Modeling logistics location & truck route by Tokyo Metro Freight Survey 1??
MODELI G LOGISTICS LOCATIO CHOICEA D TRUCK ROUTE CHOICE BEHAVIOR BY
TOKYO METROPOLITA REGIO FREIGHTSURVEY
Tetsuro Hyodo, Tokyo Univ. of Marine Science and Technology, Japan
Hirohito Kuse, Tokyo Univ. of Marine Science and Technology, Japan
Yasukatsu Hagino, Institute of Behavioral Sciences, Japan
Hideki Takebayashi, Ministry of Land, Infrastructure and Transport, Japan
Kotaro Endo, LITEC CO., LTD Tokyo, Japan

Abstract
Road network and land use regulations has a great impact on logistics functions andelements particularly in elements of commodity flow such as route choice and volume,
facility location and SCM structure to name a few. Accordingly, public sectors should
develop appropriate demand models to describe these elements for future infrastructureplans or policies. In this paper, we studied logistics location choice model and truck route
choice model in evaluating the impacts of newly constructed roads in Tokyo Metropolitan
Region (TMR). The actual data of the large-scale public freight survey was applied to themodel estimation and several policies were analyzed.

PROFILE OF TOKYO METROPOLITA
2003

FREIGHT SURVEY (TMFS) I

In TMR, the decennial freight surveys were conducted in 1972, 1982 and 1993 by theTransport Planning Commission of TMR. The fourth survey was conducted in 2003. It
consisted of: 1) questionnaire survey to logistics establishments (30,000 respondents) for2

City Logistics V2

daily commodity flow and logistics activities of their facilities, 2) questionnaire survey tolarge size truck drivers for actual routes, and 3) local delivery survey of loading andunloading activities in five Central Business Districts (CBD).

Ibaraki pref.
Saitama pref.
Saitamacity
Tokyo Mtro.
Kawasaki city

Chiba
city

Yokohama city
Chiba pref.
Kanagawa pref.

Figure 1 Tokyo Metropolitan Region
The main part of the survey was the questionnaires to logistics establishments. The
survey questionnaire was primarily sent to manufacturing, shipping, warehouse, andwholesale establishments. About 120,000 questionnaires were mailed out to thoseestablishments, and about 30,000 of them replied.
SurveyMain part

Questions to Establishment

Sub part

Interview Survey

Large Truck Route Survey

Local Delivery Survey in CBD

Figure 2 Structure of the Tokyo Metropolitan Freight Survey

Current situations of freight in TMR
In this section, current situations of freight in TMR are described based on the survey
questionnaires. Figure 3 shows the freight distribution in TMR. The total volume ofModeling logistics location & truck route by Tokyo Metro Freight Survey 3

commodity flow has not changed compared to 1982, but the flow from outside to TMR
has increased due to increased imports. Of the transportation modes in TMR, almost all
were trucks (Figure 4). Large portions carried by trucks are goods for citizens’ living
necessity.
Figure 5 illustrates the goods flow from production/import to consumption based on the
supplemental business objective survey as a part of the TMFS. From the urban transport
perspective, the figure implies that freight measures in “regional” “city” and “local”
levels and the network connecting those different levels respectively are critical to control
regional freight facilities (e.g. warehouse, logistics center, etc.) as well as the leveled
urban freight structure.
（1982）（2003）In内外 Out
388千トン
千トン/
388
千トン
388/日

In 内外
 Out
338千トン
千トン/
338
千トン/日
338

In内々
 In
1,263千トン
千トン/
1,263
千トン/日
1,263

In内々
 In
1,228千トン
千トン/
1,228
千トン/日
1,228
Out
 In
外内
530千トン
千トン/
530
千トン/日
530

Out外内
 In
423千トン
423千トン/
千トン
423/日(Unit = 1000 ton / day)Figure 3 Daily Commodity Flow in TMR

0

20
Commercial
Truck

Private Truck

Intra-TMR
Between TMR &other regions

40
Commercial(consolidator)

33

10

60

80

Rail

100 （％）

Shipothers58

53

30 5 1

6

2

Figure 4 Commodity Flow by Modal Share“Private Truck” is owned by shippers, “Commercial Truck” is owned by freight companies

28

1

4

City Logistics V

4

Plant
Overseas

Plant
Outside TMR

Shipped by
Vessel or Air

Shipped by
Vessel, Air, or Rail

Freight Facility
Outside TMR

Plant in TMRPort / Airport

Regional Freight Facilities(Regional warehouses or distribution centers etc.)

Freight Facility
for Local Delivery(Distribution Center, etc.)

Retailetc.

Retailetc.

Freight Facility
for Local Delivery(Distribution Center, etc.)

Retailetc.

Retailetc.

Freight Facility
for Local Delivery(Distribution Center, etc.)

Retailetc.

Retailetc.

Shipped byLarge size
Truck
Shipped by
Small size
Truck

Tokyo Metropolitan Region (TMR)
Figure 5 Schematic Diagram of Goods Flow in TMR

Four freight policies
The Commission analyzed the survey results in collaboration with many academic
researchers and established various transportation policies as well as city and regional
planning policies in 2005. Those are classified into four areas;
Policy A: support location of regional logistics facilities,
Policy B: control location of local delivery facilities in urban areas,
Policy C: improve road network for large trucks,
Policy D: promote effective loading/unloading activities in CBD.
The Commission developed new demand forecasting models for logistics facility’s
location choice, truck route choice model, and physical distribution channel patternmodel. One of the major policies is to evaluate the impact of newly constructed roads on
logistics activities. For the policy analyses, the location choice model for “Policy A” andthe route choice model for “Policy C” are introduced in the following two Chapters.

Modeling logistics location & truck route by Tokyo Metro Freight Survey 5

LOGISTICS FACILITY LOCATIO CHOICE MODEL FOR MEASURI G
IMPACT OF ROAD CO STRUCTIO
Lately, logistics facilities have grown larger through merging for cost reduction purposes.
Those large facilities tend to locate in undeveloped areas where space is still available.
These facilities generate flow of large-size trucks, so controlling location of thosefacilities has become a crucial issue in city and regional planning in TMR.
In this study, a model was developed to quantitatively estimate location choiceprobabilities for regional logistics facilities. The study also analyzes “Policy A” to direct
locations for those facilities.
A discrete choice Logit model was estimated quantitatively for the likelihood of locationof large-scale logistics facilities in 1km2-meshes within TMR. The variables used in themodel are “transportation service-level,” “whether appropriate land-use for logistics,” etc.
Logit model of logistics facility location was estimated in the areas around expressway
interchanges (IC) currently under construction. With the location probabilities estimated,
we proposed a policy to support the location for logistics efficiency near expressway ICs
and limiting the location for those areas for environmental preservation purposes.

Estimation of location choice model
The Location Choice Model was established to quantitatively estimate potential areas forlarge-scale regional logistics facilities in TMR and the anticipated effects of location
choice and guiding location for such facilities. A discrete choice Logit model was
selected for location choice by 1km-mesh in TMR using the logistics establishment
survey (main part) data from the 4th TMFS.
Areas and zoning for estimation
The areas for estimation were: Southern Ibaraki and 4 other prefectures (Tokyo, Chiba,
Saitama, and Kanagawa). The tertiary mesh (1km mesh) of the National Land Numerical
Information was used for zoning to enable microscopic analysis. The total number ofmesh was 15,230 in the areas surveyed.
Location data used in the estimation and model categories/segments
Among logistics facility samples of the Logistics Survey (Main Survey), those built in1990 or later at current location were used for the estimation. In Table 1, observations
were divided into 2 categories, Regional Freight Facility and Local Distribution Center.
The study assumed to apply discrete choice Logit models and we selected the following
model commonly used in zone selection1).

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City Logistics V

Regional
Freight
Facility

6

Table 1 Model Categories/Segments
Property w/ 3000 ㎡ Freight facility with major
Model 1
destinations outside TMR or
and over
Property w/ less their prefecture, and average
Model 2
travel distance over 40km
than 3000 ㎡

Freight, warehouse Facility other than
Local
Model 3sectors
freight facility
Distributi
on Center Model 4 Other industries

Pi r =()

exp vir + ln M i∑ exp v jr + ln M j(regional

)

j

=

( )
( )

exp Vi r∑ exp V jrj(1)
Pi ：Probability of logistics facility of segment r to locate in area i (tertiary mesh)rrv i ：Utility function of the average utility for available lot in the area i and its
variance
M i ：Number of available lots in the area i

Vi r ：Utility function of the area i
Since the number of available lots, M i , is not directly observable, so, we suppose thevariable Si as the number of observable lots in the area i,

M i = αS i

(2)

S i : Scale variable of the area i (inhabitable area – residential area), α : parameter.
The utility function of Logit model then becomes

Vi r = ∑ β kr x ki + ln S ik(3)
kth variable for the average location utility in a lot in the area i (location
factor)r
β k : parameterxki:

Explanatory variables for location choice model
Considering the location factor and data availability, explanatory variables were selected
for Equation (3). The scale variable in equation (3) was obtained by subtracting theresidential area from the inhabitable area.

Modeling logistics location & truck route by Tokyo Metro Freight Survey 7

Since the number of location options is the number of tertiary mesh 15,230 in the entire
TMR, parameter estimation would be difficult with the Logit model theoretically.
However, if the size of subset randomly selected from the choice set is large enough, theestimated parameters will converge. Thus, in this study, a total of 200 meshes, 199 asalternatives and one with actual facility, were selected among 15,230 for each observation,and then, estimated with maximum likelihood method, considering the capacity of
personal computers and software (GAUSS) used.
Table 2 shows the estimation results which were confident for key explanatory variables.
Correlation coefficient ranged between 0.6 and 0.8 when comparing the actual number offacilities and the estimate, so we concluded that the model was quite accurate.

CALIBRATIOOF LOCATIOPOTE TIAL FOR LOGISTICS FACILITY

Definition of location potential
With the utility value calibrated for each mesh with utility function (location utility,
hereafter) for location choice equation (3), the following deviation was defined as apotential value,

Pi r =

10 × (Vi r − V r )σrwhere,Pi r : potential of segment r, in mesh i ,

+ 50
(4)

Vi r : location utility of segment r, in mesh i ,V r : average location utility in segment r

σ r : standard deviation of location utility in segment r.
Calibration of potential for large-scale regional logistics facilities
With model (regional logistics facility with area over 3,000m2) 1 in Table 2, current
location potential for regional logistics facilities with over 3,000m2 was calibrated andcompared with actual distributions of these facilities. It is presented in Figure 6 which
implies that areas with high potential will be located in coastal and suburban areas. 77%of actual large-scale logistics facilities were located in the highest 20% potential meshes.
Calibration of potential changes by road development
Since beltways (expressways) are to be completed in TMR in the near future, demand forlarge-scale logistics facilities around future expressway ICs is expected to increase.
Estimating the demand quantitatively for those facilities is essential for appropriate
location guiding measures.
Location potential for prospective expressways was calibrated as in the previous
paragraph. Figure 7 shows the planned expressway network in TMR and the areas with

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City Logistics V

8

Table 2 Estimated Results of Location Choice Model
Parameters(t-value in parenthesis)
Explantory variablesln(population density ※1)(1000persons/km )ln(working population in commuting distance)(1000persons)2

Regional Freight Facility
Model 1Model 2
2

3000m
-0.2876

Transport/
warehouse-0.2971

Other
industries-0.2443(-4.918)(-4.653)(-4.715)(-2.452)

0.9672

1.02440.77050.6214(5.401)(6.678)(5.637)(3.328)

Area 3000m
and over-0.3634

Area less than

Distribution Center
Model 3Model 42

0.5361ln(Accessibility to machine & assembly sectors)

0.8332(1.404)ln(Minutes to Keihin Port)(minutes)ln(Distance to expressway IC)(km)-2.083-0.485-0.3986(-1.777)(-1.471)-0.1566-0.1575-0.1414(-1.731)(-2.078)(-1.621)-0.8878-0.6117(-3.932)(-3.440)-0.1044(-1.017)ln(Land price)(1,000Yen/km)

Urban control area

Yes = 10.2831Otherwise = 0(1.516)

Ratio to inhabitable area

3.10142.35073.08462.8124(7.715)(7.053)(8.918)(6.411)

Land use category

Semi-industrial
Industrial
Exclusive industrial

Land Attribute Dummy

Ratio to inhabitable area
Ratio to inhabitable area

-0.2761(-1.158)
0.2659(1.294)

3.54903.46623.80451.1305(6.922)(8.820)(9.028)(1.307)

3.61742.68973.242

2.0056(11.182)(7.594)-0.1695(9.669)(3.709)InlandYes = 1Otherwise = 0(-0.910)

CoastalYes = 1-0.89106.4518Otherwise = 0(-3.102)(3.794)

Suburb

Yes = 1

-6.5950

Otherwise = 0(-2.419)ln(sample land area) x land Inland
attribute dummy
CoastalSuburb-0.0354-0.0833(-1.256)(-2.165)-0.1544-1.0206(-3.667)(-3.970)0.8501(2.729)Container use dummy x land Coastal
attribute dummy
Suburb

-1.0952(-1.838)

Number of employees x land Coastal
attribute dummy
Suburb-0.0048(-2.519)ln(road density in mesh)0.4042

1.17280.45500.6022(1.748)(5.336)(2.072)(2.499)Initial maximum likelihood

1.0000(—)-899.51.0000(—)-1248.1

1.0000(—)-1007.5

1.0000(—)-633.7ln(available area※2)

Final maximum likelihood-718.6-1019.2-796.2-633.7

Likelihood ratio adjusted to degrees of freedom

0.201

0.183

0.209

0.129

Correlation coefficient between numbers of actual and estimated locations bylocal government

0.593

0.625

0.783

0.645

Sample size

168

237

189

139

※1 Population density: Night population in mesh/Inhabitable area
※2 Available area: Inhabitable area – General building area

Modeling logistics location & truck route by Tokyo Metro Freight Survey 9

Figure 6 Calibrated Potential for Large-scale Regional Logistics Facilities

Figure 7 Planned Expressway Network (dotted line) andCalibrated High Potential area after Road Development (dark color area)10City Logistics V

10increased potential after the expressway development. Consequently, potential forlarge-scale regional logistics facility will be high around suburban expressway ICs and it
will increase in the future. Many of those areas are under urbanization control (green
areas in both figures), so the Committee concluded that effective location control is an
important issue for the future.
Calibration of demand for relocation due to guiding measures
Since this location choice equation (1) is capable of estimating the location likelihoodusing location utility change by mesh, as in the previous section, the effects on location
demand by guiding measures were estimated for logistics facilities over 3,000km2.
Specifically, changes in location probability after the expressway development (currently
under construction) in meshes where those facilities should be located, were estimated bychanging the proportion of exclusive-industrial areas within mesh. The selected areas
have a potential value of 57 and over, and near ICs on Ken-o-do (within 2km diameter oralong weight-designated road within 5km diameter).
The ratio of exclusive-industrial area was set at 25% (average in meshes with
exclusive-industrial zones in TMR) to calculate location probabilities. Then, changes in
relocation demand were estimated using the current location probabilities. Major findings
are summarized below:
1) The number of large-scale regional logistics facilities relocating to guided areas insuburb would be 180, 7% of total facilities
2) Of those, approximately 41% are from suburb (7% of large-scale regional
logistics facilities in suburban areas)3) Approximately 16% are from coastal area (3% of those in coastal areas)
4) Approximately 43% are from urban areas (11% of those in urban areas)
Since the model is static, the estimated results should not be implied that these changes
will occur instantly upon implementing these measures. However, the estimation shouldbe implied that the estimated number of relocation could happen due to the measures.

TRUCK ROUTE CHOICE A ALYSIS BY MAXIMUM OVERLAPPI G
ROUTE MODEL
Current issues on large size truck movement in TMR
The increase of international cargo causes more heavy or tall containers movement inroad network. Deregulation of truck size and weight in 1990’s has also raised the averagetruck size. In Japan, trucks over total 20 ton or 3.8 m height can be driven only “thedesignated link for heavy vehicle” or “the designated link for tall vehicle” without
permission. However, there are many bottlenecks in road network for large size trucks
because of the lack of these designated links. Figures 8 and 9 illustrate the presentModeling logistics location & truck route by Tokyo Metro Freight Survey 11

networks of designated links. It is shown that the road running north-south for heavy
vehicle in western region is not constructed, because some beltways in TMR have notbeen developed by the citizens’ objections.
We supposed that some methods for evaluating the priority of road improvement or
construction for truck should be required. The conventional multi-class assignment model
can not follow the large truck’s route choice behavior under these constraints, therefore,
we examined “Maximum Overlapping Route (MOR) Model” as detailed in the next
subsection.

Figure 8 Designated Link for
Heavy Vehicle

Figure 9 Designated Link for
Tall Vehicle

Maximum overlapping route (MOR) model for truck route choice behavior
This method was developed for describing bicycle route choice behavior by Hyodo et al.
The objective function of this model is to maximize the overlapping link length between“cognitive minimum cost route” and actual route as follows.
First, we define the actual trip length of the n-th sample ( X n ) as

X n = ∑a δ na ⋅ l a

,

(5)

where

δ na : dummy variable; if the n-th sample passes the a-th link=1, otherwise=0,l a : the length of the a-th link.
Assuming that perceived link length is affected by the link attributes, we propose thefollowing relationship.
*l an(β ) = la ⋅ ∏ β kZ ank

k

l

*
an

(β ) : the cognitive length of the a-th link of the n-th sample

Z ank : the dummy variable of k-th attribute for the a-th link of the n-th sample

β k : unknown parameter for the k-th attribute

(6)

12

City Logistics V

12Equation (6) states that the physical attributes of a link affect the cognitive length of thelink. Therefore, cognitive link length may be different for each driver. In this paper, weapply this MOR model to truck drivers’ route choice under the many conditions such as
travel time, travel cost, expressway toll, designated link for heavy vehicle etc. Thesevariables can be considered by using the following general cost function instead of thecognitive length in Equation (6).

GC *a (ω , β ) = (Cost a + ω・Time a ・) ∏ β kZ ak(7)“ω” is value of time (VOT) and it is also an unknown parameter. The parameters are
estimated by the following process. The overlapping rate of n-th sample is defined byka∑ δ ・δ (ω ,β )・l
*
na

na

Dn (ω , β ) =

a

Xn

.

(8)

δ (ω , β )*na: dummy variable: if the n-th sample passes the a-th link of theminimum generalized cost path=1, otherwise=0
The weighted overlapping rate with whole samples is

D (ω ,β ) =∑ X ・D (ω,β ) ∑∑ δ δ (ω,β ) ⋅ l
n

na

n

= n

n

∑X

n

*
na

a

a

∑X

n

.
(9)
This objective function is not continuous with parameters, because the minimum
generalized cost path is changed discretely based on (ω ,β ) . Therefore, we drawn thenncontours of D(ω ,β ) with appropriate interval of (ω ,β ) , and confirmed the maximum
point. If the number of parameters is over two, this method is not feasible so that
Genetic Algorithm (GA) should be applied 2).

Estimation result and the applicability
We applied the model to the truck drivers’ questionnaire survey which is a part of TMR
Freight Survey in 2003. The survey provides about 700 actual large truck routes in TMR.
The MOR model can involve many variables as attributes of links as mentioned before.
We have already prepared digital road network data and the results of traffic assignment
analysis. In this paper, we introduced the results for the parameter estimation. Table 3 isthe initial overlapping rate which means all link attribute parameters (β) equal to one.
Because of the variation of variables, we can define several kinds of “shortest path”,
minimum distance, travel time and travel cost. Hereafter, we apply “minimum actual
distance route” as for the simple initial overlapping rate.

Modeling logistics location & truck route by Tokyo Metro Freight Survey

13

Table 3 Initial Overlapping Rate by Definition of The Shortest Path
Weighted Overlapping Rate (Eq.(9))1) Minimum actual distance route
0.481512) Minimum travel time route
0.49623
3) Minimum travel cost route
0.38662
We examined a lot of combinations of many variables. Owing to the limitation of space,
the results of two variable cases are reported. Table 4 shows the six kinds of estimation
results. The highest weighted overlapping rate is given by model with “Value of Time
(VOT)” and “Dummy variable of designated heavy vehicle link”. The estimated VOT is
74 [Yen/min.], which is similar to the more commonly used value in Japan. The
parameter of the dummy variable (0.550) means that the designated link for heavy
vehicle provides 45% shorter cognitive distance for drivers. Aggregate log likelihood is
calculated as ρ = 1 − (log(0.64962 ) log(0.48151)) = 0.410 . This is very high when it
is compared to the past results of bicycle route choice examples2).
The variables related to land use are not so significant (“Model numbers 2-5”, “2-6”).
However, the link condition is an important factor for truck drivers in TMR.
Figure 10 illustrates the contour of weighted overlapping rate of “Model number 2-1” inTable 4. It is confirmed that the objective function is not smooth and has several local
optimum points.
Table 4 Parameter Estimation Results of Two Variable Case
Model
Variable
Parameter
Weighted
numberoverlapping rate
Value of Time [Yen/min.]74
2-1
0.649621
Dummy of “heavy” link
0.550
Value of Time [Yen/min.]88
2-2
0.63677
Dummy of “tall” link2
0.775
Value of Time [Yen/min.]102
2-3
0.63754
Dummy of 4 lanes3
0.725
Value of Time [Yen/min.]58
2-4
0.64372
Dummy of expressway4
0.625
Value of Time [Yen/min.]80
2-50.59173
Dummy of D.I.D.5
1.100
Value of Time [Yen/min.]86
2-6
0.58515
6
Dummy of Ring Road 7
0.900
1 If designated link for heavy vehicle=1, otherwise=0
2 If designated link for tallvehicle=1, otherwise=0
3 If number of lanes equals to 4 or over=1, otherwise=0 4 If expressway=1, otherwise=0
5 If link is in D.I.D.=1, otherwise=0
6 If link is inside of Ring Road
#7=1, otherwise=0

14City Logistics V

14

Figure 10 Contour of Weighted Overlapping Rate with Two Variables(Horizontal axis=Dummy of “heavy” link, Vertical axis=Value of Time)
By using the truck OD tables and MOR model, we can forecast the future link volume oflarge sized trucks in TMR. The OD table of large sized truck was calculated by the resultof TMR Freight Survey. And truck route for each OD pair was assumed that theminimum generalized cost estimated by MOR model number 2-1 in Table 4. Three cases,“Present”, “Completion of roads under construction” and “Completion of Long Termroad network,” were examined as the case studies (Figure 11). “Present” result suggeststhat present network is generating excessive concentration of large sized trucks in thecentral area. The outer beltways are now under construction, and they will be completed
in 2012. Figure 11 “b) Completion of roads under construction” displays that the new
network would shift the inconvenient concentrated traffic to the suburban beltways.
However, there are some missing links in TMR road network. “c) Completion of Long
Term road network” case hypothesizes construction of two important sections. One
section completes the inner beltway, and other connects large port and principle
expressway in Japan. It is represented that these sections are important “pieces” toaccomplish smooth and sustainable road network in TMR.
As we introduced in this subsection, the MOR model has applicability for demand
forecasting. Now we are examining this methodology to other metropolitan areas andtesting the evaluation procedure for road construction priority for large sized trucks.

Modeling logistics location & truck route by Tokyo Metro Freight Surveya) Present Network

15b) Completion of Roads under Constructionc) Completion of Long Term Road Network
Figure 11 Calibrated Large Size Truck Traffic Volume

REMARKSIn this paper, logistics location choice model and truck route choice model weredeveloped and its applicability with the actual freight survey data was examined. The
advancement of internationalization and expansion of road network, especially newly
constructed beltways, cause more active goods movement in TMR. These two models
would give the important information for infrastructure planning to meet the increasing
demand. The former model is for “node facility” and the latter model is for “linkfacility”.

16City Logistics V

16

We represented two models as separate tools, however, these models and theconventional four step model should be integrated. It is one of further topics to be
examined.
We reported the results to many public sectors in TMR, and several sectors launched orrevised their transportation master plans or city plans based on the study results. Tokyo
Metropolitan Region Freight Survey and our analyses opened the way to discuss freightpolicies by quantitative methods.

Further topics to improve the choice models are summarized as follows:
1) Spatial error structure for location choice model should be tested for better effective
parameters (Bhat et al.).
2) In this paper, “Maximum Overlapping Route (MOR) Model” was applied to the
network already assigned traffic volume, however, it can be utilized as an impedanceterm of conventional BPR function. Traffic assignment with the estimated MORmodel should be examined (Prashker et al.).

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Bhat, C.R. and Guo, J. (2004). A Mixed Spatially Correlated Logit Model: Formulation
and Application to Residential Choice Modeling, Transportation Research B,
Vol.38, 147–168
Hyodo, T., N. Suzuki and K. Takahashi (2000). Modeling of Bicycle Route andDestination Choice Behavior for Bicycle Road Network Plan. Transportation
Research Record 1705, 70-76.
Prashker, J.N. and Bekhor, S. (2004). Route Choice Models Used in the Stochastic Use
Equilibrium Problem: A Review, Transport Review, Vol.24, No.4, pp.437-463,
2004
Yai T. (1986). Transportation Demand Forecasting Methods Using Disaggregate
Behavioral Models, Transportation Statistics, No.15-16 (in Japanese)