workplace choice model: insights into spatial patterns of commuting in 3 metropolitan regions
DESCRIPTION
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: - PowerPoint PPT PresentationTRANSCRIPT
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
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General Model Framework
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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
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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
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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
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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)
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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
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3 Metropolitan Regions
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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)
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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
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Observed Commuting TLD
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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
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Segmentation of Distance Decay Functions
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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
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Baseline Distance Decay
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-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
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-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
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-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
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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
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-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)
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Validation, SANDAG, 8×8 Major Statistical Areas
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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
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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
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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
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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?
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