B Discussion

It is impossible to design a replicable experiment controlling other factors to identify the effects of density. But it is possible to filter out the unsuitable or irreproducible models.

B.1 A Proposed Framework

Related theories say that travel is determined by individual’s energy, distance, and city’s opportunity. A proposed framework is to re-categorize all potential factors by three aspects: budget, distance decay, and benefit/opportunity. And assign them into multiple spatial scales.

Personal/household characteristics are travel budget that means the ability or willingness to pay for a trip. These factors are always point attribute rather than areal.

A part of built environment factors, such as road connectivity, distance to transit, relate to distance decay and measure how easily people can move. From this perspective, a high local and nearby residential density may lead to greater resistance.

Other built environment and urban form factors could represent the benefit or opportunity. The aggregated measurement only can caupure the variation between cities/regions. For disaggregated data, a complete assessment should evaluate all opportunities inside the daily travel range and their weights for each traveler.

B.2 Policy implications

  • directionality and causality might not matter

Ideally, the causal relationship between built environment and travel is preferred in studies. But as long as evidences support that these two have significant association, policy makers still can utilize this relation to achieve some social, economic, and environmental goals.

Controlling the effects of self-selection make the research results more convincing. But from the policy perspective, if a city shows a more desirable travel pattern under a policy intervention, it doesn’t matter whether people change their travel behavior or people with different behavior relocated outside or inside this city.

The nomological network among travel behavior, socioeconomic, and built environment factors is iterative and cumulative. Causal inference, such as disentangling residential self-selection, This study does not indicate .

  • Policy cost and effect size matter

There are different metrics, such as correlations, coefficients, or elasticities to measure the effect size. If it is possible, a quantified travel behavior change with respect to per unit of public investment is more attractive for policy maker and public.

  • Generalisability and Reproducibility

Scientist always want to find some generalized knowledge and hope it can be reproduced in all places. It requires a large amongt of evidences and stronger schemes.

When a study can only explain and evaluate the built environment-travel association in a specific city or region, the results are still valuable for this city/region. the trip-based model (TBM) and activity-based model (ABM) are designed for forecasting the future scenarios based on the local data. These simulation methods are very elaborate and widely used.

  • The Scales of Intervention

The urban development policies have their spatial scales. For example, UGB would affect all the people in the metropolitan.

A TOD project, and a neighborhood upzoning may change or not change people’s travel behavior who living or working inside the neighborhood.

  • A case study

In 2019, Oregon legislators passed the first law (HB2001) in the United States legalizing duplexes on city lots.14 ‘Missing Middle Housing’ (Figure ) claims that more middle-dense communities would make less reliance on cars.15

The goal of making American communities “car free” like some European cities has been widely discussed for many years.16 From “Compact City” (1970s), “New Urbanism” (1980s), to “Smart Growth” (1990s), urban planners and researchers agree that less automobile dependence has many benefits including fewer traffic accidents involving injury or fatality, less congestion, less greenhouse gas emission, more active trips and healthier lifestyle.17 The controversial part is the role of density. Does density strongly affect VMT - a primary variable representing the degree of automobile dependence? Independently or not? Does the effect exist everywhere or in a specific geographic range?

B.3 Other thoughts

Aston et al. (2020) conduct a systematic analysis on study design of built environment-transit research. Their results show that 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:

  1. Travel behavior data sources
  2. Population segments
  3. Transit modes
  4. Catchment buffer size and type

Banister (2008) suggests that urban sustainable mobility should be the third principle in addition to derived demand and cost minimization.

`r if (knitr:::is_html_output()) ’ # References {-}

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  1. https://olis.leg.state.or.us/liz/2019R1/Downloads/MeasureDocument/HB2001↩︎

  2. https://www.sightline.org/2019/06/30/oregon-just-voted-to-legalize-duplexes-on-almost-every-city-lot/

    https://olis.leg.state.or.us/liz/2019r1/Downloads/CommitteeMeetingDocument/158465↩︎

  3. https://roomfordebate.blogs.nytimes.com/2009/05/12/carless-in-america/↩︎

  4. https://www.aarp.org/livable-communities/info-2014/livability-factsheet-density.html↩︎