systems analysis group one abm for four cities: experience of abm estimation on a pooled dataset of...

Systems Analysis Group

One ABM for Four Cities: Experience of ABM Estimation on a Pooled Dataset of Multiple Surveys

Surabhi Gupta, Peter Vovsha, Gaurav Vyas,

Parsons Brinckerhoff Inc.

Rebekah Anderson, Greg Giaimo,

Ohio Department of Transportation


4 Regions

Characteristics Columbus Cleveland Cincinnati Dayton

Population 1.66 M 2.02 M 1.99 M 0.8 M

# Counties 7 5 8 ( OH, IN, KY) 3

Transit modes Express bus, Local bus

Heavy Rail, BRT, Express

bus, Local bus

Express bus, Local bus

Express bus, Local bus

Toll roads No Yes No No


4 Regional Household Travel Surveys

Characteristics Columbus Cleveland Cincinnati Dayton

MPO MORPC NOACA OKI MVPRC

# Households 5,555 4,250 2,050 1,950

# Days 1 3 3 1

Survey year 1999 2012-13 2010 2001

Type Prompted recall (PR)

GPS + partially PR

GPS + partially PR

PR

Time of the year Fall/Spring All year All year Fall/Spring


Motivation

• Develop the most possible generic ABM for

all regions:

• Transferability as desired feature rather than

post-development analysis

• Bigger and richer dataset for advanced ABM

compared to any regional HTS on its own


Data Processing• Consolidating Survey Data:

• Household File

• Person File

• Trips File

• Vehicle File

• Recoding Variables:

• Common variable codes

• Unknown for missing variables in a particular region


How to handle missing data in estimation?

• Missing independent variables (e.g.,

income, age etc)

• Create dummy for missing category

• Cannot estimate region-specific coefficients for

any attribute missing for the region

Systems Analysis GroupNYMTC, April 2, 2014

Using Pooled Dataset for Model Estimation / General Approach

7

Dependent variable Independent variables

Y1st Survey

2nd Survey Y

X1

X2

X2

X3

Still possible to estimate Y=f(X1,X2,X3)

Systems Analysis GroupNYMTC, April 2, 2014

Using Pooled Dataset for Model Estimation / Placeholders

8

Dependent variable Independent variables

Y1st Survey

2nd Survey Y

X1

X2

X2

X3

Estimated model example: Y=a1×X1×δ1 + b1×Z1×(1-δ1) +a2×X2 + b3×Z3×(1-δ2) + a3×X3×δ2

Z1

Z3

Pla

ceho

lder

s or

ap

prox

imat

ions

Applied model: Y=a1×X1 + a2×X2 + a3×X3


How to handle missing data in estimation?• Missing dependent variable (e.g., work arrangement model)

• Choice alternatives specific to region based on available data

• Component-wise utility function and generic coefficients

Alternative Number of jobs Work place type Available for

1 Single job Fixed work place Cleveland and Cincinnati

2 Single job Variable work place Cleveland and Cincinnati

3 Single job Home Cleveland and Cincinnati

4 Multiple jobs Fixed work place Cleveland and Cincinnati

5 Multiple jobs Variable work place Cleveland and Cincinnati

6 Multiple jobs Home Cleveland and Cincinnati

7 Single job NA Columbus and Dayton

8 Multiple jobs NA Columbus and Dayton


Transferability Analysis

• Every model has a rich set of variables:

• Household characteristics, person characteristics, activity

participation, LOS, accessibilities, time-space constraints

• Statistical analysis and model estimation/calibration:

• Generic model – no region-specific coefficients or

constants

• Partially segmented – some coefficients or constants are

region-specific

• Fully segmented – all or most coefficients or constants are

region-specific


Submodels: Generic or Specific?Sub-Model/ Component Generic or Region Specific

Work Arrangement Model Partially segmented

Work Location Choice Model Fully segmented

Schooling from Home Model Generic School Location Choice Model Fully segmented

Commuting Frequency Model Generic

Person Mobility Attributes Model Generic

Auto Ownership Model Generic

Auto Allocation Model GenericCoordinated Daily Activity Pattern Partially segmented

Mandatory Activity and Tour Frequency Partially segmented

Preferred Mandatory Activity Span Model Generic

Escorting children to School Generic


Submodels: Generic or Specific?Sub-Model/ Component Generic or Region Specific

Joint Tour frequency, party composition and household participation

Partially segmented

Joint Tour destination with stop frequency and location choice

Generic

Frequency of Household Maintenance tasks Generic

Allocation of Maintenance Tasks to Household Members

Generic

Person Frequency of Individual Activities Partially segmented

Tour Formation Models Generic

Tour Time-of-day Choice Model Generic

Tour Mode Combination Model Fully segmented


Fully- Segmented Models• Work and School Location Choice Models

• Size of region shapes tolerance to commuting distance

• Relative location of population and employment

0 10 20 30 40 50 60 70 80

-9.00-8.00-7.00-6.00-5.00-4.00-3.00-2.00-1.000.00

Columbus Cleveland Cincinnati Dayton

Distance to Work Location (miles)

Uti

lity


Partially Segmented Models

• Work Arrangement

• Coordinated Daily Activity Pattern

• Mandatory Activity and Tour Frequency

• Joint Tour frequency, party composition and

household participation

• Person Frequency of Individual Activities


Work Arrangement Model

• Number of Jobs ( 1, 2+)

• Region specific constants

Work Location Type (Fixed, Variable, Home)

• Generic

• Available for only 2 surveys (Cleveland and Cincinnati)

Columbus Cleveland Cincinnati Dayton

-2.5-2.0-1.5-1.0-0.50.0

Constant - Mul-tiple JobsU

tils


Coordinated Daily Activity Pattern

• Mandatory, Non-Mandatory, Home patterns

• Differences between Older (Columbus, Dayton) vs. Newer

(Cleveland, Cincinnati) Surveys

• Fall/Spring vs. All year for Mandatory frequency

• Prompted recall vs. GPS for Non-Mandatory vs. Home

FT Worker PT Worker Univ Stud Non-Worker Retiree Child (16-17)

Child (6-15) Child(0-5)0%

25%

50%

75%

100%Mandatory proportion in survey

ColumbusClevelandCincinnatiDayton


Mandatory Activity and Tour Frequency

• Tour Breaks – going home between work episodes

• Multiple work tours

• More probable for Dayton – smaller region size

Columbus Cleveland Cincinnati Dayton-2.0

-1.0

0.0

1.0

2.0

3.0

4.0

00.591

-1.299

2.962

Uti

lity

of

mu

ltip

le

tou

rs


Joint Tour Frequency and Participation

• Cleveland specific constants

• More maintenance, eating out and

discretionary joint tours

• Lower frequency of joint tours

• GPS survey, All yearForm New Tour Join Old Tour

-6.0

-5.0

-4.0

-3.0

-2.0

-1.0

0.0

Cleveland Others

Shopping Maintenance Eating Out Visiting Discretionary

-1.5

-1

-0.5

0

Cleveland Others

Activity Purpose

Uti

lity

Fully Joint Tours0%

20%

Proportion from survey

Columbus ClevelandDayton


Person Frequency of Individual Activities

• # of Eating out, visiting, and discretionary activities

• Region specific constants by purpose & frequency

• Cleveland – time trends?

• Cincinnati data was not used due to trip purpose imputation issues

Breakf

ast

Lunch

Dinner

Visitin

g

Discre

tionary

-2

-1

0

ClevelandDayton

Activity Purpose

Uti

ls

1 2 3+-4.0-3.5-3.0-2.5-2.0-1.5-1.0-0.50.0

Cleveland

# Activities


Conclusions

• Overall most of the models generic and transferable

• Pooled dataset supports more advanced behavioral analysis:

• Recommend cooperation between MPOs

• Observed differences across regions partially reflect on survey

technology and time trends

• Moving towards more generic and portable models by having a rich

set of variables and more flexible specifications

• Destination choice and travel time-cost perceptions the most

fundamental difference across regions:

• Residential self-choice

• Endogenize and equilibrate time and cost coefficients as function of regional

travel conditions

systems analysis group one abm for four cities: experience of abm estimation on a pooled dataset of...

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