Impacts of the Accuracy of Traffic Informationin Dynamic Route Guidance SystemsToshio 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

ABSTRACTIn this study, we evaluate impacts of the accuracy of traffic information in dynamic routeguidance. We focus on how the information quality (present or predicted information) and its
accuracy influence on traffic condition in relation to the share of equipped vehicles. The recent
developments in Intelligent Transport Systems are expected to bring the environment in which the
dynamic traffic information and dynamic route guidance are frequently supplied to users. On theother hand, there have been negative opinions for the system: providing traffic information may
sometimes make traffic condition worse because everyone would try to use routes recommended by
the current information. We should, therefore, investigate how and to what extent route guidancesystems (or more generally traffic information systems) can mitigate traffic condition. In addition
to categorizing two type of information quality (present or predicted), predicted information isclassified into three types with different level of accuracy (high, middle, low). The impacts of theinformation has been evaluated quantitatively by applying our dynamic traffic simulation model,SOUND[1], to the Tokyo Metropolitan Expressway network. As the result, it was confirmed thatproviding predicted information can improve traffic condition even if the information is not perfectly
accurate and the improvement seems more when the share of equipped vehicle becomes higher.

1. INTRODUCTION
One of the most expected effect of ATIS is the improvement of traffic condition by providing
traffic information. We should therefore understand the effect through realistic experiments.
Yoshii et al. have analyzed effects of dynamic route guidance systems providing present or predicted
information without prediction errors[2]. And they reported that traffic condition is improved moreby providing the predicted information in most cases, especially when an unexpected accident occurs.

Whereas in reality, it is quite difficult to precisely predict future traffic condition. In this study, weconsider different accuracy levels in the predicted information and evaluate impacts of dynamic
route guidance in relation to the share of equipped vehicles. Another feature of this study is that themodel is applied to a real network with observed time-dependent OD volumes so that the results ofthe analysis could practically more appeal.

2. SIMULATION MODEL
2.1 The Model Framework
As the model framework is written in reference[2], this chapter explains just the outline of themodel framework. Let us first define present and predicted information, which this study uses. The
present travel time of a route means the sum of present instantaneous link travel times along theroute, while the predicted travel time is the actually experienced travel time obtained based on thefuture traffic condition estimated by the prediction logic mentioned below.
As shown in Fig.1, 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. In "Prediction of future
traffic", we store future traffic condition bySimulation
0 10 20 30 40 50 60 70
Time[min]implementing the simulation assuming that
0Prediction of future traffic
everyone chooses a route based on theΔT
Prediction of future traffic
5present link travel times. Then, we return to10
Prediction of future traffic
the main course of the simulation and15
Prediction of future traffic
progress the run for ∆T (5 minutes in Fig.1)
・・
by evaluating driver's routes based on the・・・・
estimated future traffic condition above.
Then, the simulation again bypasses toCurrent Traffic
Predicted Information"Prediction of future traffic" in order toupdate future traffic condition and so on as Fig.1 The Model Structure for Predicted Information
shown in Fig.1.
2.2 Route Choice Behavior
We consider the following two different types of drivers.
(1) Fixed-Route Group
Drivers in this group do not change routes regardless of travel times, and the fixed probabilities
are calculated by the Logit assignment (with parameter θ) 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 divergingratio determined by the assignment.

(2) Route Choice Group
Drivers in this group are respondent to traffic information from control center through car
navigation equipment, which means they can access traffic condition of an entire network at any time.
And they are assumed to choose the shortest routes to their destinations based on the information
supplied.
2.3 The Accuracy of Predicted Information
In reality, predicted information may contain errors due to several factors as follows :(a)
OD volumes may not be precisely estimated for the future prediction time period,(b)
The simulation model may not be able to reproduce traffic condition perfectly,(c)Current traffic condition may not be exactly monitored,(d)Driver's route choice behavior may not be fully understood.
On the other hand, when we evaluate the impacts through the model, the errors (a), (b) and (c)are not included, since the simulated traffic condition by the model is considered as the real trafficcondition. Thus, for the more realistic evaluation of traffic information systems, we should add
error terms to the predicted information.
Since drivers in Route Choice Group respond to predicted travel time in their route choices, theerror term should be added to travel times. There are two ways to put error terms to travel times:
Method 1
Method 2

Adding the error term to each link travel time, orAdding the error term to each route travel time.

In Method 1, there is a problem that if the number of links along a route increases, the error term)
becomes less dominant. For example, suppose that travel time of link a , ta , consists of constantterm of ta and its error term is εa following the Normal distribution, N (0, (α⋅ta)2) :)t a = ta + εa .

(1))

Then, travel time of route p composed of n links, Tp , is))Tp = Σa ta =εpΣ a ta + Σ a εa = Tp + εp(2)∼ N(0,( α⋅ ∑a t a 2 )2).

Therefore, this ratio of standard deviation of the error term to the constant time on route p is smallerthan one at link a approximately by a factor of 1/ n :(α⋅ta)/ ta=α> (α⋅ ∑a t a 2 )/ Tp. ≈α/ n(3)On the other hand, in case of Method 2, instead of avoiding the problem as above, anotherproblem occurs in route travel time. Suppose there are two routes of almost the same travel times
but they share some portion of the routes as shown in Fig.2. We cannot add the error term
independently because their routes are partially overlapped each other. In general, however to add
appropriate error term considering this kind of shared portion is not systematically easy.
Route iOrigin

Destination
Route jFig.2 Example NetworkTherefore, this study employs the following method, which is a slight modification of Method 1.
For route p, its travel time is defined)
Tp = Tp + εpso thatVar ( ε p ) / Tp=)

Tp + (Σa ta - Σ a ta)⋅( Σ a ta / ∑a t a 2 )(4)is equal to α, which is shown as below:)
Var {(Σa ta - Σa ta)⋅( Σa ta / ∑a t a 2 )} ={(Σa ta )2 / (Σa ta 2)}⋅Var(εp )= {(Σa ta )2 / (Σa ta 2)}⋅ α2 ⋅ (Σa ta 2)= (α Tp ) 2
However, there is still a problem that the additive law is not warranted; that is, for route p consisting) ) )of link a and b, Tp ≠ ta + tb as illustrated in an example below.
)Tp)ta)tb

Fig.3 Violation of the Additive Law

3. APPLICATIONS TO TOKYO METROPOLITAN EXPRESSWAY3.1. Network and Traffic Demand
Table 1 and Figure 4 show the outlineof the Tokyo Metropolitan Expressway
network used in this study. The network isapproximately 240 km in length with about
800 links and 800 nodes, and a star mark in
the figure denotes the location of anaccident we creates. During the simulation
period from 4 am. to 11 am., about 350
thousands vehicle trips are generated.

D (Horikiri)★

5 kmO (Shouwajima)★ Accident Point

Fig.4 Tokyo Metropolitan Expressway Network

3.2 The Ratio of Drivers belongs to Route
Choice GroupWe change the share of Fixed RouteGroup and Route Choice Group to evaluate
effects of information at various different
components of drivers responding trafficinformation.

Table 1 Network and Traffic Demand
Length in km
240
No. of Links
800
No. of Nodes
800
No. of Trips ( 4 am. - 11 am.)
350,000

3.3 Information Quality
Four types of information with different quality are defined as follows :(a)present information,(b)predicted information without any error,(c)predicted information with error whose standard deviation is 10% of travel time (α=0.1),(d)predicted information with error whose standard deviation is 20% of travel time (α=0.2).
For information types (c) and (d), since we have to add the error term according to the Normaldistribution, we run the simulation several times and take an average of these results to evaluate
impacts on traffic condition.
3.4 Application Result
Fig.5 shows the average travel time per trip within the simulation time (from 4 a.m. to 11 a.m.)by the share of Route Choice Group. Since the average travel time per trip gets extremely large
when the share is less than 50%, the figure describes only a portion with the share greater than 50%.

Average Travel Timeper Trip

[sec]
2400
2200

present
2000
predicted

10%

20%

1800
50

Fig.5

present
predicted
predicted (α =10%)
predicted (α =20%)

60
70
80
90
The Ratio of Route Choice Group[%]
100

Changes in Traffic Condition depending on the Share of Route Choice Group

From the figure, we remark the following points :(1) Traffic condition seems to be improved more with predicted information than with present
information regardless of the share of Navigation Group, even if the information is not strictlyaccurate.
(2) The gap between the cases with predicted information and predicted information with error
becomes large as the share of Route Choice Group increases except for the case of 100%.
(3) In both cases with present and predicted information without error, when the share of Route
Choice Group is more than 80-90%, traffic condition gets worse as the share increases. On theother hand, if there are some errors in predicted information, this tendency is not apparent.
The following section explains each points in more detailed manner.
Table 2 Average Travel Time

(1) Table2 shows average travel times underprovision of predicted information with andwithout error as well as present information for
the 80% share of Route Choice Group. The
travel time is averaged over 20 calculations forthe case providing erroneous predicted
information (α=0.1). From the table, traffic
condition seems to be more improved bypredicted information even with error than bypresent information.

Predicted Information ( α =10%)
Average Travel Time
Standard Deviation
Min. Travel Time
Max. Travel Time
Predicted Information ( no error )Average Travel Time
Present Information
Average Travel Time

[sec]
1936
53185020251868
2050(2) Fig.6 shows how much time Route Choice Group saves compared to Fixed Route Group incases of 50% and 80% share of Route Choice Group under provision of predicted information withrandom error (α=0.1) for trips from Shouwajima to Horikiri. From this figure, the advantage ofRoute Choice Group in travel times with 80% share of Route Choice Group is smaller than one withAdvantage of Route ChoiceGroup in Travel Time

50% share; that is, this advantage seems smaller as the share increases, and this tendency has beenconfirmed in all cases with other type of information. This advantage would be caused by thedifference of travel time between the shortest route and the other alternative routes. When the shareof Route Choice Group is large, the travel time difference among alternative routes would be smaller,
since travel times on alternative routes could be more equalized by the majority of Route ChoiceGroup. Thus, if predicted information has some errors, Route Choice Group may have larger
chance not to choose the shortest route and consequently their advantage may be smaller comparedwith Fixed Route Group. On the contrary, if the share is small which means travel time difference
among alternative routes is large, Route Choice Group could successfully choose the shortest routeeven with erroneous predicted information.
[sec]
1800
1500
1200
900
600
300
0-300 680% share of Route Choice Group
50% share of Route Choice Group

50%

80%
7[a.m.]

891011TimeFig.6 Advantage of Route choice Group (α=0.1）
As the share of route Choice Group increases, the advantage is smaller, and there are some timeintervals with very little advantage, for example from 8 a.m. in Fig.6. Then, it seems that there isfew effect of route guidance when the share of Route Choice group increases; however, as theaverage travel time would be shorter than one for lower share, the utility of users belonging to RouteChoice Group is not reduced by the larger share of Route Choice Group.
(3) Figure 5 says that, with the share of Navigation Group of more than 80-90%, traffic condition
gets worse even if the predicted information is provided. When the present information is given,
this tendency would be brought by the concentration of vehicles to the shortest route. However,this tendency is observed even with the predicted information, although the concentration is not asmuch as the situation with the present information. The main reasons for this worth traffic
condition would be:
1. The predicted information which cannot be the exact estimate of future traffic condition.
2. The user equilibrium condition does not generally bring the optimal condition for the system as
a whole. In other words, some portion of drivers must be forced to choose the second or third
shortest routes for them in the system optimal condition. In this case study, the above condition
seems to be established when the share of Route Choice Group is more or less 80%.

On the other hand, in the case of providing predicted information with error, even if all drivers
belong to Route Choice Group, some of them may choose the second or third shortest routes becauseof the error term. Therefore, lines of =10 and 20% look monotone decreasing in the share.
4. SUMMARY AND FUTURE SCOPE
This study analyses impacts of the quality of information on traffic condition.
remarks are summarized as follows :The major

1. It is confirmed that traffic condition is more improved by providing the predicted information
than the present information, even if the information is not strictly accurate.
2. Since the gap between the cases providing predicted information and predicted information witherror becomes large with high share of Route Choice Group, the accuracy of predicted
information is more important as the share of Route Choice Group increases.
3. In the case of providing predicted information with error, the higher share of Route ChoiceGroup can achieve more improvement.
Some of future research topics would be :1. Some theoretical analyses in addition to the simulation study are required to support the above
results obtained here, since those obtained through some case studies using the Metropolitan
Expressway network may not be generally concluded.
2. We should examine how the several error factors influence the accuracy of information.
3. Although the route choice behavior of a driver with dynamic information is a base of modeling,
there are still enough rooms to be studied on the human factor.
ACKNOWLEDGMENT
We would like express our sincere thanks to Dr. Morita, director of Traffic Control Division as
well as members in Planning and Traffic Control Divisions, Metropolitan Expressway Public
Corporation, who kindly provided traffic data for the research.
REFERENCES[1] 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 (In Japanese).
[2] Yoshii, T. et al., “An Evaluation of Effects of Dynamic Route Guidance on an Urban Expressway
Network”, Proceedings of the Second World Congress on ITS ‘95 YOKOHAMA, Vol. 4,pp.1995-2000, 1995.