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Akimasa Fujiwara
IDEC, Hiroshima University
2004/04/22
Akimasa FujiwaraAkimasa Fujiwara
IDEC, Hiroshima UniversityIDEC, Hiroshima University
2004/04/222004/04/22
An Evaluation of Sustainable Urban Form Based
on Discrete Choice Models
A Case Study in the Jabotabek Region, Indonesia
An Evaluation of An Evaluation of Sustainable Urban Form Based Sustainable Urban Form Based
on Discrete Choice Modelson Discrete Choice Models
A Case Study in the Jabotabek A Case Study in the Jabotabek Region, IndonesiaRegion, Indonesia
Why are required to discuss on urban forms?
Human activity is strongly affected by urban form
Close relationship between human activity and transport network
Travel is a derived demand of activities
Limitation of single transportation policies
Change of life cycle stage affects on travel patterns
Environmental Sustainability in Urban Transportation
• Less road construction
• Fewer car trips/shortened vehicle miles traveled
• …
• Less road construction
• Fewer car trips/shortened vehicle miles traveled
• …
Transportation strategies
• Transportation demand management
• Transportation control measures
• Vehicle/fuel technologies
Transportation strategies
• Transportation demand management
• Transportation control measures
• Vehicle/fuel technologies
Land use strategies
• Transit Oriented Development(TOD)
• Compact city
Land use strategies
• Transit Oriented Development(TOD)
• Compact city
Transportation and Land Use Strategies
Transportation strategies
outcomes
T ravel Demand Management
T ransportation Systems
Management
Vehicle/Fuel T echnology
T rip patterns (mode, length,
time of day etc.)
Vehicle activity (vehicle-trips,
vehicle miles of travel etc.)
Air Quality
T raffic Flow (speed,
acceleration)
Land use strategies
T ransit Oriented
Development
Compact City
Relationship between LCS and residential & travel mode choices
Younger single
FrequentlyPublic transit
Younger single
FrequentlyPublic transit
Younger couple
FrequentlyCar + transit
Younger couple
FrequentlyCar + transit
Couple withjunior children
Occasionally Car2 + transit
Couple withjunior children
Occasionally Car2 + transit
Couple withsenior children
InfrequentlyCar2 + transit
Couple withsenior children
InfrequentlyCar2 + transit
Elder couple
InfrequentlyCar + transit
Elder couple
InfrequentlyCar + transit
Elder single
OccasionallyPublic transit
Elder single
OccasionallyPublic transit
Transit Oriented Development• “The practice of developing or intensifying residential land use near rail stations” (Boarnet and
Crane 1998).• “Development within a specified geographical area around a transit station with a variety of land
uses and a multiplicity of landowners” (Salvensen 1996).• “A mixed-use community that encourages people to live near transit services and to decrease
their dependence on driving” (Still 2002).• “A compact, mixed-use community, centered around a transit station that, by design, invites
residents, workers, and shoppers to drive their cars less and ride mass transit more. The transitvillage extends roughly a quarter mile from a transit station, a distance that can be covered inabout 5 minutes by foot. The centerpiece of the transit village is the transit station itself and thecivic and public spaces that surround it. The transit station is what connects village residents to the rest of the region…The surrounding public space serves the important function of being a community gathering spot, a site for special events, and a place for celebrations—a modern-day version of the Greek agora” (Bernick and Cervero 1997, p. 5).
• Mixed-use development
• Development that is close to and well-served by transit
• Development that is conducive to transit riding
• Compactness
• Pedestrian- and cycle-friendly environs
• Public and civic spaces near stations
• Stations as community hubs
Transit Oriented Developmentdifferent elements of design
Compact City• To limit distances between home and work and to aim for a better balance between living and
working;• To make the best possible use of existing facilities and infrastructure, and to improve the position
of public transport;• To use space as efficiently as possible by building connected properties in high densities;• To maintain and create varied residential environments in and on the outskirts of the city;• To fragment rural areas as little as possible and to reduce the environmental burden from road
traffic.'
• Minimum densities• Multi functionality• Concentration of development at nodes• Transformation of urban mobility• Congruence of spatial-functional structure and public transit system• Station areas as catalysts for development.
街なか居住研究会
http://www.thr.mlit.go.jp/compact-city/index.html
Newly Developing Asian CitiesBasic Characteristics
• Development of economic activities at a world scale• Spatial division of labor• Division of function between core and periphery of the city• Shift from single core to multiple cores• Land use change in the urban center, agricultural land conversion on
the periphery• Development of large scale infrastructure• Substantial increase in space production (by horizontal, vertical
expansions and renewal of old land uses)• High growth in commuters and increasing commuting time
(Firman, 1998)
JA(karta)BO(gor)TA(ngerang)BEK(asi)metropolitan area
面積=6418km2
Murakami et al. 2004
Cybriwsky and Ford, 2001
11,886,579
20,159,65517,131,356
0
5,000,000
10,000,000
15,000,000
20,000,000
25,000,000
1980 1990 1995
D.K.I. Jakarta
Bogor
Bekasi
Tangerang
Jabotabek Total
人口
JA(karta)BO(gor)TA(ngerang)BEK(asi)metropolitan area
Firman, 1998
D.(Daerah) K.(Khurus) I.(Ibukota) Jakarta- 1990 -
1.5 - 2.0
2.0 - 4.0
1.0 - 1.5
Population Employment RatioDaerah Khurus Ibukota (DKI) Jakarta
adapted from Kenworthy and Laube 1999
401.9
170.6
438.6
Activity Ratio (Population+# of Jobs)/AreaDaerah Khurus Ibukota (DKI) Jakarta
adopted from Kenworthy and Laube 1999
Stated Preference Survey
• Household based survey• Based on three hypothetical city forms
– Transit oriented development– Compact city– Metropolitan suburban setting
• Household makes the decision– The place of residence
• Transit oriented development• Compact city• Metropolitan suburban setting
– Commute trip mode• Private car• Bus• Train
Sample Characteristics• 303 Surveyed households
• 4 instances of choices on residence and commute mode
– (data points 303X4=1212)
• Total number of individuals in households=1536
• Average household size=5.07 (s=1.93)
• Autumn, 2003
DKI Jakarta
Bekasi
Bogor
Tangerang
# of Households Surveyed
150
140
130
120
110
100
90
80
70
60
50
40
30
20
10
0
Tangerang Region
Tangerang Mun.
Depok Mun.
Bogor Region Count
Bogor Mun.
Bekasi Region
Bekasi Mun.
Central Jakarta
West Jakarta
South Jakarta
East Jakarta
North Jakarta
Depok Municipality
Jakarta West
Jakarta Central
Jakarta South
Jakarta East
Jakarta North
Bekasi Semi-urban
Bogor Semi-urban
Tangerang Semi-urban
Bekasi Municipality
Bogor Municipality
Tangerang Mun.
Average # of Years of Residence
30.025.020.015.010.05.00.0
sample average
Depok Municipality
Jakarta West
Jakarta Central
Jakarta South
Jakarta East
Jakarta North
Bekasi Semi-urban
Bogor Semi-urban
Tangerang Semi-urban
Bekasi Municipality
Bogor Municipality
Tangerang Mun.
Average household size by sub-districts.
5.85.65.45.25.04.84.64.44.24.0
sample average
Sample Characteristics
Own House
Rented House
Company House
Count
300250200150100500
26
273
Sample Characteristics
North Jakarta
East Jakarta
South Jakarta
West Jakarta
Central Jakarta
Bekasi Municipality
Bekasi Region
Bogor Municipality
Bogor Region
Depok Municipality
Tangerang Municipali
Tangerang Region
%
100
90
80
70
60
50
40
30
20
10
0
Dist to Market = d
d > 2km
1km< d < 2km
500mt< d < 1km
d < 500mt
North Jakarta
East Jakarta
South Jakarta
West Jakarta
Central Jakarta
Bekasi Municipality
Bekasi Region
Bogor Municipality
Bogor Region
Depok Municipality
Tangerang Municipali
Tangerang Region
%
100
90
80
70
60
50
40
30
20
10
0
Dist to Restaurants
d > 2km
1km < d < 2km
500mt < d < 1km
500mt < d
North Jakarta
East Jakarta
South Jakarta
West Jakarta
Central Jakarta
Bekasi Municipality
Bekasi Region
Bogor Municipality
Bogor Region
Depok Municipality
Tangerang Municipali
Tangerang Region
%
100
90
80
70
60
50
40
30
20
10
0
Dist to Parks
d > 2km
1km < d < 2km
500mt < d < 1km
d < 500mt
North Jakarta
East Jakarta
South Jakarta
West Jakarta
Central Jakarta
Bekasi Municipality
Bekasi Region
Bogor Municipality
Bogor Region
Depok Municipality
Tangerang Municipali
Tangerang Region
%
100
90
80
70
60
50
40
30
20
10
0
D to Department St.
d > 2km
1km < d < 2km
500mt < d < 1km
d < 500mt
Distance to markets Distance to restaurants
Distance to parks Distance to department stores
Simultaneous Residence and Commute Mode Choice-survey design
Transit Oriented City Suburban in a Metropolitan Area
Compact City
Taman=parkBelanja= shopping
Simultaneous Residence and Commute Mode Choice-survey design
0
20
40
60
80
100
120
140Private Car-Time
Bus-TimeRail-Time
Transit Oriented City
Compact City
MetropolitanSuburban Setting
0
10000
20000
30000
40000Private Car-Cost
Bus-CostRail-Cost
Transit Oriented City
Compact City
MetropolitanSuburban Setting
0
0.5
1
1.5
2
2.5
3Park
Shop-restaurant
Department storeBus stop
Rail stop
Transit Oriented City
Compact City
MetropolitanSuburban Setting
Responses (uncontrolled)
TOD-Car
TOD-Bus
TOD-Rail
Compact City-Car
Compact City-Bus
Compact City-Rail
Suburban-Car
Suburban-Bus
Suburban-Rail
(X100) % of Choices (1st Setting)
.3.2.10.0
TOD-Car
TOD-Bus
TOD-Rail
Compact City-Car
Compact City-Bus
Compact City-Rail
Suburban-Car
Suburban-Bus
Suburban-Rail
(X100) % of Choices (2nd Setting)
.3.2.10.0
TOD-Car
TOD-Bus
TOD-Rail
Compact City-Car
Compact City-Bus
Compact City-Rail
Suburban-Car
Suburban-Bus
Suburban-Rail
(X100) % of Choices (3rd Setting)
.3.2.10.0
TODCCMS
CCMSTOD
MSTODCC
TODMSCC
TOD= Transit Oriented Development; CC= Compact City; MS= Metropolitan Suburban
TOD-Car
TOD-Bus
TOD-Rail
Compact City-Car
Compact City-Bus
Compact City-Rail
Suburban-Car
Suburban-Bus
Suburban-Rail
(X100) % of Choices (4th Setting)
.3.2.10.0
Responses (controlled by super districts-pooled)
DKI Jakarta
TOD-Car
TOD-Bus
TOD-Rail
Compact City-Car
Compact City-Bus
Compact City-Rail
Suburban-Car
Suburban-Bus
Suburban-Rail
Sum
140.0120.0100.080.060.040.020.00.0
Bekasi
TOD-Car
TOD-Bus
TOD-Rail
Compact City-Car
Compact City-Bus
Compact City-Rail
Suburban-Car
Suburban-Bus
Suburban-Rail
Sum
60.050.040.030.020.010.00.0
Bogor
TOD-Car
TOD-Bus
TOD-Rail
Compact City-Car
Compact City-Bus
Compact City-Rail
Suburban-Car
Suburban-Bus
Suburban-Rail
Sum
70.060.050.040.030.020.010.00.0
Tangerang
TOD-Car
TOD-Bus
TOD-Rail
Compact City-Car
Compact City-Bus
Compact City-Rail
Suburban-Car
Suburban-Bus
Suburban-Rail
Sum
50.040.030.020.010.0
Initial definition of DC models
• Models describe individual behaviour choosing a best option among discretealternatives.
• Decision maker – an individual person/a group of persons
• Alternatives– car, bus and railway
• Attributes– =attractiveness: travel cost, time
• Decision rule– dominance, lexicographic, utility
• Dependent variable: unobserved probability between 0 and 1
• Observation: individual choices which are either 0 or 1– Depart/arrive at a given time or not– Visit on a given destination or not– Use a given travel mode or not– Choose a given route or not
DC models=disaggregate models
1
1011 9 8
67
5432
• Individual behaviour theory
• 11 samples of individual travelers
• Common parameters over homogenous groups of individuals
β
Individual models
• Individual behaviour theory
• 5 samples from repetitive observations of travelers
• 11 parameters for 11 persons
βn1
1011 9 8
67
5432
Advantages
• calibrated by maximum likelihood estimation (MLE) rather than standard curve fitting (least squares)
• more stable (transferable) in time and space
• more efficient in data collection
• avoid ‘ecological correlation’
• more flexible representation of policy variables
Random utility theory
• Individuals belong to a homogeneous population
• Individuals act rationally and possess perfect information
i.e. They always select the option which maximizes their net personal utility subject to legal, social, physical and constraints.
• Individual’s choice set is predetermined
utility function
utility systematic error= +
systematic
error
observable variableseg.Individual SE characteristics
LOSs of alternatives constraint (omitted variables)
unobservable variableseg.Individual idiosyncrasies, taste, attitude,
habit, principle measurement errors of observable variables
Advanced Econometric Model
• Mixed (Random coefficients) Logit Model– Earlier applications by Boyd and Mellman (1980),
Cardell and Dunbar (1980) on automobile demand,– Customer-level data, such as Train et al. (1987) and
Ben-Akiva et al. (1993),– Lately, Bhat (1998) and Brownstone and Train (1999)
on cross-sectional data, and Erdem (1996), Revelt and Train (1998), and Bhat (2000) on panel data,
– A very general introduction by Train (1999),
– Does not show IIA property
– (but) Requires simulation
Why Mixed Logit Model?
1. Mixed logit is a highly flexible model that can approximate any random utility model (McFadden and Train, 2000),
2. Mixed Logit model accounts for Independence from Irrelevant Alternatives (IIA) shortcoming of the traditional (multinomial) Logit model,
3. Mixed Logit Model accounts for individual heterogeneity (allows controlling random taste variation),
4. (Once simulation techniques are taken for granted ! ) simple extension to panel data estimation (allows correlation in unobserved factors over Time).
Mathematical Specification
ninini VU ε+=
( )∑
′
′
=
j
nite
eL
njt
nit
xβ
xβ
nβ
( ) ( )∏=
=T
tnitn LR
1nn ββ
( ) ( ) ( ) nnn dβθββθ ∗∗ ∫= fRP nn
( ) ( )( )∑= n nP θθ lnl
Utility is comprised of observed and unobserved components (individual n and alternative i)
The unobserved component, i.e., distributed as IID Gumbel lead us to Logit model (individual n, alternative i and time t)
Similar choices made on multiple time points are multiplied with each other. (individual n)
The paramaters β are assumed to be distributed IID based on parameter vector Θ. [We choose:
The loglikelihood function.
Estimation
{random
random
1
ticdeterminis
11ni
K
niknkk
V
K
nikkini
K
niknkini xsxxU
ni
εσμαεβα +++=++= ∑∑∑4434421
44 844 76
44 344 21
∑∑+∑+
∑+∑+
=
j
xsx
xsx
nit K
njktnkk
K
njktkqj
K
niktnkk
K
niktkqi
e
eL
11
11
σμα
σμα
∏∑
∑+
∑+
=t
j
xsV
xsV
n K
njktnkknjt
K
niktnkknit
e
eR
1
1
σ
σ
( ) ( ) ( ) ( )nKnn
s
s
s
s
s
s t
j
xsV
xsV
n sdsdsd
e
eP
n
n
n
n
nK
nK
K
njktnkknjt
K
niktnkknit
Φ⋅⋅⋅ΦΦ⋅⋅⋅= ∫ ∫ ∫ ∏∑
∞=
−∞=
∞=
−∞=
∞=
−∞= ∑+
∑+
∗∗21
1
1
2
2 1
1
,σ
σ
σμ
( ) ( ) ( ) ( )∑ ∫ ∫ ∫ ∏∑
Φ⋅⋅⋅ΦΦ⋅⋅⋅=∞=
−∞=
∞=
−∞=
∞=
−∞= ∑+
∑+
nnKnn
s
s
s
s
s
s t
j
xsV
xsV
sdsdsd
e
en
n
n
n
nK
nK
K
njktnkknjt
K
niktnkknit
21
1
1
2
2 1
1
log,σ
σ
σμl
nkβ ( )kkN σμ ,~
nkkknk sσμβ +=
Estimation Results
0.008(0.05)-0.02(0.002)# of Household Members (relative to transit oriented development, car option i.e., the coefficient of this alternative is fixed to 0)
0.02(0.07)-0.03(0.01)Number of Four Wheeled Vehicles (relative to transit oriented development, car option i.e., the coefficient of this alternative is fixed to 0)
0.01(0.02)-0.01(0.01)Income (relative to transit oriented development, car option, i.e., the coefficient of this alternative is fixed to 0)
0.09(0.02)-0.02(0.01)Distance to Rail Station (relevant for Transit Oriented Development)
0.04(0.02)-0.02(0.007)Distance to Supermarkets (relevant for Transit Oriented Development and Compact City Development)
0.06(0.01)-0.02(0.01)Distance to Restaurants and Shops (relevant for Transit Oriented Development and Compact City Development)
0.04(0.02)-0.01(0.007)Distance to Park, Recreation etc. activities (relevant for Transit Oriented Development)
0.05(0.02)-0.02(0.009)Distance to CBD (relative to Transit Oriented Development)
0.02(0.06)-0.00 (0.00)Dummy Variable Transfers for Bus (relevant for Transit Oriented Development)
0.08(0.02)0.02 (0.01)Travel Time by mode (with respect to car)
0.05 (0.02)-0.04 (0.01)Travel Cost by mode (with respect to car)
--0.07 (0.01)Constant (with respect to Transit Oriented Development and Car)
Standard DeviationMeanVariable
Log Likelihood with only constant term=-2622.71Log Likelihood at convergence full model=-2577.45With respect to Log-likelihood Ratio Test (with 22 degrees of freedom), the model issignificant at a value lower than 0.005.Sample Size=303
ConclusionsIncrease of distance from relevant land uses affect residential choice such that if residential location is far away from relevant land uses individuals prefer to live in suburban setting. But if they are not far away, then the use of the public transit and its combination with accessibility to different land uses make household choose TOD and compact city environments.Travel cost when increased with respect to private car costs, household turn to private cars as their commuting mode of travel,But surprisingly travel time increase make people use transit option, which can only be explained by other psychological factors that are not captured by thecurrent model, e.g., the frustration that might be caused by staying behind the steering wheel and wait in the midst of the congested roads. This is supportive for an argument that automobile is the best option for a moderate ranges in time (also take this in distance)…
Further ResearchIn this study we have used simulation based on the normality of the parameter values; further research is needed on distributional assumptions of theparameters,The estimation is solely based on the stated intentions, the parameter values have to be weighted by the inclusion of the revealed preferences of the individuals. This requires the inclusion of the residential area properties of the households along with their actual living environments. The spatial separation of the sample is required with careful examination of the urban amenities.
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