23rd ITS World Congress, Melbourne, Australia, 10–14 October 2016

Paper number AP-TP0533

Advanced simulation model in the region of Tokyo metropolitan
urban expressway rings*T. Oguchi1, M. Chikaraishi2, M. Iijima3, H. Oka4, R. Horiguchi3, J. Tanabe5 andY. Mohri4
1. Institute of Industrial Sciences, the University of Tokyo, 4-6-1 Komaba, Meguro, Tokyo, +81-35452-6387, takog@iis.u-tokyo.ac.jp, Japan
2. Graduate School for International Development and Cooperation, Hiroshima University, Japan
3. i-Transport Lab. Co., Ltd., Japan
4. The Institute of Behavioral Sciences, Japan
5. Regional Futures Research Center Co., Ltd., Japan

Abstract
In the near future, Tokyo metropolitan region will be covered by urban expressway networkwith ring and radial roads after the completion of three ring roads. The connectivity provided
by networked expressways will provide many route choice options for many Origin-Destination
patterns, and the needs for effective and real time dynamic traffic management become much
more important. In addition, the significance to complete rings exists in the function to guidecommercials trucks to ring road routes rather than radial routes. For the development of theadvanced function of such effective and dynamic traffic management scheme, the existing
network traffic simulation SOUND shall be modified to accommodate commercial truck typicalbehavior based on the analysis of real behavior of commercial trucks. The model framework to
estimate Origin-Destination demands of commercial trucks based on land use model which isalso affected by network traffic operation efficiency is proposed and established.
Keywords:
network traffic simulation, commercial truck, truck terminal location.

Introduction
The Olympic and Paralympic games in 2020 will be held in Tokyo. Tokyo road network will
be changed in the near future and will have possibility to accommodate traffic demand much
more smoothly and efficiently than before the completion of the three ring expressways. Tokyo
metropolitan region, in the near future, will be covered by urban expressway network with threering roads including the Central Circular Route (the 1st ring road), Tokyo Outer Loop Road

Advanced simulation model in the region of Tokyo metropolitan urban expressway rings

Figure 1 - Urban expressway network of three ring roads and radial roads in Tokyo
metropolitan region(Gaikan: the 2nd rind road), and National Capital Region Central Loop Road (Ken-o-do: the
3rd ring road), and radial roads as shown in Figure 1. The connectivity of expressway network
will provide many route choice options for many Origin-Destination patterns and the needs for
effective and real time dynamic traffic management become much more important.
In other words, even after completion of urban expressway network, traffic congestion cannot
be avoided because of the existence of several traffic capacity bottlenecks, and the congestion
effects caused by traffic accidents cannot be also. The intelligent traffic management scheme isneeded to utilize these bottlenecks and real time monitoring system of sensors including probing
technology and volume detection as well as sophisticated and real time estimation model
framework, such as 'now-cast' traffic simulator [Hanabusa, et. al. 2013a, b], will be able toprovide several alternatives for the most effective management of all the road network of theTokyo metropolitan region covered by urban expressway network.
In addition, the significance to complete rings exists in the function to guide commercials trucks
to ring roads rather than radial and through routes is high. For the development of the advanced
function of such effective and dynamic traffic management scheme, the existing model"SOUND" [Horiguchi, et. al., 2015] shall be modified to accommodate commercial truck
typical behavior based on the analysis of real behavior of commercial trucks. The model2

Advanced simulation model in the region of Tokyo metropolitan urban expressway rings

framework to estimate Origin-Destination demands of commercial trucks based on land use
model which is also affected by network operational efficiency is proposed and established.
Commercial Truck Behavior
Background
One of the important aims of developing ring roads surrounding an urban area is that to reduce
through-traffic in the central area of Tokyo. Thus, understanding drivers’ route choice decision,that is, whether they use inner or outer ring road, is crucial for traffic management. One problem
applying the conventional logit model to this practical work is to explicitly generate alternativeroutes prior to the choice model development, known as the choice set generation problem. Thisis particularly problematic in the current study, since the road network in the central area ofTokyo is quite dense, providing a large number of competitive routes which are difficult to be
enumerated.
Because of this difficulty, the authors employ the recursive logit model where the route choiceproblem is formulated as a sequence of link choices, and thus alternative routes do not need to
be enumerated [Fosgerau, et. al., 2013, Mai et. al., 2015].
Note that, although the recursive logit model can avoid the route enumeration, there are twomain remaining issues. First, the correlation across routes is not dealt with in the conventional
recursive logit model. For this issue, Mai et al., (2015) proposed the nested version of therecursive logit model. Second, the perception variance of generalized travel cost is assumed tobe constant. The larger perception variance on travel time may exist when they choose whether
or not the 3rd ring road is used, while it would be smaller they choose whether or not the 1st
ring road is used [Chikaraishi et. al., 2015]. This is because travel time in the 1st ring road is
far shorter and its estimation may be easier for drivers, resulting in the smaller perception
variance. Though the conventional recursive logit model is used in this paper as a first trial, theabove mentioned two limitations need to be addressed in future.
Empirical analysis
Commercial truck route choice behavior was never known before "Commercial Truck Probe
Data" comes to be available. A private data provider has made it available. Data used in theempirical analysis of this study is freight vehicle GPS data provided by FUJITSU Ltd., collectedfrom July 25 to 31 in 2015. In total, 164 trips which destination is off-ramp "Haneda Airport
Central", Westbound (Out-bound) on the Bay-line of Tokyo Metropolitan Expressways. The
observed total number of link choice is 8,362.
The utility function used in this analysis is assumed as below.
v(a|k) = time xtime + cost xcost + pena xpena + RC xRC 20xU turn3

Advanced simulation model in the region of Tokyo metropolitan urban expressway rings

Where, xtime: travel time [minutes], xcost: monetary travel cost [100 JPY], xpena: 1 (which implies
that the generalized cost caused by running on multiple links), xRC: right turn dummy (1: 70170 degree right turn, 0: others), xUturn: U-turn dummy (1: 175-185 degree turn, 0: others), andβtime, βcost, βpena, βRC are parameters to be estimated. Travel time here is set according to static
road network information based on the types and width of roads. Monetary travel costs are set
based on fuel costs 20 JPY per kilometer, toll fare on expressways 26 JPY per kilometer withterminal charge 150 JPY. Note that all of the expressways in Japan are generally toll roads.
Table 1 - Results of recursive logit model estimation
parameters

t-value!"#$%-0.681-11.89!&'("-1.504

-7.11

!)%*+

-0.680-155.6!,--2.502-9.13

sample size (number of trips)

164

sample size (link choice numbers)

8,362

final log-likelihood-1279.05

Table 1 shows the estimation results of proposed recursive logit model using the data. All the
explanatory parameters show logically acceptable positive/negative values. From the results,
the value of time for commercial trucks is estimated as 45.3 JPY per minute and the value ofright turn is estimated as 166.4 JPY per turn, or 3.5 minutes per turn. The value of right turnshows rather higher costs which imply that commercial trucks, especially those trucks which
may transport relatively valuable goods, may prefer to go through rather than make turns.
By the way, the results are only obtained using very limited trips which destination is the off
ramp "Haneda Airport Central" so that the generalized analysis with rather wide variation of
Origin-Destination patters is needed in the near future. The utility function may need to bemodified; for example, considering left turn costs, direction difference to the destination, andso forth.
Land use
The construction of expressway network surrounding Tokyo Metropolis is considered to change
the place of commercial truck terminals so that the Origin-Destination truck volume will bechanged significantly. The authors try to utilize the data and the model framework of land use
estimation model established by the "Tokyo Metropolitan Freight Survey, 2014"[Tokyo
Metropolitan Area Council for Transport Planning, 2015].
4

Advanced simulation model in the region of Tokyo metropolitan urban expressway rings

Land use is estimated with a logit model proposed by the authors using the obtained land use
data " Tokyo Metropolitan Freight Survey, 2014". The land use potential for physicaldistribution facilities is estimated with this model after the construction of the three ring roads.
The result shows that the land use potential of physical distribution facilities will be higher
along with the ring roads, especially along with the National Capital Region Central Loop Road
(Ken-o-do; 3rd ring road). The proposed model to estimate the land use potential is represented
as below.

rexp(Vir )Pir = P
r) ,
exp(V
i
j

Vir =

X

r r
k xki + ln Sikrwhere, Pi is selection probability of area i, sample r ; Vi is selection utility of the area i,rsample r ; xki is k-th explanatory variables of the area i , sample r ; Si is a variable torrepresent the size of the area i as livable land area size; and k is a parameter. The
explanatory variables for selection utility used in this model are those such as; population
density, commutable labor force, accessibility to manufacturing industries, accessibility tobusiness district, industrialized-use purpose land use ratio, and travel time to the nearest
expressway interchange. The last variable "travel time to the nearest expressway interchange"
is provided by the calculated output from the network traffic simulator introduced in thisresearch as those for morning and evening peak-hour travel time.
Given the land use potential of physical distribution facilities by the estimation model describedabove, the number of commercial truck trips generated from each estimated area is calculatedby the model as below. This model is proposed with empirical analysis.
Ptr ri = ↵Dirrr
where, Ptr i is the land use potential of physical distribution facility of area i, sample r , Di
is truck trip generation demand of area i , sample r . α and β are parameters estimated as
2.08*10-5 and 5.10 with t-value of 0.95 and 19.94 respectively.
Figure 2 shows an example of the estimated results of the commercial truck demand increaseobtained by the comparison between current land use potential and estimated future land use
potential caused by the completion of three ring roads construction. It is found that the truck
trip generation is increased in the areas along with the National Capital Region Central Loop
Road (Ken-o-do; 3rd ring road) such as the vicinities of "Atsugi city" of Kanagawa prefecture,"Ichikawa city" of Chiba prefecture, "Kuki-city" of Saitama prefecture, and "Sakai town" of
Ibaraki prefecture, on the other hand truck trip generation is decreased in other areas. This
results in that the commercial truck trip efficiency will be improved after the completion of thethree ring roads construction.
Implementation of commercial truck route choice model to network traffic simulator

5

Advanced simulation model in the region of Tokyo metropolitan urban expressway rings

Figure 2 - Truck demand increase affected by the three rings completion
Parameter settings
The authors have already proposed to construct a prototype of a network simulation model todescribe network traffic flow surrounding Tokyo Metroplitan area (Tokyo, Kanagawa, Chiba,
Saitama, Ibaraki, Tochigi, and Gumma) based on 'SOUND' as presented last year [Horiguchi,
et. al., 2015]. The route choice model parameters not only for small cars but also for trucks(based on the analysis described above) are set to describe the difference of their behavior.
The formula describing route choice model in SOUND is as below in the case of binary choice
between choice 1 and 2. The probability to choose choice 1 p1 is given by the equation below.
1p1 =
1 + exp(✓(c1 c2 ))where θ is logit sensitivity parameter, c1 and c2 is the generalized cost for route 1 and 2
respectively. The generalized cost in SOUND is calculated by the equation below.
c = 3.6CD

D
M+ CT T + CL NL + CR NR + 60
VFEwhere D [m] is distance to the destination, VF [km/h] is free speed without traffic congestion, T[sec] is travel time to the destination, NL is number of left turns to the destination, NR is number
6Advanced simulation model in the region of Tokyo metropolitan urban expressway ringsof right turns to the destination, M [JPY] is total toll price to the destination, E [JPY/minute] isvalue of time, and CD, CT, CL, CR are parameters for distance, time, left-turn penalty, right-turnpenalty, respectively.
The route choice parameters for small cars are shown in table 2 [Murakami et. al., 2002] aspresented last year [Horiguchi et. al., 2015].
Table 2 - Route choice parameters for small cars used in the simulation model
parameters

values

unit

CD

0.5

CT

0.5

CL

30

secondCR

60

second

E

90

JPY/minute

θ

0.01

The route choice parameters for trucks are set based on the derived values of model parameters

βtime, βcost, βpena, βRC shown in table 1 as following. CD is calculated as the fraction of theparameters for number of links (βpena) divided by the average link length in the study area (330[m], derived by taking average of Digital Road Map data). CT is given just converting the value
βtime in minute to the value in second. E is derived by the fraction of travel time parameter (βtime)
divided by monetary travel cost parameter (βcost). CR is estimated as the fraction of right-turnparameter (βRC) divided by monetary travel cost parameter (βcost), and converted with value oftime E. CL is set to about the half of CR. The summary of the estimated parameters for trucks
are shown in table 3.
Table 3 - Route choice parameters for trucks used in the simulation model
parameters

valuesunit

CD0.0020CT

0.0114CL100

secondCR

219

secondE

45.3

JPY/minuteθ

1

To compare route choice behavior of cars and commercial trucks, table 4 shows the normalized
value of parameters divided by time parameter CT and multiplied by the logit sensitivity
parameter θ. The interpretation of the results shown in the table can be summarized as below.
7Advanced simulation model in the region of Tokyo metropolitan urban expressway rings-

Commercial trucks are more sensitive to travel time instead of travel distance than cars,
therefore, they tend to select route without congestion and faster route.
Commercial trucks are very sensitive to the monetary travel cost (toll price) compared withcars so that they try not to use toll roads (expressways).
Commercial trucks have higher penalty costs for turning right and left than cars so that they
try to go straight and try to avoid turning right or left.
Table 4 - Comparison between cars and trucks
parameters

Cars

Trucksnormalized CD

1

0.17

normalized CT

11normalized CL

60

8800

120

19000

0.022

1.9

normalized CR
normalized sensitivity** The "normalized sensitivity" here is the reciprocal of value of time.

Figure 3 - Example of validation checks(left: correlation between observed and simulated;right: time change comparison at an example location)
The Origin-Destination demands are modified to adjust hourly volume of trunk road links
calculated by the simulation and observed (Figure 3) with the route choice parameters for carsand commercial trucks identified above. The obtained land use potential of physical distribution
facilities is applied to modify the Origin-Destination truck volume which will be fed for the
network simulation model.

8Advanced simulation model in the region of Tokyo metropolitan urban expressway rings

Case studies
The simulation systems have huge potential to evaluate the impact of the introduction of several
types of traffic operation policy. Here, one example to evaluate the impact of a toll fare policychange as already noted that all of the expressways in Japan are generally toll roads. To
introduce higher demand of ring roads, the scenario that the National Capital Region Central
Loop Road (Ken-o-do; 3rd ring road) is set to be free of charge rather than existing toll farebased on Origin-Destination distance was set to the simulation system and the calculated results
are compared with that of existing toll fare scheme both after the completion of three ring roads.
Figure 4 shows the comparison results of daily truck volume for each links.
difference of daily
truck volume [veh/day]Figure 4 - Difference of truck volume between two scenarios
From the figure, the increase of traffic volume on the 3rd ring road and access road to this ringroad definitely increased because of the introduction of free fare scheme on this ring road. The
traffic volume on inner trunk roads including expressway network than the 3rd ring road isbasically decreased. The simulation model frame reveals the capability to describe and evaluate
the impact of change of traffic and/or transport management policy properly.
Conclusions
The developed model is sophisticated and advanced so that it can provide reliable picture of9Advanced simulation model in the region of Tokyo metropolitan urban expressway rings

network dynamic traffic flow with reasonable sensitivity of any kinds of traffic management
schemes. It is very useful for road authorities. For the future study, land use change, truck route
choice behavior models should be modified. In addition, the parameters of traffic network
simulator should be calibrated much more systematically, and the validity of the simulation
results shall be increased. Comprehensive and systematic discussion for several types of traffic
management scheme introduction should be conducted based on the proposed simulation.
Acknowledgement
The study presented in this paper conducted in fiscal year 2015 is "Research on the effective
management of three ring roads in the Tokyo Metropolitan Area" supported by Committee on
Advanced Road Technology (CART) Trust fund by Ministry of Land, Infrastructure, Transport
and Tourism, Japan lead by the first author of this paper.
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