ITE Journal - May 2021 - 45

The bad news is that most practitioners are not exposed to those
measures or the methods behind them. Understandably, they tend
to base decisions on longstanding, mode-specific measures of speed
and level of service (LOS), which imperfectly capture accessibility.
The basic principles of accessibility sometimes come into play as
a step in conventional travel demand modeling, but rarely as an
outcome measure in decision-making.
A new practitioner guide aims to bridge the gap between
research and practice. Measuring Accessibility was released in
January of this year by the State Smart Transportation Initiative
(SSTI) at the University of Wisconsin-Madison.2 It is downloadable
at www.ssti.us/accessibility-analysis.

Mobility and Accessibility
While conventional mobility measures have their place, such as
in managing traffic friction from driveways or setting standards
for appropriate levels of transit service, they are poor indicators of
overall system performance.3 They are modally specific, making
comparisons across modes difficult. And by failing to consider
proximity, they do not reflect overall travel times. The best-known
reaction to these problems, in terms of moving away from conventional metrics, has happened in California, where legislators removed
highway LOS from consideration in critical environmental reviews
for both transportation and land-use projects, pushing practitioners
instead to consider vehicle-miles traveled (VMT) impacts.*
In one of the most notable moves toward accessibility in
practice, Virginia requires major state-funded transportation-capacity projects to be evaluated based on five to six criteria,
including accessibility.** In implementing the law, the Virginia
Department of Transportation considers both access to jobs by
various modes and access to everyday non-work destinations by
walking. At the regional level, the Metropolitan Transportation
Commission (the Bay Area Metropolitan Planning Organization in
San Francisco, CA, USA) prioritizes projects that provide access to
jobs in 30 minutes or less by auto or 45 minutes or less by transit.
In all three cases, the move from LOS and the moves toward
accessibility required substantial time and resources that most
practitioners lack. The resources needed generally go beyond
those readily available, such as the U.S. Environmental Protection
Agency's Smart Location Database, the Accessibility Observatory at
the University of Minnesota, PeopleForBikes' Bike Network Analysis
tool, and Walk Score. Those are all useful in illustrating the concept
of accessibility and considering baseline conditions, but less helpful
in evaluating transportation projects and programs for accessibility.
Tools and methods to support such analyses do exist, but there
are barriers such as technological challenges (data availability and
computing power) and a lack of standards or best practices to rely
*	
**	

California S.B. 375 " California Environmental Quality Act, " 2013.
Virginia H.B. 2, 2014.

on.1, 4 The SSTI guide, based on the authors' work with transportation agencies around the country, attempts to lower those barriers.
Some key takeaways from the guide are outlined in the
following sections.

The Basics
With so many methods and countless studies to draw from,
practitioners can find it difficult to get started. Fortunately, some
the earliest and simplest approaches are beginning to emerge
as standards of practice.5 For instance, practitioners should feel
comfortable reporting the number of jobs reachable in a region
from any given location within a certain travel time (often called
" cumulative accessibility " ) or, taking it one step further, incorporating a travel-time decay function to assign more weight to closer
jobs, just like in a gravity-based travel demand model. The former is
simpler to measure and report, but the latter avoids issues that arise
from choosing an arbitrary hard travel time cutoff. Reporting these
weighted values can be as simple as saying, " weighted jobs " or " the
number jobs within typical travel times. "
A second emerging practice is to measure local access to
important non-work destinations separately from access to jobs.
Commuting and other work-related travel accounts for just 19
percent of household trips and 22 percent of household personmiles traveled.6 Therefore, people's well-being and travel behavior
often depends on access to things like food, schools, parks and
other essential services, as well as more discretionary destinations
such as restaurants. Walk Score is a successful commercial example
of a non-work accessibility score.
There are more complex methods that let analysts measure
accessibility, like those based on utility (as in travel-demand
models), those that account for competition among different opportunities and destinations, and some that even rely on time-space
prisms to measure individuals' daily accessibility. But the two basic
concepts outlined above-regional access to jobs and local access
to non-work destinations-are enough to build a strong foundation
and drive or inform most decision-making around project
development, selection, and operation.

Technical Considerations
Before getting started with accessibility analysis, transportation
professionals will need the right data and analytical tools. Travel
demand models are an option for some, but they typically lack the
granularity and detailed transportation networks to capture walking
and biking especially well. In addition, running those models for a
large number of projects or scenarios can be time-consuming and
cost prohibitive. An important consideration is that agency staff
should be able to easily input projects or bundles of projects and see
the impacts to accessibility without much time or effort. There are
now leaner, prepackaged options for doing just that.
w w w .i t e.or g

May 2021

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http://www.ssti.us/accessibility-analysis http://www.ite.org

ITE Journal - May 2021

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