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1. A study of passengers’ willingness to pay for business
class seats of high speed rail in Taiwan
JOU, Rong-Chang*a
, JIAN, R.-Y.a
and WU, Yuan-Chana
a
Institute of Civil Engineering, National Chi Nan University, No.1, University Rd, Puli, Nantou
County,54561 Taiwan
Abstract
To enhance the transportation market share of High Speed Rail, the operator has
conducted a promotion for off-peak periods since early 2008. However, it has been
difficult to increase the usage rates for business class seats after the implementation of
this promotion. The objective of this paper is to investigate passengers’ willingness to
pay for business class seats of the HSR in Taiwan. The survey framework is designed
to determine the value of WTP. Results obtained from both the conventional model
and the spike model, a model that can handle zero responses in WTP, show that the
fare for business seats is the most important factor in choice behavior. Long distance
travelers strongly emphasize compartment noise levels and security, while medium
distance travelers primarily consider the cost of the ticket relative to income. This
paper also verifies that the application of the spike model is similar to the
conventional model when there are not a high proportion of zero responses.
Keywords: High speed rail, Willingness to pay, Spike model
*
Corresponding author: Tel: +886- 049- 2910-960 ext. 4956
E-mail address: rcjou@ncnu.edu.tw (JOU, Rong-Chang)

2. 1. Introduction
According to the Ministry of Transport and Communications (MOTC), the
THSR reduced the demand for domestic airlines by 2.1 million passengers and
highway throughput by 5.1 million vehicles after it began operations in 2007. As a
result, domestic airline transport demand fell 27% as a result of the HSR. Similar
studies were done by Milan (1993) and Park and Ha (2006). The latter study showed
that 86% of travelers chose HSR over domestic airlines in South Korea.
A total of 24,400 frequencies were operated and around 15 million passengers
were transported by HSR in 2007. The average rate of seat utilization was 44.91% at
the same time. While in 2008, the frequencies and passengers increased to 45,900 and
30 million, respectively. However, its average rate of seat utilization declined to
43.51% in that year. In order to improve the seat utilization rates, the Taiwan HSR
company offered a discount promotion of up to 35% for off-peak periods. However,
while the usage rates for general seats rose, business class seat use remained flat,
accounting for only 2.6-3% of the total passengers.
While many studies have focused on transportation demand forecast after an
HSR enters the transportation market (Wong et al., 2002; González-Savignat, 2004;
Foridh, 2005; Román, 2007; Park et al., 2006; Martin and Nombela, 2007; Chang et
al., 2004, 2008) and its market share (Park et al., 2006, Martin and Nombela, 2007,
Foridh, 2005 and Román, 2007), little attention had been paid to the analysis of
business class seat choice behavior and willingness to pay. The issues of business
class and WTP were only investigated in different fields (Tam et al., 2008; Zito et al.,
2009).
This paper therefore uses the random utility model and adopts the stated
preference method to explore the choice behavior and willingness to pay (WTP) for
business class seats on the Taiwan HSR. To gain a better understanding of choice
behavior and WTP for business seating, this paper adopts Double-Bounded
Dichotomous Choice (DBDC) questions to ask whether travelers are willing to pay
for business class seats on the Taiwan HSR.
DBDC is a closed ended form, Hanemann, Loomis and Kanninen (1991)
proposed, the simplest way of asking the respondent whether he or she would be
willing to pay a specified amount of money to obtain the value of a non-market good.
Hoehn et al. (1987) demonstrated that the closed-ended forms are less biased since
they have the simplest decision making. Hackl et al. (1997) and Calia et al. (2000)
extended their application and addressed that it is a good way of increasing the
statistical efficiency of estimation.
If the respondent rejects a series of questionnaires, a case of “protest” zeroes will

3. result (Freeman, 2003). Since it is difficult to recognize whether the respondent’s
response equals zero, Vaughan et al., (1999) pointed out that the statistical estimation
will result in a negative estimator. Kristroöm (1997) thus proposed the ‘Spike
model’ to deal with zero bids. Many studies (Yoo et al., 2002; 2006; Aurelia et al.,
2005; Jou et al., 2010) found that the spike model is an appropriate method for
handling data with a high proportion of zero values.
In this study, state preference method is used to measure travelers’ willingness to
pay for business class seats of the Taiwan high speed rail. Double-Bounded
Dichotomous Choice questions are used to obtain the traveler’s willingness to pay for
business class seats. The spike model, an effective way to remedy respondents with
zero WTP, is applied to estimate the amount of money willing to pay for business
class seats.
The remainder of this paper is organized as follows: Section 2 presents the
conventional and spike models. Section 3 discusses the empirical contents and survey
instrument, including the sample, followed by Section 4 which presents the empirical
findings. The final section offers conclusions and suggestions.
2. The Model
2.1 Conventional model
This paper explores the traveler willingness to pay for business class seats using
the random utility theory. We follow Hanemann (1984) and assume that the utility
function consists of observable and unobservable components. The individual’s utility
function can be written as:
( , , ) ( , , )
U Y X Q V Y X Q 
  (1)
where Y is income, X is a vector of socio-economic characteristics, Q is a vector
of the asset value and  is a random disturbance term with zero expected value.
When the HSR traveler will pay for business class seats, it means that the traveler
prefers the new state (V1) over the current state (V0). Thus, the individual’s utility can
be rewritten as:
1 1 1 0 0 0
( , , ) ( , , )
V Y WTP X Q V Y X Q
 
    (2)
when the traveler is willing to pay to take business class seats, his income will be
reduced by WTP ( 1
Y Y WTP
  ), though he still prefers the new utility V1. Assume
income equal to Y in the initial stage V0 ( 0
Y Y
 ). 0
 and 1
 are random terms with
independently and identically (iid) Gumbel distribution. Thus, the probability function
that a given individual pays the amount WTP in the new state can be derived as

4. follows:
1 1 0 0
0 1
Pr( ) Pr( (.) ) ( (.))
( , , ) ( , , )
Yes V F V
V V Y WTP X Q V Y X Q


  
    
   
 
(3)
Moreover, if the bid (A) offered in the DBDC questionnaire is smaller than the
WTP value that means the traveler will pay that amount A to take HSR business class
seats. The probability of individual paying the amount WTP in the new state can be
derived as follows:
Pr( ) Pr( )
1 ( )
( (.))
WTP
Yes WTP A
G A
F V

 
 
 
(4)
where ( )
WTP
G A is the cumulative distribution function (c.d.f.) of the respondent
who pays amount A. If the dependant variable is binary, Aurelia et al. (2005) argued
that using the MLE estimation is better than OLS. The log-likelihood function for the
sample is then given by Equation錯誤! 找不到參照來源。.
ln ln(1 ( )) ln( ( ))
WTP WTP
yes no
L G A G A
  
  (5)
We can derive the expected WTP as Equation(6).
0 0
0 0
( ) ( ( )) (1 ( ))
( ( (.))) (1 ( (.)))
WTP WTP
E WTP G A dA G A dA
F V dA F V dA
 
 
 
  
    
 
 
(6)
If we assume that the utility function is a linear utility function, and consider
only the effect of income Y, the model simply ignores the effect of social variable X
and asset value Q. The utility function can then be rewritten as Equation(7).
( , , ) , 0,1
j j
V Y X Q Y j
 
  
(7)
Equation (3) can be further rewritten as Equation(8). Since the WTP value cannot
be obtained at new state V1, we substitute it for bid A from the DBDC questionnaire.
1 0
1 0
(.) ( ) ( )
V Y A Y
A A
   
    
     
    
(8)
We then assume ( )
WTO
G A has the form of a logistical function, meaning that
( (.))
F V
  can be shown as:

5. 1
( (.))
1 exp( )
F V
A

 
 
   (9)
According to Equation(6), we can derive the expected WTP as Equation(10).
( )
E WTP


  (10)
2.2 Spike model
The conventional model, traditionally, easily leads to statistical deviations since
the binary probability models (such as Logit or Probit model) do not deal with WTP
values between ~
 . Kristroöm (1997) proposed the spike model to resolve the
problem of when willingness to pay is zero or negative.
In the spike model, the domain of the cumulative distribution function can be
expressed as Equation(11).
0, 0
( ) , 0
( ), 0
WTP
WTP
A
G A P A
F A A



 

 

(11)
where P belongs to (0,1) and ( )
WTP
F A is a continuous and increasing function
such that ( 0)
WTP
F A p
  and lim A  ( ) 1
WTP
F   . The maximum likelihood
function for the sample is then given by Equation(12).
[ ln(1 ( ))
(1 )ln( ( ) (0))
(1 )ln( (0))]
i i WTP
i
i i WTP WTP
i WTP
L M W G A
M W G A G
M G
 
  
 

(12)
where M indicates whether the WTP is greater than 0 or not. If the respondent
rejects a series of bids it will generate a WTP smaller than 0. Another W is defined by
whether the WTP is greater than the bid A or not, as Equations (13) and (14)
respectively.
1, 0
0,
WTP
M
other


 

(13)
1,
0,
WTP A
W
other


 

(14)
We also assume ( )
WTO
G A has the form of a logistical function, thus
Equation(11) can be further rewritten as Equation(15).

6. 1
1
0, 0
( ) [1 exp( )] , 0
[1 exp( )] , 0
WTO
A
G A A
A A

 





   

    

(15)
The expected WTP in Equation (6) can be expressed as Equation(16).
1
[ ln(1 ) ln(1 )]
A
e e
  


     (16)
Kristroöm (1997) defines the spike value when 0
A  as follows:
1
1 exp( )
Spike


 
(17)
3. Survey Setting and Instrument
3.1 Survey Instrument
To examine HSR travelers’ willingness to pay for business class seats using the
state preference method, a questionnaire survey targeting travelers in Taiwan was
conducted. A total of 500 travelers waiting for high speed railway in Taipei station
from November to December, 2007, were interviewed. Travelers were asked to
complete the survey before they left the waiting area. A total of 309 valid responses
were obtained.
The survey instrument consisted of three parts. First, it sought the information on
the travelers’ socioeconomic characteristics including gender, age, marriage,
education, car ownership rate, occupation, and income. Second, a number of questions
on travel characteristics were surveyed, such as trip purpose, OD trip, usage frequency,
the number of peers, and traveling with children. The survey further asked the
respondents trip costs, spending, and waiting time for ticket purchases. Finally, the
state preference questions were presented to obtain information on travelers’
willingness to pay for HSR business class seats. The state preference questions were
also designed to explore the effect of wireless internet on the preference for HSR
business class seats. A variety of starting bids were constructed, coordinating with the
current THSR promotion strategies, to avoid biased estimation and obtain stabilized
estimation results (Kanninen and Kristrom, 1993).
3.2 Data Analysis
A number of socioeconomic and trip characteristics were obtained and
summarized in Table 1. Interestingly, the HSR travelers at Taipei station are roughly

7. evenly distributed in gender and marriage. Moreover, the majorities (84%) of travelers
are highly educated and are between 21 and 30 years old (43%), suggesting that
highly educated and young people are the major users of the high speed rail.
Furthermore, most of respondents (61%) are working at service industry, business and
government. Most of the travelers have a monthly income of less than 60,000 New
Taiwan dollars.
Additionally, the most common trip destinations are Taichung (25%) and
Kaohsiung (42%), while the most common trip purposes are business (38%) and
returning home (30%). Further, a high percentage of travelers did not often take the
HSR in the previous year. This result is consistent with the observation that most
individuals take the HSR for business purposes. Most trips are medium (the distance
from Taipei to Taichung is about 159.83 km) and long distance (the distance from
Taipei to Kaohsiung is about 339.28 km). Roughly 80% travelers on the HSR are
self-funded, while others may be government employees. Moreover, the majorities of
travelers buy their tickets at the ticket windows and wait less than 1 minute. The short
wait times show that either the quality of service is good, or fewer individuals use the
HSR.
---------------------INSERT TABLE 1 HERE------------------------
---------------------INSERT TABLE 2 HERE------------------------
Data on traveler’s willingness to pay for business class seats are summarized and
presented in Table 2. Double-Bounded Dichotomous Choice questions are used to
obtain the traveler’s willingness to pay for business class seats under different trip
lengths. As shown in Table 2, travelers’ WTPs range from 41% to 62%, lower than the
current fares of business class seats. The results indicate that the traveler’s willingness
to pay for the business seat is lower than its current fare. That is, the traveler may not
need the service of business seat for his/her trip. This may explain the low usage rate
of business class seats. Meanwhile, the WTP increases as the trip distance increases.
In the other word, the long-distance traveler may enjoy and need the service of
business class seat more than the short-distance traveler. The results obtained above
are true for both zero WTP included and excluded.
Moreover, if the scenarios add wireless internet service to the business class seats,
the WTP remains 50% to 65% lower than current fare pricing. Furthermore, the value

8. of wireless internet rises with travel distance. Thus, we can infer that short-haul
travelers attach importance to the HSR fare, not to the quality of service. The medium
or long trip, by contrast, focuses on the quality of service though the observation of
difference was deflated by distance.
4. Results
To investigate the effect of potential passengers on willingness to pay, two
groups of travelers are defined as follows. The first one is highly potential users and
defined as those travelers who are willing to and will take business class seats in the
near future. The second one is potential users and defined as those travelers who are
willing to but won’t take business class seats due to lack of incentives (ex. price is too
high).
Furthermore, in order to explore the effect of distance on willingness to pay, this
study explores willingness to pay for business class seats for the Taipei-Taichung
route and Taipei-Kaohsiung. The MLE approach is used and estimated by Limdep
software to analyze the travelers’ willingness to pay for high speed rail business class
seats using both the conventional and spike models. The estimation results are
presented in Tables 3 and 4. The results show that all models are statistically
significant, overall, in terms of the likelihood ratio tests and WALD tests.
Our results offer several insights. Firstly, the fare is the main factor in traveler
choice of business class seats since it is significant and has a negative effect in all
models. This means that as fares rise, travelers’ willingness to purchase business class
seats falls. Secondly, the value of WTP from the multivariate model estimation is
smaller than that of the single variable model since the multivariate model considers
more factors. These results are consistent with prior knowledge. Thirdly, travelers’
willingness to pay for medium (Taipei-Taichung) and long distance trips
(Taipei-Kaohsiung) business class tickets is 646 TWD and 1,341 TWD, respectively.
Adding wireless internet services will increase the WTP value (around 30-50 TWD),
suggesting that wireless adds values in business class seats service.
Trip distance is also an important factor. The medium distance trip
(Taipei-Taichung) significantly affects travelers who travel more than once per month
by high-speed rail and whose monthly income is greater than average. This indicates
that such passengers have a high level of income and are more willing to take
business class seats in the high speed rail. Furthermore, being over 30 years of age is
also a significant negative factor in business class choice. This means that attracting
potential passengers over 30 will be difficult. For long distance trips
(Taipei-Kaohsiung), highly potential passengers with a higher level of income are

9. more likely to accept the fare for business class seats, meaning that it has a positive
effect on the choice of business class seats. Potential business class passengers have
university degrees or above, suggesting that individuals with higher education levels
are more willing to obtain better service and thus to pay more. Males whose
frequency of taking high speed rail is less than once per month will have positive
effects. In sum, if respondents perceive that the services offered in business class seats
on the HSR are not consistent with its price travelers are less likely to choose it.
After adding wireless internet services to business class, the medium distance
trip will attract potential passengers with a university education or above. The long
distance trip will attract highly potential passengers with a university education or
above and who use the HSR at least once a month. The estimation results show that
long-distance travelers emphasize more on compartment noise levels and security and
are more willing to take business class with wireless internet service.
Finally, the conventional model and spike model have the same results since the
ratio of responses equaling zero is extremely small. These results verify the
application of the spike model. It should be noted that the spike value of the
Taipei-Kaohsiung model is 0.03, close to the ratio of the sample willing to pay equal
to zero (0.027). This result is consistent with Kristroöm (1997). Since the two models
have no significant differences, it shows that the conventional model does not
encounter statistical biases when there are a low proportion zero responses. This result
is consistent with Yoo (2002, 2006).
--------------------INSERT TABLE 3 HERE------------------------
--------------------INSERT TABLE 4 HERE------------------------
To gain a better understanding of effects of variables on taking business class
seats, the elasticity analysis is performed in this study. Since Logit and Spike models
both have similar results, the former one is applied to calculate the elasticity. Several
insights are presented in Table 5 and addressed as follows. As for the price of business
class seat increases 1%, the probability of taking business class seats decreases,
ranging from 2.87% to 3.38% in different distances and w/o wireless internet service.
It proves that the fare is the most important factors in choice behavior of business
class seats which is consistent with the studies by Park et al. (2006) and Martin and
Nombela (2007).

10. Individuals who are over the age of 30 are 0.02% more willing to take the
business class seats. After adding wireless, it will up to 0.088% in short-distance trips.
Moreover, passengers who travel more than once by high-speed rail and have monthly
incomes greater than the average are 0.048% more willing to take the business class
seats. The probability will rise to 0.09% if the wireless internet is introduced.
Other variables, such as highly potential users whose monthly income is more
than 60,000 TWD, potential users with a university education or above, highly
potential users university educated or above, prefer a quiet compartment and security,
and highly potential users taking the high-speed rail more than once/month all have
positive effects on the probability of taking business seats, the elasticities ranging
from 0.02% to 0.092%.
--------------------INSERT TABLE 5 HERE------------------------
5. Conclusions
In this study, we measure travelers’ willingness to pay for business class seats
on the Taiwan high speed rail using the state preference method. To achieve this,
Double-Bounded Dichotomous Choice questions are used to obtain the traveler’s
willingness to pay for business class seats with and without wireless internet. The
spike model is further estimated to capture the amount of money for business class
seats needed to overcome the estimation problem due to the number of responses
equaling zero.
Our results offer several insights in traveler preferences. The estimated value of
WTP for passengers taking business class seats is far lower than the current price of
high speed rail of Business class seats. Passengers on shorter trips perceive prices that
are somewhat high. Therefore, pricing is the most important factor in encouraging
travelers to choose business class seats on the HSR. Travelers going long distances
strongly emphasize compartment noise levels and security, while medium distance
travelers consider whether their incomes can bear the fare.
Further, passengers with high incomes are the main source of business class
travelers on the HSR. Groups of highly educated travelers who take the high speed
rail at least once a month are potential users of business class seats. From the
operator’s viewpoint, promotion of strategies to attract potential users and increase
service should be focused on highly potential users. In addition, when business class
adds wireless internet services, it will attract highly educated people. Wireless internet

11. also increases its value as trip distance lengthens. Finally, the results verify the
application of the spike model, since the two models show no significant differences.
This demonstrates that the conventional model does not have a statistical bias when
the proportion of zero responses is low. This result is consistent with Yoo (2002,
2006).
The elasticity analysis is performed in the end of this study. The results indicate
the significant effect of fare on the probability of taking business seats which is
consistent with the studies by Park et al. (2006) and Martin and Nombela (2007).
Other variables, such as passengers over the age of 30, highly potential users whose
monthly income is more than 60,000 TWD, potential users with a university
education or above, highly potential users university educated or above, prefer a quiet
compartment and security, and highly potential users taking the high-speed rail more
than once/month all have positive effects on the probability of taking business seats,
This paper explores business class seat choice behavior and willingness to pay
for high speed rail. Our results may be useful to HSR operators and marketers in
devising their marketing strategies. For academics, it contributes to our understanding
and application of the spike model and the conventional model when the proportion of
zero responses is low.
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14. Table 1. Socioeconomic and trip characteristics of the sample (total sample=309)
Socioeconomic profile Trip characteristics
Variabl
e
Item
Number
of
Respondents
Percenta
ge
(%)
Variable Item
Number of
Respondents
Percentage
(%)
Gende
r
Male 148 48% Destinati
on
Taoyuan 10 3%
Female 161 52% Hsinchu 18 6%
Age 20 or younger 11 4% Taichung 78 25%
21~30 133 43% Chiayi 29 9%
31~40 85 28% Tainan 43 14%
41~50 47 15% Kaohsiung 131 42%
51~60 25 8% Peers 0 93 30%
61 or older 7 2% 1 95 31%
missing value 1 0% 2 52 17%
Marria
ge
Marriage 132 43% 3 32 10%
Non-marriage
177 57%
4 or more
individuals
37 12%
Incom
e
Below 20,000
60 19%
Traveling
with child
No 303 98%
Yes 6 2%
2~40,000 97 31% Number
of HSR trips
in previous
year
12 or below 221 72%
4~60,000 63 20% 13-24 48 16%
6~80,000 25 8% 25-36 16 5%
Above 80,000
25 8%
37 or above 23 7%
missing value 1 0%
missing value 39 13% Trip
purpose
Travel 28 9%
Educat
ion
high school or
below
44 14%
Business
117 38%
University
192 62%
Visiting friends and
relatives
47 15%
Master 63 20% Commuting 3 1%
PHD 6 2% Go home 94 30%
missing value 4 1% Other 19 6%
Owner
ship
No owner 135 44% Expendit
ure
Self-funded 246 80%
1 vehicle
135 44%
Full fare paid by
industry
58 19%
2 vehicle or
above
31 10%
Partial fare paid by
industry
5 2%
missing value 8 3% Payment Cash 161 52%
Occup
ation
Student 43 14% Credit card 148 48%
Business 60 19% Ticketing
type
Ticket windows 193 62%
Labor 22 7% Ticket machine 84 27%
government
employee
57 18%
Internet booking
32 10%
service industry 74 24% Waiting
time for ticket
purchase
1minute or below 261 84%
Freeman 9 3% 1~3 19 6%
Housewife 11 4% 3~5 15 5%
Other 33 11% 5 minutes or above 14 5%
Note: 31.22 Taiwan TWD was equivalent to $1 USD (April, 2010).

15. Table 2. Results of WTP for business seat (total sample=309)
From Taipei to
(distance:km)
Current
fare of
business seat
(TWD)a
Without wireless internet With wireless internet Wir
eless
value
(TW
D)
Bi
ds
Sam
ple
Average
WTPb
(excluding
WTP=0)
b/a
100%
Sam
ple
Average
WTPb
(excluding
WTP=0)
b/a
100%
Taoyuan
(36.38)
350
3
50
0
144 41%
2
180 51% 36
1
60
6 4
1
30
2 1
1
10
2 3
0 0 0
Hsinchu
(66.28)
510
5
10
1
243
(257)
48%
(50
%)
1
256 50% 13
2
90
7 7
2
40
3 4
1
85
6 6
0 1 0
Taichung
(159.83)
1000
1
000
1
557
(595)
56%
(60
%)
3
587
(619)
59%
(62
%)
30
7
00
23 31
6
00
28 20
4
55
21 20
0 5 4
Chiayi
(245.68)
1455
1
455
2
907 62%
2
940 65% 32
1
080
5 8
9
00
13 12
7
00
9 7
0 0 0
Tainan
(307.96)
1780
1
780
1
1075
(1127)
60%
(63
%)
1
1122
(1148)
63%
(64
%)
47
1
350
10 13
1
150
17 16
8
75
13 12
0 2 1
Kaohsiung
(339.28)
1950
1
950
4
1124
(1237)
58%
(63
6
1186
(1263)
61%
(65
62

16. 1
490
27
%)
32
%)
1
250
50 49
9
65
38 36
0 12 8
Note: 31.22 Taiwan TWD was equivalent to $1 USD (April, 2010).

17. Table 3. The estimation results of Conventional Models (t-value in bracket)
Variable
Without wireless internet With wireless internet
Taipei→Taichung Taipei→Kaohsiung Taipei→Taichung Taipei→Kaohsiung
Single
variable
Multipl
e variables
Single
variable
Multipl
e variables
Single
variable
Multipl
e variables
Single
variable
Multipl
e variables
Constant 5.73(7.4
1)
5.66(7.1
8)
5.02(9.2
4)
5.41(9.0
4)
5.7(7.86
)
5.65(7.4
9)
6.26(10.
24)
6.53(10.
03)
The price of business class seats -8.87(-7
.42)
-9.29(-7
.49)
-3.74(-9
.54)
-4.17(-9
.55)
-8.34(-7
.69)
-9.13(-7
.79)
-4.48(-1
0.25)
-4.84(-1
0.19)
Travelers who travel more than once/month by high-speed
rail and have monthly incomes greater than the average
0.96(2.2
6)
1.06(2.3
7)
Potential users who are over the age of 30 1.42(2.6
5)
1.82(3.1
6)
Males who travel on the HSR less than once per month -0.57(-2
.04)
Highly potential users whose monthly income is more
than 60,000 TWD
3.38(5.0
3)
Potential users with a university education or above 0.72(2.4
4)
Highly potential users university educated or above
1.14(2.4
4)
0.81(1.9
8)
Prefer a quiet compartment and security
2.02(1.9
9)
Highly potential users taking the high-speed rail more than
once/month
2.03(2.3
9)
Sample 264 264 436 436 264 264 436 436

18. Expected WTP (TWD) 646 609 1341 1297 683 618 1398 1349
Log likelihood function -126.13
6
-120.51 -228.66 -209.34
9
-127.73
7
-117.61 -212.05
3
-199.43
2
Restricted log likelihood function -182.50
6
-182.51 -301.17
9
-301.17
9
-182.80
1
-182.80
1
-302.19
4
-302.19
4
Rho-square 0.31 0.34 0.24 0.31 0.3 0.36 0.3 0.34
Likelihood ratio test -11.252* 38.62* 20.254* 25.242*
Table 4. The estimation results of Spike Models (t-value in bracket)
Variable
Without wireless internet With wireless internet
Taipei→Taichung Taipei→Kaohsiung Taipei→Taichung Taipei→Kaohsiung
Single
variable
Multiple
variables
Single
variable
Multiple
variables
Single
variable
Multiple
variables
Single
variable
Multiple
variables
Constant 6.03(13.87) 5.93(13.62) 5.46(18.27) 5.78(17.39) 5.97(12.98) 5.87(12.52) 6.46(17.40) 6.68(17.63)
The price of business class seats -9.33(-12.91) -9.71(-12.74) -4.06(-17.63) -4.44(-17.42) -8.75(-12.22) -9.48(-11.73) -4.62(-17.06) -4.95(-17.23)
Travelers who travel more than once/month by high-speed
rail and have monthly incomes greater than the average
0.98(2.06) 1.08(2.24)
Potential users who are over the age of 30 1.44(2.12) 1.85(2.84)
Males who travel on the HSR less than once per month -0.58(-1.87)
Highly potential users whose monthly income is more than
60,000 TWD
3.48(4.26)
Potential users with a university education or above 0.73(2.31)
Highly potential users university educated or above 1.16(2.27) 0.82(1.66)

19. Prefer a quiet compartment and security 2.05(1.65)
Highly potential users taking the high-speed rail more than
once/month
2.06(2.40)
Sample 264 264 436 436 264 264 436 436
Spike value 0.024 0.03 0.042 0.03 0.025 0.028 0.016 0.012
Expected WTP (TWD) 646 610 1346 1301 682 620 1399 1350
Wald Statistic(p-value) 1119.37(0.00) 767.90(0.00) 1749.54(0.00) 1070.02(0.00) 1177.39(0.00) 676.99(0.00) 2388.58(0.00) 2089.97(0.00)

20. 20
Table 5. The elasticity analysis of logit models (%)
Variable
Without wireless internet With wireless internet
Taipei →
Taichung
Taipei →
Kaohsiung
Taipei →
Taichung
Taipei →
Kaohsiung
The price of business class seats -2.873 -3.165 -3.073 -3.375
Travelers who travel more than once/month by high-speed
rail and have monthly incomes greater than the average
0.048 0.090
Potential users who are over the age of 30 0.021 0.088
Males who travel on the HSR less than once per month -0.084
Highly potential users whose monthly income is more than
60,000 TWD
0.078
Potential users with a university education or above 0.086
Highly potential users university educated or above 0.092 0.061
Prefer a quiet compartment and security 0.021
Highly potential users taking the high-speed rail more than
once/month
0.029