RE-EXAMINING THE BUILT ENVIRONMENT-TRAVEL BEHAVIOR CONNECTION:
A CASE STUDY OF JAPANESE CITIES

都市の物的環境と交通行動の因果関係に関する研究—日本の諸都市を事例として—

Giancarlos Troncoso Parady
トロンコソ

パラディ

ジアンカルロス東京大学大学院工学系研究科都市工学専攻都市交通研究室

RATIONALE• Paradigm shift in the conceptualization of what constitutes good urban
development: New Urbanism, Smart Growth, Compact cities• In the case of Japan(Kaido, 2001):• Ministry of Construction (Now MLIT) shifts from suburban development toexisting urban areas in the late 1990’s• Cities like Aomori, Kobe, Kanazawa, Fukui, Toyama incorporated Compact
City ideas in their master plans• The premise: mixed-use, high density developments can significantly reduce
automobile dependency and promote the use of alternative modes. Moreaccessible, livable and inclusive neighborhoods and cities.

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RATIONALE• The underlying assumption: The built environment exerts a strong enoughinfluence on individuals and households to effectively change their travel
behavior, that is, A non-spurious, causal relation.

Built environment

Travel Behavior

RESEARCH QUESTIONS•Is the effect of the built environment on travel behavior a causal effect?

•

If so, what is the nature of this effect?

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RE-EXAMINING THE BUILT ENVIRONMENT-TRAVEL BEHAVIOR CONNECTION:
A CASE STUDY OF JAPANESE CITIES
都市の物的環境と交通行動の因果関係に関する研究—日本の諸都市を事例として—

SECTION 2

LITERATURE REVIEW東京大学大学院工学系研究科都市工学専攻都市交通研究室

CONDITIONS TO ESTABLISH A CAUSAL RELATION(Mokhtarian and Cao,2008):• Existence of a statistically significant association• Non-spuriousness of this association• Time precedence of the effect of interest

RESIDENTIAL

MORE GENERALLY:(Meyer,1994):

SELF-SELECTION

Internal validity threats:• Omitted variables• Trends in outcomes• Misspecified variances• Mismeasurement• Political economy• Simultaneity• Selection• Attrition• Omitted interaction

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THE SELF-SELECTION PROBLEM IN ITS SIMPLEST FORM
• Consider as the treatment 𝑇 𝐴 of interest a vector of built environment variables• Consider as the (continuous) outcome variable 𝑓𝑖 𝑇 𝐴 , a measurement of thetravel behavior of interest• Consider a naïve hypothesis:fi T A = α + T A β + ε• The treatment of interest is a function of residential location, a non-random

process.
• Residential self-selection bias stems from a correlation between thetreatment assignment and observed outcomes. In other words acorrelation with the error term.
RE-EXAMINING THE BUILT ENVIRONMENT-TRAVEL BEHAVIOR CONNECTION:
A CASE STUDY OF JAPANESE CITIES41STUDIES ADDRESSING THE SELF-SELECTION PROBLEM
Cross-sectional approaches (25)
•
•
•
•
•

Statistical control (8)Instrumental variables (3)Sample selection and propensity score (6)Discrete choice joint modeling (5)Structural equation models (SEM) (3)Quasi-longitudinal approaches (4)• Same as above (4; including all approaches)

Longitudinal approaches (4)• Pooled OLS (1)• First differenced models and Fixed effect models (2*)• Difference in differences models (1*)*Not directly related to planning or transportation
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GENERAL FINDINGS FROM THE LITERATURE [IN A NUTSHELL]• In general there is a rather well established statistical association between built
environment and travel behavior.
• Higher population density & mixed land use with less car travel• Higher population density & mixed land use with more travel by alternativemodes• Some studies also suggest the effect of the built environment might notsignificant• The effect of the built environment differs given activity types.
•

Strongest effects observed for non-work trips (with the exception ofleisure activities)• Factors associated with travel attitudes, personality and lifestyle might mitigate
self-selection. Habits might also play a key role in explaining behavior.
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RE-EXAMINING THE BUILT ENVIRONMENT-TRAVEL BEHAVIOR CONNECTION:
A CASE STUDY OF JAPANESE CITIES
都市の物的環境と交通行動の因果関係に関する研究—日本の諸都市を事例として—

SECTION 3

CROSS-SECTIONAL ANALYSIS AND THE
CAUSALITY PROBLEM

東京大学大学院工学系研究科都市工学専攻都市交通研究室

RE-EXAMINING THE BUILT ENVIRONMENT-TRAVEL BEHAVIOR CONNECTION:
A CASE STUDY OF JAPANESE CITIES
都市の物的環境と交通行動の因果関係に関する研究—日本の諸都市を事例として—

SECTION OBJECTIVES①

Understand the conditions for establishing causality
using cross-sectional data [and its limitations]②Introduction of the propensity score as an approachto overcome these limitations [binary treatment case]③

Introduction of a generalization of the propensity score
To continuous treatments

東京大学大学院工学系研究科都市工学専攻都市交通研究室

Understanding the conditions for establishing causality using cross-sectional data

① [and its limitations]• To motivate the problem at hand, first consider a naïve hypothesis of therelationship between the built environment and travel behavior.
Built environment

Travel Behavior

(z)

(Y)

• Further assume that the built environment treatment variable of interest isbinary (i.e. urban vs suburban), following Rubin (1977), we can define:
Average treatment effectATE →E[y1i − Y0i = E[Y1i − E[Y0iAverage treatment on treated

ATT →E Y1i − Y0i zi = 1 = E Y1i zi = 1 − E[Y0i |zi = 1
𝐄 𝐘𝟎 𝒛 = 𝟏 however, is not observed. It’s a COUNTERFACTUAL!

Where

1 Treated (Urban)𝑧=
0 Untreated (Suburban)

𝑌1𝑖 = Outcome when treated
𝑌0𝑖 = Outcome when untreated

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Understanding the conditions for establishing causality using cross-sectional data

① [and its limitations]• However, to the extent that the treatment is truly randomly allocated,
treatment is independent from outcomes (Y0i , Y1i ⊥ zi ) thus
This we CANNOT observe

𝐸 𝑌0𝑖 𝑧𝑖 = 1 = 𝐸 𝑌0𝑖 𝑧𝑖 = 0
This we can observe
so we can substitute and get

E Y1i − Y0i zi = 1 = E Y1i zi = 1 − E Y0i zi = 0
Where the Average Treatment Effect (ATE) equals the Average Treatment on Treated (ATT).

The problem with this naïve estimator that a truly random experiment ofresidential location is virtually impossible.

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Understanding the conditions for establishing causality using cross-sectional data

① [and its limitations]• In the absence of randomization the estimated coefficients are biased andinconsistent. This can be easily seen by restating the ATT equations as

E Y1i − Y0i zi = 1= E Y1i zi = 1 − E Y0i zi = 1 + (𝐄 𝐘𝟎𝐢 𝐳𝐢 = 𝟏 − 𝐄 𝐘𝟎𝐢 𝐳𝐢 = 𝟎 )Selection Bias
Under randomization Y0i , Y1i ⊥ zi ; Bias = 0

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Understanding the conditions for establishing causality using cross-sectional data

① [and its limitations]• Consider now, under the conditional independence assumption (CIA), that thetreatment of interest is independent from the observed outcomes given a set ofcovariates X (Y0i , Y1i ⊥ zi |𝐗 i ).
• Angrist (1998) shows that by matching on all values of X, and by the law of
iterated expectations, in the case of discrete covariates, the Average Treatment
on Treated (ATT) can be estimated as:

𝐸 𝑌1𝑖 − 𝑌0𝑖 𝑧𝑖 = 1 = 𝐸{𝐸 𝑌1𝑖 − 𝑌0𝑖 𝑧𝑖 = 1, 𝑿𝒊 |𝑧𝑖 = 1}
(By CIA)= 𝐸{𝐸 𝑌1𝑖 𝑧𝑖 = 1, 𝑿𝒊 − 𝐸 𝑌0𝑖 𝑧𝑖 = 0, 𝑿𝒊 |𝑧𝑖 = 1]}=𝑥 𝛽𝑥 𝑃 𝑧𝑖 = 1 𝑿𝒊 = 𝑥 𝑃 𝑿𝒊 = 𝑥
𝑥 𝑃 𝑧𝑖 = 1 𝑿𝒊 = 𝑥 𝑃 𝑿𝒊 = 𝑥

Where 𝛽𝑥 = 𝐸 𝑌1𝑖 𝑧𝑖 = 1, 𝑿 = 𝑥 − 𝐸 𝑌0𝑖 𝑧𝑖 = 0, 𝑿 = 𝑥 . That is, hedifference of the mean between treated an untreated for any given value of x.
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Understanding the conditions for establishing causality using cross-sectional data

① [and its limitations]• Empirical evidence suggests that violations to the linearity condition might
result in strong estimation bias in OLS case (Imai & van Dyk, 2004). which mightfavor the use of matching and stratification approaches to estimate causal
effects in the absence of randomization.
• However, as the number of covariates increases, the number of sub-classes
increases exponentially, rendering many subclasses empty, or with either nocontrol or treated units, making impossible to draw estimates for the whole
population (Corchran, 1965; Rosenbaum & Rubin, 1984).
• To address that problem Rosenbaum and Rubin (1983) proposed a scalarfunction that summarizes the information necessary to balance the covariate
distribution, this function is called the propensity score.

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②

Introduction of the propensity score as an approach to overcome these limitations[binary treatment case]

THE PROPENSITY SCORE APPROACH• The propensity score, defined as the conditional probability of treatment given
observed covariates, was proposed by Rosenbaum and Rubin (1984) as a way toremove bias due to observed covariates in binary treatments:

THEORETICAL BASIS:
Following Rosenbaum and Rubin (1983)① “The propensity score is a balancing score”: conditional on P(Xi), thedistribution of Xi and z are independent

P r{ zi |𝐗 𝐢 , P 𝐗 𝐢 } = Pr{zi |P(𝐗 𝐢 )}The propensity score makes inherently different groups comparable

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②

Introduction of the propensity score as an approach to overcome these limitations[binary treatment case]

THE PROPENSITY SCORE APPROACH② “If treatment assignment is strongly ignorable given x, it is strongly
ignorable given any balancing score”
strong ignorability of treatment implies that outcomes (Y0i,Y1i) are
independent from treatment assignment given P(Xi). In addition, every unit
has a chance to receive either treatment state

P 𝑌0𝑖 , Y1i zi , P 𝐗 𝐢= P 𝑌0𝑖 , Y1i P 𝐗 𝐢

0 < P(zi = 1|P 𝐗 𝐢 < 1③ “At any value of a balancing score, the difference between the treatment
and control means is an unbiased estimate of the average treatment effectat the value of the balancing score if treatment assignment is strongly
ignorable”

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②

Introduction of the propensity score as an approach to overcome these limitations[binary treatment case]

THE PROPENSITY SCORE APPROACH
P(X) ESTIMATIONThis function is not known but can be estimated from observed data, using
limited dependent variable models such as the logit model or probit model.

ATE ESTIMATION𝑁• MATCHING

ATEmatching = 𝑁1−1

𝑌1𝑖 −
𝑖=1

𝑊𝑁0𝑁1 𝑖, 𝑗 𝑌0𝑗𝑗∈ 𝑧=0

Where 𝑊𝑁0𝑁1 𝑖, 𝑗 is a weight function (several estimators exist)𝑁• WEIGHTING

ATEweighting= 𝑁 −1𝑖=1

𝑌𝑖 ∙ 𝑧𝑖 𝑌𝑖 ∙ 1 − 𝑧𝑖−
𝑃 𝑿𝒊
1 − 𝑃 𝑿𝒊𝐽

• STRATIFYING

𝐴𝑇𝐸𝑠𝑡𝑟𝑎𝑡𝑖𝑓𝑖𝑐𝑎𝑡𝑖𝑜𝑛 =𝑌𝑗1 − 𝑌𝑗0 ∙ 𝑊𝑗
𝑗=1

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②

Introduction of the propensity score as an approach to overcome these limitations[binary treatment case]

THE PROPENSITY SCORE APPROACH IN PLANNING STUDIES• Several studies in the transport literature have implemented propensity score
methodologies as a way to address the self-selection issue. :• Boer et al(2007), Cao et al (2010), Cao (2010)• Nevertheless, polarizing the built environment to a binary treatment (usuallyurban vs. suburban) is a rather strong assumption.
• Ignores the spectrum of variability in terms of how “urban” or how “suburban”
a neighborhood might be .
• Binary approach is adequate in the case of neighborhood to neighborhoodcomparisons as previous studies demonstrated but not practical for large scale
analysis at the city level.

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③ Introduction of a generalization of the propensity score to continuous treatments
GENERALIZING THE PROPENSITY SCORE TO CONTINUOUS
TREATMENTS• Following a propensity score generalization under continuous treatment
regimes, proposed by Imai and Van Dyk (2004):• The distribution of an arbitrary treatment TA given a vector of covariates X,
is modeled as T A |𝐗~N(𝐗 ⊺ 𝛃, σ2 )• The propensity score function is solely characterized by the scalar𝛉𝛙 𝐗 = 𝐗 ⊺ 𝛃 (The conditional mean function, estimated via OLS)
• The propensity score serves as a balancing score:
Pr{T A |𝐗 , P 𝐗 } = Pr{T A |P 𝐗 }• Strong ignorability of treatment:

𝑃𝑟{𝑌(𝑡 𝑃 )| 𝑇 𝐴 , 𝑃(𝑿)} = 𝑃𝑟{𝑌(𝑡 𝑃 )|𝑃 𝑿 }RE-EXAMINING THE BUILT ENVIRONMENT-TRAVEL BEHAVIOR CONNECTION:
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③ Introduction of a generalization of the propensity score to continuous treatments
GENERALIZING THE PROPENSITY SCORE TO CONTINUOUS
TREATMENTS• By averaging Pr{Y(t P )|P 𝐗 } over the distribution of P(X), the distribution of
the outcome of interest can be obtained as

Pr 𝑌 𝑡 𝑃=

Pr Y t P T A = t P , θ Pr θ dθ .

Thus, the distribution of Y(tP) can be approximated by stratifying on the propensity
score estimate 𝛉

Pr 𝑌 𝑡 𝑃

J≈
j=1

Prϕ𝑗 Y t P T A = t P ∙ Wj

where ϕj is the within strata estimate of unknown parameter 𝛟 in strata j, and Wj
is the relative weight of strata j

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③ Introduction of a generalization of the propensity score to continuous treatments
GENERALIZING THE PROPENSITY SCORE TO CONTINUOUS
TREATMENTS

The object of interest (ATE) is thus:J

ϕj Y t P T A = t P , 𝐗 ∙ Wj

𝛟=
j=1

Where ϕj Y t P T A = t P , 𝐗 is the within strata estimated treatment parameter

Covariates X are included to control for variability of θ within strata.

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HYPOTHESIZED RELATION OF TREATMENT AND COVARIATESLife stage

Lifestyle

Built environment at
previous locations

Attitudes

HabitsSocio-demographics

Main mode of transport
during previous life-stages

Work location
Residential Location choice:
Built environment

Subsistence activities
Maintenance activities

Change in car ownership

Discretionary activities
Travel behavior

Social network of ego

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RE-EXAMINING THE BUILT ENVIRONMENT-TRAVEL BEHAVIOR CONNECTION:
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都市の物的環境と交通行動の因果関係に関する研究—日本の諸都市を事例として—

SECTION 4

TREATMENT VARIABLE ESTIMATION: A LATENT CONSTRUCTOF URBANIZATIONTHE CASE OF FUKUOKA CITY東京大学大学院工学系研究科都市工学専攻都市交通研究室

ESTIMATING THE TREATMENT OF INTEREST
How to define the continuous treatment?
Urbanization level operationalized as a latent variable
Estimated via Confirmatory Factor Analysis• Allows for calculation of goodness of fit statistics
Addressing the MAUP problem:• Use of a regular aggregation scheme (300m diameter hexagon tessellation)• Sensitivity analysis of scale of spatial unit
Hexagon diameter: 100m, 300m*, 600m, and 1000m
Definition of indicator variables• Guided by urban economics and planning theory• Urbanization level is conceptualized as a latent construct that accounts forthe observed spatial distribution of the city in terms of supply of goods andservices , land use intensity, transport mobility and land prices.
Model validation via a fixed-weight partial cross-validation test
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1COMMERCIAL KERNEL DENSITY
1f x = nh21
nK
(𝐱 − 𝐗 i )i=1h

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POPULATION DENSITY

RE-EXAMINING THE BUILT ENVIRONMENT-TRAVEL BEHAVIOR CONNECTION:
A CASE STUDY OF JAPANESE CITIES221WEEKDAY TRANSIT FREQUENCY

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LAND PRICESRE-EXAMINING THE BUILT ENVIRONMENT-TRAVEL BEHAVIOR CONNECTION:
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MODEL RESULTS
0.141

0.362

0.362

0.439

Log ofcommercial
Kernel density

Log ofpopulation
densityLog ofweekdaytransitfrequency

Log of land
prices0.927(0.859)0.799(0.638)0.799(0.638)0.749(0.561)URBANIZATIONLEVEL

Chi-Square test of model fit (d.f.) 51.38 (2); p-value: 0.000; RMSEA (C.I. 90%) : 0.037 (0.028, 0.046)
Probability RMSEA ≤.05 : 0.994; CFI: 0.999; TLI: 0.996; SRMR: 0.005
Value in parenthesis is total explained variance by the factor.
All parameter estimates are significant at the p < 0.01 level.
Due to multivariate non-normality, estimator is Robust Maximum Likelihood.

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MODEL RESULTS

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RE-EXAMINING THE BUILT ENVIRONMENT-TRAVEL BEHAVIOR CONNECTION:
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都市の物的環境と交通行動の因果関係に関する研究—日本の諸都市を事例として—

SECTION 5

IMPLEMENTATION AND VALIDATION OF THE PROPENSITY SCORE:
EMPIRICAL APPLICATION IN FUKUOKA CITY東京大学大学院工学系研究科都市工学専攻都市交通研究室

RE-EXAMINING THE BUILT ENVIRONMENT-TRAVEL BEHAVIOR CONNECTION:
A CASE STUDY OF JAPANESE CITIES
都市の物的環境と交通行動の因果関係に関する研究—日本の諸都市を事例として—

SECTION OBJECTIVES①

Briefly summarize survey characteristics②

Measure the performance of the propensity scoreapproachestimates against ordinary least squares
estimates through Monte Carlo simulation③

Estimate causal effects of the built environment on travel
behavior using empirical data

東京大学大学院工学系研究科都市工学専攻都市交通研究室

① Briefly summarize survey characteristics
GENERAL CHARACTERISTICS OF THE SURVEY• Survey target: Residents in Fukuoka city, Over 20 years old• Sampling criteria: Stratified random sampling. Stratified on household
structure (See Table 3 for details on sampling strata and population
distribution)• Survey Medium: Online survey• Survey period: 2013年12月14日（土） ～ 2013年12月16日（月）SAMPLE HOUSEHOLD STRUCTURE DISTRIBUTION
Household type
Frequency
Single household314
Of which: Young (age 20-64)
302
Of which: Elder (age 65 and over)
12
Couples only101
Of which: Young (age 20-64)
60
Of which: Elder (age 65 and over)41
Nuclear household
201(including single parent households)
Three generation household & others
40
Total
656
Population data source: 2010 population census of Japan

Sample percentage
47.9%
46.0%
1.8%
15.4%
9.1%
6.3%

Population percentage
47.7%
39.2%
8.5%
15.1%
8.7%
6.5%

30.6%

31.3%

6.1%
100%

6.0%
100%

1
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② Measure the performance of the propensity score approach via Monte Carlo simulation
METHODOLOGICAL COMPARISON THROUGH SIMULATIONGOAL: Use the survey data to simulate outcomes and compare the performance of
OLS and propensity score stratification estimates in terms of bias reduction.
• Monte Carlo simulation (1000 iterations)• Exponential functions were used to specify two data generating processes(DGP): (Following Thomas and Rubin (2000) and Imai and van Dyk (2004))• Additive model of the form:

Yi = 𝛿𝑖 𝑇𝑖𝐴 +𝐾
𝑚𝑘 𝑿𝑖𝑘𝝀𝑒
𝑘=1 𝑘• Multiplicative model of the form:
𝐾𝐴
Yi = 𝛿𝑖 𝑇𝑖 + 𝑒 𝑘=1 𝜆𝑘𝑿𝑖𝑘
𝛿𝑖 = treatment effect
𝑇𝑖𝐴 =assigned treatment

Λk = vector of simulated coefficients N~(0,σ)
Xik = vector of observed covariates in data

𝑚=

+1
−1

(of k dimensions)
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② Measure the performance of the propensity score approach via Monte Carlo simulation
METHODOLOGICAL COMPARISON THROUGH SIMULATION• Specifying the variance of Λk to control the level of linearity of the simulations• Highly linear

R2 ≈ .95• Moderately linear

R2 ≈ .85• Moderately non-linear

R2 ≈ .75• Additive model of the form:

Yi = 𝛿𝑖 𝑇𝑖𝐴 +𝐾
𝑚𝑘 𝑿𝑖𝑘𝝀𝑒
𝑘=1 𝑘• Multiplicative model of the form:
𝐾𝐴
Yi = 𝛿𝑖 𝑇𝑖 + 𝑒 𝑘=1 𝜆𝑘𝑿𝑖𝑘
𝛿𝑖 = treatment effect
𝑇𝑖𝐴 =assigned treatment

Λk = vector of simulated coefficients (N~(0,σ)
Xik = vector of observed covariates in data

𝑚=

+1
−1

(of k dimensions)
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② Measure the performance of the propensity score approach via Monte Carlo simulation
METHODOLOGICAL COMPARISON THROUGH SIMULATION• Constant treatment models:•

Assumes effect is the same for all individuals• Variable treatment defined as a function of a covariate:𝛿mi = 10−1 (10 − H) δm
Where H is the car use habit index as measured by the Response Frequency Index
method, and βm is equivalent to the constant treatment parameter for mode m.
Under this function, the treatment effect tends to zero as the car use habit
increases. This is however an arbitrary function in order to illustrate the variable
treatment case, but another function might have been used as well.

Total simulated models: 144
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1PROPENSITY SCORE OLS ESTIMATION RESULTS - FUKUOKA
491
19
472
134.14

S.E. of e
R-squareAdj. R-square
F test (p-value)

Variable
Constant
Household characteristics
Household size
Number of children
Number of cars
Driver to car ratio
Number of workers
High Income
House is company/school lodge
Job located in city center
Lifetime events motivating relocation
School(Start, change)
Wedding
Empty nest
Job promotion
Individual characteristics
University degree holder
Attitudes and habits
Attitude: Car lover
Attitude: Urbanite
Car use Habit
Life ratio using transit
Log of weighted population density at previous
locationsβ1.505

0.53310.25
0.22
8.66 (.0000)
S.E.
t-Stat0.337
4.467-0.0870.110-0.164
0.249
0.049
0.141
-0.193
0.072

0.039
0.053
0.060
0.100
0.037
0.066
0.132
0.048

-2.2192.079-2.726
2.477
1.339
2.144
-1.4651.487

0.132-0.1560.707-0.201

0.080
0.079
0.327
0.149

1.648
-1.9812.161-1.354

0.060

0.047

1.258

-0.0350.059-0.034
0.103

0.025
0.025
0.012
0.068

-1.3922.368-2.796
1.503

0.049

0.033

1.517

N
Parameters
d.f.
RSS

1

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PROPENSITY SCORE AS A BALANCING SCORE

8.0

8.0

4.0

4.0

Quantiles of t-statistics

Quantiles of t-statistics

STANDARD NORMAL QUANTILE PLOTS OF T-STATISTICS OF COVARIATES

0.0
-4.0
-8.0
-12.0

0.0
-4.0

-8.0
-12.0

-3.0

-2.0

-1.0

0.0

1.0

2.0

Standard normal quantiles(Without controlling for θ)

3.0

-3.0

-2.0

-1.0

0.0

1.0

2.0

3.0

Standard normal quantiles(Controlling for θ)

The propensity score serves as a balancing score:
Pr{T A |𝐗 , P 𝐗 } = Pr{T A |P 𝐗 }RE-EXAMINING THE BUILT ENVIRONMENT-TRAVEL BEHAVIOR CONNECTION:
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② Measure the performance of the propensity score approach via Monte Carlo simulation
MEASURING THE PERFORMANCE OF THE PROPENSITY SCORESTRATIFICATION AGAINST OLS• The performance of each model is compared against the OLS estimates,
measured in terms of absolute bias where:

1
𝐴𝐵𝑖𝑎𝑠 =
𝑅𝑅

( 𝛿 − 𝛿)𝑟=1

and mean squared error where:

1
𝑀𝑆𝐸 =
𝑅

𝑅

( 𝛿 − 𝛿)2
𝑟=1

where 𝛿 is the estimated treatment effect and R is the number of replications.
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② Measure the performance of the propensity score approach via Monte Carlo simulation
SIMULATED CHANGES IN ABSOLUTE BIAS AND MEAN SQUARED ERRORCOMPARED AGAINST THE OLS ESTIMATES- HOME-BASED
MAINTENANCE TRIPS BY CAR (CONSTANT TREATMENT)% Change in absolute bias
Additive models
Highly linear
Moderately linear
Moderately non-linear
Multiplicative models
Highly linear
Moderately linear
Moderately non-linear%Change in mean squared error
Additive models
Highly linear
Moderately linear
Moderately non-linear
Multiplicative models
Highly linear
Moderately linear
Moderately non-linear

3 strata
N.C.
A.C.

5 strata
N.C.
A.C.

7 strata
N.C.
A.C.

7.43%
9.94%
6.08%

-1.90%-2.82%-2.12%-1.89%
0.42%-3.12%-52.34%-51.25%-52.90%-15.24%-13.63%-16.03%-26.89%-27.67%-27.09%90.73%
54.19%-20.54%-11.06%22.33%
5.59%-41.93%-40.21%

2.09%-4.28%-34.58%-12.54%17.14%-17.68%6.65%-28.08%2.28%-10.91%

N.C.

A.C.

N.C.

A.C.

N.C.

A.C.

36.36%
44.43%
33.26%-5.17%-7.51%-5.13%

13.61%
20.41%
11.06%-73.88%-72.30%-74.36%-20.05%-15.76%-21.19%-47.31%-48.76%-47.31%384.55%137.92%19.32%-41.61%-45.90%-49.49%131.18%
9.11%
2.47%

-70.69%-82.97%-62.45%41.03%-4.41%-4.59%-49.44%-45.50%-51.94%

Positive values:
Bias increase
Relative to OLS
Negative values:
Bias decrease
Relative to OLS

N.C.: No covariates; A.C.: All Covariates
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② Measure the performance of the propensity score approach via Monte Carlo simulation
SIMULATED CHANGES IN ABSOLUTE BIAS AND MEAN SQUARED ERRORCOMPARED AGAINST THE OLS ESTIMATES- HOME-BASED
MAINTENANCE TRIPS BY NMM (CONSTANT TREATMENT)% Change in absolute bias
Additive models
Highly linear
Moderately linear
Moderately non-linear
Multiplicative models
Highly linear
Moderately linear
Moderately non-linear% Change in mean squared error
Additive models
Highly linear
Moderately linear
Moderately non-linear
Multiplicative models
Highly linear
Moderately linear
Moderately non-linear

3 strata
N.C.
A.C.

5 strata
N.C.
A.C.

7 strata
N.C.
A.C.

91.45%
42.77%
41.74%-20.17%-16.80%-3.95%

13.69%
3.80%
13.14%-45.48%-31.81%-26.27%-10.20%-7.11%
4.21%-44.82%-32.77%
1.93%

5.54%
2.65%

-1.76%-1.46%-3.63%-6.26%-53.11%-54.25%-16.29%-17.87%-26.78%-26.56%

9.66%-2.54%

0.13%-51.49%-13.93%-27.41%

N.C.

A.C.

N.C.

A.C.

N.C.

173.19%69.67%
61.30%-34.20%-42.41%-22.94%23.96%
4.01%
12.58%-73.37%-72.85%-60.43%-23.26%-13.35%1.35%-70.55%-68.13%-33.21%36.44%
28.86%
40.15%-5.62%-4.73%-6.19%

13.71%
7.41%
16.79%-73.80%-75.11%-73.16%-19.76%-23.02%-18.09%-47.59%-47.08%-47.94%

Positive values:
Bias increase
Relative to OLS
Negative values:
Bias decrease
Relative to OLS

A.C.

N.C.: No covariates; A.C.: All Covariates
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② Measure the performance of the propensity score approach via Monte Carlo simulation
SIMULATED CHANGES IN ABSOLUTE BIAS AND MEAN SQUARED ERRORCOMPARED AGAINST THE OLS ESTIMATES- HOME-BASED
MAINTENANCE TRIPS BY CAR (VARIABLE TREATMENT)
3 strata
Change in absolute bias
Additive models
Highly linear
Moderately linear
Moderately non-linear
Multiplicative models
Highly linear
Moderately linear

N.C.

5 strata

A.C.

7 strata

N.C.

A.C.

N.C.

A.C.

71.56%
43.31%
13.46%

17.37%
5.39%-0.57%-22.48%-5.28%-3.98%-11.00%-30.05%-48.47%-76.12%-34.15%-19.91%-47.57%-35.12%-28.47%83.80%
52.12%

4.16%-15.31%-10.38%-0.26%-27.72%-42.93%-49.54%-10.83%-50.47%-30.01%Moderately non-linear

24.86%-15.01%

7.74%-28.15%1.67%-16.06%

Change in mean squared error
Additive models
Highly linear
Moderately linear
Moderately non-linear
Multiplicative models
Highly linear
Moderately linear
Moderately non-linear

N.C.

A.C.

N.C.

A.C.

N.C.

A.C.

193.78%87.20%
36.40%

37.61%
6.09%-4.15%-39.72%
1.98%
9.90%

-20.98%-57.34%-73.25%-94.03%-38.92%-22.50%-72.42%-55.69%-47.78%385.66%128.22%27.17%-1.44%-40.64%-45.21%

6.43%
1.95%
1.31%

-51.12%-83.00%-62.79%-61.59%-17.77%-9.19%-74.62%-65.17%-57.75%

Positive values:
Bias increase
Relative to OLS
Negative values:
Bias decrease
Relative to OLS

N.C.: No covariates; A.C.: All Covariates
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② Measure the performance of the propensity score approach via Monte Carlo simulation
SIMULATED CHANGES IN ABSOLUTE BIAS AND MEAN SQUARED ERRORCOMPARED AGAINST THE OLS ESTIMATES- HOME-BASED
MAINTENANCE TRIPS BY NMM (VARIABLE TREATMENT)
3 strata

5 strata

7 strata

Change in absolute bias
Additive models
Highly linear
Moderately linear
Moderately non-linear
Multiplicative models
Highly linear
Moderately linear

N.C.

A.C.

N.C.

A.C.

N.C.

A.C.

71.74%
40.45%
12.27%

17.39%
4.83%-1.04%-22.46%-6.05%-3.37%-10.90%-31.68%-49.34%-76.19%-32.52%-18.75%-47.61%-33.98%-28.26%121.29%49.74%-15.90%-24.81%

16.92%
12.46%-33.12%-43.38%-2.26%-0.72%-45.30%-39.99%Moderately non-linear

55.90%-22.06%28.87%-32.75%17.65%-17.53%

N.C.

A.C.

N.C.

A.C.

N.C.

A.C.

194.43%80.91%
39.71%

37.67%
6.03%-4.94%-39.70%-0.14%
13.05%-20.80%-59.00%-72.74%-94.08%-39.30%-20.79%-72.48%-54.36%-48.04%210.75%152.05%209.94%-40.19%-41.89%-40.94%16.72%
45.23%
100.00%-71.60%-72.62%-64.68%-24.21%18.82%
80.73%-74.01%-61.61%-14.71%

Change in mean squared error
Additive models
Highly linear
Moderately linear
Moderately non-linear
Multiplicative models
Highly linear
Moderately linear
Moderately non-linear

Positive values:
Bias increase
Relative to OLS
Negative values:
Bias decrease
Relative to OLS

N.C.: No covariates; A.C.: All Covariates
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② Measure the performance of the propensity score approach via Monte Carlo simulation
SIMULATION RESULTS• The propensity score achieves:• Absolute bias reductions of up to 76% against the OLS estimates.
• MSE reductions up to 94% against the OLS estimates.
• Propensity score models with no covariates performed poorly, including
covariates is recommended.
• For moderate sample sizes (400-600) stratification on 5 strata is recommended• This is most likely due to sample size and multivariate distribution ofcovariates (Imbens and Wooldridge 2008)

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③ Estimate causal effects of the built environment on travel behavior using empirical data
MULTI-SCALE ANALYSIS OF EMPIRICAL DATA
Having demonstrated the bias reduction potential of the propensity scoreapproach, the method is applied to the Fukuoka survey dataset. In addition, amulti-scale analysis is conducted, largely motivated by the modifiable areal unit
problem (Fotheringham & Wong, 1991).
Different scales of treatment considered:1

f x = nh21
ni=1 K h (𝐱 − 𝐗 i )R=0.15km

R=1.5km

R=3.0km

Scale 1

Scale 2

Scale 3

Scale 4

(unweighted average)(unweighted average)(Kernel density function)

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③ Estimate causal effects of the built environment on travel behavior using empirical data
MULTI-SCALE ANALYSIS OF EMPIRICAL DATAMULTI-SCALE ANALYSIS OF URBANIZATION EFFECT ON HOME-BASED
MAINTENANCE TRIPS
Treatment scale
Model
Car tripfrequency
model
NMM tripfrequency
model

βt-Stat%diff.
βt-Stat%diff.

Scale 1OLS
5 Strata-0.201-0.200-4.7940.002

Scale 2OLS
5 Strata-0.145-0.217-3.191-5.020Scale 3OLS
5 Strata-0.127-0.178-2.477-4.106

Scale 4OLS
5 Strata-0.131-0.217-3.273-5.110

-0.1%
0.152
2.604
0.4%

50.0%
0.156
2.710
24.8%

39.5%
0.179
3.230
101.0%

65.7%
0.177
3.025
71.9%

0.151
2.595

0.125
1.924

0.089
1.215

0.103
1.746

1
Model

Scale 1OLS
7 Strata-0.201-0.196

Scale 2OLS
7 Strata-0.145-0.223

Scale 3OLS
7 Strata-0.127-0.205

Scale 4OLS
7 Strata-0.131-0.217-3.191-2.477-3.273Cartrip
frequency
model

βt-Stat%Δ-4.794

NMM tripfrequency
model

β

0.151
2.595

t-Stat%Δ-4.326
-2.4%
0.160
2.545
5.9%

0.125
1.924

-4.592
54.1%
0.181
2.989
45.3%

0.089
1.215

-4.220
61.0%
0.172
3.023
92.4%

0.103
1.746

-4.381
65.8%
0.187
3.245
81.7%

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③ Estimate causal effects of the built environment on travel behavior using empirical data
SUMMARY OF FINDINGS• Under the ignorability of treatment assumption a casual effect can be
estimated using cross-sectional data
• Findings suggest the existence of a mode substitution mechanism between car
and NMM for non-work trips given changes in urbanization, as measured by theurbanization level index.
• For the 5 strata – Scale 4 case one standard unit increase of urbanization on
average translates into:• 21% less home-based maintenance car trips• 17% more home-based maintenance non-motorized trips• A multi-scale analysis suggest that OLS is very sensitive to the scale of analysis
with difference in estimates of up to 100%.

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③ Estimate causal effects of the built environment on travel behavior using empirical data
A CAVEAT• In terms of the propensity score function, the importance of the strong
ignorability of treatment assumption cannot be over-emphasized. This
assumption is crucial to the unbiasedness of estimates.
• In practice it is impossible to know how well the estimated function
approximates the true population function.
• Although in order to estimate the propensity score function, relevant variables
largely cited in the literature introduced in the model, it is assumed at theestimated function is a good estimate of the true unknown function. However,
the risk of misspecification is certainly non-trivial.
• Much care should be place in estimating the propensity score function, as
much of the validity of the analysis depends on it.

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RE-EXAMINING THE BUILT ENVIRONMENT-TRAVEL BEHAVIOR CONNECTION:
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都市の物的環境と交通行動の因果関係に関する研究—日本の諸都市を事例として—

SECTION 6

CONCLUSION

東京大学大学院工学系研究科都市工学専攻都市交通研究室

FINDINGS AND CONTRIBUTIONS• Operationalization of a continuous index to measure built environment
characteristics.
• This index can then be used as a continuous treatment variable inpropensity score estimations of causal effects using cross-sectional data.
• Implementation and validation of a methodology to assess the causality
problem from a cross-sectional approach.
• This approach relaxes the binary treatment assumption of the traditional
propensity score approaches. [First in the planning literature]• The effectiveness of the proposed methodology in reducing bias against
OLS was validated via Monte Carlo simulation.
• Estimation of causal effects of the built environment on travel behavior fromcross-sectional perspective.

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1

POLICY IMPLICATIONS• In general, findings support the notion that the built environment has asignificant effect on travel behavior, specifically, on trip frequency by mode.
• Data from Fukuoka city supports the notion that living in more urbanized areas
is conducive to less car use and more non-motorized trips.
• Nevertheless, the issue at hand is more complex that just retrofitting or
promoting a certain (re)development model. Even after establishing a causal
relation, residential location is still a self-selecting process guided by household
life stage, lifestyle and preferences, so a mismatch between supply and demandmight hamper efforts to promote compact city paradigms.

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1FUTURE RESEARCH DIRECTIONS• Cross sectional analysis:• Gross misspecification of the propensity score function can result inserious bias (Imai & van Dyk, 2004). Hence further research efforts should
be directed towards improving the propensity score estimates.
• Attitudes measurement and use as control variable• No overarching guiding theory• Research efforts should thus be directed towards the development of atheory that guides the attitudes and preference measurements in theplanning field.

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1FUTURE RESEARCH DIRECTIONS• Assessing other dimensions of travel behavior• The main travel behavior dimension analyzed in this study relate to trip
frequencies by mode.
• Other relevant dimensions should be analyzed to strengthen the
conclusions presented in this dissertation.
• The propensity score approach presented here can be used to analyze
continuous variables such as travel distance, or fuel consumption,
provided reliable data is available.

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1

RE-EXAMINING THE BUILT ENVIRONMENT-TRAVEL BEHAVIOR CONNECTION:
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都市の物的環境と交通行動の因果関係に関する研究—日本の諸都市を事例として—

Giancarlos Troncoso Parady
トロンコソ

パラディ

ジアンカルロス東京大学大学院工学系研究科都市工学専攻都市交通研究室