Discussion Papers In Economics
And Business

Is Individual Environmental Consciousness One of theDeterminants in Transport Mode Choice?
Junyi Shen, Yusuke Sakata, and Yoshizo Hashimoto

Discussion Paper 05-29

Graduate School of Economics andOsaka School of International Public Policy (OSIPP)
Osaka University, Toyonaka, Osaka 560-0043, JAPANIs Individual Environmental Consciousness One of theDeterminants in Transport Mode Choice?
Junyi Shen, Yusuke Sakata, and Yoshizo Hashimoto

Discussion Paper 05-29

October 2005

Graduate School of Economics andOsaka School of International Public Policy (OSIPP)
Osaka University, Toyonaka, Osaka 560-0043, JAPANIs Individual Environmental Consciousness One of theDeterminants in Transport Mode Choice?

Junyi Shen a,#, Yusuke Sakata b, and Yoshizo Hashimoto a
Osaka School of International Public Policy, Osaka Universitya

1-3 Machikaneyama, Toyonaka City, Osaka 560-0043, Japan
School of Economics, Kinki Universityb

3-4-1 Kowakae, Higashi-Osaka City, 577-8502, Japan
October 2005

Abstract
This paper models a negative impact on environment as one of the attributes oftransport mode. By this modeling, we are able to examine whether individual
environmental consciousness has a significant effect on his/her choice of transport mode.
A survey data from Saito and Onohara Area in Northern Osaka of Japan is used toestimate the model with the Heteroscedastic Extreme Value (HEV) specification. Bothof the estimated and simulated results imply that individual environmental
consciousness does influence his/her decision on transport mode choice. Furthermore,
the likelihood ratio tests indicate that both the utility and scale parameters are notequal across sub-samples of university commuters, research-facility commuters, andresidents. The details of the comparison across sub-samples suggest that we may learn
more from subdividing a whole sample into several sub-samples if we could select thembased on their characteristics.
Keywords: Environmental consciousness; Transport mode choice; Stated choice
experiment; Heteroscedastic Extreme Value (HEV) model; Value of timesaving (VOTS)
JEL classification: C35, D12, Q51, R41# Corresponding author. Tel/Fax: +81-6-68505652
E-mail address: junyi@osipp.osaka-u.ac.jp

11. Introduction
This paper examines variations in individuals’ choice of transport mode for their dairy
trips from and to a newly developed metropolis in Northern Osaka of Japan, whentransport condition is changed due to the monorail line being extended to thismetropolis. We consider the most important route from a currently developed area
(Saito West Center) to Senri-chuou which is terminal connecting from Northern Osaka
to Central Osaka. Three transport modes are selected for our analysis: monorail, bus,
and car. Among these modes, bus is currently run directly from Saito West Center toSenri-chuou, while monorail is not directly connected to Saito until the extension from
Handai Byoin Mae to Saito West Center operates in spring of 2007.
In previous studies on transport mode choice, several common attributes such as
in-vehicle time, access and excess time, Fare or cost, frequency of the service, etc. areproved to be the key determinants of making decision on selecting which transportmode to use. In recent years, people’s environmental consciousness has been greatly
enhanced with the income growth. In Japan, due to Kyoto Protocol, Japanese
government has made and will still make a great endeavor to attain the aim of the
protocol. Reconsideration of current transport policy is one of these targets.
Under this consideration, however, whether individual environmental consciousness
affects his/her decision on transport mode selection still remains an unknown issue.
Limited to our knowledge, we cannot find any literature currently discussing about thisissue, therefore, the first purpose of this paper is to manipulate an attribute ofindividual environmental consciousness into a transport mode choice experiment andexamine whether it really has impact on determining which mode to choose. A further
simulation experiment on choice share changes of each transport mode based on an
increase of its negative impact on environment also provides useful information to thepolicy makers.
The second purpose of this paper is that we observe three sub-samples and examine
the differences among them on values of time saving, direct choice elasticities,
influences of individual socio-economic characteristics, and so on. The significant
differences in some cases indicate that we may learn more from subdividing a wholesample into several sub-samples, at least in the case like this study.
A Heteroscedastic Extreme Value (HEV) model is used to derive the results of thispaper. The HEV model is based on the same random utility theory as the Multinomial
Logit (MNL) model, which is normally applied in discrete choice model. However, theHEV model is more plausible than the basic MNL model since it relaxes the assumptionof equal variances across alternatives and avoids the independence of irrelevant2alternatives (IIA) assumption in the MNL model. It is especially valid in the case thatthe tree for a nested model is difficult to be specified.
The rest of the paper is organized as follows. In Section 2, the random utility theory,
the MNL specification and the HEV specification of the model are discussed. Section 3
describes the survey issues, focusing on the basic information of the survey,
experimental design, and sampling strategy and data collection. Alternative-specificand attribute-specific individual socio-economic characteristic variables are defined in
Section 4. Section 5 presents the estimated results based on the HEV model
specification. Finally, our general conclusions are discussed in Section 6.

2. Model structure and specification
Stated Choice (SC) 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 of alternatives the decision maker will choose thealternative that maximizes his/her utility. Since the utility of an alternative for anindividual (U ) cannot be observed, however, it could be assumed to consist of adeterministic component (V ) and a random error term ( ε ). Formally, individual q ’s
utility of alternative i can be expresses as:

U iq = Viq + ε iq

(1)Hence the probability that individual q chooses alternative i from a particular set J,
which comprises j alternatives, can be written as:
Piq = P(U iq > U jq ; ∀ j (≠ i) ∈ J ) = P(ε jq < ε iq + Viq − V jq ; ∀ j (≠ i) ∈ 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 are assumed to follow the extreme value type I (EV1) distribution and be
independently and identically distributed (IID) across alternatives and cases (orobservations), the multinomial (or sometimes called conditional) logit (MNL) model(McFadden 1974) is obtained. In the MNL model, the choice probability in Equation (2)is expressed as:
J

Piq = exp(µViq ) / ∑ exp(µV jq )

(3)

j =1

Then, making further assumption for the deterministic component of utility to be linear
in parameters, Viq = β ′ Xiq, the probability in Equation (3) is given as:

3J

Piq = exp(µβ ′X iq ) / ∑ exp(µβ ′X jq )

(4)

j =1

where µ represents a scale parameter that determines the scale of the utilities, which
is proportional to the inverse of the distribution of the error terms. It is typicallynormalized to 1 in MNL model. Xiq are explanatory variables of Viq, normally including
alternative-specific constants (ASCs), the attributes of the alternative i and thesocial-economic characteristics of the individual q , β ′ is the parameter vector
associated with the matrix Xiq.
An important assumption of the MNL model is the independence of irrelevantalternatives (IIA) property. This property, which follows from the assumption of
independently and identically distributed (IID) across alternatives and cases, implies
that the relative choice probabilities between any two alternatives of choice set J are notaffected by the inclusion or exclusion of other alternatives in that set. The IIA property
is a strict assumption of the MNL model and a “reasonable approximation of more
complex relationships” (Ben-Akiva and Lerman 1985). Although the inclusion of ASCs,
to some extents, helps mitigate inaccuracies caused by IIA in the MNL model, it is notenough in most cases if the IIA assumption is violated.
One way of circumventing the IIA property is to allow alternatives having different
scale parameters by estimating a Heteroscedastic Extreme Value (HEV) model (Bhat,
1995; Allenby and Ginter, 1995). The HEV model is based on the same random utility
structure discussed above and simply relaxes the assumption of equal variances acrossalternatives. A frequently used Nested Logit (NL) model with a unique inclusive value
parameter for each alternative (with one arbitrarily chosen variance to 1 for
identification) is equivalent to a HEV specification (Louviere et al. 2000). However,compared with the NL model, the HEV model is obviously valid when the tree for anested model is difficult to be specified. Moreover, the magnitude of the estimatedalternative-specific scale parameters and their standard deviations may help analysts
determine the tree structure of a nested model.
In mathematical term for the HEV model, the choice probability of alternative ifrom a choice set J

by individual q is expressed as
J

Piq = exp(µ i β ′X iq ) / ∑ exp(µ j β ′X jq )

(5)

j =1

where µ i denotes the different scale parameters across alternatives. Intuitively, thisscale parameter represents uncertainty associated with the expected utility (or theobserved part of utility) of an alternative. Therefore, the lower the scale parameter is,
the higher the uncertainty would be (Louviere et al. 2000).

43. Stated choice survey
3.1 Basic information on a new metropolis−Saito
Saito (International Culture-Park City) is aimed at developing a new and uniquemetropolis on hills in the Northern Osaka of Japan, which is planned to be equippedwith such distinctive functions as culture and academic center, research anddevelopment, and international exchange. As the process of this development, a part of
the West Center in the metropolis has started to be sold as its residential areas since
May 2003. By the end of May 2005, about 600 households have been located with a bit
more than 1800 people. In addition, two research facilities, i.e. Saito Bio-Incubator since
the end of 2004 and Pharmaceutical Basic Technology Research Facility since thebeginning of 2005, have started to operate, with a total number of 180 staffs in current.
Osaka Monorail Saito Line, as a main transportation access to Saito, is planed to run
a total of 9 kilometers from Saito East Center to Bampaku Kinenkoen (Expo’70
Commemoration Park). The line is planned to have totally 7 stations from south to→ Handai Byoin
Mae → Toyokawa → Saito West Center (Saito Nishi Center) → Saito Central (Saito
Chuou) → Saito East Center (Saito Higashi Center). The first part from Bampaku
north:

Bampaku

Kinenkoen→

Koen

Higashiguchi

Kinenkoen to Handai Byoin Mae has already operated since October 1st 1998 and thesecond part from Handai Byoin Mae to Saito West Center will be operating in the springof 2007. The survey is based on the extension of the second part of the line to examine
how individuals choose their transport mode from Saito West Center to Senri-chuou due
to the coming operation of this extension.
3.2 Design of the stated choice experiment
In the stated choice experiment, a number of attributes and assigned levels are used
to generate hypothetical scenarios. The attributes and their levels included in eachscenario for this study are summarized in Table 1. We have three alternatives(transport modes), with each five common attributes. For each attribute, we adopt a
2-level design with an exception of access time and frequency in car and a negative
impact on environment in monorail. Therefore, in full factorial design, there are totally
212 (=24・23・25) choice sets. Obviously, it is too much for respondents to answer. Then,
fractional factorial designs were used to reduce the number of choice sets to a
manageable level. Thirty-two choice sets were constructed in such a way thatorthogonality both between and within alternatives was ensured. These choice sets
were further systematically blocked into four versions to avoid dominant selection. Eachsurveyed respondent was presented with one version of eight choice sets and asked to5Table 1

Transport attributes and their levels used in the survey

Attributes

Levels of attributes
MonorailCarBus

15 minutes
20 minutes

20 minutes
40 minutes

25 minutes
50 minutes

Access time

10 minutes
15 minutes

almost 0

3 minutes
6 minutes

Frequency

10 minutes
20 minutes

at any time

15 minutes
30 minutes

Fare or cost

360 JP yen
420 JP yen

400 JP yen
800 JP yen

220 JP yen
280 JP yen

Negative impact on environment

low

2 times as monorail
3 times as monorail

1.5 times as monorail
2.5 times as monorailIn-vehicle time including delay timecaused by traffic jam(such as CO2 emission)

Table 2

An example of choice sets

Transport mode
In-vehicle time including delay timecaused by traffic jam (minutes)
Access time (minutes)
Frequency (minutes)
Fare or cost (JP yen)

Monorail

Car

Bus

15

40

50

15
10

almost 0
at any time

3
15

360

800

280

Negative impact on environment(such as CO2 emission)Low

3 times asmonorail

2.5 times asmonorail

Please choose one most desirable
transport mode and ✔ in □

□

□

□

select the most favorite mode in the choice sets, with several questions related toindividual socio-economic characteristics.
A pilot test of six residents from Saito West Center has been executed on May 21st
2005. According to the pilot test, the context and presentation of the questionnaire havebeen modified. Then, with a further reconsideration on the questionnaire based on thecomments from transport planner and monorail operator, we finally fixed the form.
Table 2 provides an example of choice sets for the final questionnaire.

6

3.3 Sampling and data collection
We consider three sub-samples in the survey. The first one is the residents in Saito
West Center and Onohara Area in the neighborhood of Toyokawa, another new monorail
station. We apply a method called posting1 for this sub-sample. To avoid the possible
result that most of the respondents are housewives, we deliver two different versions ofquestionnaires and two self-addressed postage-prepaid envelopes to each household,and ask two different household members older than 15 years to answer the questions
respectively. Of 2440 questionnaires distributed on July 7th 2005, 467 valid responses
were returned, with a response rate at 19.14%.2
The second sub-sample is selected as work commuters to Saito Bio-Incubator andPharmaceutical Basic Technology Research Facility. Since there are currently 180 staffs
in these two facilities, 180 questionnaires with self-addressed postage-prepaid
envelopes were distributed on July 7th 2005. As a result, 85 responses were obtained,with a response rate at 47.22%.
The third sub-sample is for students commuting to Osaka University of Foreign
Studies which is quite near to Saito West Center. A total of 300 questionnaires with
self-addressed postage-prepaid envelopes were distributed face-to-face to the students
at the campus of the university on July 7th 2005. A response rate at 28.33% was reachedupon 85 responses being mailed back.

4. Individual socio-economic variables
Table 3 defines the alternative-specific constants and the individual socio-economic
interaction variables included in the stated choice model’s estimation. The individual
socio-economic characteristics are manipulated in two ways: an alternative-specific way
and an attribute-specific way. In the alternative-specific way, such individual
socio-economic characteristics as current bus users, current car users, car numbers heldby each household, and work commuters are interacted with ASCs of monorail and car.
In addition, the average number of days traveling in the surveyed section in a week is
It is a similar method to mail survey, which can be used in some simple and familiar
products choice experiments. It is executed by delivering questionnaires into thesurveyed respondents’ posts and asking the respondents to mail them back in
self-addressed postage-prepaid envelopes after they completing the questions.
2 Two questions involving the number of household members and the number ofchildren below 15 years in the household are designed partly to accurately calculate theresponse rate, since several families may probably be single-parent with children or justsingle. After considering this issue, we estimate that the valid response rate is adjusted
to 19.71%.
1

7

Table 3

Definitions of individual socio-economic interaction variables

Variable

Definition

ASC_mono

Dummy variable taking on a value of 1 for monorail, and 0 forothersASC_carDummy variable taking on a value of 1 for car, and 0 for others

Busnow_mono

An interaction term of ASC_mono with a dummy variable taking
on value of 1 for current bus users and 0 for others

Carnow_mono

An interaction term of ASC_mono with a dummy variable taking
on value of 1 for current car users and 0 for others

No.Car_mono

An interaction term of ASC_mono with car numbers held byhouseholdAn interaction term of ASC_mono with a dummy variable taking
on value of 1 for work commuters and 0 for others

Work-commuter_mono

Busnow_car

An interaction term of ASC_car with a dummy variable taking onvalue of 1 for current bus users and 0 for others

Carnow_car

An interaction term of ASC_car with a dummy variable taking onvalue of 1 for current car users and 0 for others

No.Car_car

An interaction term of ASC_car with car numbers held byhouseholdWork-commuter_car

An interaction term of ASC_car with a dummy variable taking onvalue of 1 for work commuters and 0 for others

Days_car

An interaction term of ASC_car with average number of days
traveling in the surveyed section in a week

Envi_higheduc.

An interaction term of environmental attribute with a dummy
variable taking on a value of 1 for higher education above
university and 0 for others

Envi_inc.abo.600

An interaction term of environmental attribute with a dummy
variable taking on a value of 1 for household’s annual income
above six million JP yen and 0 for others

Envi_age.bel.49

An interaction term of environmental attribute with a dummy
variable taking on a value of 1 for age below 49 and 0 for others8also interacted with ASC of car. In the attribute-specific way, respondent’s education,age, and household’s income were interacted with the key attribute of this study−transport mode’s negative impact on environment. After counting these individual
socio-economic characteristics into the choice model, we employ both the MNL and HEV
specifications for estimation.

5. Results and analysis
5.1 Specification issue
Tables 4 and 5 report the results estimated by the MNL model and the HEV model forthree sub-samples mentioned in section 3.3 and wholly pooled sample. All the results
are obtained using NLOGIT Version 3.0 (Econometric Software 2002).
The first issue concerning on the model specification is whether the IIA property of
the MNL model is violated or not. The results of Hausman test in all cases provided inTable 4 significantly reject the IIA assumption in all sub-samples and wholly pooled
sample. It is to say that the MNL model is inappropriate in this sense. The violation of
IIA in the MNL model can also be confirmed from the estimated alternatives’ scaleparameters in the HEV specification. Results for the estimated scale parameters formonorail and car in all cases indicate that they are significantly different from 1.0 at
either 1% or 5% level3, implying that the assumption of independently and identically
distributed (IID) across alternatives is violated.
The second issue concerns on the hypothesis of equal utility parameters among eachsub-samples and pooled samples. Since there are three sub-samples, four groups can be
possibly pooled. They are pooled samples of university commuters + research-facility
commuters, university commuters + residents, research-facility commuters + residents,
and university commuters + research-facility commuters + residents4. Therefore, thenull hypotheses can be formally stated as follows:

H 01 : β university = β research− facility = β university + research− facility
H 02 : β university = β resident = β university + resident
H 03 : β research− facility = β resident = β research− facility + resident
H 04 : β university = β research− facility = β resident = β whole
The statistics of the likelihood ratio test suggested by Swait and Louviere (1993) are:
The scale parameter of bus is set to one for identification.
The results for listed pooled sample in Tables 4 and 5 are for university commuters +research-facility commuters + residents sample. To conserve space, we do not list theresults of other pooled samples. These results are available upon request.
3
4

9

Table 4

MNL results for sub-samples and wholly pooled sample

In-vehicle time
Access time
Frequency
Fare or cost
Environment
Envi_higheduc.
Envi_inc.abo.600
Envi_age.bel.49
ASC_mono
Busnow_mono
Carnow_mono
No. Car_mono
Work-commuter_mono
ASC_carBusnow_car
Carnow_car
No. Car_car
Days_car
Work-commuter_car

University

Research-facility

Resident

Pooled sample-0.08291***0.00156-0.01658*-0.00359***-0.641457−−−-1.64426***
0.46312**-1.36694*-0.09568−-0.88921-0.28916
0.356110.11527-0.10913−-0.08018***-0.10604***-0.04941***-0.00267***-1.73916**-0.83308*-0.09796
0.92467*-0.42016
0.05906
1.12373***
0.20643−-1.09955**-1.27860***
1.14179***0.17810−−-0.07584***-0.02652**-0.01555***-0.00266***-0.70660***-0.50334***-0.49052***
0.35160**-0.76123***0.021220.18071
0.28616***0.43587***-1.39350***-0.97117***0.84037***0.53534***-0.11327***0.46454***-0.07636***-0.03152***-0.01915***-0.00269***-0.91034***-0.58513***-0.31021**
0.39249***-1.03457***
0.25145**0.54531***0.16202***0.51315***-1.43559***-0.87194***
1.10189***0.27612***-0.06451***0.74883***

30.081***33.442***80.854***116.257***
(5)
(8)
(8)
(8)
LL convergence-530.9535-547.5648-2853.0621-3989.0072
ρ2
0.17980.20420.18440.1830Observation
672
664
3584
4920
Note: t-statistics and standard deviations are not reported to save space.
***, ** and * denote significance at the 1%, 5% and 10% significance level, respectively
Hausman test for IIA

LR1=−2[−1128.685−(−530.9535＋−547.5648)]＝100.3334
LR2=−2[−3424.773−(−530.9535＋−2853.0621)]＝81.5148
LR3=−2[−3445.949−(−547.5648＋−2853.0621)]＝90.6442
LR4=−2[−3989.0072−(−530.9535＋−547.5648＋−2853.0621)]＝114.8536
The critical value of theχ 2 distribution is 49.80 and 67.50 at the 95% significance levelon 35 and 50 degree of freedom, respectively. Therefore, the hypothesis that the vector
of common utility parameters is equal across sub-samples can be rejected in all cases.
To test whether the scale parameters are equal across sub-samples requires a further
likelihood ratio test. The models for four pooled samples are re-run with the restrictionthat the scale parameters is no longer allowed to differ across sub-samples. The LR

10

Table 5

HEV results for sub-samples and wholly pooled sample

In-vehicle time
Access time
Frequency
Fare or cost
Environment
Envi_higheduc.
Envi_inc.abo.600
Envi_age.bel.49
ASC_mono
Busnow_mono
Carnow_mono
No. Car_mono
Work-commuter_mono
ASC_carBusnow_car
Carnow_car
No. Car_car
Days_car
Work-commuter_car

University

Research facility

Resident

Pooled sample-0.08186***-0.01386-0.01685*-0.00517**-0.78494*−−−-1.17532*
0.43139**-1.15019-0.09668−-1.52804-0.51432
0.781240.19000-0.13732−-0.09319***-0.11303***-0.05334***-0.00335***-1.67719*-0.88097-0.30983**
0.84831-0.48238-0.00503
1.13661**
0.19513−-1.55418**-1.78273***1.23067**
0.25397−−-0.07593***-0.02308*-0.01476***-0.00307***-0.64773**-0.49577***-0.45831***0.35651**-0.64561**
0.025340.210770.27535***0.41078***-1.59834***-1.08142***0.96014***0.55632***-0.12710***0.46974***-0.07614***-0.02778***-0.01777***-0.00315***-0.89740***-0.58069***-0.30140**
0.37951**-0.85608***
0.22838***
0.54747***
0.14983***
0.46764***-1.70499***-1.04014***1.26194***
0.27316***-0.06758**
0.78812***1.46071**
0.59376***
1 (fixed)

1.06629**0.62269***
1 (fixed)

1.24873***
0.80387***
1 (fixed)

1.37301***
0.75201***
1 (fixed)

0.878032.160031.28255

1.202812.05969
1.28255

1.02708
1.59547
1.28255

0.934121.70550
1.28255

Scale parameters
Monorail
Car
Bus

Std. deviation
MonorailCarBus-489.5722-515.057-2627.048-3680.383
LL constants-647.3135-688.0841-3598.0438-4882.4911
ρ2
0.24370.25150.26990.2462
observation
672
664
3584
4920
Note: t-statistics and standard deviations are not reported to save space.
***, ** and * denote significance at the 1%, 5% and 10% significance level, respectively
LL convergencestatistics for this test are:LR5=−2[−1143.325−(−1128.685)]＝29.28
LR6=−2[−3445.462−(−3424.773)]＝41.378
LR7=−2[−3477.245−(−3445.949)]＝62.592
LR8=−2[−4023.733−(−3989.0072)]＝69.4516
11

These LR statistics well exceed the critical value of 3.84 at the 95% significant level,
indicating that the scale parameters are different across sub-samples in all cases.
5.2 Results based on the HEV model
Due to the violation of the IIA property in the MNL model, we focus, based on the
HEV results, our following discussions on fare and time-associated attributes,

environmental

consciousness

attributes,

andalternative-specificindividual

socio-economic variables. We have checked the cross effects of environmental
consciousness attribute with fare and time-associated attributes and found that thesecross effects are not significant in all the sub-samples. Thus, we only deal with the main
effects in our estimation.
・fare and time-associated attributes
Most of fare and time-associated attributes in each sub-sample have been estimatedwith significantly negative signs, which is consistent with the theoretical expectation.
However, in the sub-sample of university commuters, access time is not statisticallysignificant even at 10% level.
Evaluating the absolute parameter estimates across sub-samples is not informative
because of scale differences. However comparisons of willingness to pay indicators suchas values of time savings (VOTS) and elasticities are very informative. Summaries of
VOTS for each sub-sample are presented in Table 6. The estimates of VOTS differ agreat deal across sub-samples. Research-facility commuters have the highest VOTS ineach kind of time such as in-vehicle time, access time, and frequency, whilst university
commuters have the lowest VOTS among the surveyed samples. An interest andprobably important result is that in the sub-sample of research-facility commuters, theVOTS of access time has the highest value, while in the other two sub-samples,
in-vehicle time is the highest one. It is intuitive that work commuters evaluate accesstime as the most important determinant of transport mode choice probably because they
dislike spending too much time on accessing the mode everyday.
Summaries of the choice elasticities for in-vehicle time, access time, frequency andFare or cost are given in Table 7. The results suggest that the choice elasticities ofin-vehicle time for each transport mode are somewhat similar among sub-samples.
However, for access time and frequency, sub-sample of research-facility commuters is
relatively much more sensitive than the other two sub-samples, with a range from thelowest ratio of 2.405(=-0.291/-0.121) in comparing to university commuters forfrequency to the highest ratio of 4.470(=-0.514/-0.115) in comparing to residents for12Table 6

Value of time savings (JP yen per hour)
University

Research-facility

Residents

950
1669
1484
ns
Access time
161
2024
451
Frequency
196
955
288
Note: ns = not statistically significant. The results are based on the HEV specification.
In-vehicle time

Table 7

Choice direct elasticities (probability weighted)
University

Research-facility

Residents-0.681-1.169-1.277-0.595-1.123-1.593-0.527-1.194-1.455−−−-0.514−-0.266-0.115−-0.061-0.121−-0.165-0.291−-0.620-0.088−-0.194-0.964-1.413-0.565-0.447-0.838-0.445-0.474-0.986-0.450In-vehicle time
Monorail
Car
Bus

Access time
Monorail
Car
Bus

Frequency
Monorail
Car
Bus

Fare or cost
MonorailCar
Bus

access time. This issue indicates that access time and frequency are relatively
important to the work commuters other than students and residents. With respect tochoice elasticity on fare, university commuters are the most sensitive in all modes,
while the other two sub-samples are almost with the same elasticities. This is probably
plausible because students normally have stricter constraint of income than the othertwo sub-samples, therefore, they are more sensitive on fare.
・environmental consciousness attributes
The key issue being worthy of remark in this paper is the environmental
consciousness attribute. In all the sub-samples estimated by the HEV specification, thevariable of transport negative impact on environment is estimated with a significantly

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Table 8

Prediction of change in choice share in response to a 50% increase in negative
impact on environment caused by each transport mode
University

Research-facility

Residents-0.8360.252
0.584-5.8642.414
3.450-2.675
0.767
1.908

0.637
-1.1590.523

5.353-7.3062.953

1.804-2.537
0.733

1.148
0.412
-1.5606.250
1.595-7.8453.681
0.597-4.278

Monorail increase a
Monorail
Car
Bus

Car increase b
Monorail
Car
Bus

Bus increase cMonorail
Car
Bus

Note: Share changes are in percentage (%)
a Corresponding to an increase in the negative impact on environment for a monorailb Corresponding to an increase in the negative impact on environment for a car
c Corresponding to an increase in the negative impact on environment for a bus

negative sign, indicating that the more negative impact a transport mode causes onenvironment, the more disutility individual has when he/she chooses that mode. By
interacting the individual socio-economic characteristics with the variable ofenvironmental consciousness, we may conclude that, for those who have at least
educational background over university and whose annual household incomes are morethan six million JP yen, more negative impact on environment of a transport mode leads
to a less choice probability of that mode. In contrast, young and middle age (below
forty-nine years old) group may receive relatively more utility than old age (older thanfifty) group when they choose a mode with more negative impact on environment, suchas car comparing to monorail.
To examine more explicitly how environmental impact caused by a transport mode
affects individual choice of it, we execute a simulation of predicting the change in choiceshares of each transport mode after changing the attribute level of one mode associatedwith environmental consciousness. We suppose a 50% increase of the environmental
negative impact to simulate its impact on each alternative, and report the results inTable 8.
It can be seen that, based on the HEV specification, a 50% increase in monorail’s
negative impact on environment brings about 0.836% reduction of the monorail share14

for university commuters, 5.864% reduction for research-facility commuters and 2.675%
reduction for residents. These reductions are almost lower than any other reduction
caused by the same increase for car and bus in each sub-sample, except in the scenario
of car for residents. This indicates that the reduction in the choice share of a transportmode which has relatively small negative impact on environment is less than those ofwhich have relatively more negative environmental impact. In addition, in any
sub-sample, for any loss of the share from the increased negative impact, more shares
go to the relatively “cleaner” mode. That is, for the reduction in monorail’s share, more
people change to bus than car; for the reduction in share of car, more people change tomonorail than bus; and for the reduction in share of bus, more people change tomonorail than bus.
The above two evidences from the simulation strongly support our hypothesis that
individual environmental consciousness is an important determinant in his/hertransport mode choice. Therefore, “cleaner” transport modes are worth being developed.
Finally, a relatively lower reduction in each mode from the increase of negative
impact on environment for university commuters than for both research-facility
commuters and residents is probably true in reality, since students, due to theirstringent income constraint, are less substitutable any other than their usually selectedtransport mode.
・alternative-specific individual socio-economic variables
Individual social-economic variables such as current bus user, current car user, carnumbers held by household, and work commuters, etc. are interacted with alternative
specific constants of monorail and car. The results of these interaction terms arepresented below.
＊Busnow_mono and Busnow_car
The positive and significant parameter of Busnow_mono in the sub-sample ofuniversity commuters implies that for university commuters, current bus users are
more willing to change to monorail other than continuing taking bus. Whilst, thesignificantly negative parameters of Busnow_car in research-facility and resident
sub-samples suggest that a current bus user is unwilling to change his current
transport mode to car.
＊Carnow_mono and Carnow_car
Current car users in the sub-sample of research-facility commuters intend to choose
either monorail or car other than bus due to the estimated positive and significant
parameters of Carnow_mono and Carnow_car. However, the current car users in15

resident sub-sample seem only receive more utility for continuing using car.
＊No. car._mono and No. car_car

No. car._mono variable parameter is estimated with a significantly positive sign inresident sample. It indicates that for residents in Saito West Center and Onohara
Area, the more cars a household has, the more utility they obtain from choosing
monorail than the bus. The estimated positive sign of No. car_car means that morecars held by a household bring to higher possibility in which they choose car as theirtransport mode.
＊Work-commuter_mono and Work-commuter_car
These two variables are only interacted in the sub-sample of residents. The result issomewhat counterintuitive to us. Both the positive and significant parameters of

Work-commuter_mono and Work-commuter_car imply that work commuters of thoseresidents are more willing to choose either monorail or car other than bus. However,
the magnitude of marginal utility of work-commuter_car is estimated slightly larger
than that of work-commuter_mono, which is out of our expectation. The probablereason may be that some of work commuters are current car users and prefercontinuing choosing car due to their habits.
＊Days_car is considered in sub-samples of university commuters and residents, withthe result that it is only significant in residents’ sample. The significantly negative
parameter of this variable implies that the more days residents travel in the section,
the less probably they choose car as their transport mode.5
All the above discussions are very intuitive to the decision makers of transport policy.
The significant and substantial differences of value of time savings, elasticities of fareand time-associated attributes, environmental consciousness attributes, and individual
socio-economic interaction variables across sub-samples suggest that we can learn morefrom subdividing a whole sample into several sub-samples if we could select them basedon their characteristics.

6. Conclusion
This paper estimates various parameters of discrete transport choice model, based on
the survey for Saito West Center and Onohara Area in Northern Osaka of Japan. We

We have also tried interacting days (average number of days traveling in the surveyed
section in a week) with ASC of monorail. However, this interaction term is not
significant in any sub-sample. Therefore, we drop it in our final estimation.

5

16

have shown that a more general Heteroscedastic Extreme Value model than a
traditional Multinomial Logit model provides a better fitness and better explanation of
the data.
Our modeling on individual environmental consciousness is a try to connect transporteconomics with environmental economics. This modeling allows us to examine whether
individual environmental consciousness is one of the determinants of transport mode
choice. Our results suggest that people intend to choose a mode which has less negative
impact on environment. Furthermore, the simulation of predicting the change in choiceshares of each transport mode in response to a change on the level of environmental
attribute brings to a result that the environmental deterioration of one mode reduces its
choice share and increases more the share of a relative “cleaner” mode.
Finally, we are able to compare the model estimates across sub-samples due to oursampling strategy and data collection of the survey. The results of two different
likelihood ratio tests suggest that neither utility parameters nor scale parameters areequal across sub-samples. With a detailed comparison among the estimated values oftime saving, direct choice elasticities, and alternative-specific individual socio-economic
variables across the sub-samples, we conclude that at least in the cases like this study,
more implication can be found by subdividing a whole sample into several different
sub-samples.

Acknowledgement
The authors 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 thesole responsibility of the authors.

References
Adamowicz W. and Swait J. D. (1994), “Combining stated and revealed preference
methods for valuing environmental amenities”. Journal of Environmental Economics

and Management 26: 271-292.
Allenby G. and Ginter J. (1995), “The effects of in-store displays and feature advertisingon consideration sets”. International Journal of Research in Marketing 12: 67-80.
Ben-Akiva M. E., Bolduc D. and Bradley M. (1993), “Estimation of travel choice models
with randomly distributed values of time”. Transportation Research Record 1413:

17

88-97.
Ben-Akiva M. E. and Lerman S. (1985), Discrete choice analysis: theory and application

to travel demand, Cambridge, Mass: MIT Press.
Bhat C. R. (1995), “ A heteroscedastic extreme value model of intercity travel mode
choice”. Transportation Research 29B(6): 471-483.
Blamey R. K., Bennett J. W., Louviere J. J. Morrison M. D. and Rolfe J. (2000), “A test ofpolicy labels in environmental choice modeling studies”. Ecological Economics 32:
269-286.
Boxall P., Adamowicz W., Williams M., Swait J. D. and Louviere J. J. (1996), “A
comparison of stated preference approaches to the measurement of environmental
values”. Ecological Economics 18: 243-253.
Bradley M. A. and Gunn H. (1990), “Stated preference analysis of values of travel timein the Netherlands”. Transportation Research Record 1285: 78-89.
Hanley N., Wright R. E. and Adamowicz V. (1998), “Using choice experiments to value
the environment: design issues, current experience and future prospects”.

Environmental and Resource Economics 11(3-4): 413-428.
Hausman J. A. and McFadden D. (1984), “Specification tests for the multinomial logit
model”. Econometrica 52: 1219-1240.
Hensher D. A. (1994), “Stated preference analysis of travel choices: the state of thepractice”. Transportation 21: 107-133.
Hensher D. A. (2001), “The sensitivity of the valuation of travel time saving to thespecification of unobserved effects”. Transportation Research part E 37: 129-142.
Keane M. (1994), “Modeling heterogeneity and state dependence in consumer choice
behavior”. Working paper, Department of Economics, University of Minnesota.
Louviere J. J., Hensher D. A. and Swait J. D. (2000), Stated choice methods: analysis

and applications, Cambridge University Press.
McFadden D. (1974), Conditional logit analysis of quanlitative choice behavior,
Zarembka P. (ed.), Frontiers in econometrics, New York: Academic Press. 105-142.
Menendez L. G., Zarzoso I. M. and Miguel D. P. (2004), “Determinants of mode choice
between road and shipping for freight transport”. Journal of Transport Economics andPolicy 38(3): 447-466.
Swait J. D. and Louviere J. J. (1993), “The role of the scale parameter in the estimationand comparison of multinomial logit models”. Journal of Marketing Research 30:
305-314.

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