Workplace Choice Model: Insights into Spatial Patterns of Commuting in 3 Metropolitan Regions

Planning Applications Conference, Reno, NV, May 2011 1

Workplace Choice Model:Insights into Spatial Patterns of Commuting in 3 Metropolitan Regions

Peter Vovsha, Surabhi Gupta, Joel Freedman, Heather Fujioka (PB)Wu Sun (SANDAG)Vladimir Livshits (MAG)

Importance of Workplace Choice Cornerstone of demand model:

Usual workplace choice in ABM HBW trip distribution in 4-Step

New observed phenomena and tendencies: Growing share of work from home & telecommuting More specialized occupations

Advantages of ABM framework: Directly comparable to Census/ACS Unlimited segmentation (occupation, income, gender) Disaggregate estimation & application of utility

functions

Planning Applications Conference, Reno, NV, 9-12 May 2011 2

General Model Framework


Worker characteristics:• Person (age, occupation, gender, education, etc)• HH (income, composition, age of children)• Residential location (accessibility to relevant jobs)

Work at home permanently

Usual workplace out of home

TAZ 1:Jobs

TAZ 2:Jobs

TAZ N:Jobs

…

Individual accessibility

Workplace Type Choice Utility


Work out of home:

Work at home:

Occupation Person type

Residential zone

Workplace zone

Workplace zone choice

utility

Accessibility to jobs

Person & HH attributes

Workplace Location Choice Utility


Occupation Person type

Residential zone

Workplace zone

/Zone size term (relevant jobs)/Mode choice logsum/Distance decay function/Agglomeration & competition effects

Mode

Elemental

functions

Competing

locations

Distance Decay Function Linear combination of

elemental distance (D) functions:

LN(D) D0.5

D D2

D3

Great degree of flexibility in describing various non-linear effects


0

10

20

30

40

50

60

0 10 20 30 40 50 60 70

Deca

y

Distance

0

0.5

1

1.5

2

2.5

0 10 20 30 40 50 60 70

Deca

y

Distance

Research Focus Factors affecting work from home Factors affecting choice of out-of-

home location: Level of segmentation of workers & jobs

in the size variable (income group, occupation)

Individual perception of accessibility to job (willingness to spend time on commuting)


Transferability Workplace location choice model with a

rich set of socio-economic and travel/accessibility variables transferable from region to region?

If not, what are the specific regional conditions that create uniqueness and are not incorporated in the model?

Same model structure estimated and validated for 3 different regions


3 Metropolitan Regions


Characteristic San Diego, CA

Phoenix, AZ

Tucson, AZ

MPO for which the ABM is developed

San Diego Association of Governments (SANDAG)

Maricopa Association of Governments (MAG)

Pima Association of Governments (PAG)

Population 3,095,000 4,261,000 1,035,000HHs in the survey

3,651 3,357 1,710

Workers in the survey

4,151 3,001 1,323

Working from home

11.2% (466) 13.5% (405) 14.2% (188)

Workplace Type Choice – Work from Home (MAG/PAG)


Variable Coefficient t-stat

ConstantsGeneral -0.851 -2.46Tucson -0.034 -0.33

Status Full Time Worker -1.178 -11.04Gender Female -0.346 -3.43

Household composition

Female Worker with Preschool Child Child in the HH 0.382 1.68

Non-Working Adults in the HH -0.192 -1.54

Occupation Sales or marketing 0.765 5.89

Age Group

Age <= 35 years -0.230 -1.31

35 years to 44 years (reference)

45 years to 54 years 0.332 2.3455 years to 64 years 0.348 2.37Age 65 years or older 0.432 2.38

Household Income group

$49,999 or Less -0.090 -0.63

$50,000 to $74,999 (reference)

$75,000 to $99,999 0.160 1.07$100,000 or more 0.267 1.95

Education Level

Less than High School Educated -0.398 -0.95

High School completed (reference)Bachelor's or Some College degree holder 0.295 2.28

Master's or higher degree holder 0.300 1.89

Accessibility Accessibility to Employment Locations by Job Category (Logged)

-0.069 -2.22

Model stats

Number of Observations 4,324

Likelihood with Constants only -1728.4776

Final likelihood -1601.4239

Rho-Squared (0): 0.4657

Rho-Squared (constant): 0.0735

Predicting Future for Working from Home & Telecommuting Rapidly growing %:

Work from home Full or partial telecommuting Compressed & flexible work schedules

Result of: Communication technology Structural shifts in occupation and industries

One of the biggest unknowns: Saturation or trends will hold?

Significant impacts on congestion levels (reduction) and VMT (mixed):

Effective policy variable Sensitivity tests possible with model that has this component as

policy lever


Observed Commuting TLD


0%

5%

10%

15%

20%

25%

30%

35%

40%

45%

0 - 5 5 - 10 10 - 15 15 - 20 20 - 25 25 - 30 30 - 35 35 - 40 40 - 45 45 - 50 50 - 55 55 - 60 60 - 65 65 - 70 70 - 75 75 - 80

Perc

enta

ge o

f Wor

kers

Distance, miles

SANDAG

MAG

PAG

Segmentation of Workers and Jobs by Occupation (MAG/PAG) Workers in NHTS 2008 are classified by 5

occupation categories: Sales, marketing Clerical, administrative, retail, Production, construction, farming, transport Professional, managerial, technical Personal care or services

Jobs in each TAZ are classified by 2-digit NAICS codes (26 categories)

26 to 5 correspondence used to segment the size variables by 5 categories


Segmentation of Distance Decay Functions


2 worker status categories: Full-time (30+hours per week) Part-time (<30 hours per week)

3 gender / household composition categories: Male Female w/child U6 Female w/o child U6

3 household income groups: Low (<$50K) Medium ($50K-$100K) High ($100K+)

Results in 2×3×3=18 segments

Estimation of Distance Decay Functions Baseline worker case:

Male Full-time Medium HH income ($50K-$100K)

Main impacts on top of the baseline found in all 3 regions: Female gender:

With preschool child U6 W/o preschool child U6

Part-time Low income (<$50K) High income (>=$100K)

Mode choice logsum coefficient kept 0.5 across all three regions that is close to the original estimated values


Baseline Distance Decay

Planning Applications Conference, Reno, NV, 9-12 May 2011 16-12

-10

-8

-6

-4

-2

0

0 10 20 30 40 50 60 70 80 90

Utils

Distance, miles

SANDAG

MAG

PAG

SANDAG jobs are closer to population compared to MAG while PAG is a smaller compact region

Impact of Part-Time Work


-9

-8

-7

-6

-5

-4

-3

-2

-1

0

0.0 10.0 20.0 30.0 40.0 50.0 60.0 70.0 80.0 90.0

Utils

Distance, miles

SANDAG

MAG

PAG

Part-time workers look for local jobs; the tendency is most prominent in small regions like PAG for short commuting under 10 miles (majority of cases)

Impact of Low Income


-5

-4

-3

-2

-1

0

1

0.0 10.0 20.0 30.0 40.0 50.0 60.0 70.0 80.0 90.0

Util

s

Distance, miles

SANDAG

MAG

PAG

Low-income workers look for local jobs and are less specialized in occupation; the tendency is less prominent in small regions like PAG

Impact of High Income

Planning Applications Conference, Reno, NV, 9-12 May 2011 19-0.5

0

0.5

1

1.5

2

2.5

3

3.5

4

0.0 10.0 20.0 30.0 40.0 50.0 60.0 70.0 80.0 90.0

Util

s

Distance, miles

SANDAG

MAG

PAG

High-income workers do not look for local jobs; for MAG high-income workers could not be distinguished from medium-income workers (baseline)

Impact of Female Gender


-7

-6

-5

-4

-3

-2

-1

0

0.0 10.0 20.0 30.0 40.0 50.0 60.0 70.0 80.0 90.0

Util

s

Distance, miles

SANDAG

MAG-w/o child U6

PAG-w/o child U6

MAG-w/child U6

PAG-w/child U6

There is still a gender bias; females, especially with small children tend to avoid long-distance commuting; w/o children the bias is less prominent, especially in a small region like PAG

Composition of All Impacts (MAG)


Validation, SANDAG, 8×8 Major Statistical Areas


0

50,000

100,000

150,000

200,000

250,000

0 50000 100000 150000 200000 250000

Estim

ated

Wor

ker F

low

s (N

orm

alize

d)

CTPP Worker Flows

Normalized Estimated

Linear (trend)

No K-factors

needed!

Conclusions / Main Factors Segmentation by occupation to connect right workers by

place of residence to the right jobs Commuting distance has a complex non-linear effect on

workplace choice differentiated by person type: Constrained time budgets result in cut-off thresholds (40-60 min) Minimal commuting time is acceptable and usable resulting in a

low-sensitivity region (0-30 min) Incorporation of these non-linear effects in mode choice

logsum instead of distance-based terms: Theoretically appealing Practically difficult to achieve: mode choice and destination

choice are subject to different considerations, time scales, and constraints


Conclusions / Transferability Main factors and effects generic

across regions Function forms and coefficients

specific to each region (more rigorous stat tests needed)

Region size, transportation accessibility, and spatial structure of population & jobs affect the results


Conclusions / Differences Aggregate constraints shape spatial structure:

SANDAG and MAG regions are bigger than PAG; most PAG specifics stem from the smaller size; however:

SANDAG region has less separation between population and employment; SANDAG TLD is closer to PAG; SANDAG basline distance decay function is the strongest

Individual behavior adjusted to regional “norms”: In MAG region both medium and high income workers

equally tolerant to longer commuting In small region like PAG gender differences not

prominent w/o small children


Conclusions / Application In principle, results applicable to all types of

models (ABM and 4-Step): Segmentation of size variables (constraints) by

occupation (5+ categories) Segmentation of impedance function by income,

gender, and worker status (18 categories) In practice:

Difficult to apply with 4-Step because of the limited segmentation (60+ segments needed)

Easy to incorporate in microsimulation ABM Segmentation of workers and jobs by occupation

require LU model Planning Applications Conference, Reno, NV, 9-12

May 2011 26

Thanks for Your Attention! Q?


Documents

Workplace Choice Model: Insights into Spatial Patterns of Commuting in 3 Metropolitan Regions