Study design impacts on built environment and transit use research

Journal of Transport Geography 82 (2020) 102625

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Journal of Transport Geography journal homepage: www.elsevier.com/locate/jtrangeo

Study design impacts on built environment and transit use research a,⁎

a

b

a

c

Laura Aston , Graham Currie , Md. Kamruzzaman , Alexa Delbosc , David Teller a b c

T

Department of Civil Engineering, Monash University, Melbourne, Australia Monash Art Design and Architecture, Monash University, Melbourne, Australia Department of Transport, Victoria, Australia

ARTICLE INFO

ABSTRACT

Keywords: Study design Built environment Transit Public transport Meta-regression Publication bias

The different factors examined in studies linking the built environment and transit use explain about half of the variability in findings for travel behavior. Despite many differences in the research design of these studies, it is not known if choices about study design impact theoretical consistency in results and account for some of the unexplained variance between studies. This gap exists because multiple studies must be analyzed together to explore the topic. This study aims to fill this gap, using a sample of data points and statistical models from 146 studies identified through a comprehensive database search. This paper first synthesizes the study design adopted in empirical studies of the built environment and transit use. Meta-regression is then used to identify study design aspects causing significant differences. Selective reporting bias appears to slightly exaggerate estimates for built environment Density and Accessibility. Over 40% of variability in findings for Density and Diversity was explained by study design aspects. These include whether collinearity of variables is accounted for, the specificity of the sample population and transit mode, catchment size; and the number of explanatory variables specified. Overall the average correlations for built environment and transit use are weak (< 0.2). Predictions of transit ridership based on built environment factors are likely to be imprecise, so models should be carefully specified. Given the impact of study design, adherence to best practice conventions could reduce variance within studies and dispersion between studies. For ambiguous specification issues, sensitivity testing could be used to generate prediction intervals. Further investigation of factors such as transit mode and catchment size would be useful to determine if there is a theoretically plausible reason to favor certain specifications.

1. Introduction Empirical research has demonstrated significant associations between the built environment and transit ridership. However, the transferability of these findings into practice is limited, as most studies do not adhere to a causal research design. Meta-analysis has been used to generalize the findings of this research, finding a significant relationship between vehicle travel and built environment variables including Density, Diversity, Design and Accessibility (Ewing and Cervero, 2010; Stevens, 2017). The emphasis in research has predominantly been on built environment impacts on automobile travel, rather than its association with transit use. Nevertheless, studies that do focus on transit use tend to find increases in transit for the same variables that reduce automobile travel (Ewing and Cervero, 2010). The practical significance of these findings is limited by variability in the magnitude and direction of findings between studies (Stevens,

2017). This dispersion limits the ability to make precise predictions, when considering tactical built environment interventions to increase transit use. Dispersion may reflect natural differences in the relationship, but usually comprises some variability due to differences in study design (Borenstein et al., 2009). Researchers face many decisions when designing an experiment, including which variables to include, what kind of data to use and at which unit of analysis. Different research designs can lead to differences in results (Stanley and Doucouliagos, 2012). Researchers have identified the importance of correctly specifying the variate and choosing the correct statistical procedure in built environment and transit use research (Boarnet and Crane, 2001; Maat et al., 2005a; Stevens, 2017). Self-selection and publication bias have been demonstrated to impact findings for the built environment and automobile use (Stevens, 2017). Less attention has been given to choices about data, units of analysis and indicators, although their

⁎ Corresponding author at: Public Transport Research Group, Monash Institute of Transport Studies, Department of Civil Engineering, Monash University, Clayton, Victoria 3800, Australia. E-mail address: [email protected] (L. Aston).

https://doi.org/10.1016/j.jtrangeo.2019.102625 Received 11 April 2019; Received in revised form 10 December 2019; Accepted 14 December 2019 0966-6923/ © 2019 Elsevier Ltd. All rights reserved.

Journal of Transport Geography 82 (2020) 102625

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potential to impact results has been acknowledged (Guerra et al., 2011; Handy et al., 2005). Little guidance is available to researchers when choosing between alternatives that cannot be differentiated on theoretical grounds. This study seeks to address these gaps in knowledge by conducting a synthesis and empirical meta-regression of prior research. The paper has three aims, to:

The anticipated impact on ‘Theoretical Consistency’ conveys how varying the study design may change the likelihood of obtaining an estimate that is of the expected direction and magnitude. Data aggregation and variable specification are two aspects of study design that present researchers with decisions that impact results. Predictions of travel behavior may use data aggregated before or after statistical testing. Aggregating data before modelling captures more latent factors and therefore commonly explains greater variance. Models into which disaggregated data is input more accurately capture associations linked to individuals; at the cost of reduced explanatory power (Ben-Akiva, 1985). Variable specification can also impact observations. Increasing the number of variables provides greater explanatory power at the cost of increased measurement error and risk of over-specification, resulting in spurious correlations (Alonso, 1968; Elvik, 2005). To balance theory and expedience when specifying variables, the evidence-based convention of incorporating up to eight ‘D-variables’ has been adopted in built environment and travel behavior research. These include Density, Diversity, Design, Distance to transit, Destination accessibility, Demand management and Development scale (Cervero and Kockelman, 1997; Ewing et al., 2013). These variables affect the generalized cost of travel options by influencing the ease with which destinations can be accessed by a particular mode (Cervero, 2002). In addition to built environment and land use properties, Demographic characteristics are often used as a control. ‘Group’ variables, including population segments, seasonality, time of day and trip purposes, as well as properties of the transport system are also important influences on travel behavior (Ortúzar and Willumsen, 2011).

1. Synthesize research design in studies exploring the relationship between the built environment and transit use. 2. Determine whether publication selection is a potential source of bias in results. 3. Examine whether study design is associated with empirically meaningful differences in results. The first section of this paper defines key measures of variability and gives evidence of this problem in built environment and transit use research. Considerations when specifying observational built environment and transit use studies are summarized. The approach to synthesizing estimates, and detecting the presence of publication selection, is outlined. Meta-regression is used to identify study design aspects that significantly impact findings, and their contribution to variability between studies. Recommendations are provided to improve consistency and transferability in future research design, within the context of observational study design. 2. Literature review 2.1. Variability in built environment and transit use research The average relationship between Density, Diversity, transitfriendly Design, automobile-friendly Design and transit use has been calculated using meta-analysis and found to be significant (Ewing and Cervero, 2010). However, differences in the direction and magnitude of results from study to study, a symptom of the large variability in findings, limits their practical significance (Borenstein et al., 2009; Maat et al., 2005b). The amount by which observations vary is called dispersion. If the amount of variance explained by factors controlled in the study is high, then dispersion is unlikely to interfere with future predictions as long as the same features are controlled (Borenstein et al., 2009). If the variance explained by the study is low, then dispersion can threaten the precision of predictions. The amount of variance that can be explained by factors under the control of the researcher in observational studies is given by the coefficient of determination, R2, or Pseudo- R2 in the case of discrete outcome variables. The average variance explained by variables under observation in findings for transit ridership and built environment variables is 58% (n = 217) [1]. For studies that employ probabilistic methods, such as logistic regression, the pseudo R2 explains far less variance, with an average of 0.372 or 37% (n = 117) [2]. Identifying factors that contribute to the unexplained portion of variance can increase the reliability of predictions about transit use.

2.3. Methods for exploring study design impacts The impact of differences in study design on results in the field of travel behavior and land use has been explored using meta-regression analysis, rough set analysis and subgroup analysis. Table 2 provides a summary of the variables that have been tested, and the methods used. Meta-regression analysis can be used to determine if attributes that vary between studies, but which are not controlled, impact the magnitude of findings (Stanley and Jarrell, 2005). Meta-regression has been used in built environment and travel behavior research to determine if studies that control self-selection predict significantly different relationships to those that do not (Stevens, 2017). In this example, study design was found to significantly impact associations of vehicle miles travelled for some variables and not others. One limitation of metaregression analysis is the requirement for a large enough sample of comparable estimates. Due to small sample sizes, Stevens (2017) used a significance threshold of 0.1 to detect ‘significant’ differences between results with and without self-selection. Stevens also checked whether selecting reporting of findings was causing a systematic bias in understanding the role of the built environment in travel behavior (Stevens, 2017). The test used is known as the funnel asymmetry test, or “funnel plot”. If this funnel plot has vertical asymmetry, it is possible that less precise estimates (signified by larger standard errors) are being reported in favor of more precise ones (Stanley and Doucouliagos, 2012). Studies with small sample sizes require larger effects to be observed for those effects to be statistically significant. A tendency to report only significant results can lead to an over-representation of larger effect sizes from imprecise studies, resulting in vertical funnel plot asymmetry. Horizontal asymmetry is also suggestive of selective reporting, which can occur if publication favors results that support expected hypotheses. To develop parsimonious models, researchers might iterate and observe the variance explained by the model (R2) when different variables are added. This method was employed in a study that sought to determine if a particular unit of analysis explained more variance in the built environment and transit ridership relationship (Guerra et al., 2011). This approach is similar to sensitivity testing, used to observe

2.2. The impact of study design on relationships Researchers face many decisions when specifying their models, and such decisions can impact results and cause dispersion (Borenstein et al., 2009). Study design can also impact results if it is correlated to the dependent variable, therefore introducing systematic bias (Stanley and Doucouliagos, 2012, p. 81). Publication bias can also distort understanding of the relationship between the built environment and transit use. Such bias may result from selective reporting or favoring model specifications that yield theoretically consistent findings (Stanley and Doucouliagos, 2012). The observational study design that the majority of studies in the field of transport and land use research employ is highly susceptible to such impacts (van de Coevering et al., 2015). Table 1 summarizes the attributes that may influence results. 2

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L. Aston, et al.

Table 1 Hypothesized impacts of study design. Study design

Theoretical consistency

Impact

Accounting for residential self-selection Accounting for relative regional accessibility of study sites Increasing number of explanatory variables in variate Increasing number of built environment Variables in variate

Varies by indicator

Delineating group variables

Varies by segment

Regional accessibility and self-selection have been found to suppress some BE-TB relationships, resulting in larger relationships once controlled; whilst confounding others (Næss, 2009; Stevens, 2017). Little is known about specific impacts on transit ridership. The omission of third factors that impact travel behavior increases the likelihood of observing spurious relationships (Shadish, 2002). Third factors can confound or mask relationships, so spurious correlations may make it both more likely to detect significant effects when there are none (Type II error) or not to detect significant effects (Type I error) (Alonso, 1968; MacKinnon et al., 2000). Travel behaviour differs for certain segments of the population and trip purpose, so theoretical consistency of each group may vary from the sample mean (Ortúzar and Willumsen, 2011). It is not known whether travel behaviours differ by public transport mode. Uncontrolled collinearity may mask or exaggerate impacts (Ortúzar and Willumsen, 2011) Aggregation of data reduces its sensitivity to individual differences and increases the likelihood of attributing associations to the built environment (Ben-Akiva, 1985) Data sources at the same level of aggregation should convey comparable estimates of travel behavior. Significant effects may be masked for larger/ less specific units of analysis of the independent variables, however the effect is unlikely to be significant (Guerra et al., 2011)

Varies

Population Mode Trip Accounting for collinearity of variables Aggregating data

Varies Increase

Data source

Constant

Enlarging the unit of analysis of the independent variable Decreasing the specificity of the catchment Differentiating journey leg

Reduces

Time intervals

Unknown

Use more sensitive model

Increase

Increases

Trip origin variables are most commonly associated with built environment and transit use research, however impacts at both the origin and destination are relevant and might aid in explaining observed behavior. Multi-period studies enable dynamic effects of travel behavior change to be attributed to changes in built environment (van de Coevering et al., 2015). Choice behaviour is associated with individuals. Using models formulated on theories (structural equations), with nests or multiple levels; or using discrete choice models will increase the consistency of results (Mokhtarian and Cao, 2008; Ortúzar and Willumsen, 2011)

categorical one, such as a neighborhood being of the “neotraditional” American architectural style (score = 1 if yes, 0 otherwise), is not meaningful. A one-point increase in intersection density represents a marginal change, whereas a one-point increase on the scale for neighborhood age represents an entire spectrum. For this reason, a subset of the data was chosen for comparison of averages and the impact of study design. The largest subset was available for continuous outcome variables (e.g. ridership, number of transit users) and continuous independent variables. The converted correlation estimates from several studies were consistently larger than those from other studies. To guard against the possibility of systematic conversion errors, which may result from misinterpretation of reported estimates across entire studies, outliers were screened out. Outliers were identified as estimates that were 1.5 standard deviations outside the interquartile range of estimates for each indicator. After removing 22 outliers, a total of 270 data points from 51 studies were available for analysis. Publication bias was examined for samples of all four indicators. The sample size for Design and Accessibility were too small to detect associations with study design. As such, meta-regression was conducted for estimates of the relationship between ridership and Density (n = 147) and Diversity (n = 79) only.

the impact of methodological choices on results (Higgs et al., 2019). 3. Method This study aims to determine if study design is related to meaningful differences in results of built environment and transit use research. A descriptive summary of the study design of 146 empirical studies, and their corresponding theoretical consistency, is first provided. Average correlations for four built environment variables and transit ridership are estimated. A test for publication bias is conducted to determine if this is a potential source of distortion in findings. Meta-regression analysis is used to identify study design attributes that explain variability in observations for built environment impacts on transit ridership. Their quantitative impact on observed relationships is explored by solving the meta-regression equation for different study design scenarios. 3.1. Data Data for this study were extracted from 146 studies, identified through a comprehensive database search [3,4]. A summary of citation information as well as variables, data points and models analyzed from each of these priors is included in the Appendix to this paper. Fig. 1 illustrates the break-down of data points for synthesis of study design, followed by meta-regression analysis. Correlations, r, were chosen as a common metric to represent average relationships in meta-regression. The correlation parameter (ρ), represents the strength of a relationship between the independent and dependent variable for a population (Borenstein et al., 2009). Conversion to a common effect size is meaningful when the relationships being examined measure the same thing (Borenstein et al., 2009, p. 18). To achieve this, results were segmented into the established D-variable constructs of Density, Diversity, Design and Accessibility (Ewing and Cervero, 2010). Measurements must be taken on the same scale, so that incremental increases are equivalent from one relationship to another. Comparison between a continuous indicator of urban design quality, such as the number of pedestrian crossing opportunities, with a

3.2. Coding and synthesizing study design Fifteen study design attributes were coded in total. Twelve attributes were relevant to the specification of empirical models from which observations derived, while three were properties of the individual data points. Each attribute was extracted according to a pre-specified coding framework. Group variables, including transport mode, population subset and trip purpose, were discretized to indicate whether they were delineated into specific modes. For example, studies that referred to ‘transit’ as the mode of travel, were differentiated from studies that identified which type of transit was chosen, such as light rail or bus. The presence of external variables commonly analyzed in the field of built environment and transit use, was also considered. These included demand management, destination accessibility, distance to transit and demography (D-variables), direct cost, trip attributes, level of service, 3

Journal of Transport Geography 82 (2020) 102625

Coefficient of variation and descriptive statistics Sensitivity testing on subgroups

No reported effect

306

Insufficient information

658

Indicator out of scope (auto-oriented)

144

Variable not continuous

262

outliers

22

2. Meta-averages and publication bias

R2

R2

270

51 studies Density

Diversity

Design

Accessibility

147

70

36

17

3. Meta-regression analysis 217

Accounting for self-selection; presence of reporting bias

Built environment and vehicle miles travelled Public transport demand elasticities

Activity dispersion

Built environment and public transport ridership Built environment and liveability

(Nikjamp and Pepping, 1998)

(Buliung et al., 2008)

(Guerra et al., 2011)

42 studies

(Stevens, 2017)

Unit of analysis of population, unit of analysis of jobs, dummy variable for scale

Mean effect

146 studies

Study design aspects tested

Country, year of data collection, mode, indicator of the dependent variable, scale, number of competing modes, data type, estimation method Weekday-to-weekend, day-to-day, mode, gender of sample

Mean effect

Meta-regression and funnel asymmetry test Rough set analysis

1,662

Scope

Mean effect (elasticity)

1. Synthesis

Study

Fig. 1. Data point screening flow diagram.

Unit of analysis for all land use variables

self-reported preference for transit and regional accessibility. The complete coding framework and accompanying metadata are published online [5]. The number of models or observations adopting each study design alternative was tabulated. The proportion of theoretically consistent results was also evaluated for each. The hypotheses pertaining to each observation were derived from commonly accepted values in the literature that suggest transit-friendly design, land use intensity and mix and accessibility increase transit use, while car-oriented design and isolation reduce it (Ewing and Cervero, 2010). A hypothesis test was used to determine whether theoretical consistency differed significantly depending on study design. This was achieved using a z-test to determine if the proportion of theoretically consistent studies adopting a particular study design attribute was significantly different to the remainder of the sample. The significance level for these tests was set to 0.05. The alternate hypothesis was accepted if the z-value for the proportion of theoretically consistent results for a given study design was in the upper or lower critical region ( ± 1.96). 3.3. Meta-regression analysis To obtain comparable z-value estimates, data pertaining to observations were entered into the software Comprehensive-Meta Analysis (CMA) (Borenstein et al., 2013). CMA performs conversions using the formula in Table 3. Standardized scores (Fisher's Z) are estimated by converting either direct correlations, z-scores, t-statistics or pvalues into a common metric. From these values, a correlation for each built-environment and transit ridership finding, r, is derived.

(Higgs et al., 2019)

Table 2 Approaches to analyzing the impact of study design on empirical relationships.

Metric

Method

L. Aston, et al.

SEr = (1

SEFisher sZ =

4

r 2)

SEFisher sZ

1 N

3

(1)

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L. Aston, et al.

predictor reduced the model's explanatory power. The next analysis involved sensitivity testing for the impact of any study design choices identified as being significant predictors of the relationship between either density or diversity and transit ridership. To do this, the intercept (β0) and significant coefficients (βi) regression coefficients were substituted into the random-effects regression equation. Solving for ρ′ in eq. (5) produces a corrected estimate of the relationship between a built environment variable and transit ridership. To do this, a value had to be chosen for the categorical study design variables. If ‘best practice’ was known, then the corresponding value category and its value, (0 or 1 for categorical variables, or count data) was used (Stanley and Doucouliagos, 2012, p. 93). If not, the impact of substituting different alternatives into the equation for ρ was sensitivity tested.

Table 3 Formulae for computing correlations for observations (adapted from Sharmin and Kamruzzaman (2018)). Type

Quantitative data to extract or Spearman's • Pearson's correlation coefficient size • Sample (Fisher's Z) • z-value size (n) • Sample (t) • t-statistic • Sample size (N) • p-value in t-test • Tails • Sample size (N)

1 2 3 4

Conversion formula No conversion

r=

r=

(2)

e2 Z 1 e2 Z + 1

(3)

t2 t 2 + (N

2)

- t-value associated with p-value, assuming 2-tailed test for significance - Eq. (3)

4. Results

3.3.1. Diagnosing publication bias An informal test for reporting bias was conducted to determine whether findings showed symptoms of publication selection bias. A plot of standardized estimates (z-scores) and standard errors provides a visual representation of precision against the magnitude of estimates and inspecting for asymmetry in the distribution of the estimates. Eq. (4) presents the weighted least squares equation that is modelled in the funnel asymmetry test (Stanley and Doucouliagos, 2012, p. 78).

ti =

1

+

1 + vi SEi

0

4.1. Synthesis of study design in built environment and transit use research The first aim of this study was to synthesize research design in studies exploring the relationship between the built environment and transit use. Observations from 146 prior studies were segmented according to study design attributes theorized to impact results in a meaningful way. A total of 467 statistical models, containing 1662 data points for built environment and transit use relationships were extracted from these priors. The proportion of study design alternatives pertaining to each model or observation is summarized in Tables 4 and 5 respectively. Thirteen of the fifteen study design characteristics analyzed have at least one alternative that is associated with significantly higher or lower theoretical consistency than the population of studies. Segmenting studies according to trip purpose did not impact the proportion of findings that were theoretically consistent. Contrary to expectation, controlling collinearity did not impact theoretical consistency of findings. Studies that control regional accessibility are associated with higher theoretical consistency, as are those that control Self-selection in terms of attitudinal variables (but not sociodemographic). Including three or more built environment variables in the variate is associated with reduced theoretical consistency. The impact of adding explanatory variables is nonlinear, which attests to the need to be selective rather than take an ‘all-in’ approach to explanatory variables. Census and travel survey data also had significantly higher theoretical consistency than other data sources. Aggregate data was associated with higher theoretical consistency (47%) than disaggregate data (39%). Studies that used data collected over multiple time periods only found significant positive associations with transit ridership in 26% of cases.

(4)

Asymmetry in the funnel plot is cause to reject the null hypothesis (H0 : β1 = 0), and suggests publication selection is impacting results. Estimates were adjusted for publication bias in conjunction with an analysis of other study design impacts, explained in the next section. 3.3.2. Sensitivity testing the impact of study design Meta-regression analysis was used to identify study design impacts that significantly impacts the magnitude of findings for the built environment and transit use. A random effects model that weights z-scores by inverse variance was used to account for variance within and between estimates (Borenstein et al., 2010). Random effects was chosen because it assumes the underlying association between built environment variables and transit ridership is not the same from one study context to another. The random-effects model is derived from the unweighted ordinary least squares meta-regression equation, shown in Eq. (5) (Borenstein et al., 2009, pp. 69–75).

=

0

+

1

x1 + …+

i1

xi +

(5)

Attributes of each study pertaining to the categories in Table 1 (hypothesized impacts of study design) were entered into CMA. These were converted into categorical entries, except in the case of the number of explanatory variables and the number of built environment variables, which were entered as integers. Precision, represented by standard error, was also included as a covariate in the model to account for publication selection. The meta-regression was set up to estimate the impact of changing a given study design attribute from the reference category. For example, the impact of changing the land use unit of analysis from a 400 to 800 m catchment, to a catchment larger than 1600 m, was analyzed. Each study design attribute (Table 1) was examined for a direct association with the strength of relationship between transit ridership and Density or Diversity. Those with a significant direct correlation, using the relaxed threshold of p < .25, were included in a multiple linear model. This included standard error, where publication bias was detected. Study design predictors of effect size were removed in a stepwise manner if the significance test of the null hypothesis (p-value) was > 0.1, starting with the predictor with the largest p-value. The impact of removing each predictor on the model's coefficient of determination was observed. Stepwise removal of variables continued until either no insignificant variables remained, or the removal of a subsequent

4.2. Meta-regression results Average correlations for the relationship between Density, Diversity, Design, Dccessibility and transit ridership are shown in Table 6. The impact of study design on the relationship between Density and ridership or Diversity and ridership is also shown. Accessibility indicators showed the strongest raw average correlation with ridership (ρ = 0.158). The second largest average correlation was identified for density and ridership (ρ = 0.136). However, the estimates for these relationships showed evidence of reporting bias, detected by inspecting a plot of their standard error on z-score for asymmetry. Since the sample for Accessibility was small, it was not tested for significant impacts of other study design factors. Setting the coefficient of the standard error term to 0 in the meta-regression model produced a corrected estimate of 0.105. Density was also corrected for publication bias using meta-regression. Correcting for publication bias and study design impacts simultaneously maximized the explanatory power of the model. Four study design attributes had significant impacts on the 5

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Table 4 Synthesis of study design properties relevant to models (n = 467 models). Attribute

Segments

Number of models (%)

Sig. variation in Theoretical Consistency?

Regional accessibility

Not controlled Controlled Not controlled Controlled Not controlled Controlled 0 1 2 3 4 5 6 7+ 0 1 2 3+ Not Controlled Controlled Aggregate Disaggregate Patronage Census Travel Survey Stated Preference Census Travel Survey Stated preference Commuter Specific pop. General Aggregate modes Specific mode Aggregated trip type/purpose Specific type/purpose Multi-period 1 Logistic OLS Negative binomial regression SEM Probit

250 217 373 94 145 322 52 22 46 77 40 84 50 96 185 128 66 88 162 305 249 218 133 45 62 9 29 127 59 107 188 172 234 233 169 298 421 46 153 218 55 3 20

✓ ✓ ✓ ✓

− + − +

✓ ✓ ✓

+ − +

✓

−

✓ ✓

+ +

✓

−

✓ ✓ ✓ ✓ ✓

+ − − + +

✓ ✓ ✓ ✓ ✓ ✓

− + − + + −

✓ ✓

− +

✓

+

Self-selection

Attitudes Socio-demographic

Explanatory variables

Additional built environment variables

Collinearity Level of aggregation of data Data source

Aggregate

Disaggregate Population Mode Trip Subset Time periods Estimation method

population correlation for Density. The impact of publication bias on study design was strongest, exerting a positive impact on the magnitude of the estimate. The second largest impact was for controlling Covariance (β = −0.133). Changing the catchment size from the standard of 401–800 to a catchment exceeding 1600 m also had a negative impact (β = −0.101), as did examining associations at the destination of a trp rather than the origin (β = −0.060). These four aspects of study design explained 41% of the variability in effect size for the sample of ridership data points. Assuming the best practice convention of eliminating collinearity between variables, estimates for density varied in the range of 0.025–0.125, after correcting for publication bias. Five study design aspects had significant impacts on the magnitude of the relationship between Diversity and ridership. The strongest impact was for studies whose sample comprised workers, as opposed to the general population (β = 0.160). The second largest impact was for specifying the mode of transport, as distinct from analyzing “transit modes” in aggregate. This was also positive, with β = 0.110. The third largest impact was for controlling for collinearity, however the impact was in the opposite direction than for density estimates. According to the model, screening or correcting for collinearity would increase the observed relationship between diversity and ridership (β = 0.078). Controlling regional accessibility and adding built environment variables were also significant predictors. Adding explanatory variables did

Table 5 Synthesis of study design properties relevant to data-points (n = 1662 data points). Attribute

Segments

Number of data points (%)

Sig. variation in theoretical consistency?

Buffer size

≤400 m 400 < x ≤ 800 m 800 m < x ≤ 1600 m > 1600 m City or metropolitan area Other Route Radial catchment Walk catchment Access corridor Census geography Other Origin/residence Destination/ workplace Both

114 507 95

(6.9%) (31%) (5.7%)

✓

−

56 65

(3.4%) (3.9%)

✓

+

825 10 670 64 27 417 358 1129 222

(50%) (0.6%) (40%) (3.9%) (1.6%) (25%) (22%) (68%) (13%)

✓ ✓ ✓ ✓

− + − −

58

(3.5%)

✓

+

Buffer type

Journey leg

(54%) (46%) (80%) (20%) (31%) (69%) (11%) (5%) (10%) (16%) (9%) (18%) (11%) (21%) (40%) (27%) (14%) (19%) (35%) (65%) (53%) (47%) (59%) (20%) (28%) (4.0%) (11%) (50%) (23%) (23%) (40%) (37%) (50%) (50%) (36%) (64%) (10%) (90%) (33%) (47%) (12%) (0.6%) (4.3%)

‘+’ implies the test statistic falls in the upper, or right-hand side of the critical region, and that the test proportion is higher than the population proportion. The opposite applied for ‘-‘.

6

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Table 6 Meta-regression of study design and findings for built environment and transit ridership (final estimates shown in bold). Dependent variable

Estimated correlation with transit ridership Density a,b

Diversity

Average correlation, ρ Independent variables (study design) SEr Control for regional accessibility (ref: no control) Add explanatory variables Add built environment variables Control for covariance (ref: no control) Delineation of sample Transit mode (ref: by combined transit) Worker population (ref: general pop.) Catchment size (ref: 801–1600 401–800): > 1600 m Measure built environment at destination (ref: origin) Intercept R2 Base solution (best practice)

0.136 Rank 1

β 3.48

p 0.08

2

−0.133

< 0.001

Sensitivity-testing of ave. correlations

Catchment size: 801–1600 (1) Covariance: Y (1) Catchment size: > 1600 (1) Covariance: Y (1) Journey leg: Destination (1) 0.125

ρadjustedc

4 3 5

−0.070 −0.101 −0.060

0.259 0.41 SE (0), Covariance controlled (1)

0.014 Rank

Design β

p

5 3 2

−0.039 −0.007 0.016 0.078 0.111

0.096 0.4215 0.076 0.049 0.008

1

0.160

0.047

4

0.056 β

Accessibility p

0.158 β p 1.42 0.008

0.0156 0.0085 0.0807

0.066

−0.104 0.43 SE (0), Regional accessibility controlled (1), BE Variables (2), Covariance controlled (1) Mode: specific (1)

0.055

Population: workers (1)

0.127

0.025

Mode: transit (0), Population: general (0)

−0.003

0.125

0.056 0.00

−0.0329

0.078

0.105 0.00

0.078

0.056

0.105

a

All reported correlations are significant at the 0.001 level. Reported correlation statistics are Pearson product moment correlations. Formulae for conversions based on sample size and either z-statistics, t-statistics or pvalues are listed in Table 3 c Includes adjustment for publication bias where significant (density, accessibility) b

not have a significant impact on the magnitude, however it did add explanatory power, and so remained in the model. These six aspects explained 43% of the variability in the observations between built environment diversity indicators and ridership. The population correlation parameter was adjusted to correct for data that did not adhere to best practice. If ridership was predicted for specific transit modes across all studies, and best practice was applied to the other variables, the adjusted correlation was predicted to increase to ρ = 0.078. Similarly, limiting the sample to worker populations would increase the estimate to ρ = 0.127. However, if the more general alternatives were applied, that is examining transit ridership aggregated for different modes and the general population, diversity was predicted to change the direction of its relationship with ridership, with ρ = −0.003.

and transit use. Increasing density and land use diversity, implementing pedestrian-friendly design and increasing access to activities at a local and regional scale is associated with increasing transit use. 5.1. Examining potential sources of bias Correcting for publication bias in estimates of Density and ridership and Accessibility and ridership decreased the magnitude of the population correlation parameter. This may occur due to null results or theoretically inconsistent results not being reported. Selective reporting bias has been identified as a potential threat to the validity to the general understanding of built environment and travel behavior research (Stevens, 2017). One source of selective reporting bias is the selection of study designs that favor expected results (Stanley and Doucouliagos, 2012). In cases where the most common study design was also the most theoretically consistent, there is a risk that the study design might be favored for its ability to produce expected results. The plots for Density and Accessibility showed signs of asymmetry, but not for Diversity or Design. The inconsistency of reporting bias across the four variables suggest that bias is specific to the variables, rather than a result of preferencing study designs that favor expected results.

5. Discussion This study first segmented 146 studies, encompassing 467 models and 1662 data points, according to 15 different study design aspects. The proportion of findings that accorded with expected (theoretically consistent) relationships was estimated to each study design alternaive. This approach enabled detailed comparison of a sample for which estimates were presented in many different forms (Borenstein et al., 2009). Thirteen of the study design aspects were associated with significant differences in the direction and significance of findings. Findings for built environment and transit use were subsequently examined for distortion resulting from publication selection. Meta-regression analysis was used to estimate average correlations, while accounting for significant impacts of study design and publication bias. After correcting for impacts of study design and selection bias (relevant for Density and Dccessibility) and study design (Density and Diversity), average estimates concord with hypotheses about built environment

5.2. Study design impact on findings Table 7 summarizes the most common study design attributes (Aim 1) as well as observations for study design impacts on built environment and transit ridership findings. The third and fourth columns in Table 7 summarize the attributes which significantly increase or decrease theoretical consistency; and columns five and six show those which impact effect size estimates for Density or Diversity. The 7

8

Travel survey Specific population subsets

50% aggregate modes, 50% specific modes Specific trip purpose 1 OLS

Data source, disaggregate Population

Mode

Census geography Both

Buffer type Journey leg

Radial catchment Origin

> 1600 m

1 Negative Binomial Regression

Aggregate modes

Commuter, General

Study design properties of data points (n – 1662) Buffer size 400 < x ≤ 800 m1

Trip Time periods Estimation method

Controlled Aggregate Patronage

Collinearity Level of aggregation Data source, aggregate Aggregate Census data, Travel survey

0, 1

0

Built environment variables

Access corridor, other Origin

400 < x ≤ 800 m

Multi-period

Stated preference Specific population subset Specific modes

Disaggregate Patronage

3+

Not Controlled Attitudinal SS not controlled 1, 5

Observing BE at destination reduces estimate

Reduces for buffers other than 400 < x ≤ 800 m

Decreases if controlled

Density

Significantly higher

Significantly lower

Impact on findings

Theoretical Consistency (%)

Controlled Attitudinal SS controlled 0, 2

Most common

Study design properties of model (n = 467) Regional accessibility Not controlled Self-selection (SS) Controls socio-demographic only Explanatory variables 5

Attribute

Table 7 - Synthesis of study design attributes by prevalence, impact and best practice.

Increases for ‘workers/ commuters’ Increases if specific modes analyzed

Control all theoretical relevant variables while maintaining statistical power for a given sample size (Alonso, 1968; Elvik, 2005)

Decreases as explanatory variables added Increases as other built environment variables added Increases if controlled

400 < x ≤ 800 m aligns to most planning conventions. Guerra et al. (2011) suggests choosing based on data availability and quality. Walk or access catchment Both

n/a Multi-period SEM (Mokhtarian and Cao, 2008), discrete choice (BenAkiva, 1985)

n/a

Stated preference (Dill et al., 2014; Handy et al., 2005) n/a

Control Disaggregate n/a

Account for self-selection and regional accessibility (Cao et al., 2009; Renne et al., 2016)

Decreases if controlled

Diversity

Best practice

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discussion that follows explores the implications of these findings for future research. Some of the findings confirmed hypotheses about study design aspects overstating the role of built environment variables, mainly due to a lack of control or specificity. Firstly, examining only one built environment variable (the variable of interest), was associated with higher theoretical consistency. When other relevant built environment variables are not included, the interaction and concurrent impacts of multiple built environment factors is not captured. Models with aggregate data were also the most common and the most theoretically consistent. The less specific the data, the more likely results are to be attributed to the built environment variables under observation. A lower proportion of studies find a significant relationship when data from multiple time periods is used. This demonstrates that correlation is not causation, and attests to the need to control for the potential reverse causality in built environment and transit ridership studies by examining data from multiple time periods (van de Coevering et al., 2015). Presently, “multi-period” data is used in only 10% of models. Census data increased theoretical consistency, which may be due to the prevalence of journey to work data in the census. Generalizing studies that examine these populations is not advisable as they may overstate the relationship for other population segments. The higher theoretical consistency of studies that analyzed the built environment at the origin and destination of trips was also expected. The finding suggests that attributes of both the origin and destination are significant determinants in the decision to use transit. In some cases, findings for theoretical consistency contrast with those for effect size. This was the case for Regional Accessibility and Transit Mode. These differences are likely due to systematic differences in the samples used for each analysis. While the test for theoretical consistency examined all estimation methods, meta-regression was limited to continuous (correlational estimates). Irrespective of divergence in the specific direction of impacts, the significant explanatory power afforded by study design for estimates of Density and Diversity demonstrates the potential impact of study design on results. Corrected estimates for density (ρ = 0.125) and accessibility (ρ = 0.136) were smaller than their raw estimate, while diversity (ρ = 0.078) increased slightly after adjusting for study design. The smallest raw correlation was observed between design and ridership ρ = 0.056, while the largest was for accessibility. Correlations are useful for understanding the direction and strength of a relationship. Smaller correlations have less reliability for prediction (Hair et al., 2014). Predictions of ridership from the four variables examined in this study, all of which are below 0.20, are unlikely to yield reliable results. Furthermore, correction for study design does not change the practical significance of the correlations. In all cases the magnitude remains ± 0.1 of the original estimate, which is considered a small and unreliable prediction interval for correlations. Results concord with the findings of meta-analysis for private vehicle travel (Stevens, 2017), that meaningful changes in travel behavior are unlikely to result from changes in the built environment. Nevertheless, since meta-regression analysis suggests study design accounts for 41% of variance between estimates for Density and ridership, and 43% of variance for Diversity and ridership, study design poses a serious threat to the reliability of predictions. Given the imprecision of the estimates, it is important that ridership models based on the built environment be properly specified and calibrated.

particular threat to the validity of meta-analysis, which grouped results into D-variable constructs. However, the indicators used to represent each variable may measure impacts at different scales (neighborhood or regional). This study focuses on observational (non-causal) research; which constitutes the majority of the built environment and transit use literature; although it does not represent best practice (Mokhtarian and Cao, 2008). The field is moving away from such studies for the purpose of understanding travel behavior. As the synthesis of literature demonstrates, time series studies and studies that use primary user perceptions and preferences make up a small portion of empirical evidence. Boarnet (2011) advocates for the development of a retrospective tradition of analysis. Multi-period research is better suited to establishing a cause and effect relationship (van de Coevering et al., 2015). Researchers have also promoted a shift in emphasis to choices and perceptions, to better understand the mechanisms by which the built environment influence mode choice (Handy, 1996; Miller, 2019). Nevertheless, direct observational analysis will continue to play a role in transport system management. This research is relevant for practitioners and modelers who should be aware of the impact that calibration decisions can have on predicted transit use.

5.3. Limitations

Notes

This study has some important limitations. This study assumes that study design influences all built environment-transit use relationships in the same way. As noted in the methodology, comparison is only meaningful for indicators that measure the same underlying construct. While the behavioral mechanisms at play are likely to be similar, there may also be some differences between indicators. This poses a

1. Coefficients of determination calculated from priors included in this study. 2. Pseudo coefficient of determination calculated from priors included in this study. 3. Descriptive information pertaining to these studies is available through the open-access Built Environment and Transit Use Meta-

6. Conclusions and recommendations The aims of this study were to summarize existing study design in built environment and transit literature and to determine if study design accounts for meaningful differences in results. This was achieved by examining differences in theoretical consistency associated with study design alternatives, as well through meta-regression to detect study-level drivers of different effect sizes. The results show study design has significant impacts on findings for the relationship between the built environment and transit use. Three methodological recommendations are made for future research: 1. Where applicable, best practice approaches to specification should be adopted. Table 7 assembles best practice approaches according to theory. 2. In the absence of best practice, researchers should use Sensitivity Testing to demonstrate a range of results generated when different methodological choices are made. 3. Study design characteristics associated with significant differences in theoretical consistency or effect size should be further examined to determine whether there is a theoretically plausible reason for favoring certain alternatives. These include: a. Travel behavior data sources b. Population segments c. Transit modes d. Catchment buffer size and type The impact of study design is small, although in some cases it was observed to change the direction of the relationship between built environment and transit use. The weak correlations calculated for all variables indicate that predictions based on the built environment are inherently imprecise. The implication of this is that models are highly sensitive to changes in model specification. Adopting these three recommendations would therefore serve to mitigate bias and promote consistency between studies.

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database, available in figshare at: https://doi.org/10.26180/ 5d921ac827289 4. Comprehensive literature review protocol available in figshare at: https://doi.org/10.26180/5c3fd9990600c

Acknowledgements This work was supported by the Department of Transport (State of Victoria); and a Department of Education and Training (Commonwealth of Australia) Research Training Program Stipend.

Declaration of Competing Interest None. Appendix A Table A1

Citation information and data extracted from priors for meta-analysis of the impact of study design on built environment and transit. 1. Study Design impacts on theoretical consistency

2. Meta-averages and publication bias (Includes outliers)

ID

Citation

Data points

Models

Transit-friendly Design

Transit-friendly Accessibility

Density

Diversity

Subtotal

1662

467

39

20

154

79

(Siddiqui, 2000) (Hess, 2001) (Meurs and Haaijer, 2001) (Asensio, 2002) (Cervero, 2002) (Shiftan and Barlach, 2002) (Zhang, 2002) (Zhao et al., 2002) (Greenwald, 2003) (Rajamani et al., 2003) (Chapman and Frank, 2004) (Gordon, 2004) (Kuby et al., 2004) (Lund et al., 2004) (Zhang, 2004) (Bento et al., 2005) (Næss, 2005) (Sung, 2005) (Bhiromkaew, 2006) (Brown et al., 2006) (Cervero, 2006) (Chow et al., 2006) (Lane et al., 2006) (van de Coevering and Schwanen, 2006) (Cervero, 2007) (Chen et al., 2007) (Dill and Wardell, 2007) (Pinjari et al., 2007) (Polzin et al., 2007) (Brown and Thompson, 2008) (Cervero and Day, 2008) (Cervero and Murakami, 2008) (Concas and DeSalvo, 2008) (Frank et al., 2008) (Lin and Shin, 2008) (Yarlagadda and Srinivasan, 2008) (Blumenberg and Smart, 2009) (Cao et al., 2009a, 2009b) (Hess, 2009) (Lawrence Frank and Co. Inc et al., 2009) (Pitombo et al., 2009) (Ryan and Frank, 2009) (Taylor et al., 2009) (Armbruster, 2010) (Cervero et al., 2010) (Chatman et al., 2010) (Loo et al., 2010) (Marshall and Garrick, 2010) (Thérèse et al., 2010) (Currie et al., 2011) (Deka et al., 2011) (Guerra et al., 2011) (Gutiérrez et al., 2011)

5 2 45 4 5 3 1 52 20 3 16 258 3 27 16 20 2 10 10 16 6 2 5 2 3 3 2 1 4 4 2 14 15 11 8 20 3 12 12 3 4 1 2 1 7 10 10 18 8 2 5 14 2

2 2 4 2 1 1 1 19 4 1 4 29 1 4 4 2 2 5 3 2 4 2 2 1 2 1 1 1 2 2 1 6 3 2 1 4 1 2 6 1 4 1 2 1 1 5 2 1 2 1 5 13 1

3. Meta-regression analysis

6 7 6 5

10 2

10

2 1

1 5

5 4

15

4

5 7

6 1

1 1

1 1

4 2 1 2

1 2 14 1

10

17

16 6 146 28 3 5 13 10 24 117 110 15 9 115 131 22 116 25 7 30 2 26 21 20 27 19 8 4 18 23 120 119 11 31 118 12 29 109 1 114 121 17 14 66 51 122 87 55 101 64 83 70 105

(continued on next page)

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Table A1 (continued) 1. Study Design impacts on theoretical consistency

2. Meta-averages and publication bias (Includes outliers)

ID

Citation

Data points

Models

Transit-friendly Design

Transit-friendly Accessibility

Density

Diversity

Subtotal

1662

467

39

20

154

79

(Lane, 2011) (Peterson, 2011) (Talbott, 2011) (Thompson et al., 2011) (Tracy et al., 2011) (Zhao and Lu, 2011) (Appleyard, 2012) (Brown and Neog, 2012) (Buehler and Pucher, 2012) (Cardozo et al., 2012) (Chakraborty et al., 2012) (Choi et al., 2012) (de Grange et al., 2012) (Dong et al., 2012) (Imam and Tarawneh, 2012) (Mitra et al., 2012) (Moniruzzaman and Páez, 2012) (Pulugurtha and Agurla, 2012) (Song et al., 2012) (Susilo et al., 2012) (Tsai et al., 2012) (Zahabi et al., 2012) (Asad, 2013) (Bhattacharya, 2013) (Bordoloi et al., 2013) (Chan and Miranda-Moreno, 2013) (Currie and Delbosc, 2013) (Dalton et al., 2013) (Deng et al., 2013) (Ewing et al., 2013) (Foletta et al., 2013) (Ho and Mulley, 2013) (Jun et al., 2013) (Lee, 2013) (Lee and Lee, 2013) (Mangan, 2013) (Spears, 2013) (Zhao et al., 2013) (Zhu et al., 2013) (Brown et al., 2014) (Caulfield and Ahern, 2014) (Concas and DeSalvo, 2014) (Feng et al., 2014) (Gehrke and Clifton, 2014) (Hamidi and Ewing, 2014) (Imani et al., 2014) (Nawrocki et al., 2014) (Rubin et al., 2014) (Shaaban and Hassan, 2014) (Sung et al., 2014) (Tsai et al., 2014) (Zamir et al., 2014) (Zhao et al., 2014) (Cao et al., 2015) (Chiou et al., 2015) (Durning and Townsend, 2015) (Ewing et al., 2015) (Hammadou and Papaix, 2015) (Kamruzzaman et al., 2015a) (Kamruzzaman et al., 2015b) (Kerkman et al., 2015) (Noland and Dipetrillo, 2015) (Papaioannou and Martinez, 2015) (Verbas et al., 2015) (Aditjandra et al., 2016) (Arbués et al., 2016) (Chakour and Eluru, 2016) (Chen and Zegras, 2016)

3 3 17 7 11 4 2 3 8 10 9 108 2 20 1 6 9 20 8 4 4 3 18 8 6 2 2 3 1 2 8 8 4 18 12 20 21 7 12 36 16 8 6 22 5 2 2 4 1 90 5 10 8 6 2 17 5 1 4 6 4 5 2 3 5 2 47 9

3 2 16 1 1 2 1 3 2 2 4 6 2 20 1 4 3 5 4 1 1 1 5 4 6 2 1 1 1 2 1 1 2 3 3 14 4 1 3 9 2 3 2 19 2 2 2 3 1 9 1 4 1 3 1 1 2 1 2 1 2 1 1 1 1 1 19 4

1

3 1

3. Meta-regression analysis

4

2 2

8 1 1

2

1 2 8 1 1

4 1

4 1

4

2 40 2

2 1

2

2

1

4 2

1

2 2

3 2 4

11

1

1

95 104 43 108 41 123 92 44 48 40 32 38 39 124 60 74 37 42 125 99 102 57 45 72 127 36 62 81 126 112 68 79 98 84 47 73 100 128 54 107 132 97 133 82 34 35 77 96 130 65 69 56 129 63 134 52 113 85 53 46 67 106 135 91 61 49 59 86

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Table A1 (continued) 1. Study Design impacts on theoretical consistency

2. Meta-averages and publication bias (Includes outliers)

ID

Citation

Data points

Models

Transit-friendly Design

Transit-friendly Accessibility

Density

Diversity

Subtotal

1662

467

39

20

154

79

(Dai et al., 2016) (Duggal et al., 2016) (Goel and Tiwari, 2016) (Husein and Maoh, 2016) (Kim et al., 2016) (Liu et al., 2016) (Qing et al., 2016) (Rahman et al., 2016) (Ramos-Santiago and Brown, 2016) (Renne et al., 2016) (Saghapour et al., 2016) (Sun et al., 2016) (Vergel-Tovar, 2016) (Woldeamanuel and Kent, 2016) (Zhou and Li, 2016) (Boulange et al., 2017) (Lee et al., 2017) (Ma et al., 2017) (Ramezani et al., 2017) (Schneider et al., 2017) (Sun et al., 2017a) (Sun et al., 2017b) (Voulgaris et al., 2017) (Wu and Hong, 2017)

2 6 2 15 6 15 12 9 16 5 2 1 13 2 6 4 5 2 19 3 6 2 18 1

1 2 2 4 1 3 4 4 4 1 1 1 4 1 2 1 1 1 2 1 1 1 3 1

3. Meta-regression analysis

2 2 2

1 2

0 4

141 89 136 75 93 71 140 78 33 103 80 138 139 76 137 58 88 90 142 50 143 144 94 145

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