traffic
condition is considered to be brought by theconcentration of vehicles to routes with currently shorter
travel times. However, this tendency is observed evenwith the predicted information, although theconcentration would not as much as the situation withthe present information.
The main reason for this worse traffic condition would
be due to the predicted information which cannot exactly
estimate future condition. Especially, when the shareof Navigation Group is 100%, the average travel timewith the predicted information and present one arealmost the same in the case of no accident. Since, inthis study, the travel time is predicted assuming thatdrivers choose routes based on current traffic condition,
the gap between the predicted and the actual traffic
condition may become large with the very high share of
Navigation Group. Another reason would be foundfrom the fact that the user equilibrium condition is not
generally the optimal condition for the system as a whole.
In other words, some portion of drivers must be forced
to choose routes not giving the shortest travel times forthem in the system optimal condition. In this casestudy, the above condition seems to be established when
the share of Navigation Group is more or less 80%.
(3) Areas covered by Traffic Information
In scenario 2 and 3, keeping the share of Fixed-RouteGroup 50% and 20% respectively, we change the sharesof VMS Group who can get traffic information of alimited part of a network and Navigation Group who can
get information of an entire network. Figures 7 and 8
respectively show the result without and with theaccident.

Average Travel Time

[sec]
2400

Present
Predicted
Scenario 2(FR=50%)2000

Changes in Traffic Condition depending on

Fig.8

Information Sources (with the accident)
In both scenarios, the traffic condition is improved as theshare of Navigation Group increases. Traffic condition
with the predicted information becomes clearly
improved compared to the condition with the present
information, when the share of Navigation Group out ofdrivers utilizing information exceeds a certain level.
This boundary level is higher as the share of
Fixed-Route Group decreases and in a case with theaccident.
Also, when the share of Navigation Group is zero,
traffic condition with the predicted information
sometimes gets worse than one with the present
information. Fig. 9 shows a variation of distances tothe head of a queue shown on the variable message signfrom the diverging point at which the variable message
sign is installed. From the figure, we see that the area
shown on the variable message sign with the predicted
information is quite different from one with the present
information. Because of this difference in the area
shown on the sign, sometimes traffic condition with thepredicted information, which often shows more narrow
area than one by present information, happens to beworse. In reality, this situation would not likely occur,
since drivers can see the approximate traffic condition ofa wider area from graphic information signs which havebeen installed on the network in addition to variable
message signs.
Present
Predicted[km]

Scenario 3

30(FR=20%)0

Average Travel Time

Fig.7

20
40
60
80
100
The Share of Navigation Group to [%]Route Choice Group

Changes in Traffic Condition depending on
Information Sources (without the accident)
[sec]
2400

Scenario 2

2000

Scenario 3

Distance

1600

20
10
0
6

8

10

12

Time [am.](FR=50%)

Fig.9 Variations of Distances to the Head of a Queuefrom the VMS Location

4. SUMMARY AND FUTURE SCOPE(FR=20%)Present
Predicted
1600
0

20
40
60
80
100
The Share of Navigation Group to
Route Choice Group [%]This study analyses impacts of the quality of information(the present and predicted information) on traffic
condition. The major remarks are summarized asfollows:
1. It is confirmed that, in most situations, traffic
condition is more improved by providing thepredicted information than the present information.

2. Especially, the above improvement becomessignificant when an unexpected accident occurs.
Also, traffic condition gets better as the share ofdrivers utilizing information increases.
3. Since the predicted information cannot exactly
estimate future condition, and the user equilibrium
condition is not generally optimal for the system as awhole, traffic condition sometimes gets worse as theshare of Navigation Group increases even if thepredicted information is provided, especially with therate more than 80%.
4. Compared to traffic information through variable
message signs covering a part of the network,
information through navigation equipment covering awider area improve traffic condition more.
5. When traffic information of only a part of the
network is given, traffic condition with the predicted
information may sometimes get worse than one withthe present information. Hence, it is important toselect one or two proper areas shown on a variable
message signs, which can provide only a limited
number of messages at once.
Some of future research topics would be
1. Some theoretical analyses in addition to thesimulation study are required to support the above
results obtained here, since those obtained throughsome case studies using the Metropolitan Expressway
network may not be generally concluded.
2. We should examine how the accuracy of information
affects traffic condition. For instance, the OD
volumes have no errors in this simulation model, andthus the predicted information seems more accurate
than one in the real situation, if the same prediction
logic is employed. Furthermore, if the bi-directional
communication system were common, the amount of
information acquired from vehicles traveling on a
network could be substantially more and the predicted
information could be more accurate.
3. Effects of graphic information boards should beanalysed, since drivers can roughly recognize traffic
condition of a wider area than one covered by VMS.
4. Although the route choice behavior of a driver is abase of modelling, there are still enough rooms to bestudied.

ACKNOWLEDGMENT
We would like express our sincere thanks to Dr. Morita,
vice director of Engineering Department as well as
members in Planning and Traffic Control Divisions,
Metropolitan Expressway Public Corporation, whokindly provided traffic data for the research.

REFERENCES[1] Arnott, R. et al., “Does Providing Information to
Driver Reduce Traffic Congestion ? ”, Transpn. Res.
Vol.25A , No.5, pp.309-318 , 1991.
[2] Yoshii, T. et al., “Development of Traffic Network
Simulation Model for Oversaturated Traffic Flow on
Urban Expressways”, Traffic Engineering Vol.30, No.1,
pp.33-41,1995.
[3] Moritsu, H. et al., “Analysis of Traffic Network Flowby Adapting Route Guidance”, proceedings ofInfrastructure Planning, Vol.9, pp.37-44, 1991.
[4] Mahmassani, H. S. & Jayakrishnan, R., “System
Performance and User Response under real-time
information in a congested traffic corridor”, Transpn.
Res. Vol.25A, No.5, pp.293-307,1991.
[5] Center for Tokyo Metropolitan
expressway
Technology, “The Report of Basic Research about
Traffic Engineering and Traffic Economy in Tokyo
Metropolitan Expressway”, pp.275-287,1993.2.