Proceedings of the Eastern Asia Society for Transportation Studies, Vol.11,2017

Study on Transportation Behavior of Middle-Class Households withChildren in Bangkok, Thailand
Mihoko MATSUYUKI a, Damrongsak RINCHUMPHU ba

Institute of Urban Innovation, Yokohama National University, Kanagawa,
240-8501, Japan; E-mail: mihoko@ynu.ac.jpbFaculty of Architecture and Planning, Thammasat University, Pathum Thani,
12120, Thailand; E-mail: damrongsak@ap.tu.ac.th

Abstract: This study focuses on households with children in Bangkok and vicinity, Thailandand explore their automobile ownership and commuting transportation behavior and affecting
factor. Data is collected by questionnaire survey, and logistic analysis is employed. As results,
their heavy dependence on an automobile is revealed. Automobile ownership is affected byexisting of children who commute to school by automobile and household income. Regardingwith affecting factors, the followings are identified: Parents’ transportation mode to work
place is affected by existing of children who commute to school by automobile and officelocation close to a station. Children’s transportation mode to school is their age and housing
location close to a station.
Keywords: Household with children, Middle class, Transportation Behavior, Bangkok

1. INTRODUCTION
It is reported that use of private automobiles in Asian countries has been increasing in recent
years, accounting for 34 per cent of trips in Jakarta, 32 percent in Bangkok, and 30 per cent inMetropolitan Manila (UN Habitat, 2010). And these automobile dependency causes several
serious problems, such as traffic jam, air pollution, global warming, etc.,
On the other hand, another trend in Asian cities is the introduction of rail-based transportsystems (UN Habitat, 2010), for example, Hanoi and Ho Chi Minh, capital city and economic
center of Vietnam is now constructing urban rail transit and in Dhaka, a capital of Bangladesh,construction of Metro is in the process. Bangkok, a capital city of Thailand, is one of the
progressive cities in Asian developing countries regarding urban rail transit development. The
first urban rail transit, called BTS started its operation in 1999, and now five lines (BTS
Sukhumvit line, Silom line, MRT subway, Airport Rail Link (ARL) and MRT purple line)
makes urban rail transit network. However, motorization in Bangkok is still progressing andhas still heavy traffic jam and serious air pollution problem (Tulyasuwan, 2013).
To tackle with problems cause by motorization, to understand people’s travel behavior
and its effecting factors are essential. Various scholars emphasis impact of household
structure on transportation demand and behavior (Gordon et al. :1988, Oster:1979, Strathman
et al., 1994), so to understand transportation behavior by the household structure is very
important.
This study selects Bangkok as a study area because it is one of the progressive cities
which have developed urban rail transit network in Asian developing countries. In Bangkok,
School trips made by private cars is identified one of major factors of traffic congestion(Bangkok Metropolitan Administration, 2001). Therefore, this study focuses transportation
behavior of household with schoolchildren in Bangkok and vicinity. As middle-class people
can afford automobiles and they are now a major social class in Bangkok, and it is still further

increasing in size, this study focuses on middle-class households.
Objectives of this study are as follows; 1) to identify middle-class households’ automobile
ownership and parents’ and children’s commuting transportation behavior in Bangkok andvicinity, and 2) to identify affecting factors on their automobile ownership and transportation
behavior.
There are a lot of studies on transportation behavior in Bangkok and vicinity. For example,
Dissabayake et al. (2010) investigate household travel behavior in Bangkok Metropolitan
Area by a Nested Logit Model.
Recently, the number of studies on transportation behavior related with urban rail transit has
been increasing. Matsuyuki et al.(2013) investigate transportation behavior of condominium
residents along urban rail transit in Bangkok and reveals that most of the residents are single
or couple household without children and they decrease the number of owned automobile anddecrease automobile use ratio and commuting distance by moving in the proximity of stations.
Sanit et al.(2012) examined condominium residents' BTS ridership and its affecting factors.
Some scholars focus on a specific household structure, for example, Sanit et al.(2013)
focuses on a multi-worker household in Bangkok and explore factors affecting transportation
mode choice.
Almost of all above studies have variables of having child/not, and some of them include
a variable of child’s age in their numerical analysis, but they didn’t analyze how children’stransportation behavior affect parents’ transportation behavior. Additionally, studies whichanalyze children’s transportation behavior in Bangkok is very rare.

2. METHODOLOGY
2.1 Data Collection
In this study, data was collected by questionnaire survey. The target of the questionnaire
survey is middle-class households with school-year children. Therefore, a questionnaire
survey was conducted to a parent who came to pick up their child/ren at elementary, juniorhigh and senior high schools in Bangkok (21 schools), Nakhon Pathom (9 schools),
Nonthaburi (8 schools), Samut Prakan (7 schools), and Pathum Thani (8 schools). The survey
was conducted from Nov. 1st to 20th, 2014. The questionnaire asks respondent’s
characteristics (age, gender, education, etc.,), household’s characteristics (location of house,household income, tenure of a house, number of household member, age of household
members, number of child, number of owned car, etc.,), respondents and his/her spouse’scommuting transportation behavior, if they have jobs (location of work place, commuting
mode, commuting time) and school-year children’s commuting transportation behavior toschool (location of school, transportation mode, transportation time, etc.,).
Totally, 204 samples were collected, but some data were missed in 19 samples, so 185
samples were the valid response. When a statistical population is more than one million, 384
samples are necessary for the 95% confidence interval. The registered population in Bangkok
and its surrounding five provinces is about 10,650,000 (as of 2015), and the share of
middle-income household (monthly household income is 15,001-50,000 Baht) is 54% inGreater Bangkok (Bangkok and surrounding five provinces)(Household socio-economic
survey, 2009) and average household size is 2.7 in Bangkok and its vicinity respectively in2010 (population and household senses). From these figures, the number of middle-classhousehold in Bangkok and its vicinity is expected 2,130,000. According to the National
Senses 2010, the share of the population from 7 to 22 years old in about 20% in Bangkok.

Therefore, the maximum estimation of middle-class households with school-year children isaround 420,000 households. The required number of samples for the statistical population
420,000 is 385 for the 95% confidence interval and, by comparing this figure with our sample
number, 185, the sample number might not be sufficient. Summary of socio-economic
characteristics of respondents and their household is summarized in Table 1.
As the monthly income of a household in Greater Bangkok (Bangkok, Nonthaburi,
Pathumthani and Samut Prakarn) is 35,004 Baht (WHO, 2011), almost all of the respondents
can be categorized as middle class or upper than middle class. 96% of them graduate
university or graduate school, so their education level is quite high. The age of respondents is43.0 years old, so most of the respondents are middle age. An average number of children is
1.37 children, and the average number of household is 3.62 persons, so most of the
respondents’ families are nuclear families. Location of a house, work place and a school issummarized in Table 2. About 70 % of households lives in Bangkok and rest of them lives in
Bangkok Vicinity.

Table 1. Socio economic characteristics of respondents
Sex
Male
Female44%
56%

Household Number
2 persons 2%
3 persons 59%
4 persons 23%
5 persons 10%
6 persons 3%
7 persons 2%
8 persons 1%
Average=3.62 persons

Location of Residence
Bangkok69%
Nonthaburi
Nakhon Pathom
Samut Prakarn
Samut Sakhon
Pathumthani
Other

12%
1%
11%
0%
7%
1%

Age
20’s 3%30’s 37%
40’s 38%
50’s 18%
60’s- 3%
Average=43.0 years old
No. of Children
1child67%
2 children 24%
3 children 5%
4 children 1%
Average=1.37 persons
Age of Children
0-5 years old 28 persons
6-11 years old 85 persons
12-15 years old 47 persons
16-18 years old 43 persons
19 years old- 34 persons
House Ownership
Owner 73%
Rental 7%
Rental fromparents/relatives
21%
Types of Residence
Detached house 54%
Condominium
21%
Town house16%
Shop house
9%

Education
High School
Undergraduate
Graduate

4%
58%
38%

Occupation
Government officer
5%
Office worker
62%
Self-owned business21 %Teacher
1%
Profession
2%(ex. Lawyer, doctor, accountant)
Housewife3%
Student
1%
Retired
2%
Other
3%
No job0%Monthly Household Income
Less than 30,000THB
30,000-Less than 40,000THB
40,000-Less than 50,000THB
50,000-Less than 60,000THB
60,000-Less than 70,000THB
70,000-Less than 80,000THB
80,000-Less than 90,000THB
More than 90,000THB

8%
3%
4%
15%
11%
10%
12%
36%

Table 2. Location of house, work place and school
House
Province
BangkokNonthaburi
Nakhon Pathom
Samut Prakarn
Samut Sakhon
Pathumthani
Other
Linear distance from city center(Silom station)
0-5km*
5-10km
10-15km15-20km20-25km25-30km
30kmProximity of a station
Within a radius of 1km
Outside a radius of 1km
Linear distance from a house
0-5km
5-10km
10-15km15-20km20-25km25-30km
30km* including 0km (work at home)

Work Place*

School

69%
12%
1%
11%
0%
7%
1%

84%
4%
0%
8%
0%
2%
2%

81%
4%
2%
5%
0%
6%
3%

7%
29%
11%
16%
12%
12%
4%

25%
35%
17%
11%
7%
2%
3%

28%
25%
17%
8%
8%
6%
7%

15%
85%

33%
67%

13%
87%

35%
18%
18%
9%
10%
5%
5%

39%
16%
16%
13%
6%
6%
4%

3. AUTOMOBILE OWNERSHIP AND TRANSPORTATION BEHAVIOR
3.1 Household Automobile Ownership
The number of owned automobiles is summarized in Table 2. Most of the households have atleast one automobile, and about 60% of households have not less than two automobiles.
Therefore, the households with children are considered to heavily depend on an automobile.
Table 3. Household automobile ownership
No. of automobile No. oh household
4 (2%)
0
71 (38%)1
70 (38%)2
22 (12%)3
12 (6%)4
6 (3%)
More than 5
1.93
Average3.2 Commuting Transportation Behavior
In this section, parents’ and schoolchildren’s commuting transportation behaviors areanalyzed.
3.2.1 Parents’ commuting transportation to work
(1) Workers’ Commuting Transportation Mode
Figure 1 shows parents’ commuting transportation mode, excluding parents who work athome and do not need to commute. In total, 79% commute by automobile, so it shows that
middle-class people with children are heavily depend on an automobile in their commuting towork place. Mothers tend to be fellow passengers in automobile, and about 10% of
automobiles is shared by parents.
Only 10% uses public transportation, such as urban rail transit (BTS/MRT/ARL), bus androt-tu (van-style mini bus). Mothers tend to use more public transportation thanfathers.

Figure 1. Commuting transportation mode to work
Parents’ commuting time linear distance and time are summarized in Table 4. Theiraverage linear commuting distance is 13.3km and commuting time is 43.5 min.. Fathers’commuting distance is longer than mothers’ for 1.1km but their commuting time is almost
same.
Table 4. Parents commuting distance and time
Linear distance*

Commuting time* (one way.)
43.2 min. (SD:26.6)
43.7 min. (SD:25.2)
43.5 min. (SD:26.0)

Father
13.8km (SD:17.3)
Mother
12.7km (SD:10.2)
Total
13.3km (SD:14.4)*Exclude parents who works at home

Figure 2. Linear commuting distance and transportation mode
The relationship between linear commuting distance and transportation mode issummarized in Figure 2. From this figure, a clear relationship between commuting distanceand transportation mode can not be found. However, regarding public transportation, we canfind a tendency that urban rail transit and bus are used for short, and medium-distancetransportation and rot-tu is used for ling-distance transportation.
3.2.2 Children’s commuting transportation to schools(1) Children’s commuting transportation mode
Figure 3 shows schoolchildren’s commuting mode by generation and in total. Totally, about
58% of children goes to their school by automobiles, and only 23% use public transportation.
75% of elementary school students commute by automobiles and 13% by public
transportation. Rates of automobile passengers in elementary school, junior high, school, highschool and university are 75%, 56%, 45% and 33% respectively. The automobile dependence
decrease as children get older. Rates of public transportation passengers in elementary school,
junior high, school, high school and university are 13%, 32%, 34%, 20% respectively. The
rate of public transportation passengers increases as children get older until high school. As
university students have more variety of mode choice such as driving bike, so public
transportation user rate is not so high.
Commuting distance has only slight difference between elementary school, junior highschool and high school students but university students’ commuting distance is much longer
than lower schools as shown in Table 5. It means even in elementary school, children need tocommute for long distance, In Bangkok, middle-class parents are very eager for theirchildren’s education, and many of them try to enter their children into ‘big-name school’,
private schools, old public schools and university demonstration schools, etc., even though it
locates far from their house.
Therefore, their commuting distance is long, and parents
need to drive them to school.

Figure 3. Age and transportation mode to school
Table 5. Average linear commuting distance (km)Year/kind of school
6-11years old: Elementary school
12-15 years old: Junior high school
16-18 years old: High school
Not younger than 19 years old:
University/graduate school

Linear distance
10.9km10.3 km
9.4 km16.5 km

Commuting time (one way.)
29.3 min.
32.9 min.
37.4 min.
40.3 min.

4. MODEL ESTIMATIONIn this section, logistic regression is employed to identify affecting factors on household’s car
ownership, parents’ commuting transportation mode to their work place and schoolchildren’scommuting transportation mode to their schools. Table 7 shows variables that are included in
the analyses. SPSS statistics 24 were used for the model estimation.

Table 7. Variables of models
Household Characteristics
Household income

Car numberChild number
Car commute child
Individual characteristics
Age
GenderLocation of a house
Distance city-center house

Distance station house

1=Less than 30,000THB
2=30,000-Less than 40,000THB
3=40,000-Less than 50,000THB
4=50,000-Less than 60,000THB
5=60,000-Less than 70,000THB
6=70,000-Less than 80,000THB
7=80,000-Less than 90,000THB
8=More than 90,000THB
1=1 car, 2=2cars, 3= not less than3 cars
persons
Does a household have children who commute to school by a car?
0=yes, 1=noAge (years old)
0=male, 1=female
Linear distance between city center (Silom station) and a house
1=0-5km
2=5-10km
3=10-15km
4=15-20km
5=20-25km
6=25-30km
7=30kmDistance between the nearest station and a house
0=within 1km (catchment area)
1=further than 1km (not catchment area)

Location of Work place/school
Distance house work
Linear distance between a house and work place (km)Distance school work
Linear distance between a house and school (km)Distance city-center work
Linear distance between city center (Silom station) and work
place (km)Distance city-center school
Linear distance between city center (Silom station) and a school(km)
Distance station work
Distance between the nearest station and work place
1=within 1km (catchment area)
2=further than 1km (not catchment area)
Distance station school
Distance between the nearest station and a school
1=within 1km (catchment area)
2=further than 1km (not catchment area)
Transportation mode
Transportation mode
0=automobile, bike
1=public transportation (BTS/MRT/ARL, bus rot-tu)(1) Factors influencing household car ownership
Ordered logistic regression is employed to analyze the relationship between household carownership and household characteristics. The dependent variable is the number of owned car:
1=1 car, 2=2 cars and 3=not less than three cars. As household characteristics, child number,household income, distance between a house and city center, the distance between a houseand the nearest station and the average parents’ distance between work place and a house areincluded as independent variables from Table 7. By using a stepwise method, the best set ofpredictors was finally found and summarized in Table 8.
Due to the magnitude of a coefficient, child number is the best predictor of household carownership. As described in the previous chapter, more than half of schoolchildren are driven
to their school by family members, if there are more than one children and their school
locations are not close, the household needs to have plural automobiles.
The second best predictor is household income, and it has a positive impact on the numberof owned cars. If a household can not afford to buy more than one car, they all family member
who commute by automobile need to share one car, and a driver needs to stop several places
to drop family member and should drive for a long distance until his/her work place. To avoid
such situation, the higher income households have, the more automobile they purchase.
Additionally, there is also the possibility that some high-income households have plural
automobiles to show their socio-economic status.
Table 8. Model estimation of household car ownership
Coefficient Sig.
Odd Ratio
.798
.012
**
15.90
Child Number
.379
.000
***
2.22
Household income
2.767.000 ***
1.46
Constant 1n
Cox & Snell R Square
Nagelkerke R Square
McFaddenThresholdSig. of the regression

181
.174
.236
.143
2.767
.000******p value<.01, **p value<05, *p value<.10(2) Factors influencing parent’s commuting transportation mode
Next, to analyze factors influencing parents’ commuting transportation mode, especially auto
(automobile and bike) or public transportation, binary logistic regression analysis was
employed. The dependent variable is transportation mode: 0=auto, 1= public transportation.
Parents who commute on foot or by taxi, motorcycle taxi or other mode are excluded fromanalysis. From Table 7, distance between a house and city center, distance between a houseand the nearest station, distance between a house and work place, distance between a work
place and city center, distance between a work place and the nearest station, gender, age,household’s monthly income, existing of children commuting by a car, are used as
independent variables. A stepwise method was used and finally, a set of three explanatory
variables were found (Table 9).
The result shows that parents who have children who commute to schools by cars andwhose work place is far from a station and whose household income is higher, tend tocommute by automobiles. Due to the magnitude of a coefficient, we can say that children’stransportation behavior affect parents’ transportation behavior greater than parents’ work
place and household economic situation.

Table 9. Model estimation of commuting transportation mode
Coefficient
Sig.
Odd Ratio
Car commute child
Household income
Distance station work 12

Constant-2.143-.315-1.968
2.765

.050
.081
.013
.091

**
**
**
**

n
-2 log Likelihood
Cox & Snell R Square
Nagelkerke R Square% of Cases correctly predicted
Sig. of regression

.117
.730
.140
15.874370
52.875
.127
.301
92.2
.001*****p value<.01, **p value<05, *p value<.10(2) Factors influencing children’s commuting transportation mode
Next, to analyze factors influencing children’ commuting transportation mode, especially auto
(automobile and bike) or public transportation, binary logistic regression was employed. The
dependent variable is transportation mode that is 0=auto, 1=public transportation. Children
who commute on foot or by taxi, motorcycle taxi or other mode are excluded from theanalysis. From Table 7, age, distance between a house and a school, distance between a school
and the nearest station, distance between a school and city center, distance between a houseand city center, distance between a house and the nearest station and household income, areused as independent variables. By using a stepwise method, two variables are selected as thebest set of predictors. The result is summarized in Table 10.
According to Table 10, children whose house is not in the catchment area of a station and
who is younger tend to commute by automobiles. The magnitude of coefficient shows that
effect of house location is greater than age. This result is consistent with Figure 3.
Table 10. Model estimation of children’s commuting mode to school
Coefficient Sig.
Odd Ratio Exp(B)AgeDistance station school

Constant

.094
-.622
-.846

n
-2 log Likelihood
Cox & Snell R Square
Nagelkerke R Square% of Cases correctly predicted
P value of regression

.029
.013
.268

**
**

1.099
.537
.429
248
131.115
.069
.105
80.0
0.09*

***p value<.01, **p value<05, *p value<.10

5. CONCLUSION

This paper deals with transportation behavior of middle-class households with children in
Bangkok and vicinity. A questionnaire survey was conducted, and 185 valid responses werecollected. The result of the questionnaire survey, household car ownership, parents’commuting transportation mode and schoolchildren’ commuting transportation mode are
analyzed based on logistic regression model to identify affecting factors.

It is revealed that in Bangkok and vicinity, middle-class households with children areheavily depend on the automobile. The average number of owned car is 1.93, even though anaverage number of family member is only 3.62 persons. 79% of parents and 58% of children
commute to their work places and schools respectively by automobile.
From the analysis, it is identified that child number in households, and householdincome affects the number of automobiles positively. Now Thailand is still developing its
economy, and then middle-class households are expected to increase their income. Therefore,
middle-class households have high possibility to increase their automobile shortly.
The analysis reveals that if there a child/ren who commutes to a school by anautomobile and a work place is not in the catchment area of a station, more parent commuteby automobiles. Regarding with children’s commuting mode to school, children’s age andlocation of their house, that is, their house is in the catchment area of a station, effect their
public transportation ride. When children become university students, a variety of theirtransportation mode widens, and more children commute by public transportation.
To summarize, children’s commuting transportation behavior effect both households’ carownership and parents’ commuting transportation behavior. Especially, most of younger
schoolchildren need to be driven to their school by family members and parents need to havemore automobiles and commute by automobiles. When children grow up and change theircommuting transportation mode to public transportation, parents can decrease theirautomobile dependency. One of the reasons that parents drive their children to schools is‘safety’ (Bangkok Metropolitan Administration, 2001). To reduce elder children’s automobile
commuting, enhancement of security in public transportation and catchment area of stations
and bus/rot-tu stops are required, and it leads to decrease of parents’ automobile dependency.
As a locational relationship between a house and station effect children’s transportation
behavior, when a house is in the catchment area of a station, more elder children go to theirschool by public transportation, then more parents use public transportation on theircommuting. Additionally, if parent’ work place is in the catchment area of a station, moreparents use public transportation. Therefore, locating houses and offices in the catchment areaof a station is considered to be an effective way to decrease automobile dependency of
middle-class households with children.
Now condominium developing along urban rail transit is booming in Bangkok(Chalermpong et al.:2011) but most of the units in the condominium is for a single or couple
use because of the high land price. In 2016, a new urban rail transit line, MRT purple line
started its operation, and it connects Bangkok and Nonthaburi province. It is the first urban
rail transit to connect Bangkok, and other province and extension of the line are planned.
There are plans to develop more urban rail transit in Bangkok and vicinity. This study reveals
that distance of house from city center does not affect respondents’ transportation behavior.
Therefore, a housing development in proximity to a station in suburbs is expected to decreaseautomobile dependency of middle-class households with children.

ACKNOWLEDGEMENT
The authors would like to acknowledge support of Research Unit for Architecture and Urban
Development, Faculty of Architecture and Planning, Thammasat University.

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