Discussion Papers In Economics
And Business

The Influence of Environmental Deterioration and Network Improvement
on Transport Modal Choice

Yusuke Sakata, Junyi Shen, and Yoshizo Hashimoto

Discussion Paper 06-04

Graduate School of Economics andOsaka School of International Public Policy (OSIPP)
Osaka University, Toyonaka, Osaka 560-0043, JAPANThe Influence of Environmental Deterioration and Network Improvement
on Transport Modal Choice

Yusuke Sakata, Junyi Shen, and Yoshizo Hashimoto

Discussion Paper 06-04

January 2006

Graduate School of Economics andOsaka School of International Public Policy (OSIPP)
Osaka University, Toyonaka, Osaka 560-0043, JAPANThe Influence of Environmental Deterioration and Network Improvement
on Transport Modal Choice

Yusuke Sakataa1,

Junyi Shen b and Yoshizo Hashimotob

aDepartment of Economics, Kinki University

3-4-1 Kowakae, Higashi-Osaka City, 577-8502, JapanbOsaka School of International Public Policy, Osaka university

1-3 Machikaneyama, Toyonaka City, Osaka 560-0043, Japan
Abstract

Transport modal choice is considered to be influenced by natural environmental change andtransport network improvement. This paper reveals how these impacts affect individuals’ decisions
on selecting transport mode under an extension plan of Osaka Monorail Loop-line. To estimate
these impacts, a Stated Choice (SC) experiment is carried out for collecting the neighborhood dataaround the monorail’s extended area. Our model is estimated by the Heteroscedastic Extreme Value(HEV) specification in order to avoid Independence from Irrelevant Alternatives (IIA) assumption
in the Multinomial Logit (MNL) model. Both the results of full-sample and sub-sample data imply
that residents prefer public transport modes (monorail or bus) to private car when either naturalenvironment becomes worse or transport network is improved.

Keywords: Environmental Deterioration; Network improvement; Network externality; Choice
Model (CM); Heteroscedastic Extreme Value (HEV) Model

JEL classification: C35, D12, Q51, R41

Corresponding author. Tel: +81-6-6730-5880(7063), Fax: +81-6-6726-3213
E-mail address: ysakata@msa.kindai.ac.jp

11

1. Introduction
Public transport is an essential element for urban life since it reduces car traffic and gives
mobility to city residents. In addition, more use of public transport modes instead of private cars is
also considered being able to reduce emissions such as carbon dioxide from urban transport. Thisfeature has been getting more important after the Kyoto Protocol had been taken effect in March
2005.

These social situations require policy makers to support modal change from private cars topublic transportation. Differences in accessibility to destinations such as city center affect users’
decision on choosing transport modes. In well developed cities of Japan like Osaka and Tokyo, newpublic transports are built to improve connectivity and accessibility of existing lines and stations.
Introducing a new line to such cities is thought to bring the improvement of network accessibility
and to promote transport modal shift of residents in its neighborhood. Besides direct effect of the
public transport investment, additionally-generated traffic caused by the network externality(indirect effect) would be distributed over the whole city. These direct and indirect effects could be
examined by the analysis of transport modal choice.2
Analyses of transport modal choice have been implemented in various ways. Discrete choicemodel is a mainstream among them. In this approach, transport modal choice analysis is usuallytreated as an application of the random utility theory which assumes that an individual makes arational choice among discrete alternatives so as to maximize his/her utility. There have been anumber of studies on transport modal choice in literature based on discrete choice approach (see, forinstance, McFadden 1974, Garrod and Millius 1983, Ben-Akiva and Lerman 1985, Bates 1987,
Louviere 1988, Hensher 1994, Louviere et al. 2000, Asensio 2002, Cervero 2002, Rodriguez and Joo
2004, Schwanen and Moktarian 2005, etc.). The traditional determinants in selection of transportmodes are attributes associated with travel time and cost. Alternatively, the determinants oftransport modal choice can also be estimated by other modeling using revealed preference data, forexample, Bresson et al. (2004) analyzed the determinants of demand for public transport by using
panel data in France.
The climate change problem requires transport policy to regard sustainability as an importantelement (e.g. Ech 2005, Steg and Gifford 2005). Some evidences suggest that the growing concern ofenvironmental protection, which has important external effects on our life, has begun to affectresidents’ behaviors including transport modal choice (Himanen et al. 2005, Shen et al. 2005).
The effect of individual environmental concern on transport modal choice comes not only fromthe recognition of negative impacts on the environment caused by various transport modes, but also
from the awareness on local natural environmental state. There are a number of factors which
affect local natural environment, such as industrial activities, geographical features or built
In this study, due to the financial constraint, we only focus on the direct effect of an extension of
Osaka Monorail Loop-line on residents around the Eastern Osaka of Japan.

22environment, consumers’ behaviors including transport modal choice, and weather condition, etc. A
feedback effect caused by local natural environment seems also possible to influence these
constitutional factors. For instance, heavily polluted environment may encourage people to look forthe less pollution-intensive goods. In transportation, it is well known that public transport modes
are relatively “cleaner” than private car. If individual environmental consciousness does work as adeterminant in transport modal choice (Shen et al. 2005), then his/her recognition of a bad local
natural environment from the position of victim might lead to a higher probability of choosing
public transport modes than private cars. There are several previous studies analyzing how local
physical environment affects transport modal choice (see, for instance, Cervero 2002, Rodriguez and
Joo 2004, etc.). Concerning about the impact of local natural environment on modal choice, however,
we find that it is not yet well studied.
In addition, utilization of public transport can also be stimulated by improvement of its
network accessibility. Overall, changes in travel conditions are commonly summarized by thegeneric concept of accessibility, which can be defined as “the ease of access between spatial
opportunities”. This definition implies that variables like travel time and costs, associated with atrip to a location, are the key components that determine accessibility. Alternatively, accessibility
can be defined as “the potential attainment of a set of transportation choices” (Banister andBerechman, 2000, p.174). The latter definition indicates that transport network accessibility would
influence individuals’ decision on making a selection from different transport modes.
Then, based on the above discussions, we can formally propose two hypotheses in relation to
the influences of transport milieu upon individual modal choice: (1) environmental deterioration
will cause people more likely to choose public transport modes; (2) improved accessibility of public
transport network will lead people to have higher probability of selecting public transport modes. To
test these two hypotheses, the transport modal choice data recently collected in Eastern Osaka ofJapan are used. As a result, both hypotheses are strongly supported by the estimated results.
The rest of the paper is organized as follows. In the next section, the empirical models arepresented, while in section 3, the experimental design and brief summary of the surveyed areas aredescribed. In section 4 we examine our estimated results, and finally we sum up our main
fact-findings in section 5.

2. ModelIndividual preference for transport modes can be estimated by using a choice model, whichstarted to be applied to transportation research around thirty years ago and has rapidly developedsince then (e.g. McFadden 1974; Garrod and Millius 1983; Maier et al. 2002). The choice model (CM)
had been historically developed in Mathematical psychology (Luce and Tukey 1964) and marketing3research (Actio and Jain, 1980; Green and Srinivasan 1990).3 In the 1990s, CM has been widely
applied to environmental economics for evaluating non-market goods and to transport economics formodal choice analysis (Haefele 2001; Hensher 1994; Louviere 1988; Louviere et al. 2000; Takeuchi

et al. 1999). Recently, the application of CM has been extended to various areas such as healtheconomics (Miguel et al. 2000) and waste management (Sasao 2002, Sakata 2005).
Choice model is based on random utility theory. The basic assumption embodied in the random
utility approach to choice modeling is that decision makers are utility maximizers, i.e. given a set ofalternatives the decision maker will choose the alternative that maximizes his/her utility. Usually
the utility of an alternative for an individual (U) cannot be observed, however, it could be assumed
to consist of a deterministic component (V ) and a random error term ( ε ). Formally, the utility
function for choosing a mode k by individual i can be denoted as follows:

U ik = Vik + ε ik

(1)

According to utility maximization, individual i chooses a mode k which prefers to any j ( ≠ k ) fromchoice set J if U ik > U ij . Hence, the probability of choosing a mode k over choice j ( k, j ∈ J ) can be
written as:

Pik = (U ik > U ij ; ∀ j (≠ k ) ∈ J ) = P(ε ij < ε ik + Vik − Vij ; ∀ j (≠ k ) ∈ J )(2)To transform the random utility model into a choice model, certain assumption about the joint
distribution of the vector of random error terms are required. If the random error terms ε areassumed to follow the extreme value type I (EV1) distribution and be independently and identically
distributed (IID) across alternatives and cases (or observations), the multinomial logit (MNL) model(McFadden 1974) is obtained. In the MNL model, the choice probability in Equation (2) is expressed
as:

Pik =

exp( µVik )∑ exp(µV )
J

j =1

(3)

ij

where µ is a scale parameter, which is usually set to 1 in the MNL model. Then, making further
assumption for the deterministic component of utility to be linear in parameters, Viq = β ′ Xiq, theprobability in Equation (3) is given as:

Pik =exp( β ′X ik )∑ exp(β ′X )
J

j =1

(4)

ij

where Xiq are explanatory variables of Vik, normally including alternative-specific constants (ASCs),
the attributes of the alternative k and the social-economic characteristics of the individual i , β ′is the parameter vector associated with the matrix Xik.
MNL is often criticized because it requires the assumption of independence from irrelevant

Several literatures include CM as one of approaches of Conjoint Analysis (CA). However, due tothe differences between judgment data (from conjoint rating and ranking) and choice data (fromchoice modeling), we define CM as an additional method other than CA in this study.

34alternatives (IIA).

IIA assumption is a very strong constrain on applying the choice model to policy

studies. To relax this assumption, Generalized Extreme Value (GEV) models are proposed (e.g.
Train, 2003, p.80). The Nested logit (NL) model (e.g. Tsuge 2001) and the Heterosckedastic Extreme
Value (HEV) model are typical examples of the GEV models.
In the HEV model, the choice probability is changed toPik =exp( µ k β ′xik )∑(5)exp( µ j β ′xij )j =1

Jwhich differs in allowing scale parameters ( µ k ) to vary across alternatives from the MNL model. A
nested logit model with a unique inclusive value parameter for each alternative (with one
arbitrarily chosen variance to 1 for identification) is equivalent to an HEV specification (Louviere etal. 2000, p. 189). Compared with the NL model, the HEV model is especially valid in the case thatthe tree for a nested model is difficult to be specified. In addition, the magnitude of the estimatedalternative-specific scale parameters and their standard deviations may help analysts determine
the tree structure of a nested model.

3. Choice experiment3.1. The plan of extending Osaka Monorail Loop-line
Osaka monorail has two lines currently in operation. One is called Osaka Monorail Loop-line,
running from Osaka International (Itami) Airport toKadoma-Shi. Another one is currently runningfrom Banpaku-Kinenkoen to Handai-Byoin-Mae,

Figure 1 The planning area

named as Osaka Monorail Saito line. Osaka

Monorail PlanêVñ•ñ

Monorail

Monorail Loop-line at present covers an area

Future NetworkêÁó¢ íÜ âõåuÅ@ír

Present Networkfrom Northwestern Osaka to Eastern Osaka. 4
In 2003, Kinki Public Transport Committee

ìÏ
àÔ
ñÿ

ëÂ
Å@
ìÝ

(KPTC) has authorized the plan of extending

Extending Plan
Kadoma Shi

Kadoma Minami

the Loop-line from one of the current terminals,
Kadoma-Shi, to Uryudo station (south toKadoma-Shi). Although the date of operation of

í½ í¨

this plan has not been fixed yet, it can be
obviously viewed as an essential part of public
transport network composing of monorail andother railways or subways. According to the plan, there will be four new stations in this extension
from north to south: Kadoma-Shi→ Kadoma-Minami → Konoike-Shinden → Aramoto→ Uryudo (Figure 1). Table 1 provides information about these connections to other lines. The mostOsaka Monorail Loop-line is listed in the Guinness Book of Records with the longest total
operational line of monorails.

4

5

Table 1
Prospected stations and connections to other lines
Monorail station

Kadomashi

Connection

Keihan Railway

Main Destinations ofPopulation within 1

connected line

km of neighborhood

Kyoto, Yodoyabashi,

45,113

Kitahama
Kadomaminami

Subway Tsurumi

Kyobashi, Shinsaibashi

34,614

Japan Railway

Gakkentoshi, Kitashinchi,

42,997

Gakkentoshi Line

Kobe

Kintetsu Higashi

Higashiikoma, Honmachi

17,873

Nara, Ikoma, Nanba

48,278

Ryokuchi Line
Konoikeshinden

AramotoOsaka Line
UryudoKintetsu Nara Linenotable characteristic of this link is that each station has a connection to other lines which havedifferent destinations as shown in Table 1. The new monorail extension is therefore expected tobring better accessibility to residents and to shift modes from private cars to the monorail.
Several large plants are also located in the main residential areas along the planned monorailline. Most of residents commute to Osaka City Center or work in those plants. In addition,
metropolitan trunk roads (Osaka Central Loop Road and Kinki Highway) run in the parallel withthe planned line. Traffic volume of cars on the roads is huge and congestions daily occur in rush
hours. Air pollution caused by emission from cars, and noise & hubbub are serious.

3.2. Survey design
The questionnaire used for this study is composed of three parts. The first part is intended to be
an introduction of the questionnaire and to give respondents basic information of the new line andthe environmental changes in these days. The second part is an experiment on transport modal
choice and the last part is a face sheet for individual social-economic characteristics. In the choice
experiment, respondents are asked to choose one transport mode from a choice set which isconsisted of monorail, bus and car with each different combination of attribute levels. In the facesheet, respondents are asked not only their social-economic characteristics but also their current
travel behavior including whether they hold cars or not and they use them or not.
Trying to estimate impacts of the planned monorail which is a part of improvement of larger
public transport network, we must specify the attributes levels which are comparable among those6

Table 2
Attributes and their levels
Attributes

Levels of attributes
Monorail

Car

Bus

Average in-vehicle time for 1 section

3 minutes
6 minutes

2 minutes
4 minutes

5 minutes
10 minutes

Average delay time for 1 section

0 minutes

Frequency

10 minutes
15 minutes

0 minutes
8 minutes
at any time

0 minutes
10 minutes
6 minutes
12 minutes

Average cost for 1 section

100 JP yen
200 JP yen

200 JP yen
400 JP yen

60 JP yen
120 JP yen

Local natural environment

Current state/ Worsethan current state

Current state/ Worse thancurrent state

Current state/ Worse thancurrent state

Network accessibility

Only monorail/
Fully realized

Only monorail/
Fully realized

Only monorail/
Fully realized

Table 3Sample choice sheet
Transport mode
Average in-vehicle time for 1 section (minutes)
Average delay time for 1 section (minutes)

Monorail
6
0

Car
4
8

Bus
5
10

Average cost for 1 section (JP yen)

100

400

60

Frequency (numbers/hour)
Local natural environment(Please refer to the examples in Sheet A)
Network accessibility(Please refer to the map in Sheet A)

4

at any time10

Current state

Current state

Current state

Fully realized

Fully realized

Fully realized

□

□

□

Please choose one most preferable transportmode and ✔ in □alternatives. Therefore, we design the levels on the basis of traveling 1 section between the newstations of monorail which is about 2 km on average and ask respondents to select the most
preferable transport mode in a choice set when you are going to trip any part of these sections.
A choice set used in this experiment has 3 alternatives and each of them has 6 attributes. Two
attributes (state of local natural environment and public transport network) are manipulated to becommon among 3 alternatives. Table 2 shows the attributes of each alternative and the levels ofeach attribute. With these attributes and their levels, we obtained 32 choice sets by fractional

7

factorial design from 4096(=212) combinations. 8 choice sets are combined as one version anddistributed to the respondent. A sample choice sheet is shown in Table 3. Respondents are asked 8times in total to choose one most preferable mode in each different choice set.
Most of attributes except for natural environmental state and transport network are chosenfrom typical transport modal choice experiments. Their levels used in this experiment were
adjusted based on the results of pilot tests held in July 2005. We found it impossible to specify thepatterns of individual trips in the surveyed area, and therefore calibrated the levels of in-vehicle
time, delay time and cost based on the distance of one section of the monorail. On top of that, due tothe reason that residents are likely to include parking cost into an average cost of car using, we
decide to fix the level to car running and parking costs.
The environmental state used in the choice experiment is intended to reveal respondents’attitudes to transport modal choices caused by the environmental change like air pollution, global
warming problems, and so on. Two environmental scenarios (‘current state’ and ‘worse thancurrent’) are used to evaluate this point. 5 The environmental state is treated as a common
condition in each choice set and allowed not to be variable across alternatives, but choice sets.
On the other hand, KPTC has 19 plans to improve network accessibility of public transport in
Osaka Prefecture. Most of them will extend an existing line to link with other lines to improvetransit and access to city center. The improvement of network accessibility brings network
externality to each line. According to the KPTC’s plans, we find that the monorail loop-line
extension combined with other plans is obviously beneficial to the residents in the surveyed areas.
One typical impact of the network accessibility can be observed in a change of modal choice. To
estimate this impact, an attribute of ‘Network’ is divided into two levels, i.e. ‘Only monorail
extension is realized’ or ‘All proposals are fully realized’, and included as a common attribute in the
choice profile. If we could estimate a significant change of respondents’ choice behavior between the
two levels, we may conclude that external effects of the public transport network on the residents
could be observed.
The network accessibility may also affect car users through the other route.

Especially, there

are little parking lots around the planned monorail stations and bus stops in the surveyed areas,
thus, this improvement of public transport is additionally expected to cause a modal change fromIn Osaka Prefecture, however, it is officially said that problems of air pollution have been almost
overcome because SOx and NOx have become too little to be observed in the atmosphere these days.
Despite few victims from serious air pollution, there are certainly growing concerns for PM(Particular Matter) from diesel, heat island, and global warming. It is unbelievable that naturalenvironment has been getting better, especially in the surveyed areas. Another issue of treating
environmental problem in our experiment is that ordinary respondents may feel difficult tounderstand meanings of detailed numerical data of pollution. Therefore, we divide environmental
state into two levels, i.e. ‘current state’ or ‘worse than current state’, and use a dummy variable
designating 0 to the former and 1 to the latter.
58

private cars to public transports.

3.3. Data collection
The survey is focused on the areas within 1 kilometer from each station which is listed inmonorail extension plan shown in Figure 1. The questionnaires were randomly delivered to 1200
households per station between 25 July and 8 August 2005. The total number of delivered
households for this experiment is 6000 (1200∗5). As a result, we have 516 responses mailed back,and 453 (7.9%) of them are valid for this analysis.

4. Results
4.1. Descriptive statistics
An overview of respondents’ social-economic characteristics and current pattern of behaviors isshown in Appendix. Of the 453 respondents, about 90% are above 30 years old and more than half
have finished high school or special college (polytechnic).
Potential impacts of monorail extension plan on residents’ daily lives are listed (see Appendix).
Among their responses, 82% expect an improved traffic, and 49% expect that the neighborhood
around each new station would get more activating, while about 10% concern possibly increased
noise and decreased bus service.
At present, only 3.8% use Train or Subway and 7.4% use Car as their transport modes whencurrently traveling the monorail’s extended sections. Many of them take Bus (27.1%) or
Walk/Bicycle (30.7%). Responses about potentially using monorail in the future indicate that 81.1%
will use monorail after the extension is realized.

4.2. Estimation result
Table 4 summarizes the results estimated by the Multinomial Logit (MNL) model and theHeteroscedastic Extreme Value (HEV) model in which the scale parameters of monorail and bus are
set to be consistent to one. This assumption of equivalent scale parameters for monorail and bus in
the HEV seems to be plausible since, in the surveyed areas, both monorail and bus are thought toplay a same role of transporting customers in the north-south direction and/or linking with otherlines. In addition, both impacts in local environmental state and transport network accessibility on
individual modal choice can be almost viewed as selection between public modes and privatemodes.6
This setting is similar to the degenerated Nested Logit (NL) model which includes monorail andbus in a common branch for public mode and regards car as a degenerated branch for private mode,
however, in the case of NL model specification, the independence of irrelevant alternatives (IIA)
property is still assumed in the branch of public mode, while in the restricted HEV setting here, thescale parameters of monorail and bus are assumed to be same, but the IIA property is released

6

9

Table 4
Estimated results of transport modal choice (t statistics in parentheses)

Variable
Average in-vehicle time
Average delay time
Frequency
Average cost
ASC_Monorail
ASC_CarEnvironment (worse=1)
Network (fully realized=1)
Age above 65
Household annual income
above 6 million JP yen
Numbers of car held
Current bus user ∗ worse
environment
Current bus user ∗ fully
realized network
Current car user ∗ worse
environment
Current car user ∗ fully
realized network
Scale parameters
Monorail and bus
CarStandard deviation
Monorail and bus
Car
Log likelihood
ρ 2 (McFadden)
Observations

Alternative
All
All
All
All
MonorailCar
MonorailBus
MonorailBus
Monorail
Car
Car

MNL
-0.1241 (-8.635)-0.0613 (-8.239)-0.0324 (-3.156)-0.0050 (-13.371)
1.0957 (8.976)-0.2056 (-1.036)
0.3481 (2.608)
0.1247 (0.764)
0.3318 (2.489)
0.4865 (3.025)
0.3506 (2.900)-0.3529 (-2.006)-0.1590 (-1.48)

HEV-0.1178 (-6.987)-0.0623 (-6.826)-0.0473 (-3.874)-0.0105 (-11.786)
1.5564 (10.589)-1.7766 (-3.363)
1.3264 (3.775)
1.0465 (2.881)
0.9158 (2.847)
1.0873 (3.240)
0.3679 (3.018)-1.1285 (-2.790)-0.3105 (-1.264)

Car
MonorailCar
MonorailCar
MonorailCar
MonorailCar

0.5019 (6.808)-0.5615 (-2.469)-0.7273 (-2.086)-0.5374 (-2.558)-1.1789 (-3.092)-0.0152 (-0.095)
0.6460 (3.334)
0.0759 (0.509)
0.5686 (3.040)

1.1403 (5.490)-0.5846 (-2.394)-1.1531 (-1.493)-0.5491 (-2.490)-2.0356 (-2.433)-0.0626 (-0.386)
1.9788 (4.676)
0.0974 (0.639)
1.1347 (3.049)

Monorail, BusCar

1 (fixed)
0.3921 (8.553)

Monorail, BusCar

1.2825 (fixed)
3.2713 (8.553)-2824.817
0.08653624-2802.078
0.09393624

Comparing the specifications between the MNL and HEV models, the Likelihood Ratio (LR)test indicates that the LR statistics 45.478 (=2∗(-2802.078-(-2824.817))) is larger thanχ 2 (1) =7.88(the difference in the degree of freedom of the two models is 1) at 0.5% significance level, suggestingthat the HEV model is superior to the MNL model.7 According to this test, the following discussions
between monorail and bus.
7 The independence of irrelevant alternatives (IIA) assumption in the MNL model can be rejected10

are therefore based on the results of the HEV specification.
Time and cost coefficients are estimated generically for all alternatives. As expected, all of them
are strongly significant with negatives signs, which indicates that more time spent on travel, delay
and waiting, and more cost needed for traveling cause residents more disutility. The values of timesavings (VOTS) for in-vehicle time, delay time, and frequency are calculated through dividing thecoefficients of time variables by the coefficient of cost variable. The VOTS of in-vehicle time isestimated at 673 JP yen per hour (about 6 US$ per hour), which is the highest one among the three
types of time. Whilst, for delay time and frequency, the VOTS are estimated at 356 and 270 JP yen
per hour respectively. The difference between these two VOTS can be explained by the different
stresses caused by these additional times during the travel line. It is to say that frequency can bepre-checked so that people may go to the station or bus stop just before departure in order todecrease wait time, however, delay time which is usually varied by road congestion can not be well
predicted by the passengers.
Local natural environment variable (‘worse than current state’ = 1; ‘current state’ = 0) andtransport network accessibility variable (‘fully realized’ = 1; ‘monorail only’ = 0) are estimated byinteraction terms with alternative-specific constants (ASCs) of monorail and bus. 8 All theestimated coefficients of these four interaction terms are positive and significantly different fromzero. This result suggests that, in the case of either a worse local natural environment or a more
improved accessibility of public transport network, individual will prefer his/her choosing public
transport modes (monorail or bus) to private car. Furthermore, a deterioration of local naturalenvironment suggests that the marginal utility of selecting monorail is slightly higher than that ofchoosing bus (see the magnitudes of Environment with Monorail and Bus in Table 4). In contrast,
when all the public transport investments are fully realized, the marginal utility of selecting bus isslightly higher than that of choosing monorail (see the magnitudes of Network with Monorail and
Bus in Table 4). At first glance, the latter result seems counter-intuitive. However, it is probably
true due to the reason that respondents may be influenced by the fact that current bus lines would
have, in some cases, a better linkage with other new lines planned by KPTC, e.g. individuals
frequently going to Shin-Osaka station and taking a Shinkansen will find that current bus service
can link more easily with a new line directly bound for Shin-Osaka than monorail.
With respect to other variables in Table 4, the respondents who are older than sixty-five areliable to prefer selecting monorail to car (see terms of Age above 65). In addition, household’s
income interacted with ASC of car is not significant here, whilst the interaction term of numbers ofon the basis of the result that scale parameter of car in the HEV model is quite different from 1.0.
8 A possible cross effect between local natural environmental state and transport network
accessibility has been tested and found not statistically significant. Therefore, concerning these twovariables, main effects are only specified in this study.

11

Table 5
Predicted probabilities of four sub-samples
Sub-sample 1

Sub-sample 2

DifferenceSub-sample 3

Sub-sample 4

Difference

(1)

(2)

(2) – (1)

(3)

(4)

(4) – (3)Monorail

39.26171.14631.88573.31075.1741.864

Bus10.72517.0136.288

17.22022.8535.633

Car50.01411.841-38.173

9.470

1.974

-7.496

Note: Probabilities are in percentage (%).

car held by a household with ASC of car is estimated with a significantly positive sign. This implies
that the more cars a household holds, the higher is the probability with which its members choose
car as a traffic mode.

Finally, let us turn to investigating how current bus users and current car users behave under
the conditions of local environmental deterioration or improved network accessibility whentraveling the extended sections. For current bus users (occupying 27% of the sample), in any case,almost all the coefficients of interaction terms with ASCs of monorail and car are estimated withsignificantly negative signs, indicating that despite the monorail extension, bus users are not willingto shift from bus to either monorail or car. This evidence could be plausible because most of thesamples are selected within one kilometer from each monorail new station and bus stops are usually
located more nearly to residents than monorail. 9 On the other hand, for current car users(occupying 7.4% of the sample), interaction terms with ASC of monorail are not significant, while
the coefficients of interaction terms with ASC of car in either condition are estimated withsignificant and positive signs, implying that car users are still willing to select car for passing
through the extended sections of monorail.
4.3. Further discussions for natural environment and network accessibility
Further estimations by using sub-samples are carried out to confirm the above results about
impacts of local natural environment and network accessibility on individuals’ transport modal
choice. Sub-samples 1 and 2 are divided from the full sample based on two different environmental

states – current state or worse than current state. Alternatively, sub-samples 3 and 4 are generated
from the full sample based on two situations of network accessibility – monorail only or fully
realized. All these four sub-samples are estimated by the same HEV specification as before.
9Withinthe surveyed area of a circle with radius of 1 kilometer (almost equals to a half of amonorail section), it is said that four or five bus stops are normally allocated in Eastern Osaka.
Thus, bus is an extremely convenient mode for a short traveling within these areas.

12Table 5 summarizes the predicted choice probability of these four sub-samples. From the table,
we can find that for a change in local natural environment (a change from Sub-sample 1 toSub-sample 2), the variations of the induced choice probabilities for monorail, bus and car are
31.885%, 6.288% and -38.173%, respectively, while, for a change in public transport network
accessibility (a change from Sub-sample 3 to Sub-sample 4), the variations of the induced choiceprobabilities for monorail, bus and car are 1.864%, 5.633% and -7.496%, respectively. This result isexactly consistent with the full-sample result discussed above, i.e. when local natural environment
is worse than current or public transport network accessibility becomes more convenient,
individuals will have a higher probability to choose public transport modes (monorail or bus).
Further consistency to the full-sample estimation can be exhibited from the results of a relative
higher probability variation in monorail than bus in the case of environmental deterioration, and a
relative higher probability variation in bus than monorail in the case of improved network
accessibility.
In summary, the estimates in this study highlight the impact of local natural environment andnetwork accessibility on individuals’ transport modal choice. They are significantly supporting thetwo hypotheses proposed in the Introduction. However, the induced values of time savings seem
lower than most previous studies in Japan and other developed countries. One possible reason isthat VOTS may be influenced by relatively lower household income in the surveyed areas. A further
systematical study on this issue is necessary and left for future research.

5. Conclusions
Estimating the impacts of local natural environment and network accessibility on individualtransport modal choice is not an easy task by a stated choice experiment, partly because it is quitedifficult to deliver the analyst’s message to individuals. Trying to make the questionnaire easily
understood by ordinary residents, we specify, in this study, both local natural environment andnetwork accessibility into two respective states and affix an additional detail introduction sheet
combining figures and graphs.
By a restricted HEV specification, both results from full-sample and sub-sample estimations
suggest that the probability or utility of public transports selected by individual are higher thanthat of private car under either condition of local environmental deterioration or improved public
transport accessibility. Linking this result to sustainable transport development, this study suggests

that, for transport policy makers, the relations among natural environment, network accessibility
and individual modal choice should be recognized as an essential part of present transport policy.

13

Acknowledgement
We would like to appreciate the financial support from Japanese Ministry of Education,
Culture, Sports, Science and Technology (Grand-in-Aid for Scientific Research A(2) 16203020). All
the views expressed in this paper and any errors are the sole responsibility of the authors.

References
Actio, F. and Jain, A. K. (1980), “Evaluation of conjoint analysis results: a comparison of methods”.

Journal of Marketing Research 17:106-12.

Asensio, J. (2002). “Transport mode choice by commuters to Barcelona’s CBD”. Urban Studies
39(10): 1881-1895.
Banister, D. and Berechman, J. (2000), Transport Investment and Economic Development.
University of College London Press.
Ben-Akiva, M. E. and Lerman, S.(1985), Discrete choice analysis: theory and application to travel

demand. Cambridge, Mass: MIT Press.
Bates, J. J., Roberts, M., Gwilliam, L. and Goodwin, P., (1987), The Value of Travel Time Savings,
Newbury: Policy Journals.
Bresson, G., Dargay, J., Madre, J. and Pirotte, A. (2004), “Economic and structural determinants ofthe demand for public transport: an analysis on a panel of French urban areas using shrinkage
estimators”. Transportation Research Part A 38: 269-285.

Cervero, R. (2002), “Built environments and mode choice: toward a normative framework”.
Transportation Research Part D 7: 265-284.
Eck, J. R., Burghouwt, G. and Dijst, M. (2005), “Lifestyles, spatial configurations and quality of life
in daily travel: an explorative simulation study”. Journal of Transport Geography 13: 123-134.
Green, P. E. and Srinivasan, V. (1990), “Conjoint analysis in marketing: new developments with
implications for research and practice”. Journal of Marketing Research 54:3-19.
Garrod, P. V. and Millius, W. (1983), “Estimated value of improvements in transport services”.

Transportation Research Part A 17A:33-37.
Haefele, M. A. and Loomis, J. B. (2001), “Using the Conjoint Analysis Technique for the Estimationof Passive Use Values of Forest Health”. Journal of Forest Economics 7: 9-27.
Hensher, D. A. (1994), “Stated Preference analysis of travel choices: the state of practice”.

Transportation 21:107-33.
Himanen, V., Lee-Gosselin, M. and Perrels, A. (2005), “Sustainability and the interactions between
external effects of transport”. Journal of Transport Geography 13: 23-28.
Lancaster, K. J. (1966), “A new approach to consumer theory”. Journal of Political Economy 74:
132-57.

14

Louviere, J. J. (1988), “Conjoint analysis modeling of stated preferences: a review of theory,
methods, recent developments, and external validity”. Journal of Transport Economics andPolicy 20: 93-119.
Louviere, J. J., Hensher, D. A. and Swait, J. D. (2000), Stated choice methods: analysis andapplications. Cambridge University Press.
Luce, D. R. and Tukey, J. W. (1964), “Simultaneous conjoint measurement: A new type offundamental measurement”. Journal of Mathematical Psychology 1: 1-27.
Maier, G., Bergman, E. M. and Lehner, P.(2002), “Modelling preferences and stability amongtransport alternatives”. Transportation Research Part E 38: 319-334.

McFadden, D. (1974), Conditional logit analysis of quanlitative choice behavior. Zarembka P. (ed.),Frontiers in econometrics. New York: Academic Press. 105-142.
Miguel, F. S., Ryan, M. and Mcintosh, E. (2000), “Applying conjoint analysis in economic
evaluations: an application to menorrhagia”. Applied Economics 32: 823-833.

Rodriguez D. A. and Joo. J. (2004). “The relationship between non-motorized mode choice andthe local physical environment”. Transportation Research Part D 9: 151-173.
Sakata, Y., (2005), “An estimation of the residential utility for the local waste management services
in Kagoshima”,

Working Paper Series E-5, Kinki University.

Sasao, T.( 2002), “Evaluating Impacts of Siting Landfill Based on Public Preferences”. Journal of

the Japan Society of Waste Management Expert 13: 325-333 (in Japanese).
Schwanen, T. and Moktarian, P. L. (2005), “What affects commute mode choice: neighborhood
physical structure or preferences toward neighborhoods?”. Journal of Transport Geography 13:
83-99.
Shen, J. Y., Sakata, Y. and Hashimoto, Y. (2005), “Is individual environmental Consciousness one ofthe determinants in transport mode choice?”. Working Paper 05-29, Graduate school of
economics and Osaka school of international public policy, Osaka University.
Steg, L. and Gifford, R. (2005), “Sustainable transportation and quality of life”. Journal of Transport

Geography 13: 59-69.
Takeuchi, K., Kuriyama, K. and Washida, T. (1999), “The effect of oil tanker accidents on coastal
ecosystems–the evaluation of conjoint analysis—”. Chapter 2, 91-104, in Washida, T., Kuriyama,K., and Takeuchi, K., Environmental evaluation workshop.

(Tsukiji shokan, in Japanese).

Train, K. E. (2003), Discrete Choice Methods with Simulation. Cambridge University Press.
Tsuge, T. (2001), “The Valuation of the Externalities of Forest on Citizen’s Preferences andPossibility of Use in a Policy-Making: A Choice Experiment Study”. Environmental Science ofSociety of Environmental Science 14: 465-476 (in Japanese).
Ubiillos B. J. and Sainz, A. F. (2004), “The influence of quality and price on the demand for urban
transport: the case of university students”. Transportation Research Part A 38: 607-614.

15

Appendix .
Descriptive Statistics of respondents
a. Behavior and Knowledge
Data
Answers
Knowledge for the plan
Very wellI have heard of it
hardly heard of it
Don't know
EffectNo Effect
Better Transport
Inconvenient using Bus
Reducing Car Traffic
Increase Tax
Station get more active
Reduce Air Pollution
Increase Noise
Change in City Environment
Know Very Well
I have heard of it
hardly heard of it
Don't know
Main Transport Usage
Train/Subway
Bus
Car/Taxi
Walk/Bicycle
Other
Primary Transport Usage
CommuteGo to School
Daily Use
LeisureUse for work
Other
Secondary Transport Usage
CommuteGo to School
Daily Use
LeisureUse for work
Other
Future Potential of Usage
I don't know
Yes
No

Count
44
141
151
136

%

Mean
MedianMode
St. Dev
1.8030220.9607
9.3%
29.9%
32.0%
28.8%

42
385
38
90
78
229
84
38

8.9%
81.6%
8.1%
19.1%
16.5%
48.5%
17.8%
8.1%

46
218
178
18
12

9.7%
46.2%
37.7%
3.8%
2.5%

18
128
35
145
146

3.8%
27.1%
7.4%
30.7%
30.9%

189
7
168
34
30
8

40.0%
1.5%
35.6%
7.2%
6.4%
1.7%

11
8
159
173
24
37

2.3%
1.7%
33.7%
36.7%
5.1%
7.8%

17
383
72

3.6%
81.1%
15.3%

b. Face
Data
Sex

Answers

Count

%

Mean
MedianMode
St. Dev1.43011
10.5328

Don't know
Male
Female

9
251
212

1.9%
53.2%
44.9%

Don't know
-19
20-29
30-39
40-49
50-64
65-

7
5
41
107
83
127
102

1.5%
1.1%
8.7%
22.7%
17.6%
26.9%
21.6%

Don't know
0-1
2-3
4-5
6-7

12
64
98
135
163

2.5%
13.6%
20.8%
28.6%
34.5%

Don't know
Self Employment
Private Company
Public Company
Parttime job
Student
Housewife
Pension
Other

15
55
159
25
53
8
58
79
20

3.2%
11.7%
33.7%
5.3%
11.2%
1.7%
12.3%
16.7%
4.2%

Don't know
High School
Special School
University
Graduate SchoolStudent
Other

15
204
45
151
13
8
36

3.2%
43.2%
9.5%
32.0%
2.8%
1.7%
7.6%

Don't know
12
34More than 5

2
74
159
94
92
42

0.4%
15.7%
33.7%
19.9%
19.5%
8.9%

8
329
135

1.7%
69.7%
28.6%

59
99
179
100
35
66
69
139
89
106

12.5%
21.0%
37.9%
21.2%
7.4%
14.0%
14.6%
29.4%
18.9%
22.5%

Age
0.08900.81570.08050.19070.16530.48520.17800.08051.4322

0
1
0
0
0
0
0
0
1

0
1
0
0
0
0
0
0
1

0.28500.38820.2724
0.39330.37180.50030.38290.27240.81892.57843

41.27952.20553

1

1.4680

Goout

Job

Education

3.25853

41.5710

Family1.11651

10.4188

HavecarDon't know
Yes
No
Household Income
Don't know
-3 million
3-6 mil.
6-10 mil.
over 10 million
Station Kadomashi
Kadomaminami
Konoikeshinden
AramotoUryudo4.20974

51.41444.1186572.18193.7309322.36432.23522

11.52052.65252

2

1.26781.26911

10.48072.4089221.25010.13980.14620.29450.18860.2246

0
0
0
0
0

0
0
0
0
0

0.34720.35370.45630.3916
0.417716