DEMAND FOR MINIBUS SERVICE: KOWLOON TONG MTR STATION …libproject.hkbu.edu.hk/trsimage/hp/11050985.pdf · DEMAND FOR MINIBUS SERVICE: KOWLOON TONG MTR STATION TO HKBU BY ... Price

1

Prof. C.K. Woo

Dept. of Economics

DEMAND FOR MINIBUS SERVICE:

KOWLOON TONG MTR STATION TO HKBU

BY

Song Xuchen

11050985

Applied Economics Concentration

Zhou Zhou

11051604

Applied Economics Concentration

An Honours Degree Project Submitted to the School of

Business in Partial Fulfilment of the Graduation

Requirement for the Degree of Bachelor of Business

Administration (Honours)

Hong Kong Baptist University

Hong Kong

April 2014

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Contents

ABSTRACT ........................................................................................................................................ 3

INTRODUCTION ............................................................................................................................. 3

LITERATURE REVIEW ................................................................................................................ 4

QUESTIONNAIRE DESIGN & DATA COLLECTION ....................................................... 7

Questionnaire Design .............................................................................................................. 7

Data Collection ......................................................................................................................... 8

BINARY CHOICE MODEL .......................................................................................................... 8

GRAPHIC ANALYSIS ................................................................................................................. 10

Hypotheses .............................................................................................................................. 10

Price elasticity ........................................................................................................................ 10

Hot vs. Cool weather ............................................................................................................ 11

Rain vs. No rain ..................................................................................................................... 12

Waiting time ............................................................................................................................ 13

Budget effect ........................................................................................................................... 14

Study year effect .................................................................................................................... 16

Gender effect .......................................................................................................................... 16

FIELD OBSERVATION ANALYSIS ....................................................................................... 17

ECONOMETRIC ANALYSIS ................................................................................................... 19

CONCLUSION & RECOMMENDATION ............................................................................ 21

REFERENCES ................................................................................................................................ 24

APPENDIX 1: Price elasticity of demand ............................................................................... 25

APPENDIX 2: Questionnaire Survey Results ........................................................................ 26

APPENDIX 3: Questionnaire design ........................................................................................ 30

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ABSTRACT

This project aims to assess the demand for minibus service from the Kowloon Tong

MTR Station to the HKBU campus and suggest policies to reduce the long waiting

time. It identifies the likely factors affecting the demand and collects first-hand data

via a survey of HKBU students. It uses graphical analysis and a binary choice model

to assess HKBU students’ demand for the minibus service. The results thus obtained

lend support to our hypotheses: (a) the demand of minibus service is higher on hot

and/or rainy days; and (b) the demand is price-sensitive. To reduce the long waiting

time during the morning rush hours, the project estimates that raising the current

HK$3.9/trip price to about HK$5.5/trip would cut the waiting time by about one third

to half.

INTRODUCTION

The minibus service from the Kowloon Tong MTR Station to Hong Kong Baptist

University campus, namely 25M(S), is a busy route during the morning rush hours.

From 8 am to 10 am on weekdays, the queue waiting at the bus stop is long, up to 15

minutes. During the remaining hours, however, the demand shrinks drastically.

The 25M(S) service currently has a fixed price of HK$3.9/trip and mainly serves

HKBU students. The long waiting time is a real-world problem that deserves our

investigation using the economic theory and statistical techniques that we have learnt

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as BBA (Applied Economics) students.

This project identifies and quantifies the factors affecting students’ demand of the

25M(S) minibus service, thereby suggesting a pricing policy to reduce the long

waiting time during the morning rush hours. To this end, we design a questionnaire to

collect data from our fellow students to collect data, record the waiting time at the

minibus stop, graphically describe, and estimate a binary choice model of students’

decision to take the minibus service. Our main finding is that raising the current

HK$3.9/trip price to about HK$5.5/trip would cut the waiting time by about one third

to half.

LITERATURE REVIEW

This study is case-specific based on the collection of first-hand data. There is

currently no publication specifically on this topic. There is, however, extensive

literature on urban travel economics that prove to be enlightening to our study.

Urban Travel Demand: A Behavioral Analysis (1975) by Domencich and McFadden

advances a behavioral model that describes the causal relationship between

socioeconomic/transport system characteristics and the decision of trip-making. This

model stands at a microeconomic view and emphasizes the decision-making process

of a traveler when confronted with a complicated set of conditions. The model should

be able to explain how individuals would behave differently if certain circumstances

change and thus is useful for policy determination. This thinking applies well to our

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study, because the demand of minibus is the result of students’ decisions it closely

relates to the long waiting time problem. Therefore, our model should be

behavior-oriented; it requires an examination of relevant influencing factors on travel

decision made by students.

The book describes in detail the different aspects of trip-making decision and travel

demand. Examples of transport system characteristics include parking charges and toll

fees for auto transit, and seating capacity, schedule frequency, and use of exclusive

bus lanes for public transport. Examples of socioeconomic characteristics of traveler

include work/residential location, income, time-of-day choice, etc. There are also

other considerations like destination choices and time constraint. Since the main target

of our study is confined to trips on 25M(S), which is non-auto transit by students of

HKBU, with predetermined locations, in the early rush hours of workdays, we need to

choose carefully the important factors that affect students’ decision of taking the

minibus and screen out those that are irrelevant. Some factors that are specific to this

study are also considered, like waiting time at the bus stop.

The book also mentions the concept of substitute. In this study, the purpose of

travel is to get to the campus on time. There are essentially two modes of travel for

students, minibus and walking; presumably there are fewer people who will take a

taxi instead. These three modes are not perfect substitute but achieve the same end

objective. Since the purpose of this study is to assess the demand for minibus alone

and its relevant policy issues, it is reasonable to assume the characteristics of other

modes to be constant. There is one exception, though, regarding the weather condition.

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Hot weather or rain could be said to increase the demand for minibus; it could also be

said to decrease the demand for walking as a substitute mode of travel.

Urban Travel Demand Modeling (1995) by Norbert Oppenhein mentions that the

choice made by a traveler is the process of utility maximization; the utility of a given

mode of transportation for a certain trip is measured by a combination of its attributes

such as time, cost, comfort, and safety. We created different scenarios in our

questionnaire so that travelers’ expected utility on taking the minibus changes, which

in turn may influence their choices between different modes of transportation.

The last chapter of the book also discusses briefly about supply side

decision-making process. The author develops several models to fulfill various

objectives such as revenue maximization, ridership maximization and congestion

abatement. In our case, the objective of the minibus supplier is to reduce congestion

while maintain stable revenues. The suppliers’ actions are constrained by the demand

problem with aggregate utility function. The author mentions two approaches to solve

this bi-level problem: it can either solved by incorporating demand problem into

objective function of the supply problem or solved by replacing demand problem by

its optimality conditions. Integrating the supply decision with demand models could

help us reach an optimal decision.

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QUESTIONNAIRE DESIGN & DATA COLLECTION

Questionnaire Design

The questionnaire is designed to assess students’ choices of whether to take the

minibus when given different prices, namely HK$4, HK$6, HK$8 and HK$12. The

current price of 25M(S) is HK$3.9/trip and we use HK$4 as a round-off measurement.

The questionnaire contains twelve scenarios, each with a specific set of non-price

factors including temperature, rain, and waiting time. Temperature is defined as hot or

cool, considering the average temperature of Hong Kong in winter is higher than 10℃,

“cold” is then a redundant measurement. Zero, ten and twenty minutes are used to

measure the waiting time. It is defined as the expected waiting time when passengers

arrive the minibus station. It takes about 15 to 20 minutes walking from the Kowloon

Tong MTR Station to HKBU campus, and the frequency of 25M(S) is 3-5 minutes

during peak hours. Given the two facts above, it is neither rational nor realistic to

expect waiting time longer than 20 minutes. It turned out that our setting of waiting

time corresponds to the actual situation. In our field observation, waiting time ranges

from 0-15 minutes during peak hours (8-10 a.m.). The scenarios are created to

account for the influence of these factors on the demand for minibus. Besides, general

information about respondents’ habits and preferences on transportation choice, and

their basic background information are collected.

(See Appendix 3)

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Table 1. Scenario information

Data Collection

We collected primary data from two sources. The first one is a survey conducted on

HKBU students. The questionnaire assesses what factors influence the demand for

25M(S). The second source is the field observation at the minibus stop outside the

Kowloon Tong MTR Station. We took record of the time of intervals between

minibuses, number of people waiting and individual waiting time to assess the actual

demand during peak hours (8-10 a.m.) on weekdays.

We distributed questionnaires to HKBU students, either during breaks of classes or

via online survey platform. There were in total 46 completed questionnaire and 2,208

pieces of response given a specific scenario and at a certain price.

BINARY CHOICE MODEL

Based on the chosen factors that affect students’ decision, we use a binary choice

model that estimates the probability of a student taking the minibus. The probability

can serve as a proxy for the share of the student population who will take the minibus.

The definitions of variables are as followed:

Scenario 1 2 3 4 5 6 7 8 9 10 11 12

Hot Yes Yes No No Yes Yes No No Yes Yes No No

Rain No Yes No Yes No Yes No Yes No Yes No Yes

Waiting 0 0 0 0 10 10 10 10 20 20 20 20

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Table 2. Variables

VARIABLES VALUE DEFINITION

yes 1 The student is willing to take the minibus

0 The student is not willing to take the minibus

hot 1 The weather is hot

0 The weather is cool

rain 1 There is rain

0 There is no rain

hour_waitin

g_time

0 The student needs to wait for 0 minutes to get on a minibus

1/6 The student needs to wait for 10 minutes to get on a minibus

1/3 The student needs to wait for 20 minutes to get on a minibus

price

4 The price per trip of the minibus is HK$4




male 1 The student is male

0 The student is female

local 1 The student is local

0 The student is non-local

budget The monthly budget for transportation of the student

Model specification:

Prob(“yes”) =exp(Y)

1 + exp(Y)

where

Y=β0+ β1*hot+ β2*rain+ β3*hour_waiting_time

+ β4*price + β5*male+ β6*local+ β7*budget

From the regression result, we found that the coefficient for “budget” is not

statistically significant. Therefore we conducted another analysis without the “budget”

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variable.

By transforming the model, we can obtain the price elasticity of demand Ɛ under a

certain circumstance. (See Appendix 1)

Ɛ= β4*price*(1 – Prob(“yes”))

GRAPHIC ANALYSIS

Hypotheses

We advance the following hypotheses and they will be examined one by one using

data collected from our questionnaires and graphs constructed by data analysis.

Demand is price-sensitive

Demand is higher in hot than in cool weather

Demand is higher in rainy days than non-rainy days

Demand drops when waiting time increases

Demand increases with budget

Price elasticity

The price elasticity of demand in twelve scenarios given different prices can be

derived and a scatter graph is used to show the result.

According to the scatter, we confirm our first hypothesis that demand is indeed

price-sensitive. As price increases, the elasticity of demand changes significantly,

from around -1 when price is HK$4 to more than -9 when price is HK$12. Moreover,

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the graph shows that the demand is less elastic when it rains, which is represented by

the difference between scenarios 1&2, 3&4, 5&6, etc.; and the demand is more elastic

when it is hot (1&3, 2&4, 5&7, etc.) and when waiting time increases.

Figure 1 Price elasticity of demand

Hot vs. Cool weather

Ceteris paribus, the effect of temperature on minibus demand can be analyzed. We

divide the twelve scenarios into six groups to compare the demand under hot and cool

weather conditions:

Table 3. Comparable groups to analyze the effect of temperature

Group 1 2 3 4 5 6

Condition No rain,

0 min

Rain,

0 min

No rain,

10 mins

Rain,

10 mins

No rain,

20 mins

Rain,

20 mins

We calculate the percentage of respondents who will take the minibus under a

certain condition at a given price. Then we can draw the demand curve for each

scenario.

-10

-9

-8

-7

-6

-5

-4

-3

-2

-1

0

0 1 2 3 4 5 6 7 8 9 10 11 12

Elasticity

Scenario

4 6 8 12

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Figure 2 Hot vs. Cool (Group 1)

The above graph shows that when it is not raining and the waiting time is 0 minute,

the demand is higher in hot weather than in cool weather. Five other graphs are

derived using the same method (See Appendix 2), only the raining condition or/and

the waiting time change.

By analyzing the graphs, besides confirming our hypothesis that demand is higher

in hot weather than in cool weather, we also find out that the demand difference does

not vary with waiting time, and the effect becomes weaker when it is raining.

Rain vs. No rain

Similar to our previous analysis, we divide the scenarios into six groups and compare

the demand for minibus under rain and no rain condition, holding other factors

constant.

0

2

4

6

8

10

12

14

0 0.2 0.4 0.6 0.8 1

No RainWaiting Time: 0

Hot

Cool

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Table 4. Comparable groups to analyze the effect of rain

Group 1 2 3 4 5 6

Condition Hot,

0 min

Cool,

0 min

Hot,

10 mins

Cool,

10 mins

Hot,

20 mins

Cool,

20 mins

We use the same method as before and draw the demand curves. The effect of rain

is thus illustrated.

Figure 3 Rain vs. No rain (Group 1)

The graph tells us that when it is hot and the waiting time is 0 minute, demand for

minibus is higher when it is raining. The other five graphs (See Appendix 2) show

similar effect.

In conclusion, our hypothesis that demand is higher on rainy days is confirmed and

the demand difference keeps unchanged when the waiting time changes. Moreover, by

comparing the effect of hot weather and rain, it is obvious that rain is a stronger

incentive for passengers to take the minibus than the hot weather.

Waiting time

The third non-price factor that we would like to analyze is waiting time. The scenarios

0

2

4

6

8

10

12

14

0 0.5 1

HotWaiting Time: 0

No Rain

Rain

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are divided into four groups:

Table 5. Comparable groups to analyze the effect of waiting time

Group 1 2 3 4

Condition Hot, No rain Hot, Rain Cool, No rain Cool, Rain

Using similar methods, we draw the demand curves and the effect of waiting time

is illustrated under a specific weather condition.

Figure 4 Waiting time 0/10/20 (Group 1)

The four graphs together show that the effect of waiting time is not significant on

the demand for minibus, thus our fourth hypothesis is not valid.

Budget effect

Our last hypothesis is that budget has positive effect on minibus demand. According

to responses we got, we divide the monthly budget (measured by HK$) into six

groups: <100, 100-199, 200-299, 300-399, 400-499, >499.

Firstly, we calculate the percentage of passengers who will take the minibus for

each group:

3

5

7

9

11

13

0 0.2 0.4 0.6 0.8

Cool & No Rain

0 10 20

15

Figure 5 Percentage of students taking the minibus

The graph shows no obvious relation between the budget and the minibus demand.

To be more precise, we draw out the demand curve for different budget group:

Figure 6 Budget effect on minibus demand

The demand curve does not always shift to the right when budget increases, so

there is not clear relationship between demand and budget.

However, the demand indeed increases when the budget increases from below

HK$100 per month to above HK$200.

A reasonable explanation could be that the monthly minibus expense is around

HK$80, which takes up a smaller proportion of the transportation budget when the

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

<100 100-199 200-299 300-399 400-499 >499

Percentage

Budget

0

2

4

6

8

10

12

14

0 0.2 0.4 0.6 0.8 1

<100

100-199

200-299

300-399

400-499

>499

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budget is higher, so that the price change of minibus has a relatively weak effect on

passengers’ choices.

Study year effect

Then we examine whether different study years affect the choice of taking the

minibus. We divide questionnaire results into 4 groups according to study years,

derive the percentage of students who are willing to take the minibus for each group,

and draw the figure below:

Figure 7 Percentage of students taking the minibus in different study years

We see a slight increase on minibus demand for senior year students. However,

considering students from year one, two and three account for 95% of our respondents,

and the percentages of their willingness to take the minibus are all around 34%, we

conclude that the study year has no significant effect on minibus demand.

Gender effect

The last hypothesis we examine is whether gender effect exists. The result shows a

nine-percent difference between female and male respondents about whether to take

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

1 2 3 4

Percentage

Study Year

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the minibus under given conditions.

Figure 8 Gender effect on minibus demand

FIELD OBSERVATION ANALYSIS

We conducted two field observations at 25M(S) minibus stop outside the Kowloon

Tong MTR station and the weather conditions were different. We did the first

observation on Feb 12, 2014 when Hong Kong was faced with an unusual cold surge;

it was a drizzling day with temperature at 7°C. The second observation was conducted

on Feb 18, 2014, a sunny day with temperature at 17°C. After interviewing the

coordinator of 25M(S), we got some basic information about the management of

25M(S) during peak hours. The scheduled interval of 25M(S) minibuses is 2-3

minutes. When the number of people waiting is too many, two or more 25M(S) will

come at the same time. We took record of individual waiting time and the number of

people waiting during 8 a.m. to 10 a.m. and used the data to draw following graphs:

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

Male Female

Percentage

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Figure 9 Individual waiting time

Figure 10 Total number of people waiting

According to graphs, both individual waiting time and the number of people

waiting increase under bad weather condition. The results correspond to common

sense and our questionnaire-based survey results. The phenomenon could be

explained by an increasing demand for minibus under bad weather condition.

However, the coordinator also suggested to us that the road congestion on rainy days

would also cause delay. The minibuses got stuck on the road and couldn’t come back

in time, thus extending the interval of the minibus.

0

2

4

6

8

10

12

14

16

8:09 8:38 9:07 9:36 10:04

Wa

itin

g t

ime

Time

Individual waiting time

12-Feb

18-Feb

0

20

40

60

80

100

120

140

7:55 8:24 8:52 9:21 9:50

Number of people

Time

Total number of people waiting

12-Feb

18-Feb

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ECONOMETRIC ANALYSIS

After the previous graphical analysis, we shall conduct a regression using the binary

choice model and see if we can confirm our conclusions above. We obtain the

following result:

Table 6. Regression result 1

Coefficient Variable Estimated coefficient P-value

β0 Intercept 4.048313 0.0000

β1 hot 0.532118 0.0000

β2 rain 1.285850 0.0000

β3 hour_waiting_time -1.175062 0.0082

β4 price -0.771888 0.0000

β5 male -0.320754 0.0182

β6 local -0.854856 0.0000

β7 budget 0.000113 0.6914

We notice that the “budget” variable is not statistically significant, nor does it have

a major effect on the independent variable. This result is consistent with our graphical

analysis. Considering that this variable may be irrelevant, we conducted another

analysis without the “budget” variable. The result is as followed:

Table 7. Regression result 2

Coefficient Variable Estimated coefficient P-value

β0 Intercept 4.135892 0.0000

β1 hot 0.532766 0.0000

β2 rain 1.291281 0.0000

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β3 hour_waiting_time -1.256879 0.0043

β4 price -0.776562 0.0000

β5 male -0.321713 0.0155

β6 local -0.833710 0.0000

We observe a larger sum of squared residual but also a slightly larger R2. From

Table 7 we can see that the result confirms our graphical analysis. The probability of a

student choosing to take the minibus increases when the exponent of Y increases, or

equivalently, when Y increases. This probability can serve as a proxy for the share of

student population that will take the minibus, i.e. the demand for minibus. In general,

holding other factors constant, hot weather and rain shall increase the demand for

minibus; long waiting time and high price will decrease the demand; and when the

student is male or local, this student is less likely to take the minibus.

By holding the factors of “male” and “local” constant (“male”=1; “local”=1), we

can derive the following table that summarizes the probability of taking the minibus.

Table 8. Probability of taking the minibus

Sce.

Price

1 2 3 4 5 6 7 8 9 10 11 12

HK$4 0.6004 0.8453 0.4686 0.7623 0.5492 0.8159 0.4170 0.7223 0.4970 0.7823 0.3671 0.6784

HK$6 0.2412 0.5362 0.1572 0.4043 0.2050 0.4839 0.1314 0.3550 0.1729 0.4320 0.1093 0.3086

HK$8 0.0630 0.1966 0.0380 0.1256 0.0517 0.1656 0.0310 0.1043 0.0424 0.1386 0.0253 0.0863

HK$12 0.0030 0.0108 0.0018 0.0064 0.0024 0.0088 0.0014 0.0052 0.0020 0.0072 0.0012 0.0042

From table 8, we can see that, holding other factors constant, with price at the

current HK$4, when the weather changes from “no rain” to “rain”, the probability

increases by 25 – 30 percentage points; when the weather changes from hot weather

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to cool weather, the probability decreases by 10 – 20 percentage points; when waiting

time changes by every 10 minutes, there is a mild 5 percentage points increase in

probability.

More importantly, we observe that, on a day without rain, holding other factors

constant, when price increases from HK$4 to HK$6, the probability decreases sharply

by 2/3 in general, around 25 – 30 percentage points; on the other hand, on a rainy day,

the same price increase would cause the probability to decrease by half in general,

around 30 – 35 percentage points.

It is clear that, in both our field observation and econometric analysis, “rain” is the

prominent factor that influences the demand for minibus. Students are much more

willing to spend some money when the minibus can provide both transportation and

shelter from getting wet. Temperature also plays a less significant role. Price has a

major effect on the demand, probably because the current price is quite low and

students have become more sensitive to price increases. It is safe to say that we can

change the demand for minibus by price change.

CONCLUSION & RECOMMENDATION

Based on our previous analysis, we can conclude the following important points.

Firstly, the demand for minibus is price-sensitive in general. When price increases,

the demand drops drastically and the price elasticity of demand increases. Secondly,

the demand is higher in the condition of hot and rainy weather. The effect of rain is

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especially significant. Thirdly, although the demand is negatively related to waiting

time, the relation is not very strong. We also controlled for two demographic variables

and observe that male or local students are more likely to take the minibus than

female or non-local students.

From the field observation, we can see that the waiting time of a particular

passenger and the total number of people waiting both reach the peak at around 9:00 –

9:30am. The maximum waiting time that we recorded was 15 minutes, which is quite

long, considering that walking would only take about the same length of time. Beside

the high demand, the problem of long waiting time is partly due to congestion in the

road; according to the employees at the bus stop, sometimes several minibuses are

stuck in the road and couldn’t come back in time to pick up passengers.

The minibus service currently offers students of HKBU a relatively cheap and

convenient way to go to campus from Kowloon Tong MTR Station. The peak hour

situation necessitates some action to keep the waiting time to a reasonable limit. We

recommend that the minibus charge a higher price, around HK$5.5, at peak hour from

8 – 10am. This policy will reduce the demand roughly by a third to half. Consequently,

the waiting time shall decrease significantly and the problem can be alleviated.

Although the price for the regular 25M minibus route is HK$5.1 for the whole

route, it usually doesn’t stop at the university. Therefore, there is no need to worry

about driving passengers to the 25M route.

With a price increase at peak hours, the demand can be controlled to a certain

extent so that the waiting time wouldn’t be too long. This mechanism is consistent

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with the supply and demand theory. When a lot of students go to the campus in a short

period of time, the demand is greater in this period than in others. The most efficient

price should therefore be greater in this period than in other periods.

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REFERENCES

Domencich, T. A., McFadden, D. (1975). Urban Travel Demand: A Behavioral

Analysis. Amsterdam: North-Holland

Maddala, G. S., Lahiri, Kajal (2009). Introduction to econometrics. Chichester, U.K.:

Wiley

Oppenheim, N. (1995). Urban Travel Demand Modeling: from individual choices to

general equilibrium. New York, U.S: John Wiley & Sons. Inc.

25

APPENDIX 1: Price elasticity of demand

ln (𝑃𝑟𝑜𝑏("yes")

1 − 𝑃𝑟𝑜𝑏("yes")) = 𝑌

ln(𝑃𝑟𝑜𝑏("yes")) − ln(1 − 𝑃𝑟𝑜𝑏("yes")) = 𝑌

𝜕ln(𝑃𝑟𝑜𝑏("yes"))

𝜕𝑝𝑟𝑖𝑐𝑒−𝜕ln(1 − 𝑃𝑟𝑜𝑏("yes"))

𝜕𝑝𝑟𝑖𝑐𝑒= 𝛽4

1

𝑃𝑟𝑜𝑏("yes")×𝜕𝑃𝑟𝑜𝑏("yes")

𝜕𝑝𝑟𝑖𝑐𝑒+

1

1 − 𝑃𝑟𝑜𝑏("yes")×𝜕𝑃𝑟𝑜𝑏("yes")

𝜕𝑝𝑟𝑖𝑐𝑒= 𝛽4

𝜕𝑃𝑟𝑜𝑏("yes")

𝜕𝑝𝑟𝑖𝑐𝑒= 𝛽4 × 𝑃𝑟𝑜𝑏("yes") × (1 − 𝑃𝑟𝑜𝑏("yes"))

Therefore, the price elasticity of demand is

ε =𝑝𝑟𝑖𝑐𝑒

𝑃𝑟𝑜𝑏("yes")×𝜕𝑃𝑟𝑜𝑏("yes")

𝜕𝑝𝑟𝑖𝑐𝑒= 𝛽4 × 𝑝𝑟𝑖𝑐𝑒 × (1 − 𝑃𝑟𝑜𝑏("yes"))

26

APPENDIX 2: Questionnaire Survey Results

Temperature Effect (Hot vs. Cool)

Figure A2- 1. Hot vs. Cool (Group 1-6)

0

2

4

6

8

10

12

14

0 0.2 0.4 0.6 0.8 1


Hot Cool

0

2

4

6

8

10

12

14

0 0.5 1

RainWaiting Time: 0

Hot Cool

0

2

4

6

8

10

12

14

0 0.2 0.4 0.6 0.8 1


Hot Cool

0

2

4

6

8

10

12

14

0 0.2 0.4 0.6 0.8 1

RainWaiting Time: 10

Hot Cool

27

Rain Effect (Rain vs. No Rain)

Figure A2- 2 Rain vs. No Rain (Group 1-6)

0

2

4

6

8

10

12

14

0 0.2 0.4 0.6 0.8


Hot Cool

0

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0 0.2 0.4 0.6 0.8 1

RainWaiting Time: 20

Hot Cool

0

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0 0.2 0.4 0.6 0.8 1

HotWaiting Time: 0

No Rain Rain

0

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0 0.2 0.4 0.6 0.8 1

CoolWaiting Time: 0

No Rain Rain

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0

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0 0.5 1

HotWaiting Time: 10

No Rain Rain

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0 0.2 0.4 0.6 0.8 1

CoolWaiting Time: 10

No Rain Rain

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HotWaiting Time: 20

No Rain Rain

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0 0.2 0.4 0.6 0.8 1

CoolWaiting Time: 20

No Rain Rain

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Waiting Time Effect

Figure A2- 3. Waiting Time 0/10/20 (Group 1-4)

3

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0 0.2 0.4 0.6 0.8 1

Hot & No Rain

0 10 20

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0 0.5 1

Hot & Rain

0 10 20

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0 0.2 0.4 0.6 0.8

Cool & No Rain

0 10 20

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0 0.2 0.4 0.6 0.8 1

Cool & Rain

0 10 20

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APPENDIX 3: Questionnaire design

Demand for Minibus from MTR Station to HKBU

Introduction:

Hi there, we are two final year students from Business School at HKBU. This survey is part of our Honor

Project that focuses on travel demand. We would like to ask you about your preferences in taking the minibus

under various circumstances.

The questionnaire will take you less than 3 minutes. The information gathered is for academic purpose only

and will not be disclosed to third party in a form other than aggregate statistics.

(Indicate by ticking “√”)

1. How often do you come to the HKBU from the Kowloon Tong MTR station?

times/week.

2. How do you usually travel to HKBU (after taking MTR, if applicable)?

Walk

Minibus

Taxi

Other

3. Would you take the minibus from MTR station to HKBU if the price is:

HK$4

HK$6

HK$8

HK$12

4. Why do you take the minibus? (Select all applicable reasons)

☐Comfortable ☐Convenient ☐Fast ☐Other________

5. Why don’t you take the minibus? (Select all applicable reasons)

☐Too costly ☐Long waiting time ☐Walking is healthy ☐Other_______

Scenarios

In the following, you will see several scenarios, each with a specific set of conditions. Imagine

you are in that place, just coming out of the MTR station and intending to go to campus.

Would you take the MINIBUS (“√”) or Not (“X”)?

(Current price: approx. HK$4)

Scenario 1

Weather: Hot Would you take the minibus if the price is:

Rain: No HK$4 _____ HK$8 _____

Expected waiting time: 0 mins HK$6 _____ HK$12 _____

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Scenario 2


Rain: Yes HK$4 _____ HK$8 _____


Scenario 3

Weather: Cool Would you take the minibus if the price is:

Rain: No HK$4 _____ HK$8 _____


Scenario 4


Rain: Yes HK$4 _____ HK$8 _____


Scenario 5


Rain: No HK$4 _____ HK$8 _____


Scenario 6


Rain: Yes HK$4 _____ HK$8 _____


Scenario 7


Rain: No HK$4 _____ HK$8 _____


32

Scenario 8


Rain: Yes HK$4 _____ HK$8 _____


Scenario 9


Rain: No HK$4 _____ HK$8 _____


Scenario 10


Rain: Yes HK$4 _____ HK$8 _____


Scenario 11


Rain: No HK$4 _____ HK$8 _____


Scenario 12


Rain: Yes HK$4 _____ HK$8 _____


Basic Information: (tick “√” as appropriate)

1. Gender: M _____ F _____

2. Year 1 / 2 / 3 / 4 / 5 or above in university

3. Citizenship: Hong Kong _____ Mainland _____ Others _____

4. Monthly budget of transportation: HK$

33

You have finished! Thank you very much for your kind participation. For further

enquiries, you can get in touch with us by:

Song Xuchen

Tel: +85264893698

Email: [email protected]

Zhou Zhou

Tel: +85264326387

Email: [email protected]

mailto:[email protected]

mailto:[email protected]

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Documents

DEMAND FOR MINIBUS SERVICE: KOWLOON TONG MTR STATION …libproject.hkbu.edu.hk/trsimage/hp/11050985.pdf · DEMAND FOR MINIBUS SERVICE: KOWLOON TONG MTR STATION TO HKBU BY ... Price