padayhag - exploring the influence of social factors to travel
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Exploring the Influence of Social Factors to Travel: A case study of
university workers and college students in Metro Manila, Philippines
Grace Padayhag Co-authored by: Dr. Daisuke FukudaGraduate student Associate Professor
Tokyo Institute of Technology
Japan Society for the Promotion of Science (JSPS) SymposiumUniversity of the Philippines Diliman
March 10, 2009
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Outline of presentation1. Introduction2. Conceptual framework3. Methodology4. Results and Discussion 5. Conclusion and recommendation6. Future works
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Motivation of the studyInformation and Communications Technology (ICT) is
gradually penetrating in the developing countries and have affected the daily life in so many ways.
e.g. do more social activities, social friends expand
The use of ICT also can induce, reduce or substitutetravel.
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Examined through social interaction, social activities and
social network
Examples of how ICT affected travelcell phone use – for quick and instant deals, may
create additional travels
online shopping – may substitute travel
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IntroductionUrry (2007)
German sociologist Georg Simmel stated two important points why people travel:
(1) they are attracted to others for ulterior reasons and (2) they enjoy engaging in “free-playing sociability,” namely forms of
social interactions that are free from content, substance, and ulterior end.
Axhausen (2003) conducted the initial research referring to social factors and travelthe core of the research hypothesis was that people’s travel pattern is
shaped by his network – a social network.
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IntroductionSocial network
Technically speaking, it is defined as a set of actors and the ties among them
In layman’s term, it is composed of a person’s relatives, colleagues from work (sports club or professional organization), friends, and acquaintances.
Figure 1 Social Network
You/actor
Your friends/ties
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IntroductionSocial interactionsArentze and Timmermans (2007) With social interactions, habitual or intermittent, people are able to exchange information (especially that ICT is rampant even in developing countries in which information can be delivered instantly)
Hibbitt et al., (2001)Social interaction create obligations and expectations of reciprocation.
Harvey and Taylor (2000)working in isolation at home (telecommuting) does not really diminish travel but will try to find social interaction elsewhere consequently generating travel.
Social activitiesLu and Pas (1999) who revealed that travel behavior is better explained when the activity participation, it incorporates social activities, is included in the analysis.
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Carrasco et al. (2006) suggested a method of collecting social network to study social activity-travel patterns which is the ego-centered approach
Ego-centered approachThe prevalent method to collect the members of ego’s social network.
This approach elicits the “ties” (your friends) of the “actor” (you) and their characteristics.
Each participant has to list down his friends and then characterize them according to gender, age and sometimes according to their roles (or relationship) to the ego.
Introduction
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Objectives
The purpose of this study is to investigate the activity-travel behavior of university students as related to their patterns of socialization.The study also examines the social factors that encompass the aspects of social interactions, social activities and the composition of social contacts and their effects on travel of the university workers in Metro Manila, Philippines
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Why university students?
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Source: Urban Travel Behaviour Characteristics Of 13 Cities Based On Household Interview Survey Data (Hyodo, 2005)
Figure 3 Age structure by trips
Younger people used to travel more often than the older people in the case of Manila (age 20y.o.)
Overall conceptual framework
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Social factors
Travel behavior
Trips/trip cost
Conceptual framework
Figure 1 Proposed exploratory factors influencing after-class side-trips on the way home by university students
First conceptual model
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−+
Text messaging
−−
−−
+
Socio-economic and socio-demographic characteristics
Travel behavior
Sending letters/cards
Landline calls
Cell phone calls
Face-to-face interaction Email
Online chat
Social network
+
Social factors
Methodology1. Paper and pen interview survey2. A questionnaire survey was develop
2 parts: a main survey sheet and a name generator sheet
Main survey sheet1. Socio demographic attributese.g. gender, age, school name and type, location, car ownership, cell phone ownership
2. Social interactioninformation about the usage of mobile phones and their communication patterns e.g. how many times in a day they socially interact? how many people they interact? whom they interact?
3. Social activitiesinformation about their social activity patternse.g. how many times they do social activities a week (common social activities were listed in the questionnaire)? to whom and how many people they socialize with? How long do they plan the social activity?
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Name generator sheet1. Social network
A customized name generator was created eliciting the respondent’s circle of friends. There were 4 types of friends were considered, namely:
1. friends for important matters (e.g. a friend where you can discuss important or serious matters)2. friends for social (e.g. a friend where you can socialize with in sports, parties, celebrations, etc.)3. friends for advice (e.g. a friend where you can seek advice for job opportunities, etc.)4. friends for small matters (e.g. a friend where you can borrow equipments, small amount of money, etc.)
Friends listed in the name generator were then characterized by the ego’s relationship to them and by their age.
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Example of customized Name generator used
in the survey
age
relationship
Descriptive results of university studentsTable 1 Descriptive statistics of the respondents (N = 287)
Age M =19.96, SD =1.328Social network composition M = 23.45, SD = 13.03M: mean, SD: standard deviation
Genderuniversity
Type of universityLiving with whom
DLSU: 83, 29%
PUP: 70, 24%Male: 204, 71%
FEU-EAC: 63, 22%
UPD: 71, 25%Female:
83, 29%
State univ: 141, 49%
Private univ: 146, 51%
Not living with parents: 134, 47%
Living with parents: 152, 53%
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Social interaction per day
Average side-trips on the way home = 2.71 trips
Path analysis results
Chi-square(d.f.) = 106.47 (6) p < 0.01 Goodness of fit index (GFI) 0.933 Adjusted goodness of fit index (AGFI) 0.833Comparative fit index (CFI) 0.934 Normed fit index (NFI) 0.928 Non-normed fit index (NNFI) 0.8910
Legend:
** significant at the 0.001 level * significant at the 0.01 level ( ) t values
Figure 3 Estimated causal relationship model of socialization and number of side-trips taken on the way home for the university students
Socialization factors
Number of side-trips on the way home
Frequency of text messaging per day
Frequency of online chat per day
Frequency of Side-trips going home
Size of social networkSize of the people interacted face-to-face
β31 = 0.188 ∗∗
(5.56)
λ11= 0.730∗∗
(16.2)
β32 = 0.0610∗
(2.96)
λ31=0.775∗∗
(22.8)
λ21 = 0.508∗∗
(10.2) λ22 = 0.143∗
(2.90)
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Results and discussionThe number of side-trips made while heading home have direct and positive
effects for the number of people with whom one interacts face to face per day, the frequency of text messaging, and the size of social networks.
The number of people with whom one interacts face to face and social network size mediated the relationship among text messaging, chatting online, and side-trips on the way home.
Implications to transportation planning:Overall, the results imply that the opportunity to socialize is a sound
motivation for trip generation even in developing countries and should be considered when constructing transportation planning policies.
To better understand activity-travel behavior and motivation, the incorporation of variables related to socializing is worthwhile as part of transportation planning and research.
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Why university workers?
The survey samples was extended to university workers it will be easy to
collect the social network data of the participants (ego) as well as the data of his friends (ties) and the analysis
was deepen …
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Conceptual framework
Social factors
Travel factor
Figure 2 Conceptual model of the study for the university workers in Metro Manila, Philippines
Frequency of Social activities
Frequency of Social interaction
Social network
Degree of travel
++
+
+
Second conceptual model
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Methodology1. Paper and pen interview survey2. A questionnaire survey was develop
2 parts: a main survey sheet and a name generator sheetMain survey sheet1. Socio demographic attributese.g. gender, age, civil status, school name and type, location, household size, car ownership, cell phone ownership, monthly income
2. Social interactioninformation about the social interaction patterns for every type of contacts were emphasized and recorded. e.g. how many times in a day they socially interact with family members, with close friends, with not so close friends? how many people they interact? whom they interact?
3. Social activitiesinformation about their social activity patternse.g. how many times they do social activities a week (common social activities were listed in the questionnaire)? to whom and how many people they socialize with? How long do they plan the social activity?
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Name generator sheet1. Social network
A customized name generator was created eliciting the respondent’s circle of friends. There were 4 types of friends were considered, namely:
1. friends for important matters (e.g. a friend where you can discuss important or serious matters)2. friends for social (e.g. a friend where you can socialize with in sports, parties, celebrations, etc.)3. friends for advice (e.g. a friend where you can seek advice for job opportunities, etc.)4. friends for small matters (e.g. a friend where you can borrow equipments, small amount of money, etc.)
Friends listed in the name generator were then characterized by the ego’s relationship to them and by their age.
An additional attribute was added in the name generator, that is the estimated spatial distance of the ego to his friends.
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Descriptive results of university workersTable 2 Descriptive statistics of the university worker participants in Metro Manila (N = 235)
Age M = 29.23 years old, S.D. = 8.427Household size M = 4.510, S.D. = 2.157Monthly Income in Php M = 15,219.15, S.D. = 6731.72Number of years working M = 4.99, S.D. = 6.255Social network composition M = 24.8, S.D. = 18.8M:mean, SD: standard deviation
Gender Civil status University workers
Male: 92, 39%
University professors: 98, 42%
University staffs: 137, 58%
Single: 156, 66%
Married: 79, 34%Female:
143, 61%
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Type of university Education
level
Location of residence
State university: 156, 66%
Private university: 79, 34%
Graduate: 74, 31%
Undergraduate: 161, 69%
Outside Metro Manila: 39, 17% Within Metro
Manila: 196, 83%
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Social interaction per day
Social activities per week
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Social Network of the respondents
Descriptive resultsAverage travel time from home to work placeCar ownership
Trav
el ti
me
in m
inut
es
Num
ber o
f par
ticip
ants
Number of cars in household Mode of travel
Average total trips traveled per day 3.81 (SD 1.52)
Average travel cost from home to work place 51.24 Php
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Structural Equation Model result
Figure 3 The estimation results for social factors and travel factors
δ3
δ4
δ5
δ2
δ1
ε8
ε5
ε12
ε9
ε3
ε13
ε1
ε2
ε4
ε10
ε11
ζ2
ζ3
ζ1
Social activities, η2
Social network,η1
Social interaction, ξ
Degree of travel, η3
y8
y9
y10
y11
x1
x2
x3
x4
x5
y13y12
y1
y2
y3
y4
y5
x6δ6 ε6y6
ε7y7
1
1
0.24 *** (3.49)
0.17 *** (3.42)
0.22 ***(3.51)
0.20***(3.55)
0.23*** (3.65)
0.06***(2.9)1
1.37* (2.54)4.63**(2.90)
2.93** (2.79)
6.24** (2.85)
5.99*
* (2.09
)
0.51* (2.11)
0.14 ** (2.55) 0.074
* (2.49
)
0.29** (3.12)
1.01*** (4.32)
10.82 *** (7.15)0.47 **(2.99)
2.52** (3.00)*** significance level at 0.001** significance level at 0.005* significance level at 0.01
Chi-square = 432.6 d.f. 148 p< 0.001GFI = 0.85AGFI = 0.80SRMR = 0.90
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Results and discussionThe result of the structural model using the survey data collected from
university workers in Metro Manila indicates statistically positive and significant in all estimated parameters.
From the perspective of the university workers within Metro Manila, the structural model reveals that social interaction has a substantial causal effect on social network as well as on social activities. Moreover, social network could be a causal factor to social activities. There is also a significant effect of social activities to the degree of travel.
In addition, the strong significant effect comes from the path of social interaction via social activities then finally to the degree of travel.
Implication to transport planningThough the result is only for a small population (university workers in the
Philippines), it implies that the inclusion of social factors in transport planning should be treated with significance and should be recognized as part of the consideration of transport policies, even in the developing countries.
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Conclusion and recommendationsWith respect to the analysis of the university students data, it was found that
certain types of socialization had significant effects on trip frequencies among university students in Metro Manila, which indicate that various forms of socialization play important roles in trip generation.
From the perspective of the university workers within Metro Manila, the hypothesis that social factors, such as social interaction, social activities and social network, would have a significant effect on travel factors, i.e. total travel cost per day as well as the total traveled trip per day as considered in the study was confirmed and supported by the result of the structural model.
Though the both results (university students and university workers in the Philippines) is just a small population, it calls for an attention that transportation planning should also take into consideration in incorporating the social aspects.
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Future worksA broader survey for general population is recommended for the future
works.
Although the findings of the current study is enriching and useful, there are also new areas to explore more on the ICT interaction, since these new technologies are built for social interaction purposes but did not reflect on the effects to transport.
The ongoing research study is also considering how travel is affected by the capability of ICTs to reduce the planning time horizon of some social activities.
To explore on including the spatial distance of the friends in the social network subject, which is actually the current state of the research progress.
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Maraming salamat sa pakikinig! ☺
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Methodology
where y = p × l vector of observed dependent variables measured without error, b = m × m matrix of coefficients relating p dependent variables to one another, x = q × l vector of observed independent variables measured without error, l = m × n matrix of coefficients relating q independent variables to p dependent variables, and z = p × l vector of errors in the equation.
y = βy + λx + ζ
η = Βη+ Γξ + ζ. (2)
(1)
y = Λyη + ε, x = Λxξ + δ, (3)
1. Path analysis
2. Structural equation model (SEM)
B, Γ, Λy, Λx : unknown parameter array, ξ : endogenous or latent dependent variable vector,
ζ,ε ,δ : error term vector following a multivariable normal distribution,x : vector of observed exogenous or independent variables,y : vector of observed endogenous or dependent variables.
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