peter vovsha, parsons brinckerhoff, new york, ny, usa gaurav vyas, parsons brinckerhoff, new york,...

•Peter Vovsha, Parsons Brinckerhoff, New York, NY, USA•Gaurav Vyas, Parsons Brinckerhoff, New York, NY, USA•Danny Givon, Jerusalem Transportation Masterplan Team (JTMT), Jerusalem, Israel•Yehoshua Birotker, Jerusalem Transportation Masterplan Team (JTMT), Jerusalem, Israel

USUAL WORK ARRANGEMENTS: STATISTICAL ANALYSIS AND

MODEL IMPLEMENTATION FOR JERUSALEM

1TRB Application

Conference, May 2013

Commuting to work: Main traffic component in peak periods Cornerstone of travel demand modeling

Traditional view on commuters: Full-time worker Commuting every regular workday Commuting in peak hours AM / PM Inflexible schedule dictated by employer

New tendencies: Growing number of alternative flexible arrangements New phenomena like telecommuting

Motivation

2TRB Application


Alternative work arrangements affect: Commuting patterns and frequency Sensitivity to congestion pricing and other

policies Correspond to policy levers:

Compressed work weeks Peak spreading for work hours

Incorporation in travel models: Explicit (sub-model) or implicit (DAP, work trip

rates)? Assumptions for future (fixed or trends?)

Implications for Modeling

3TRB Application


Arrangement Normal Alternative

Job type Full time Part-time

Number of jobs 1 2+

Usual workplace location

Out of home - permanent

At home

Out of home - variable

Commuting frequency

5 days a week 1-4 days a week (compressed)

6-7 days a week (extended)

Telecommuting frequency

Low (less than once a week)

High (once a week or greater)

Schedule flexibility No / little Yes / significant

Usual schedule AM / PM Second shift, other

Taxonomy of Usual Work Arrangements

4

Population Sector # Households Full-time

workersPart-time workers

Secular 4,887

5,251

1,277

Ultra Orthodox

2,119

1,162

558

Arab 1,224

1,528

198

Total 8,230

7,941

2,033

Jerusalem Household Travel Survey, 2010

5TRB Application


Job Type

• Full-time• Part-time

Number of Jobs

• 1• 2+

Empl.Type

• Hired• Self Employed

Work Place

• Home• Permanent Work Place• Varied Work Location

24 Alternatives

If home is notthe work place

Usual Work Location Model

Commuting Frequency and Flexibility

Person and Household

Characteristics; Occupation

Main Work Arrangement (lifestyle)

Commuting Frequency and Flexibility

7

Number of Days Working

1/7

7/7

4/7

2/7

3/7

5/7

6/7

Telecommuting Frequency (8 categories)

Schedule Flexibility: 1) No, 2) Some, 3) High, 4) No

schedule

Usual Schedule (5 categories)TRB Application


Usual Schedule Categories

TRB Application Conference, May 2013

8

Usual Arrival Time to work

Usual Departure Time from work

Before Noon Noon-2 PM 2 PM-4PM

4 PM - 6 PM

6 PM - 8 PM

8 PM - 10 PM

10 PM - 12 PM

After Midnight

Before 6 AM 113 31 58 49 18 11 4 3

6 AM - 8 AM 24 142 670 872 247 39 12 23

8 AM - 10 AM 19 472 881 1,182 557 119 18 32

10 AM - Noon 0 21 43 39 65 27 8 1

Noon - 2 PM 0 1 7 25 14 14 6 3

2 PM - 4 PM 0 0 1 5 15 27 32 5

4 PM - 6 PM 0 0 0 3 8 18 14 7

After 6 PM 0 0 0 0 2 1 19 26Conventional Schedule

Early DepartureNormal Departure and late arrival

Over Workers (early arrival and late departure)Second shift workers

Main Work Arrangement: 2 for Job types 2 for number of jobs 2 for Employment types 3 for Work location

Utility function: 4 parameterized terms by main dimensions:

Job types Number of jobs Employment types Work location

Interaction terms (2-way constants)

Choice Model 1

9TRB Application


Work Location 40 TAZs are sampled from the pool of all

TAZs Size variables include inter-sector friction

variables Sampling is based on the employment

characteristics and impedance between origin and destination TAZ

Choice Model 2

10TRB Application


Commuting Frequency and Flexibility: 7 for number of days working 'n+1' alternatives for telecommuting frequency for ‘n’

number of days at work 4 for schedule flexibility 5 for usual work schedule

Utility function: 4 parameterized terms by main dimensions:

Number of days at work Telecommuting frequency Schedule flexibility Usual work schedule

Interaction terms (2-way constants)

Choice Model 3

11TRB Application


Variables Part-time 2+ jobs Self-employed

Home as work place

Variable work place

Age>65 years More More Less

Young Arab More

Arab Male Less Less More Less Less

Arab Female More Less Less Less Less

Higher Education

More Less More Less

Clerical worker More Less More More Less

Manufacturing, construction

worker

Less Less More Less More

Non-professional

More Less Less More Less

Behavioral Insights – Main Work Arrangements


12

Behavioral Insights-Main Work Arrangements


13

Interaction

2+Jobs Self-employed

Home as work place

Variable work place

Part-time Positive Positive Positive Positive

Self employed

Positive Positive

Variable Work place

Positive

Variables Part-time 2+ jobs Self-employed

Home as work place

Variable work place

Low Income More Less More More

Female and presence of

children

More Less More More Less

Only worker and

household size>1

More for Arab Sector

More

Behavioral Insights –Work Location


14

0 10 20 30 40 50 60 70

-10.00

-8.00

-6.00

-4.00

-2.00

0.00

2.00

4.00

FTW, Male, Medium Income.lower education, Secular(Base) PT Worker Effect Female Effect

Lowinc Effect Highinc Effect Female with pre-school child

Higher Education Arab Orthodox

Female with Part time

Util

ity

Base

Individual Marginal Effects

Distance, km

Behavioral Insights –Work Location (Unique Feature)


15

Inter-Sector (Social) Friction

Residential Sector/Area

Employment Sector/Area

Arab Orthodox SecularArab High High

Orthodox Very High Very HighSecular Very High High

Variables Number of days at work


Schedule Flexibility

Usual Schedule/ duration

Part-time worker Less than 5 Less More Less conventional

Self-employed Less than 5 More More Less duration

Multiple jobs More than 5 More More Less conventional

Variable work place

Less than or equal to 5

More More More conventional

Academic professional

More than 5 More More More conventional

Orthodox-female

Less than 5 Less Less Less conventional

Age>65 years Less than 5 More More Less duration

Higher Education

Less than 5 More More More conventional

Behavioral Insights –Commuting Frequency Model

16

Variables Number of days at work


Schedule Flexibility

Usual Schedule/

work duration

Female and presence of

children

More likely equal to 5

Less Less duration

Low Income More likely equal to 5

Less Less duration

Female and low income

More

Behavioral Insights –Commuting Frequency Model

17

Interaction No-schedule Some Flexibility

High Flexibility

Telecommuting Positive

Non-conventional

schedule

Negative Negative Negative

Placement in Jerusalem CT-RAMP ABM

18

Population Synthesis

Main Long-term Work Arrangements

Long-term Location Choices

Usual Commuting Freq. & Flexibility

Household & Person Mobility Attributes

Daily Activity-Travel Pattern Type & Time Allocation

Tour Formation

Location of Non-Work Act.

Tour & Trip Details

Traffic &Transit Network

Simulations

1

23

4

5


Evolution of usual work arrangements: Communication technology revolution (work from

home, telecommuting) Structural shifts in industry & occupation (flexible

work hours, self employment) Consequence of growing congestion (compress

work weeks) Choice Models:

Statistically estimated for base year Adjustments for future years:

Scenarios and trends (for example, growing telecommuting)

Policy tests (for example, shifted usual work hours)

Forecasting

19TRB Application


Understanding principal changes in commuting patterns:

Growing share of alternative work arrangements Incorporation in travel models:

Policy lever / scenario management Policy implications of alternative work

arranges: Beneficial for reduction of commuting volumes

in peak periods Demand elasticity to congestion pricing Impact on total VMT remains unclear

Conclusions and Perspectives

20TRB Application


peter vovsha, parsons brinckerhoff, new york, ny, usa gaurav vyas, parsons brinckerhoff, new york,...

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