Konferensi Nasional Teknik Sipil 10
Editor :
Harijanto Setiawan
Siswadi
Ferianto Raharjo
Menuju Masyarakat Industri Konstruksi
Berdaya Saing Tinggi
dan Pembangunan Infrastruktur Berkelanjutan
Program Studi Teknik Sipil Fakultas Teknik
Universitas Atma Jaya Yogyakarta
ISBN : 978-602-60286-0-0
Desain sampul dan Tata letak
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Editor : Harijanto Setiawan Ferianto Raharjo Siswadi Jl. Babarsari No. 44 Yogyakarta 55281 Telp : 0274 - 487711 ext: 2162 email : tsipil@mail.uajy.ac.id
Program Studi Teknik Sipil Fakultas Teknik Universitas Atma Jaya Yogyakarta
Menuju Masyarakat Industri Konstruksi
Berdaya Saing Tinggi
xii
240
ANALISIS KINERJA STRUKTUR BETON BERTULANG DENGAN KOLOM MODIFIKASI
YANG DIPERKUAT LAPIS CFRP ... 293
Ida Bagus Rai Widiarsa dan Ida Bagus Dharma Giri
241
ANALISIS PERKUATAN BALOK BAJA DENGAN MEMPERHITUNGKAN EFEK
REDISTRIBUSI MOMEN ... 299
Wiryanto Dewobroto dan Petrus Ricky
243
PENGARUH STEEL FIBER TERHADAP KUAT GESER REACTIVE POWDER CONCRETE ... 305
Daniel Christianto, Widodo Kushartomo dan Wiratman Wangsadinata
257
KINERJA STRUKTUR GEDUNG BERATURAN SISTEM GANDA BERDASARKAN
PERENCANAAN BERBASIS PERPINDAHAN LANGSUNG ... 315
Raja Parulian Purba, Zulfikar Djauhari dan Reni Suryanita
290
KAJIAN PENGARUH PERILAKU TEGANGAN REGANGAN TEKAN BETON YANG
DIPERKUAT SERAT SINTETIS TERHADAP PERILAKU MOMEN KURVATUR ... 325
Rosidawani, Iswandi Imran, Saptahari Sugiri dan Ivindra Pane
294
APLIKASI INCREMENTAL DYNAMIC ANALYSIS UNTUK PENILAIAN KERENTANAN DAN
RESIKO SEISMIK JEMBATAN ... 333
Niam A. Wibowo, Dean H. Wardana, Mutiara Puspahati C, Senot Sangadji, Edy Purwanto dan S. A. Kristiawan
295
FUNGSI FRAGILITY (KERAPUHAN) SEBAGAI ALAT EVALUASI KINERJA SEISMIK
STRUKTUR TIPIKAL JEMBATAN JALAN RAYA BETON ... 341
Enjels N. Tropormera, Agus Trisyanto, Mutiara Puspahati C, Senot Sangadji, Agus Supriyadi dan Supardi
297
PENYEDERHANAAN PERHITUNGAN GAYA GESER DASAR SEISMIK (V) SNI GEMPA 2012
UNTUK TIPIKAL BANGUNAN GEDUNG SEKOLAH DI JAWA TENGAH ... 349
Himawan Indarto dan Hanggoro Tri Cahyo Andiyarto
298
PREDIKSI RESPONS STRUKTUR BANGUNAN BERDASARKAN SPEKTRA GEMPA
INDONESIA MENGGUNAKAN JARINGAN SARAF TIRUAN ... 359
Reni Suryanita, Hendra Jingga, Harnedi Maizir dan Enno Yuniarto
Topik: TRANSPORTASI
012
THE RELATIONSHIP AMONG LAND USE PATTERN, SOCIO ECONOMIC FACTORS AND
TRAVEL BEHAVIOURS ... 369
Dewa Made Priyantha Wedagama
013
KAJIAN KELAYAKAN FINANSIAL PENGEMBANGAN ANGKUTAN WISATA DI KOTA
DENPASAR ... 377
ISBN: 978-602-60286-0-0 369
THE RELATIONSHIP AMONG LAND USE PATTERN, SOCIO ECONOMIC
FACTORS AND TRAVEL BEHAVIOURS
Dewa Made Priyantha Wedagama1
1Jurusan Teknik Sipil, Universitas Udayana, Bukit Jimbaran Badung, Bali
Email: priyantha.wedagama@gmail.com
ABSTRACT
Socio-economic factors and land use pattern have long been considered to influence changes in travel behaviours. These changes have apparently escalated environmental damages and economic loss due to traffic congestion including in Bali. To reduce these problems, the linkages among land use, socio-economic and travel behaviour measures should be well identified. This study therefore, aims to analyse the relationship among land use pattern, socio-economic factors and travel behaviours in SARBAGITA region in Bali. Household interviews by means of questionnaires were conducted providing the effective samples of 300 respondents. Linear regression models were developed to analyse such relationships for the weekdays and the weekend. The model dependent variables are travel behaviour measures consisting short and long term decisions made by households. Short term decisions include travel frequencies and distances and travel times using non-motorised transport, motorcycles, light and heavy vehicles for making trips while long term decisions cover motorcycle and car ownerships. This study found that during the weekdays, motorcycle ownerships significantly contributed to travel distance to workplaces and the frequencies of riding motorcycles. In addition, the accessible residential frontage roads by light vehicles affect the frequencies of riding motorcycles. Meanwhile, during the weekend motorcycle ownerships and the average age of adults greatly influence the frequencies of riding motorcycles. This gives an indication that socio-economic factor is likely to influence more than land use on households’ travel behaviour in SARBAGITA region.
Key words: land use patterns, socio-economic factors, travel behaviour
1.
INTRODUCTION
Journey patterns in developing countries are in fact different to those in developed countries. In addition, developing countries currently struggle with problems of integration between land use and transportations. For examples, the increase of population number, poverty and differences in income, urban density, improper road networking, spatial mismatch of residences and employment centres, environmental damages and economic loss due to traffic congestion are currently faced by developing countries. In theory, these situations can be reduced with the integration of urban land use development and transportation (Cervero, 2013) .
There have been differences found between spatial forms and the city characteristics in developed and developing countries. Those in developing countries tend to have a 'high primacy' and monocentricity (Cervero, 2013). This indicates that the economy resulted from population density concentration and employment centres and urban clusters may rapidly change to the economic setback. Urban clusters generate economic benefits from labour specialisation, an efficient market transaction, and information dissemination. In addition, the groups of 'high primacy' and monocentricity, population density, high level of motorisation, income generation, urban development, improper road networking system and land use planning significantly contribute to severe traffic conditions in developing countries ( Cervero, 2013).
Studies on land use have massively conducted in developed countries. For instance, theories about 5D consisting density, diversity, design, distance to transit, and destination accessibility has gained the attentions to evaluate the built environment in shaping travel patterns in the US. One important factor used as the measure variables is to consider land use characteristics such as urban area density. However, there are some differences occurred in developing countries for instance, the factors affecting public interest to walk and ride bicycles are different to those in developed countries (Boarnet and Crane, 2001).
To reduce such severe traffic conditions in developing countries, alternatively is to change road users travel behaviour. Such changes can be realised for instance by implementing land use policies. Therefore, an investigation on the linkages among land use, socio-economic and travel behavioural measures are importantly required.
370
ISBN: 978-602-60286-0-0 It has been found however, that travel behaviours were not mainly affected by land use pattern. These behaviours were solely due to self- selection which related to the lifestyle or socio-economic attributes such as income level (de Abreu e Silva, et.al, 2012). For example, the choices of residence location, mode of transportation and travel time are not the pushing factor but the the more important factor is the fulfillment of lifestyle. Meanwhile, the economic attributes such as an individual income is the limiting factor and not the pushing factor. In addition, socio-economic factors such as household compositions and the number of children can also be specific preferences related to particular travel behaviours of households. Socio-economic factors can be the indicator of the latent behaviour variable on the assumption that households with similar socio-economic characteristics will have relatively similar travel behaviours (de Abreu e Silva, et.al, 2012).
This study therefore, aims to analyse the relationship among land use pattern, socio-economic factors and travel behaviours in SARBAGITA region in Bali. The assumptions considered for this study are:
a. land use patterns nearby residences and workplace is influenced by socio-economic characteristic of individuals, and households,
b. socio-economic and land use factors affect the long and short-term decisions of travel behaviours, and c. car and motorcycle ownerships and distances between residences and workplaces are considered
long-term decisions of travel behaviours that may have influences on number of trips and distance travelled each day.
2.
LITERATURE REVIEW
Urban form is considered as the second factor affecting non-motorised trips consisting walking and cycling, while motivations and limitations are the principial factors (Cervero and Kockelman, 1997). Urban form basically shows the physical characteristics of urban areas despite it has a broader concept than land use. In addition, urban form is related to urban design and transportation system. Regardless of the scale, there are three basic steps of urban form associated with mode choice of walking and cycling. This includes design, diversity and density (3Ds). Quantitatively, these 3Ds technique is significant to reduce trips rate by motor vehicles and to encourage people to walk and cycle, although the effect is quite marginal (Cervero and Kockelman, 1997).
In a study with the unit of analysis at the district level, land use design factors which affecting modal choice by walking and cycling is clearly identified (The 23rd PTRC European Transport Forum, 1995). These factors include:
a. the distance from one to another type of land use, for instance a trip distance between residential and commercial areas or between residential and office areas,
b. urban structure orientation is divided into single and multi-centres. Single-centre orientation is described as a city which most of its activities are located in the city centre. Meanwhile, multi-centre orientation has extensive activities throughout the city and is considered having more influences to encourage people to walk rather than in a single centre,
c. urban activity concentrations are the level of urban activities in a wide area units compared to those in a small units, and
d. mixed land use is a proportion between diverse and similar land uses per defined area.
As a measure of land use density, compact environment, for example is able to reduce motor vehicle trips and encourage people to walk and cycle by bringing closer trip origins and destinations. Compact physical environment tends to have less parking spaces, better quality of public transport services, more extensive of mixed land use and largely consists of low-income households. In addition, commercial services such as shops and restaurants around the workplaces encourage workers to ride the vehicle together (car pools), and therefore reducing the interest and the needs to have cars. Empirically, the relative proximity of mixed land use is important for walking and cycling. For example, retail stores and residential areas within a radius of 90 metres encourage people to travel by foot rather than by car. Meanwhile, a greater distance from home to services such as employment and commercial premises are barriers to walking and cycling (The 23rd PTRC European Transport Forum, 1995).
Meanwhile, a past study found that a compact environment of a road network in a grid form, mixed land use and pedestrian facilities encourage people to walking and cycling. In an urban environment that is not private vehicles oriented developments, trips are more likely by walking or cycling, especially for non-working trips (Cevero & Kockelman, 1997). In addition, apart from reducing motor vehicle trips and encouraging trips by foot and bicycle, mixed land use may affect more evenly trip distributions during the weekdays and weekend. Land use can be utilised for different purposes on the weekdays and weekend. For example, office workers use the parking area during working hours while on the weekend or holidays the area is used by restaurant visitors.
Several findings related to non-motorised trips and land use is also summerised as follows (Cevero & Kockelman, 1997):
ISBN: 978-602-60286-0-0
a. The distance from home to a destination play a greater role in the choice of walking. For a distance of one mile or less, mixed land use mix has a great influence on trips by walking and cycling. More than a mile, people tend not to travel on foot or by bicycle. Therefore, long-distance or distance to the center of activity has a positive effect on the mode of walking, while parking capacity has negative effects.
b. The residents in high density suburban areas and mixed residential neighborhoods are more likely to walk as an access model to public transport facilities than in low density areas. In addition, the shops around residential areas have more influences on choice of walking and cycling than in dense settlement areas.
Mixed land use in the city centre and along public transport corridors such as in commercial and residential areas can be used as an indicator of a land use diversity. Further, the proximity between the different types of land use and land use design towards pedestrian facility provisions such as footways, reducing the need to travel by motor vehicles and encouraging the use of non-motorised transport modes. In fact, people who live in pedestrian oriented developments do more walking and cycling to shopping per week than those who live in car oriented developments (Hess, et.al 2002; Kato, et. al, 2013). These previous studies have also concluded that the type of living environment is such a significant predictor of transport mode selection for non-working trips. For example, mixed land use where restaurants, banks, shops, offices and other activities next to each other, people tend to less driving of private vehicles and are more likely to walking or cycling to their destinations.
A previous study describes the relationship between socio-economic factors, land use and travel behaviour as shown in Figure 1 (de Abreu e Silva, et.al, 2012). This explains that socio economic factors are the pushing and the most important factor for land use patterns and travel behaviours.Considering their socio-economic factors, land use patterns and travel distances, households have both long and short term decisions for their travel behaviour choices. Owning vehicle is considered as long term decisions of the households, while number of trips, distance travelled and trip scheduling are short term decisions (de Abreu e Silva, et.al, 2012).
Figure 1. The relationship among socio economic factors, land use pattern and travel behaviours
Multiple linear regression models are used to analyse the relationship among these three factors. This method is relevant to use when the research problem comprises a single dependent variable assumed to be related to two or more independent variables. The aim of multiple linear regression analysis is to predict the changes in the dependent variable in response to changes in the independent variables. This purpose is most frequently realised using the statistical rule of least squares.Multiple regression is suitable when one is interested in estimating the quantity or magnitude of the dependent variable. For example, car or motorcycle ownerships (i.e. the dependent variable) might be estimated from data information concerning a household's income, member of household, and the age of the head of household (i.e. the independent variables). Reader interested more in multiple regression may consult Hair, et.al (2010).
Socio economic factors
Land use patterns at residences and workplaces
Travel distances
Travel behaviours:
a. Long term decisions (private vehicle ownerships).
b. Short term decisions (number of trips, distance travelled, and trip scheduling)
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ISBN: 978-602-60286-0-0
3.
METHODS
Case study area
SARBAGITA region has an area of 723.99 km2 and population density of 2,723 persons/km2 (Statistics of Bali
Province, 2015). This region covers the areas of :
a. Denpasar city containing four districts of North Denpasar, East Denpasar, South Denpasar, and West Denpasar,
b. Badung regency consisting five districts of Abiansemal , Mengwi , North Kuta, Kuta and South Kuta, c. Gianyar regency comprising four districts of Sukawati, Blahbatuh, Ubud and Gianyar, and
d. Tabanan regency including two districts of Kediri and Tabanan.
Denpasar as a capital city of Bali Province has an area amounted to 12,778 Ha or 2.18% of the total area of the Province. The CBD is bounded to west, north and east of Denpasar city and is fully utilised to as the government and office centres, education centres such as schools and universities, city and regional trade areas, commercial areas, wholesale and supporting wholesale areas. The industrial acitivities are mostly run by small industries and scatterly located in the south, west and east of Denpasar while the residential areas are distributed dispersely in the four districts (north, east, south and west). Denpasar has a diverse road network consisting radial, continous linear and grid. There have been constant radial and continous linear road networks in the north and south of Denpasar. Grid road network however, is largely found in all areas of the city.
Tourist destination areas are mainly found in the southern part of SARBAGITA region including Jimbaran, Kuta and Sanur and in the eastern part covering Ubud and Gianyar. Meanwhile, in the western and northern parts containing Tabanan regency and district of Abiansemal respectively are mostly agricultural areas. Despite the region is dominated by tourism and agriculture, residential areas and commercial and offices are contained therein.
To identify dependent and independent variables used for model development, some previous studies (de Abreu e Silva, et.al, 2009; 2012; Mirmoghtadaee, 2012; Van Acker, 2007; Van Wee, 2011) are used for references. These past studies examined the connections among land use, socio-economic factors and travel behaviours in developed countries. Having considered local conditions as well as data availability, this study employs independent variables as follows:
1. socio-economic factors consisting of household characteristics: a. age of head and members of households,
b. the numbers of household members and men in the households, and c. household income, education and occupation of head of households. 2. land use factors involving :
a. location of households and head of household’s workplaces, b. land accessibility in the residences and workplaces,
c. the availability of public transport facilities both at residences and workplaces, d. land use activities within a radius of 1 km from residences and workplaces, and e. pedestrian facilites in residences and workplaces.
The dependent variables, used for model development, are travel behaviours factors including:
a. trip frequency of using non-motorised vehicles, motorcycles, light and heavy vehicles to trip destinations such as workplaces, schools, shopping and leisure centres and others,
b. distances and travel times to trip destinations such as workplaces, schools, shopping and leisure centres and others using non-motorised vehicles, motorcycles, light and heavy vehicles. These variables are short term decisions of households.
c. the number of motorcycles and cars owned by households which are long term decision variable.
Data collection and model development
These variables are then used as references to construct the questionnaire to carry out the interviews for households in SARBAGITA region as shown in Figure 2. The number of questionnaires distributed were 320 samples however, the effective samples were 300 providing the return rate amounted to 93.75 %. All variables are shown in Table 1.
ISBN: 978-602-60286-0-0
Figure 2. Case study area – SARBAGITA region, Province of Bali
Table 1. Data proportion for variable reduction
Variables Weekdays Weekend 1. Number of Men in Households 299 299 2. Age of Head of Households 300 300 3. Household Income per Month 300 300 4. Number of Household Members 300 300 5. Average Age of Household Members 299 299 6. Number of Workers in a Household 279 279 7. Average Age of Adult in a Household 299 299 8. Number of Car Owned by Households 163 163 9. Motorcycle Ownerships in a Household 294 294 10. Frontage Road Accesibility at Residence (by Car) 267 267 11. Frontage Road Accesibility at Workplace (by Car) 275 275 12. Reliable Public Transport Facilities around Residences 99 99 13. Unreliable Public Transport Facilities around Residences 51 51 14. Reliable Public Transport around Workplaces 108 108 15. Reliable Public Transport around Children’s Schools 94 94 16. Public Transports are accessible between Schools and Residences 101 101 17. Public Transports are accessible between Leisure Centres and Residences 94 94 18. Public Transports are accessible between Other Places and Residences 94 94 19. The Percentages of Mixed Land Use around Residences 282 282 20. The Percentages of Mixed Land Use around Workplaces 271 271 21. Trip Frequency of Walking and Cycling per Day 98 80 22. Trip Frequency of Riding Motorcycle per Day 249 197 23. Trip Frequency of Riding Public Transport per Day 31 31 24. Trip Frequency of Driving Car per Day 83 80
25. Distance to Workplace 263 157
26.. Distance to School 172 74 27. Distance to Shops/Market 157 131 28. Distance to Leisure Centre 57 74 29. Distance to Other Types of Land Use 39 31
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ISBN: 978-602-60286-0-0 Land use, travel behaviours and socio-economic data excluding income, are all numerical and continuous. Income data however, is numerical and discrete. In theory, multiple linear regression models are considered relevant to establish the relationship between the continuous data. The data has been tabulated and subsequently analysed using a multiple linear regression models which separated for the weekdays and weekend. These model are constructed using SPSS version 17.
Variable reduction technique is employed to select variables included in the model. This is carried out by looking at the proportion of 0 (zero) on each variable. If the proportion of variable data that has 0 (zero) less than 2/3 (two third) the number of samples then the variable is not included in the modeling as shown shaded in Tables 1. As the results, variables used in the model developments for the weekdays and weekend are shown in Table.2.
Table 2. Variables for model development
Weekdays’ Variable(s) Descriptions Weekend’Variable(s) Men Number of Men in Households Men
KK_Age Age of Head of Households KK_Age Income Household Income per Month Income Size Number of Household Members Size AvrAge Average Age of Household Members AvrAge Workers Number of Workers in a Household Workers AvgAdults Average Age of Adult in a Household AvgAdults MCOwn Motorcycle Ownerships in a Household MCOwn HmAccess Frontage Road Accesibility at Residence (by Car) HmAccess WPAccess Frontage Road Accesibility at Workplace (by Car) WPAccess MixLUHm The Percentages of Mixed Land Use around Residences MixLUHm MixLUWP The Percentages of Mixed Land Use around Workplaces MixLUWP MC Trip Frequency of Riding Motorcycle per Day MC DistWork Distance to Workplace ---
4.
RESULTS AND ANALYSIS
Both models calibration and validation of the relationship among land use, socio-economic factors and travel behaviour are classified into two categories of weekdays and weekend. The enter technique in linear regression model is used to draw the relationship between the dependent variables i.e. distance to the work (DistWork) and trip frequency by riding motorcycles per day (MC) and the independent variables of land use and socio- economic factors as shown in Table 3.
Table 3. Model results Weekdays
Coefficient of Determination Model Equations 1. R2 = 0.054 DistWork = 8.997 + 5.055. MCOwn
(0.011) (0.000)
2. R2 = 0.045 MC = 7.281 + 1.352.MCOwn - 0.030.HmAccess
(0.000) (0.002) (0.043) Weekend
Coefficient of Determination Model Equations
1. R2 = 0.064 MC = -1.206 + 0.765.MCOwn + 0.071. AvgAdults
(0.349) (0.001) (0.028)
Model output is interpreted according to the significance values of each independent variable. Table 3 shows that the significance of three independent variables consisting motorcycle ownerships in a household (MCOwn), frontage road accesibility by car at residence (HMAccess) and average age of adult in a household (AvgAdults) on the weekdays and weekend models are less than 5%.
ISBN: 978-602-60286-0-0
A variable of motorcycle ownerships in a households (MCOwn) significantly influences the distance travelled from residences to workplaces (DistWork) and trip frequency of riding motorcycles per day by households (MC) during the weekedays. In additiom, a variable of frontage road accesibility by car at residence (HMAccess) has a significant influence on trip frequency of riding motorcycles per day by households (MC) during the weekedays. Certainly, this is more related to working trips. In the meantime, variables of motorcycle ownerships (MCOwn) and the average age of adult in a household (AvgAdults) significantly influence trip frequency of riding motorcycles per day by households (MC). This is surely more linked with non-working trips.
Based on these findings, motorcycle ownerships are the most important factors influencing travel behaviours for both working and non-working trips. In addition, motorcycle ownerships significantly affect both long and short terms decisions of travel behaviour by households in SARBAGITA regions. This gives an indication that socio-economic factor is likely to influence more than land use on household’s travel behaviour in SARBAGITA region. This is in line with a past study by de Abreu e Silva, et.al (2012) describing that socio economic situations are the pushing factors which extremely significant to influence travel behaviours.
5.
CONCLUSIONS
This study found that during the weekdays, motorcycle ownerships significantly contributed to distance travelled to workplaces and the frequencies of riding motorcycles. In addition, the accessible residential frontage roads by light vehicles affects the frequencies of riding motorcycles. Meanwhile, during the weekend motorcycle ownerships and the average age of adults greatly influence the frequencies of riding motorcycles. This indicates that socio economic factors significantly influence motorcycle ownerships which are the pushing factors that very important to influence travel behaviours in SARBAGITA regions. The local government are encouraged to restrain the use of motorcycles on the road while implementing provisions of mass public transport in SARBAGITA region. In addition, the local government is highly suggested to be more strictly in minimising the land use coversion in the region.
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