Understanding Public Responses Towards Park-and-Ride in ...

Understanding Public Responses Towards Park-and-Ride in Conjunction

with Transportation-Eco-Point: A Latent Class Modeling Approach

Tien Dung CHU a, Tomio MIWA b, Takayuki MORIKAWA c, SUGIARTO d

aDepartment of Civil Engineering, University of Transport and Communications, Lang

Thuong, Dong Da, Hanoi, 10001 Vietnam a E-mail: [email protected] bInstitute of Materials and Systems for Sustainability, Nagoya University, Furo-cho,

Chikusa-ku, Nagoya 464-8603, Japan bEmail: [email protected] cInstitute of Innovation for Future Society, Nagoya University, Furo-cho, Chikusa-ku, Nagoya

464-8603, Japan cE-mail: [email protected] dDepartment of Civil Engineering, Syiah Kuala University Darussalam, Banda Aceh 23111,

Indonesia. dE-mail: [email protected]

Abstract: Based on stated preference (SP) data collected in Ho Chi Minh City, this paper

applied a latent class (LC) - standard ordered response model (SORM) to explore attitude of

respondents towards Park-and-Ride (P&R) and Transportation-Eco-Point (TEP) policies. The

LC model assigned the respondents into “altruistic” and “selfish” classes. Then, the SORM

determined the respondents’ attitude for each class. In the model, some latent variables

estimated from multiple-indicators multiple-causes (MIMIC) model were also considered.

Generally, the people with less knowledge, less appropriateness and less recognition of the

effects of the new policy belong to the selfish class. It was revealed that the price of prepaid

shopping ticket (PPT) and return ratio of TEP (RRT) play very important roles in the

respondents’ attitude for both altruistic selfish class. In addition, our results suggest that the

RRT of 15% and PPT from 0.3 to 0.4 million VND would satisfy both users and government.

Keywords: Park-and-Ride, Transportation-Eco-Point, Latent Class, Standard Ordered

Response, Motorcycle, Willingness to pay

1. INTRODUCTION

Global warming and climate change, in which greenhouse gases (GHG) emissions are major

causes, have been recently becoming critical issues worldwide. Among sources of GHG

emissions, transportation sector contributes the second largest one, globally 23% (IEA, 2014).

In European Union, approximately one quarter of GHG emissions are caused by the

transportation (Abrell, 2010), meanwhile, transportation is associated with nearly a third of

GHG emissions of United States (Barth and Boriboonsomsin, 2008; US. EPA, 2012). In Japan,

GHG emissions from transportation are accounted for 17% (GIO, 2012). Thus, finding

solutions to reduce GHG emissions is an important concern of nations over the world.

As an effort to control GHG emissions, Japanese government proposed a Joint Crediting

Mechanism (JCM), which is being implemented in some developing nations. The objective of

JCM is to diffuse low carbon technologies, products, systems, services, and infrastructure, in

Journal of the Eastern Asia Society for Transportation Studies, Vol.12, 2017

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addition to implement mitigation actions for a sustainable development of those nations (GJ,

2014). Among the developing nations, the Southeast Asian is one of the most vulnerable

regions in the world for climate change and the transportation significantly shares in the GHG

emissions (ADB, 2010). Therefore, those nations including Vietnam have been paid much

attention under the JCM.

Ho Chi Minh City (HCMC) is the most populous city and biggest economic center in

Vietnam. Like in the most developing countries, the development of transportation

infrastructure does not uphold the growth in private motorization. According to HCMC

department of transport (HCMCDOT, 2015), the private modes have exploded during the last

few decades. The number of motorcycles (MC) has reached about 6.3 million and passenger

cars (PC) is nearly 0.6 million, making an extremely high ratio of private mode per inhabitant

(0.82). As a result, transport situation in HCMC is worsening and traffic congestion is

becoming one of six “hot” problems that the local government made the priority to deal with

(Nguyen et al, 2013). Thus, there is an immediate need to control the explosion of private

modes in order to tackle traffic congestion and reduce GHG emissions.

One solution for this problem is Park-and-Ride (P&R) policy, which has a long history

in western countries: originated in Untied States in the 1930s (Noel, 1988) and implemented

in the 1960s in United Kingdom (Parkhurst, 1995). The policy has recently become

increasingly popular in many European cities (Mingardo, 2013) due to its established effects

on reduction of traffic congestion in terms of vehicle kilometers travelled (WSA, 1998;

Parkhurst, 1999; Hess, 2001; Meek et al, 2011) and air pollution (Mingardo, 2013; Gan and

Wang, 2013). In Asian countries, many cities have implemented P&R to promote a modal

shift to public transport (Hayashi et al, 2004). In addition, in conjunction with P&R policy,

Transportation Eco Point (TEP) has been proposed in recent years to award to commuters for

using public transit (Yamamoto, 2005). The TEP is expected to attract more people towards

public transit including P&R. In this respect, the government of HCMC is planning to

introduce P&R and TEP in collaboration with parking lots of commercial facilities (NSRI,

2013) in order to determine the citizens to switch from their private modes to public transport.

In this new system, the citizens who acquire a prepaid shopping ticket can utilize the parking

lots of commercial facilities to park their MCs/PCs and then transfer to buses. Each time of

using bus, the citizens will be awarded a TEP. The accumulated TEPs can be exchanged to

gifts or discount in the commercial facilities.

However, the payment of the prepaid shopping ticket might become a barrier that

prevents the citizens from accepting such new policy. To make sure that the new policy will

be successfully implemented and widely accepted, it is very important to understand the

citizens’ attitude towards the new policy, especially their willingness to pay (WTP) for the

prepaid shopping ticket. The goal of our research lies in exploring various factors regarding

citizens’ WTP by using stated preference (SP) data and applying a latent class - standard

ordered response model (LC-SORM). The multiple indicators multiple causes (MIMIC)

model was applied to explore relationships among latent variables, psychological indicators

and causal variables and additionally to estimate values of the latent variables applied in

LC-SORM. The results of paper are leading to recommendations to policy makers to take

actions to increase acceptability of the citizens to achieve the goals of traffic congestion

mitigation and environment improvement.

The paper is structured as follows. Next chapter presents data collection, data

characteristics and some basis analysis. After that, methodology is presented and it i, followed

by discussion of the results. The paper concludes with the possible implications of the results

and states final conclusions.


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2. DATA

2.1 Data collection

An intensive data collection started on 15 and ended 22 on December 2014 using

questionnaire method. The survey area was center of HCMC. Total 3500 questionnaire-sheets

were randomly distributed to respondents at their home or company. Finally, 2066 valid

questionnaire-sheets (the respondents answered all questions) were returned with

returned-rate of 59%. Respondents were asked to choose a response from a 4-point Likert

scale (1: Strongly disagree, 4: Strongly agree) for their opinion about the new policy, traffic

condition, the environment and the effects of new policy on traffic congestion mitigation as

well as environment improvement as shown in Table 1.

Table 1. Summary of questionnaire survey in HCMC (15th to 22th on December 2014)

No. Categories Descriptions

1

Respondents’ opinion based

on a 4-point Likert scale:

1: Strongly disagree

2: Disagree

3: Agree

4: Strongly disagree

- Opinion about the new policy implementation

- Opinion about the effects of the new policy towards traffic

congestion and environment

- Option about the necessity of private modes (PC/MC) and public

transportation and their impacts on traffic condition and

environment

- Opinion about current transportation policy of government

2

Willingness to pay (WTP)

Price of prepaid ticket (PPT)

Return ratio of TEP (RRT)

Would you agree to pay the prepaid shopping ticket?

1: Strongly disagree 2: Disagree 3: Agree 4: Strongly disagree

Which of following options would match your decision above?

PPT = 0.1 0.2 0.3 (million VND)

RRT = 5% 10% 15% of PPT

3

Socio-demographics

- Gender, age, employment status, education level, annual income,

number of member in household

4

Mobility attributes

- Annual transportation expenditure, monthly parking fee, mode

choice, frequency of bus usage

Importantly, as mentioned earlier, the price of prepaid shopping ticket (PPT) might be

the barrier for the WTP or, in other word, acceptance of the new policy. On the other hand, the

benefit gained from TEP might attract the people to enhance their WTP. The benefit is an

amount of “money” that return back to the users for exchanging price or getting discount in

the shopping center. The amount of “money” depends on the accumulated TEPs and the

amount of PPT. Our study defined the benefit as a return ratio of TEP (RRT), which is

percentage of “returned money” to users from PPR for accumulated TEPs. In our

questionnaire survey, each respondent can choose one option of PPT or RRT that is close to

their opinion about paying prepaid shopping ticket. As seen in Table 1, the options of PPT and

RRT are 0.1; 0.2; 0.3 (million VND1) and 5%; 10%; 15% of PPT (for each accumulated

25-TEP), respectively. For example, if the PPT is 0.2 million VND and RRT is 10%, P&R

users will get 0.02 million VND for 25-TEP (or 25-time of using P&R). Furthermore, their

socio-demographics and mobility attributes were collected as well in order to capture

individual heterogeneity among respondent population as displayed in Table 1.

1 1 million VND is approximately 46 USD.


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2.2 Data characteristics

2.2.1 Socio-demographic and mobility attributes

Figure 1 displays the socio-demographics and mobility attributes of respondents. Among 2066

respondents, males occupy about 12% higher than females and their ages commonly range

from 20~39 years old (73%). For education level, the respondents who have vocational level

or higher are dominant (approximately 78%) and those with bachelor level stand for the

highest one among population (41%). Looking at annual income, it is shown that the ones

with income lower than 60 million VND (2760 USD) account for nearly 43%. This income

almost equals to haft of average annual income in HCMC in 2014 (110 million VND or 5100

USD, tuoitrenews, 2015). Figure 1 also gives evidence that 71% respondents are employees

and nearly 79% household has less than 4 members. In addition, the ones with transportation

field slightly have higher population compared to those with financial/insurance and

industry/service. Switching to mobility attributes, it is concluded that the respondents are very

much dependent on private modes, especially motorcycle (84%). In addition, the ones who do

not use bus or have low bus usage (less than 2 times per month) are very common across the

respondent’s populations. Moreover, more than half of the respondents (55%) spend less than

4 million VND (185 USD) for annual transportation expenditure.

Male

< 19

High school or lower

< 60

Employee

1 or 2

Transportation

< 2

Not use

Car

Female

20~29

Vocational

60~80

Students

3 or 4

Financial/insurance

2 ~ 4

< 2 times/month

Motorcycle

30~39

Bachelor

80~100

Other

> 4

Industry/ service

4 ~ 7

>2 times/ weeks

Bus

> 40

Master or higher

100~180

Other

> 7

>180

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Gender

Age

Education

Annual income

Employment status

Family member

Professional field

Annual trans. expenditure

Frequency of bus usage

Mode choice

Figure 1. Socio-demographics and mobility attributes of respondents.

0% 10% 20% 30% 40% 50%

Very well

A litle

Just the word

Totally not

Do you know about P&BR?

0% 10% 20% 30% 40% 50%

Do you know about TEP?

Figure 2. Knowledge of respondents in HCMC about the new policy

2.2.2 General knowledge of respondents about the new policy

It is worth mentioning that although the P&R has its long history, the knowledge of HCMC

citizens about it might be very limited. As shown in Figure 2, approximately 50% of


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respondents do not know about P&R or just know the word. Moreover, the knowledge of

respondents about TEP seems worse than that of P&R where about 66% do not know about

TEP or just know the word. This may become a main drawback for HCMC citizens to accept

such the new policy.

128

595

1181

162

0

200

400

600

800

1000

1200

1400

1 2 3 4

Fre

qu

ency

Would you agree to pay prepaid shopping ticket?

Strongly disagree Disagree Agree Strongly agree

N = 2066

a) Distribution of choices of respondents

25.78

7.81

66.41

0

20

40

60

80

100

0.1 0.2 0.3

Per

cen

tag

e (%

)

Price of prepaid shopping

ticket (million VND)

N = 128

(1) Strongly disagree

34.45

10.59

54.96

0

20

40

60

80

100

0.1 0.2 0.3

Per

cen

tag

e (%

)



N = 595

(2) Disagree

62.40

30.65

6.94

0

20

40

60

80

100

0.1 0.2 0.3

Per

cen

tag

e (%

)



N = 1181

(3) Agree

68.52

25.93

5.56

0

20

40

60

80

100

0.1 0.2 0.3

Per

cen

tag

e (%

)



N = 162

(4) Strongly agree

b) Price of prepaid shopping ticket for different choices of respondents

76.56

14.848.59

0102030405060708090

100

5% 10% 15%

Per

centa

ge

(%)

Return ratio of TEP

(million VND)

N = 128

(1) Strongly disagree

60.50

20.84 18.66

0102030405060708090

100

5% 10% 15%

Per

centa

ge

(%)

Return ratio of TEP

(million VND)

N = 595

(2) Disagree

20.91

46.10

32.99

0102030405060708090

100

5% 10% 15%

Per

centa

ge

(%)

Return ratio of TEP

(million VND)

N =

1181

(3) Agree

29.81 29.8140.37

0102030405060708090

100

5% 10% 15%

Per

centa

ge

(%)

Return ratio of TEP

(million VND)

N = 162

(4) Strongly agree

c) Return ratio of TEP for different choices of respondents

Figure 3. Willingness to pay, price of prepaid ticket and return ratio of TEP

2.2.3 Willingness to pay (WPT), price of prepaid ticket (PPT) and return ratio of TEP

(RRT)

It is obvious from Figure 3 a) that the respondents who accepted to pay are very high. Of

2066 respondents, 1181 (57%) agreed and 162 (8%) strongly agreed, making a total of 65%

respondents accepted to pay for the prepaid shopping ticket. The remained 35% are the


509

respondents who disagreed (595) and strongly disagreed (128). In order to gain more insight

into the effects of PPT and RRT, we plotted the distributions of PPT patterns (0.1, 0.2 and 0.3

million VND) and RRT patterns (5%, 10% and 15% of PPT) for each choice of respondents

as respectively represented in Figure 3 b) and c).

Figure 3 b) clearly indicates that the people who accepted to pay (agree or strongly

agree) are markedly related to the low PPT (0.1 or 0.2 million VND) and, however, the people

who disagreed to pay are mainly characterized by the high PPT (0.3 million VND). In

addition, a relatively high proportion of the low PPT (0.1 million VND) was found in the

groups of people who did not want to pay. It implies those of selfish people and they did not

want to pay even the PPT is low or they wanted the PPT lower than 0.1 million VND. In

general, it can be concluded that the lower PPT may attract the citizen to have the higher

acceptance of payment. On the other hand, it is evident from Figure 3 c) the RRT of 5% were

frequently found in the distribution of the people who did not want to pay. Meanwhile, it

seems that the higher RRT (10% or 15%) result in the more acceptances of the policies.

Generally speaking, the higher RRT would be an important factor in increasing the acceptance

of respondents.

3. METHODOLOGY

3.1 Latent variable estimation

A special case of structural equation modeling (SEM), the well-known MIMIC model

(Jöreskog, and Goldberger, 1975) was applied to figure out the relationships among the latent

variables, psychological indicators and causal variables obtained from questionnaire survey.

Later on, the results of MIMIC model were used to estimate latent variables, which were

further applied in LC-SORM.

3.1.1 MIMIC model

According to (Bollen, 1989) the MIMIC model consists of two parts: structural model and

measurement model as shown in equation (1) and (2), respectively.

(1)

(2)

Here, is a vector of m latent variables, x is a vector of n causal variables, y is a vector of p

observed indicators, B is a mm matrix of structural parameters governing the relations

among the latent variables, Γ is a mn coefficient matrix governing the relations between

latent variables and causal variables, Λ is a pm matrix of coefficients relating y to , is a

vector of m latent errors in equation and is a vector of p measurement errors for y. In this

paper, B, and were estimated using a computer program named linear structural

relationship - LISREL version 9.2 (Jöreskog, K.G., Sörbom, 2012).

3.1.2 Variables of MIMIC model

Total 7 latent variables, 20 indicators and 6 casual variables were introduced to the MIMIC

model as shown in Table 2.


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Table 2. Description of latent variable, observed indicators and causal variables

No Variables Description

1. Latent variable 1.1 KPT Knowledge of P&R and TEP policy

1.2 API Appropriateness of P&R implementation

1.3 RPE Recognition of P&R's effect

1.4 PMD Private mode dependency

1.5 ATE Awareness of transportation in city center

1.6 APP Awareness of private mode’s problem

1.7 TGP Trust in government's polices

2. Psychological indicators 2.1 KOP Know about P&R? (1: totally do not know, 4: know well)

2.2 KOT Know about TEP? (1: totally do not know, 4: know well)

2.3 CRP It is correct policy? (1: totally wrong, 4: quite right)

2.4 ACP It will be accepted by the public? (1: do not accept at all, 4: well accepted)

2.5 IRC It should be implemented to reduce traffic congestion? (1: strongly disagree, 4: strongly agree)

2.6 IMG It should be implemented to mitigate global worming? (1: strongly disagree, 4: strongly agree)

2.7 IMT P&R mitigates traffic congestion? (1: absolutely impossible, 4: absolutely possible)

2.8 IIE Its implementation improves environment? (1: absolutely impossible, 4: absolutely possible)

2.9 IPC Its implementation brings a better environment for pedestrian and cyclist? (1: worse, 4: better)

2.10 PMN PC and MC are necessary in daily life? (1= absolutely unnecessary, 4: absolutely necessary)

2.11 PTN Public transport is necessary in daily life? (1= absolutely unnecessary, 4: absolutely necessary)

2.12 PEC Public transport is easy and convenient to use? (1: quite difficult, 4: quite easy)

2.13 LKD Do you like driving? (1: not at all, 4: very much)

2.14 TCL Traffic congestion level in HCMC? (1: quite congested, 4: not congested at all)

2.15 PED Pedestrian environment is dangerous? (1: quite dangerous, 4: quite safe)

2.16 TEA Do you take friendly environmental actions? (1: never, 4: frequently)

2.17 CGW PC and MC are major causes of global warming? (1: strongly disagree, 4: strongly agree)

2.18 PMU PC and MC affect public transportation usage? (1: strongly disagree, 4: strongly agree)

2.19 CTP Current transportation policy is correct? (1: strongly disagree, 4: strongly agree)

2.20 IGR Enough interaction between government and citizen? (1: strongly disagree, 4: strongly agree)

3. Causal variables 3.1 AG Age, dummy (1: >40 years old, 0: otherwise)

3.2 EDU Education, dummy (1: High level: vocational or higher, otherwise: 0)

3.3 AI Annual income, dummy (1: high-income > 60 million VND, 0: otherwise)

3.4 ES Employment status, dummy (1: Employee, 0: otherwise)

3.5 FM Number of member in household

3.6 PF Professional field, dummy (1: transportation, 0: otherwise)

3.2 Latent class - standard ordered response model

3.2.1 Upper level model: class-membership selection

It is assumed that the respondents can be classified into “altruistic class” (a) and “selfish

class” (s). The respondents in the same class have the similar structure of preference in

making their choices. The selfish respondents might have a negative feeling of unfairness to

pay the prepaid shopping ticket for using parking lots in commercial facilities. They, therefore,

are likely to have less WTP. By contrast, the ones of altruistic class perceive positive benefits

from the new policy (e.g. congestion mitigation, environment improvement, good feeling of

gaining TEP, etc.) and they feel acceptable to pay the prepaid shopping ticket.

The utility function for individual i belonging to class a and the probability of

respondent i belonging to the class membership s or a are given by equations (2) and (3),

respectively.


511

(3)

and (4)

Here, zi is a vector of the respondent’s characteristics, is a vector of the unknown

parameters, and ui is class-specific idiosyncratic random disturbance term assumed to be i.i.d

standard normal distribution.

3.2.2 Lower level model: class conditional choice

The SORM is a common disaggregate approach with ordered outcomes. According to (Boes

and Winkelmann, 2006) and (Greene and Hensher, 2010), let the ordered outcome be coded as

y {1, 2,. . .,J} where J is the total number of outcomes and suppose that a vector of the

dependent variables xi (i = 1,2,…,I) is available. In SORM, the cumulative probabilities of the

discrete outcome are related to a single index of explanatory variables in the following way:

(5)

Here, j and i denote unknown parameters and can be any monotonic increasing

function mapping the real line onto the unit interval. In this study, is assumed to follow the

standard normal distribution.

The SORM is usually based on underlying latent variable but with a different much

from the latent variable yi* and modeled by observed information yi.

(6)

Here, j and j-1 represents upper and lower thresholds for outcome j. The random error

εi is assumed to be i.i.d standard normal distribution. Then, the individual contribution to the

unconditional probability can be written as:

(7)

After assigning the respondents into selfish (s) or altruistic (a) classes by upper level

model, they are simultaneously assigned to the class conditional choices (Sugiarto et al,

2015a). Combining equation (4) and equation (7) gives final choice probabilities for latent

class based on the SORM (LC-SORM):

Pr y

i = j zi,xi

(8)

Finally, the likelihood function for the entire observations can be drawn as:

(9)

Here, hij equals 1 if the respondent i chooses outcome j, otherwise hij equals 0. In this

study, the likelihood function was coded and implemented by GAUSS econometric


512

programming version 3.2.32 (Aptech Systems, 1997).

4. RESULTS AND DISCUSSIONS

4.1 MIMIC model

The results of MIMIC model are shown in Figure 4. The model fit indices (2=1133.83,

df=365, RMSEA=0.086, SRMR=0.06667, GFI=0.8608, AGFI=0.8156) show that the MIMIC

model has reasonably good fit of the data (Hooper et al, 2008). Generally speaking, the results

indicate significantly positive relationships between the psychological indicators and the

latent variables, except the cases of PTN, PEC and TEA. However, the signs of coefficient

and level significance vary among the causal and latent variables.

-0.05*

-0.04* 0.07***

1.20***

0.08***

-0.18***

0.06**

-0.10***

-0.05***

-0.12***

KPT

ATE

APP

KOP

0.17 ***

CRP

0.53***

-0.46***

-0.84***

0.55***

1.18***

IRC

IMG

IMT

IIE

IPC

1.00

1.05***

1.00

1.23 ***

1.25 ***

0.95 ***

0.82***

1.00

PMN

PTN

PEC

LKD

-1.56***

1.00

-1.92***

0.55***

TCL

PED

TEA

1.001.01***

-0.86***

PMU

1.00

1.21***

IGR

CTP1.00

0.75***

TGP

AG

PF

EDU

AI

ES

FM

-0.04**

0.12***

0.08***

API

PMD

-0.07***

0.03*

-0.06*

-0.04*

KOT

ACP

CGW

0.03*

Latent variables Causal variables Psychological Indicators

-0.04*

RPE

-0.03*

Figure 4: Results of Multiple Indicators Multiple Causes (MIMIC) model.

Note: N=2066; *; **; *** statistically significant at p = 0.1; 0.05 and 0.01 level (2-tail)

The data in Figure 4 give evidence that three causal variables including age (AG),

education (EDU) and annual income (AI) significantly affect the latent variable KPT. Among

them, AG and EDU have positive effects on KPT, however, AI shows opposite effects with

the negative sign. It appears that elderly and high education people gain more knowledge on

both P&R and TEP. On the other hand, the people with high-income seem to have less


513

understanding about the new policy. This result is out of our expectation since we believed

that the high-income people might have better understanding of the new policy. Therefore,

further investigation is needed to prove the finding.

About the latent variable API (appropriateness of P&R implementation), the casual

variables of AI and FM (number of family member in household) both have the negative signs

on API. In addition, AI shows the stronger effect with a bigger coefficient and significant test

level. The results suggest that the high-income people and the people living in a bigger family

show negative feelings of the appropriateness of the new policy. They are likely to think that

the new policy is not correct and believe that the public may not accept the new policy. A

factor contributed to the negative feelings of the appropriateness of the new policy for

high-income people may arise from unexpected finding as mentioned earlier. That is, the less

knowledge about the new policy of high-income people is possibly connected to their

negative feelings.

Switching to the latent variable RPE (recognition of P&R's effect), the negative sign of

AG on RPE shows that the elderly people appear to not recognize the effects of the new

policy. They do not think that the new policy is able to alleviate traffic congestion and

improve environment. By contrast, the positive signs of AI and FM indicates that the

high-income people and the people living in a bigger family possibly realize the effects of the

new policy in mitigation of traffic congestion and improvement of environment. Regarding

PMD (private mode dependency) three casual variables (AG, AI and PF) show to have

significant effect on PMD, of which AG and AI result in negative signs and PF (professional

field) links to the positive sign. Looking at measurement model of PMD (see Figure 4), the

positive signs of PMN/LKD and negative signs of PTN/PEC further give evidence that the

elderly and high-income people do not depend upon private modes but public transport.

Additionally, they feel convenient and easy to use public transportation and they do not like

driving. However, the people working in transportation field (PF) apparently have an opposite

response compared to groups of elderly and high-income people.

Turning to ATE (awareness of transportation in city center), AG shows the positive

effect on ATE. Meanwhile, both EDU and FM exhibit the negative effects. It suggests that the

elderly people have positive feelings to the congestion level and the walking environment in

HCMC when they are likely to feel the traffic is not congested and the waking environment is

safe. Whereas, high education people and people living in a bigger family are aware of the

bad traffic environment in the city center that the traffic is congested and walking

environment is dangerous. These three casual variables (AG, EDU, and FM) were also found

to have significant influences on the latent variable APP (awareness of private mode’s

problems). The positive effect of AG on APP implies that the elderly people really recognize

the problems of private modes on environment (see CGW in Figure 4) and public

transportation usage (see PMU in Figure 4). However, the negative signs of EDU and FM on

APP indicate that the high-educated people and people living in a bigger family experience

the feeling that the private modes do not have any impacts on the environment and the usage

of public transport. For high-educated people, it is not an ideal finding because it was

expected that they would better recognize the problem of private modes on the society. In the

future, confirmation for this result is necessary. Finally, focusing on TGP (trust in government

policy) gives an evidence that the high-income and employed people do not believe in the

current policies of government.

4.2 LC-SORM

Table 3 displays the estimation results of LC-SORM. Three latent variables including KPT,


514

API and RPE were used for upper level model in order to define the class membership. The

remained four latent variables (PMD, ATE, APP, and TGP) were applied in class conditional

choice model. Furthermore, the socio-demographics (GE) and mobility attributes (MPF and

TE, PTU and MOC) were considered as well. Table 4 presents overall probabilities of being

altruistic and selfish classes and the shared probabilities of acceptance in each class. It

indicates that the probability of being altruistic class (0.93) is dominant compared to that of

selfish class (0.07). And it is obvious that WTP of altruistic respondents is extremely higher

(agree + strongly agree = 68%) than that of selfish respondents (agree + strongly agree = 5%).

It appears that the selfish respondents are easy to reject the new policy.

Table 3. Estimated parameters of LC-SORM Variables

Description Coefficient T-statistics

Class-membership selection model: altruistic class

Constant 1.48 13.51***

KPT Knowledge of P&R and TEP policies 0.17 1.97**

API Appropriateness of P&R implementation 0.39 2.22**

RPE Recognition of P&R's effect 3.97 3.24***

GE Gender, dummy (1: male, 0: otherwise) 0.03 1.83*

MPF Monthly parking fee (million VND) 0.17 2.00**

Class conditional choice model 1: altruistic class

PPT Price of prepaid ticket? (million VND) -9.37 -19.44***

RRT Return ratio of Transportation Eco Point (million VND) 43.14 13.10***

PMD Private mode dependency -0.18 -1.78*

ATE Awareness of transportation in city center -0.66 -2.65***

APP Awareness of private mode’s problem in society -0.14 -1.70*

TGP Trust in government's polices 0.31 2.31**

TE Annual transportation expenditure (million VND) 0.21 4.95***

MPU Monthly bus usage >2 times/weeks, dummy 0.19 4.01***

MOC Mode choice, dummy (1: private modes, 0: otherwise) -0.08 -2.34**

Class conditional choice model 2: selfish class

PPT Price of prepaid ticket? (million VND) -17.73 -2.02**

RRT Return ratio of Transportation Eco Point (million VND) 81.75 2.97***

PMD Private mode dependency -1.11 -1.78*

ATE Awareness of transportation in city center 0.85 1.77*

APP Awareness of private mode’s problem in society 8.83 2.53**

TGP Trust in government's polices 2.74 2.57***

TE Annual transportation expenditure (million VND) 0.02 1.64

MBU Monthly bus usage >2 times/weeks, dummy 0.52 2.81***

MOC Mode choice, dummy (1: private modes, 0: otherwise) -0.94 -2.80***

a,1 -4.10 -17.31***

a,2 -1.60 -10.59***

a,3 0.67 5.02***

s,1 -17.98 -2.02**

s,2 -12.54 -1.95*

s,3 4.82 1.61

N = 2066, LL(0) = -2864.08, LL() = -1775.62, Adjusted Rho-squared = 0.36, AIC = 3611.24, BIC =

3780.23

Note: *; **; *** statistically significant at p = 0.1; 0.05 and 0.01 level (2-tail)

Note: *, **, *** statistically significant at p = 0.1, 0.05 and 0.01 level (2-tail)


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https://www.google.co.jp/search?rls=com.microsoft:en-US:%7Breferrer:source?%7D&rlz=1I7SNJB_jaJP559&q=altruistic&spell=1&sa=X&ei=YSsnVYWjD6S4mwXa94HgCw&ved=0CBoQvwUoAA


Table 4 Segmentations for altruistic and selfish class-memberships

Class

memberships

Probability of

respondents’

share

Probability of choices within each segment

Strongly

disagree Disagree Agree

Strongly

agree

Altruistic 0.93 0.03 0.29 0.59 0.09

Selfish 0.07 0.67 0.26 0.05 0.00

The estimation results of class-membership selection model (upper part of Table 3)

show that the increments of KPT, API, and RPE result in significantly increasing the

probabilities of being altruistic class or in other word, decreasing the probabilities of

belonging to selfish class. Referring to previous discussion in section 0, it further implies that

the people, who have more knowledge of the new policy (well understanding the P&R and

TEP) and the ones who appropriate the new policy (the new policy are correct and will be

accepted by public), are likely to be the altruistic class. It is similar for the people who well

recognize the positive effects the new policy in alleviation of traffic congestion and

improvement of environment. In addition, the ones who pay more parking fee are possibly

shifted to the altruistic class. It is understandable since the people who pay more money for

parking fee would not feel that the prepaid ticket is a barrier for them. They, therefore, are

likely to be altruistic class. Finally, it seems that males are more likely to be the altruistic

class.

For conditional class choice model, the price of prepaid shopping ticket (PPT) and

return ratio of Transportation Eco Point (RRT) contribute significant effects on WTP of the

respondents for not only the altruistic class but also the selfish class. It suggests that the

respondents very much concern about the amount of money that they have to pay. This

finding is consistent with those of similar studies on WTP for road pricing (Sugiarto et al,

2015a; Sugiarto et al, 2015b; Zheng et al, 2014). Furthermore, they also care about the

benefits that they can get from TEP. As seen in Table 3, the negative sign of PPT and positive

sign of RRT for both altruistic and selfish classes demonstrate that the lower PPT and the

higher RRT are associated with the higher acceptance probabilities of WTP. In addition, the

bigger coefficient magnitudes of PPT and RRT of selfish class highlight that the selfish

respondents are more sensitive to the PPT and RRT compared to the altruistic ones. Our

results are in good agreement with the analyses shown in section 0. It confirms that the model

is able to reasonably represent the observed data.

Looking at PMD, it is found from Table 3 that PMD has significantly negative effects

on the altruistic and selfish respondents’ acceptability. These results imply that the increments

of PMD would result in decreasing acceptability of both altruistic and selfish classes. Notably,

the increments of PMD mean that the respondents are more dependent upon the private modes,

and of course, less dependent on the public transport as aforementioned (see Figure 4).

Additionally, the coefficient magnitude of PMD for selfish class is approximately five times

higher than that for altruistic class. Therefore, it may be reasonable to suppose that the

altruistic respondents are somehow less dependent on private modes but more dependent on

public transport compared to the selfish ones.

Focusing on ATE, Table 3 shows a different effect of ATE on the choices of respondents.

ATE is associated with the negative effect for altruistic class; however, it results in the

positive effect for selfish class. This result implies that for the altruistic class, the people who

have negative thought about the traffic condition and walking environment in HCMC (traffic

is congested and walking environment is dangerous – see discussion in section 4.1) are likely

to accept the policies. By contrast, the selfish class people seem to reject the policies even

they experience the bad traffic congestion and dangerous walking environment in the city


516


center. The similar effects are found for APP. The negative sign of APP for altruistic class

gives evidence that the respondents, who well recognize the negative impacts of the private

modes on causing global warming and reducing the public transport usage, appear to have the

higher acceptance probability. On the other hand, those people of selfish class possibly are

likely to have lower acceptance probability.

Switching to TGP, the positive signs of TGP for both altruistic and selfish classes (see

Table 3) contribute to the fact that the higher TGP results in the higher WTP. Looking back to

the results of MIMIC model, the higher TGP means the more trustfulness of citizens towards

current policies of government (current policies are correct, enough interaction between the

government and citizens). Therefore, it is extremely probable that increasing the trust of

citizens in government might lead to the higher certainty of accepting the policies. Similarly,

annual transportation expenditure (TE) and monthly bus usage (MBU) show the positive

effects on not only the altruistic class but also the selfish class. The evidence suggests that the

people spending more annual transportation expenditure and/or using public transport more

frequently are likely to have more WTP. By contrast, both negative signs of MOC (current

mode choice) for altruistic and selfish classes seem to indicate that the ones currently using

private modes (PCs/MCs) are a factor in deceasing the acceptance probability of WTP.

5. IMPLICATIONS OF RESULTS FOR POLICY MAKERS

The results of our paper show that there are many factors affecting the citizens’ acceptability

of WTP for the prepaid shopping ticket. Based on these results, policy makers (authority or

government) can make some actions in order to increase the acceptance probabilities. As

predicted, having less knowledge significantly makes the people become “selfish” and they

are likely to refuse the new policy. Those include the young, less educated and high annual

income people. Therefore, attention to those people e.g. an indoctrination of the new policy,

may be needed to deepen their knowledge about the new policy, which can increase their

acceptability. Furthermore, the awareness of the citizens on the appropriateness (API) and

effects (RPI) of the new policy also contribute to important factors for being “selfish class”.

The results of MIMIC model imply that the annual income and number of family member in

household significantly influence such factors. Taking actions to those of people in order to

enhance their awareness of the new policy may also reduce the probabilities of being “selfish

class”.

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0.1 0.2 0.3 0.4 0.5 0.6Join

t ac

cep

tan

ce p

rob

abil

ity

PPT (million VND)

5%

10%

15%

20%

RRT =

Figure 5. Scenarios of PPT and RRT


517

Furthermore, as we found, the price of prepaid ticket (PPT) and return ratio of

Transportation Eco Point (RRT) significantly influence on respondents’ WTP for both

altruistic and selfish classes. From the users’ point of view, reducing PPT and increasing RRT

are good for them and it may increase their WTP. However, from the viewpoint of policy

makers, they may have contradiction between increasing the citizens’ WTP and getting more

payment from the users. In other words, one side, the policy makers want more citizens will

accept their proposed policies to reduce traffic congestion and improve environment. Whereas,

in another side, the policy makers want to get more payment from the users and return less

ratio as TEP for them in order to maintain the parking lots as well as bus system. Therefore,

the policy makers might need a balance between acceptance probability and PPT/RRT.

Figure 5 demonstrates the joint acceptance probability for some scenarios of PPT and

RRT (effects of other variables were neglected). It shows that if the RRT is 20% of RRT, the

acceptance probability is very high, approximately 0.8 for most of the cases. By contrast, the

acceptance probability drops considerably when RRT is larger than 0.2 million VND. It seems

that the RRT of 15% and PPT from 0.3 to 0.4 million VND (about 5%~10% monthly income

of HCMC’s citizens) can be satisfy for both viewpoints of users and government. Additionally,

TGP (trust in government policies) is also an important parameter for acceptance probabilities

of not only altruistic class but also selfish class. The more people trust in movement, the

higher possibility that they will accept the policies. Thus, the authority and government need

more dialogues with the citizens in order to make them “trust in government”, especially prior

to actual implementation of the new policy.

6. CONCLUSIONS

Using SP data collected in HCMC, our paper aims at discovering factors that affects the

citizens’ attitude of WTP towards the P&R and TEP policies. The well-known MIMIC model

was applied to figure out the relationships among 7 latent variables, 20 psychological

indicators and 6 casual variables. And then, the latent variables estimated from the MIMIC

model and some socio-demographics as well as mobility attributes of respondents were

further applied in LC-SORM. Two classes including “altruistic” and “selfish” were assumed

to classify the respondent population. Those of two classes were modeled by

class-membership selection model taking into account of the latent variables KPT (knowledge

on P&R and TEP), API (appropriateness of P&R implementation), and RPE (recognition of

P&R’s effect). In addition, the socio-demographics and mobility attributes (GE - gender and

MPF - monthly parking fee) were also considered for class-membership selection model.

After assigning the respondents into “altruistic” and “selfish” classes, the standard ordered

response model (SORM) was applied to class conditional choice model, which

simultaneously estimates the WTP of respondents for each class. In the class conditional

choice model, four latent variables (PMD - private mode dependency, ATE - awareness of

transportation in city center, APP - awareness of private mode’s problem in society, and TGP -

trust in government's policies) and mobility attributes (TE - annual transportation expenditure,

PTU - monthly public transportation usage and MOC - mode choice) were considered.

The results of MIMIC model show that, the elderly and high-educated people seem to

have more knowledge of the new policy. However, the people with high-income appear to

have less understanding about the new policy. In addition, the high-income people and the

people living in a bigger family are likely to have negative feelings of appropriateness of the

new policy. By contrast, they appear to really recognize that the new policy may be able to

mitigate traffic congestion and improve environment. It is found that the elderly people well


518

recognize the negative impacts of private modes (PC and MC) on environment and the usage

of public transport. They are likely to be more dependent upon public transportation but less

dependent on private modes. Furthermore, it is evident that the high-educated people and

people living in a bigger family are aware of the bad traffic congestion level and dangerous

walking environment in the city center.

Regarding LC-SORM, it is concluded that the altruistic respondents are likely to accept

the new policy and, however, the selfish ones easily reject the new policy. We found that KPT,

API, RPE and MPF are significant factors for class-membership selection model. Generally,

the people who have less knowledge about the new policy, less appropriateness on the new

policy and less recognition of the effects of the new policy belong to the selfish class.

However, the people who pay more monthly parking fee appear to be the altruistic class. In

order to increase probabilities of being altruistic class, thus achieving the higher acceptance

probabilities, it is recommended that the policy maker should have an indoctrination of the

new policy to deepen the knowledge of citizens and enlarge their appropriateness on the new

policy as well as their recognition of the effects of the new policy.

Turning to conditional choice model, it is revealed that the price of prepaid shopping

ticket (PPT) and return ratio of Transportation Eco Point (RRT) play very important roles in

WTP for not only the altruistic but also the selfish class. The more PPT and the less RRT

would results in reduction of WTP or rejection of the new policy. Our finding suggested that,

adopting 0.3~0.4 million VND for PPT and 15% of PPT for RRT is acceptable for both users

and government. Beside PPT and RRT, the latent variables (PMD, ATE, APP and TGP) also

show their significant effects on the acceptability of each class. However, the sign and

significant level varies across variables for each class. In general, the people who trust in

government (TGP) and are aware of the bad traffic congestion level and walking environment

(ATE) are likely to have higher acceptance probabilities.

The findings from our study imply valuable suggestions for the policy makers in

effectively proposing promotional strategies in order to increase public acceptance of the

policies. However, as stated by (Boes and Winkelmann, 2006), three assumptions of the

SORM applied in our paper lead to its limitations in analyzing marginal probability effects.

These assumptions include the single index assumption, constant threshold assumption, and

the distributional assumption that does not allow for additional individual heterogeneity

between individual realizations. Therefore, our future works tend to further verify the findings

in this paper by testing latent class - generalized order response model (LC-GORM), in which

one of a approach is the to generalize threshold parameters by making them dependent on

covariates (Maddala, 1983; Ierza, 1985). The LC-GORM was recently applied in study of

(Sugiarto et al, 2015a) to explore public response of road pricing.

ACKNOWLEDGMENTS

Firstly, the authors extremely thank Nikken Sekkei Research Institute (NSRI), Japan for their

fund of this research. Secondly, we are very grateful to Dr. Nguyen Duc Trong, University of

Transport and Communications, Vietnam and his team for their collaborations in data

collection. Finally, our acknowledgments go to Dr. Hitomi Sato and the students in

Morikawa-Yamamoto-Miwa laboratory, Nagoya University (NU TREND) for their hard work

in data aggregation.


519

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