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One of my gripes is that the public sector is really bad about
measuring results.  

LUSCAT is sincere about climate protection, so we must have very
high quality data so that we can measure the progress of state and
regional land use policy implementation.  Otherwise, we will just
be proposing a series of projects without being able to ascertain
their effectiveness. If the projects don't work, we need to know
rapidly, so that we can change course to meet 2020 carbon targets.
Currently, land use related measurement is primitive. We don't
really know what is happening.   

We need innovation in measurement of journey to work information:
home origination address and work destination address.  We must
have 95% or better coverage of all CA workers and we must have
that data updated every year. 50% of household VMT occurs from
commuting.    

CURRENT, INSUFFICIENT DATA:

The Census Transportation Planning Package CTPP3 Flow Data
provides "1 in 10" coverage of journey to work, every 10 years,
but the data takes about six years to come out.  The data is stale
by the time it is available.  LEHD (Local Employment Housing
Dynamics) data holds the promise of providing 80% or better
coverage for journey to work data, but there are many problems
with the current CA implementation.  

As far as work trips.  A little bit more than 50% of household VMT
is in commuting.  See Jonathan Rose and Calthorpe:
(http://www.cities21.org/HH_NRG_consumption.htm,
http://www.cities21.org/HomeEnergyUseJonathanRoseLLC.xls ).  The
average annual Bay Area commute is about 6,720 miles.  14 mile
one-way commute and 240 commuting days.  To meet 2050 CO2 goals,
we surely need to cut average one-way commute distance
dramatically.   

The 2006 JAPA Robert Cervero / Michael Duncan paper argues for
emphasizing efforts to reduce jobs/housing distance to reduce VMT.
The article is "Which Reduces Vehicle Travel More: Jobs-Housing
Balance or Retail-Housing Mixing?" in the Autumn 2006 JAPA.  It's
not that Cervero is arguing against smart growth to minimize VMT
on the 84% of non-work trips, he's just saying that we have to
place a higher priority on the 16% of journey-to-work trips to
reduce VMT.  

RECOMMENDATION: 

The state should modify CA Income Tax forms (just slightly) to
collect work address data, to provide 95% or better coverage of CA
journey to work, updated each year.  Once state law has been
changed, then the data can be collected by the State Labor Market
Information (LMI) office. Public sector journey to work data
should also be developed.      

The resultant journey-to-work database should be "anonymized" to
the point where no "personally identifiable" data is stored.  LMI
should establish procedures to anonymize the data and safely
destroy the personally identifiable source data.    

HERETOFORE IMPOSSIBLE QUERIES MADE POSSIBLE:   

* San Ramon and Dublin were the fastest growing residential
communities in the Bay Area in 2007.  6,000 new housing units were
added.  What is the distribution of work destinations for these new
residents?  Is the average journey to work distance shorter or
longer than we expect?  Are our new policies working like we
expected?  
* We've added a super new master planned community in Tracy. 
Their marketing brochure promised that this would be an
exceptionally green place, with solar on every rooftop.  What's
the journey to work like for those 2,000 new 3,000 square foot
single family homes?   
* It's 2010.  Our RHNA policy to balance jobs/housing in affluent,
job-rich suburbs is in place.  How are we doing?     
* We implemented policies to reduce commute distance in 2009.  How
did we do?  We then made the policy stronger.  How did we do in
2010?    
* Provide a picture of the commute distribution of Bay Area
extreme commuters, covering 95% or more of those commuters. 
* Is there a need for subscription commute bus service from
Manteca to San Ramon’s Bishops' Ranch? Using NJIT's algorithmic
bus route optimization software, where should we place bus stops
to attract the most riders?  
* We have a new Alameda County dynamic ridesharing service.  Where
should we target our marketing efforts? 
* By May 2013, answer the question: In 2012, where were the new
housing units built for Bay Area workers?  

ADDITIONAL BENEFIT: 
* Provides very useful and accurate input data for MPOs and
transit agencies for travel demand forecasting models.  Makes
modeling better.  

BACKGROUND: 

The study of journey to work is a bit of its own field.  One
example of some of the things that we do with CTPP3 data can be
found in the Bay Area Business Park Catalog:
http://www.cities21.org/BABPC/ .  A three-paragraph description
follows:  

We have identified 17 Bay Area suburban major employment centers,
13 in Silicon Valley.  The 17 centers are mostly traditional
suburban office parks with many tech workers.  Exceptions to
traditional office parks include: a) Emeryville is an edge city
with more than 1MM square feet of retail and extensive
residential, b) Stanford University encompasses the University,
the regional Stanford Shopping Center, Stanford Hospital, and
downtown Palo Alto, c) SJC is the San Jose airport major activity
center, d) Walnut Creek is a suburban downtown with dense
employment.  
Each center has at least 15,000 jobs.  The 17 centers support a
total of 594,000 jobs. SOV commute mode share varies from 85% to
65%.  The Stanford University job center stands out with 16.8% of
commuters biking or walking to work. The other 16 job centers
clump between 4.9% and 0.6% bike/ped commute mode share.
Stanford's programs to put housing by jobs are shown as a singular
success in the high-mileage world of suburban job centers.

Commute distance appears longer than was previously thought.  A
mean "crow flies" one-way commute distance (Stanford Research
Park) of 14 miles translates into roughly 18.2 driving miles. 
Other commute surveys report Silicon Valley commute distance of 14
miles.  The CTPP3 data used in this EPA study uses a larger sample
than other studies and has less "self selection bias."  This
result may point out that the high income workers in job centers
live farther away than typical suburban workers, or it simply may
point out that other phone surveys underreport commute distance,
because higher income workers are more likely to hang up on
tele-market researchers.

This proposal derived from meetings with: Nanda Srinivasan
(Consultant, CTPP and National Household Travel Survey), Ed
Christopher (FHWA, Chair TRB Census Transportation Committee),
Elaine Murakami (FHWA, Mgr, CTPP and National Household Travel
Survey), Chuck Purvis (MTC), Eileen Rohlfing (State Employment
Devt Dept, Labor Market Information Division).  This policy
proposal comes from  the U.S. EPA’s “Transforming Office Parks
into Transit Villages” Study.