State of the Art & Practice in ABM: How Can We Better...

State of the Art & Practice in ABM: How Can We Better Address Planning Needs and Unique Regional Conditions in NY?

Peter Vovsha, PhD

Principal,

PB Americas, New York

NYMTC, June 8, 2011 1

Shift in Paradigm

35 large MPOs (1 million +) in US: Half of them have developed or are developing ABM

Others plan ABM in future

All large-scale model development projects for the last 5 years were ABMs

State-wide strategic decisions to move to ABM: California

Florida

Ohio

Michigan

2NYMTC, June 8, 2011

CT-RAMP Family of ABMs

Coordinated Travel Regional Activity-based Modeling Platform

Main features: Explicit intra-household interactions and

Coordinated DAP (CDAP)

Continuous temporal dimension

Integration of activity generation, location, and TOD sub-models

JAVA-based package for ABM construction


Members of CT-RAMP Family 1st generation:

Columbus, OH (MORPC) – in practice since 2004 Lake Tahoe, NV (TMPO) – in practice since 2006

2nd generation: Atlanta, GA (ARC) – in practice since 2009 San-Francisco Bay Area, CA (MTC) – in practice since 2010

3rd generation: San-Diego, CA (SANDAG) – started in 2008 Phoenix, AZ (MAG) – started in 2009 Jerusalem, Israel (JTMT) – started in 2009 Chicago, IL (CMAP) – started in 2010

Every model has many unique features


CT-RAMP Basic Structure


Other Regional ABMs in Practice

Individual Daily Pattern: San-Francisco County, CA (SFCTA) – in practice since 2001 Sacramento, CA (SACOG) – in practice since 2006 Tel-Aviv, Israel (NETA) – in practice since 2009 Denver, CO (DRCOG) – in practice since 2010

Interactive Tour Generation: New York, NY (NYMTC) – in practice since 2002

Individual Daily Pattern with some Joint Travel Components Seattle, WA (PSRC) – started in 2008 Houston, TX (HGCOG) – started in 2011

Continuous Duration: Los-Angeles, CA (SCAG) – started in 2009

Population Synthesizer: Baltimore, MD (BMC) – started in 2011

State-wide (Intercity) Models: Oregon – in practice since 2008 Ohio – in practice since 2009


ABMs in the United States

NY

San Francisco

Seattle

ColumbusDenver

Atlanta

Sacramento

Bay Area

Developed by PB

Developed by others

Oregon

Ohio

San Diego

Lake Tahoe

CT-RAMP Family

PhoenixLA

Chicago


Houston

Baltimore

What Can we Learn from These ABMs?

Selected model features relevant to BPM 2.0: Limitations of BPM 1.x

Unique conditions of NY

Forthcoming projects and policies

Examples from recent advanced ABMs: Conceptual model structure

Software & hardware solutions


Topics for Discussion & Examples of Advanced Features


Feature MORPC

ARC/MTC

SACOG

SANDAG

PS RC

MAG/PAG

JT MT

BMC C MAP

SC AG

PopSyn √ √ √

Intra-HH √ √ √ √ √ √

Short/long √ √

TOD √ √ √ √ √

Cong./price √ √ √

Spec. Mark. √

Transit/sp. √ √ √

Comm. Pat. √ √

Parking √ √

Emissions √ √

ABM-DTA √ √ √

Population Synthesis

Create list of HHs with person attributes based on: Controls specified for

each zone Sample of individual

HHs (PUMS, ACS)

Majority of existing procedures (including HAJ of BPM-1): HH-level controls only Empirical algorithm


PopSyn

Input zonal values Household distributionSeed distribution

from PUMS

Population & HHs By size (1-6)

Average HH income By income group (1-3)

By size & income (6x3) By size & income (6x3)

By size income &

workers (6x3x4)

By size income workers

& children (6x3x4x4)

List of HHs for

micro-simulation

Labor force Workers % (0,1,2,3+) by HH

size & income

Children % (0,1,2,3+) by HH

size income & workers

% HH curves (1,2,3,4,5,6+) as

function of average HH size

% HH curves (1,2,3) as function

of average HH income

SANDAG PopSyn II Features

Formulated as an entropy-maximization problem

Balance person and household controls simultaneously

Applicable to both Census 2000 and ACS data

Updated household weight discretizing step

Added household allocation from TAZ to small geography

Database-driven and OOD

PopSyn


New Balancing ProcedureType Controls A priori

weightsContribution coefficients

Multidimensional Matrix (CMF)

Row/column totals

Initial matrix Cell-row/columnincidence (0,1)

Table of categories (ARC)

Column totals Initial weight for category (row)

Row/column incidence (0,1)

Table of individual records (SANDAG)

Column totals Initial individual weight (row)

Row/columncoefficient (≥0)G

enera

lization

Each subsequent method includes the previous one as a particular case and guarantees the same result

Not every table of categories can be reduced to a matrix form! Not every table of individual records can be reduced to table of

categories!

12

PopSyn

NYMTC, June 8, 2011

Example of Table of HHs

HH ID HH size Person age HH initial weight

1 2 3 4+ 0-15 16-35 36-64 65+

1i 2i 3i 4i 5i 6i 7i 8i n

1n 1 1 20 2n 1 1 1 20 3n 1 1 2 20 4n 1 2 2 20 5n 1 1 3 2 20

…. … Control 100 200 250 300 400 400 650 250

13

PopSyn

NYMTC, June 8, 2011

Program Formulation

- Preserve initial HH weights as much as possible

- Meet all controls

Convex mathematical program with linear constraints Efficient method is applied to find solution:

14

PopSyn

NYMTC, June 8, 2011

SANDAG PopSyn Key Steps

Create Sample HHs

Balance HH Weights

Discretize HH Weights

Allocate HHs

Validate PopSyn

Create control targets

Create validation measures

PopSyn


Control Variables

Household-level controls Household size (1,2,3,4+) Household income (5 categories) Number of workers per household (0, 1, 2, 3+) Number of children in household (0, 1+) Dwelling unit type (3 categories) Group quarter status (4 categories)

Person-level controls Age (7 categories) Gender (2 categories) Race (8 categories)

PopSyn


Results – HH Characteristics

PopSyn


Results – Person Characteristics

PopSyn


NYMTC, June 8, 2011

Person Types in ABM

NUMBER PERSON-TYPE AGE WORK STATUS SCHOOL STATUS

1 Full-time worker 18+ Full-time None

2 Part-time worker 18+ Part-time None

3 Non-working adult 18 – 64 Unemployed None

4 Non-working senior 65+ Unemployed None

5 College student 18+ Any College +

6 Driving age student 16-17 Any Pre-college

7 Non-driving student 6 – 16 None Pre-college

8 Pre-school 0-5 None None

19

PopSyn

Intra-Household InteractionsType

Co

lum

bu

s

La

ke

Ta

ho

e

Atl

an

ta

Ba

y A

rea

Sa

n D

ieg

o

Ph

oe

nix

Je

rusa

lem

Ch

ica

go

Coordinated daily activity pattern type (sequential)

√ √

Coordinated daily activity pattern type (simultaneous)

√ √ √ √ √ √

Fully joint tours for shared non-mandatory activities

√ √ √ √ √ √ √ √

Allocated maintenance tasks √ √ √ √ √ √

Escorting children to school √ √

Car allocation √


Intra-HH

Why Intra-Household Interactions? For each worker:

80% probability of going to work

20% probability of not going to work

In 2-worker HH following IDAP:

64%=80%×80% - both workers going to work

4%=20%×20% - neither of workers going to work

32% - one of workers going to work

In 2-worker HH (observed and CDAP):

72% - both workers going to work

10% - neither of workers going to work

18% - one of workers going to work


Intra-HH

Coordinated Daily Activity-travel Pattern (CDAP)

Signature feature of CT-RAMP: Trinary choice for each HH member:

(M) Mandatory pattern (N) Non-mandatory travel active pattern (H) Home (non-travel pattern)

Modeled simultaneously for all HH members taking into account coordination between them

Two significant improvements for San-Diego and Phoenix ABM: Binary sub-choice of joint non-mandatory activity

added Intermediate nesting level to account for similarity

between N and H patterns


Intra-HH

CDAP for 2 Persons


Intra-HH

Why Joint Travel Explicitly?

Almost 50% of tours fully or partially joint: Destinations, modes, and TOD cannot be

modeled independently

HOV/HOT lanes: Propensity to carpool and constraints are

essential

Cannot be modeled through simple mode choice


Intra-HH

25

Travel Tours by Type

0%

10%

20%

30%

40%

50%

60%

70%

Ind Fully

Joint

Joint

Out

Joint

In

Drop

off

Get

out

Pick

up

Get-

in

NY Mid Ohio

Intra-HH

NYMTC, June 8, 2011

26

Choice Alternatives

Household generation of joint tours

Travel party composition for each joint tour

No tours

1 tour

2 tours

Shopping

Eating out

Maintenance

Discretionary

Shop/shop

Shop/eat

Shop/maint

Shop/discr

Eat/eat

Eat/maint

Eat/discr

Maint/maint

Maint/discr

Adults

Children

Mixed

Discr/discr

Intra-HH

NYMTC, June 8, 2011

Person participationin each party

Yes

No

Escorting Children to School


School tour allocation by half-tours

Outbound half-tour Inbound half-tour

Ride-sharing

Pure escort

Noescort

Ride-sharing

Pure escort

Noescort

1st 2nd 3rd 1st 2nd 3rd 1st 2nd 3rd 1st 2nd 3rd

1 2 3 4 5 6 7 1 2 3 4 5 6 7

Chauffeur outbound

half-tours

Numbered half-tour alternatives

49 entire-tour

combinations

from 11 to 77

Chauffeur inbound

half-tours Available chauffeurs Available chauffeurs

Intra-HH

Importance of Workplace Choice

Cornerstone of travel demand New observed phenomena and tendencies:

More specialized occupations Growing share of work from home, flexible

schedules, & telecommuting

Advantages of ABM framework: Directly comparable to Census/ACS Unlimited segmentation (occupation, income,

gender) Disaggregate estimation & application of utility

functions


Long & short

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

Long & short

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

De

cay

Distance

0

0.5

1

1.5

2

2.5

0 10 20 30 40 50 60 70

De

cay

Distance

Long & short

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 1,035,000

HHs 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)

Long & short

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


Long & short

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

Long & short

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


Long & short

Baseline Distance Decay

NYMTC, June 8, 2011 35-12

-10

-8

-6

-4

-2

0

0 10 20 30 40 50 60 70 80 90

Uti

ls

Distance, miles

SANDAG

MAG

PAG

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

Long & short

Impact of Part-Time Work

NYMTC, June 8, 2011 36-10

-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

Uti

ls

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)

Long & short

Impact of Low Income

NYMTC, June 8, 2011 37-6

-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

Uti

ls

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

Long & short

Impact of High Income

NYMTC, June 8, 2011 38-0.5

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

Uti

ls

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)

Long & short

Impact of Female Gender

NYMTC, June 8, 2011 39-8

-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

Uti

ls

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

Long & short

Composition of All Impacts (MAG)


Long & short

Validation, SANDAG, 8×8 Major Statistical Areas


0

50,000

100,000

150,000

200,000

250,000

0 50000 100000 150000 200000 250000

Esti

mat

ed

Wo

rke

r Fl

ow

s (N

orm

aliz

ed

)

CTPP Worker Flows

Normalized Estimated

Linear (trend)

No K-factors needed!

Long & short

Summary of Main Factors Segmentation by occupation to connect right workers by

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

workplace choice differentiated by person type: Constrained time budget results 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


Long & short

Advanced TOD Choice Models

Consider tours and activity durations, not just trips

Operate at fine level of temporal resolution: 30 min or less in discrete implementation

First continuous implementations

Generate consistent individual daily schedule w/o gaps or overlaps


TOD


Tour TOD Choice

5 23

Work tour to schedule

TOD


Tour TOD Choice

5 23

Work tour to schedule

Considerations for departure time:

•Office hours (7-10)

•Avoid congestion (10+)

•Give ride to child (7)

TOD


Tour TOD Choice

5 23

Work tour

10

TOD


Tour TOD Choice

5 23

Work tour

10

Considerations for arrival time:

•Office hours (<=20)

•Avoid congestion (<16)

•Tennis before dark (<17)

TOD


Tour TOD Choice

5 23

Work tour

10 15

Considerations for duration:

•8 work hours

•Finish presentation for NYMTC

TOD


Tour TOD Choice

5 23

Work tour

9 19

TOD

TOD Choice Dimensions Formal (820):

40 departure half-hours (5AM-24PM) by

40 arrival half-hours (departure-24PM) leads to

40×41/2=820 feasible combinations

Real & meaningful (120): 40 departure half-

hours and 40 arrival half-hours

and 40 durations

50

Departure Alternatives

Arrival Alternatives

1 2 3 4 . . n

1

2

3

4

.

.

n

TOD

NYMTC, June 8, 2011


Sequential Processing of Tours

5 23

1-Work

2-Discretionary joint

3-Shopping individual

TOD



5 23

1-Work

7-17

2-Discretionary joint


TOD



5 23

1-Work

7-17

2-Discret

20-23


TOD



5 23

1-Work

7-17

2-Discret

20-23

3-Sh

18-19

TOD

Calibration Results –SANDAG/Work


0%

2%

4%

6%

8%

10%

12%

14%

16%

18%

Befo

re 5

am

5:0

0 a

m t

o 5

:30 a

m

5:3

0 a

m t

o 6

:00 a

m

6:0

0 a

m t

o 6

:30 a

m

6:3

0 a

m t

o 7

:00 a

m

7:0

0 a

m t

o 7

:30 a

m

7:3

0 a

m t

o 8

:00 a

m

8:0

0 a

m t

o 8

:30 a

m

8:3

0 a

m t

o 9

:00 a

m

9:0

0 a

m t

o 9

:30 a

m

9:3

0 a

m t

o 1

0:0

0 a

m

10:0

0 a

m t

o 1

0:3

0 a

m

10:3

0 a

m t

o 1

1:0

0 a

m

11:0

0 a

m t

o 1

1:3

0 a

m

11:3

0 a

m t

o 1

2:0

0 p

m

12:0

0 p

m t

o 1

2:3

0 p

m

12:3

0 p

m t

o 1

:00 p

m

1:0

0 p

m t

o 1

:30 p

m

1:3

0 p

m t

o 2

:00 p

m

2:0

0 p

m t

o 2

:30 p

m

2:3

0 p

m t

o 3

:00 p

m

3:0

0 p

m t

o 3

:30 p

m

3:3

0 p

m t

o 4

:00 p

m

4:0

0 p

m t

o 4

:30 p

m

4:3

0 p

m t

o 5

:00 p

m

5:0

0 p

m t

o 5

:30 p

m

5:3

0 p

m t

o 6

:00 p

m

6:0

0 p

m t

o 6

:30 p

m

6:3

0 p

m t

o 7

:00 p

m

7:0

0 p

m t

o 7

:30 p

m

7:3

0 p

m t

o 8

:00 p

m

8:0

0 p

m t

o 8

:30 p

m

8:3

0 p

m t

o 9

:00 p

m

9:0

0 p

m t

o 9

:30 p

m

9:3

0 p

m t

o 1

0:0

0 p

m

10:0

0 p

m t

o 1

0:3

0 p

m

10:3

0 p

m t

o 1

1:0

0 p

m

11:0

0 p

m t

o 1

1:3

0 p

m

11:3

0 p

m t

o 1

2:0

0 a

m

Aft

er

12:0

0 a

m

Work Departure Observed Work Departure Estimated Work Arrival Observed Work Arrival Estimated

TOD

Calibration Results –SANDAG/Work


0%

2%

4%

6%

8%

10%

12%

14%

0 h

ours

0.5

hours

1 h

ours

1.5

hours

2 h

ours

2.5

hours

3 h

ours

3.5

hours

4 h

ours

4.5

hours

5 h

ours

5.5

hours

6 h

ours

6.5

hours

7 h

ours

7.5

hours

8 h

ours

8.5

hours

9 h

ours

9.5

hours

10 h

ours

10.5

hours

11 h

ours

11.5

hours

12 h

ours

12.5

hours

13 h

ours

13.5

hours

14 h

ours

14.5

hours

15 h

ours

15.5

hours

16 h

ours

16.5

hours

17 h

ours

17.5

hours

18 h

ours

18.5

hours

19 h

ours

19.5

hours

Work Duration Observed Work Duration Estimated

TOD

Basic Generalized Cost Function (Starting Point of SHRP 2 C04)

U=b×Time+c×Cost b = travel time coefficient

c = travel cost coefficient

VOT = b/c (constant)

99% of research and 100% of models in practice use this function

This function is simplistic and masks many important effects of congestion and pricing

57

Congestion & Pricing

NYMTC, June 8, 2011

VOT Growth with Journey Lengths

Cost damping: Poor perception of car operating cost vs. parking and tolls Relative rather than absolute perception of cost Cheaper housing and higher disposable income for long-

distance commuters Trip frequency inversely proportional to trip length (for non-

work travel) Higher car occupancy for longer trips (if car occupancy is not

accounted)

Time valuing: Risk aversion (if reliability is not accounted ) Unfamiliarity with distant locations Time budget constraints

58


NYMTC, June 8, 2011

Non-Linear Distance Effects (NY)

U=b×Time+c×Cost

U=(b1+b2×Dist+b3×Dist2+…)×Time+c×Cost

59

Distance

VOT

30 miles


NYMTC, June 8, 2011

VOT Drop for Long-Distance Commuters

Self-selection of low-VOT commuters by residential choice

Long commuting time used productively (laptops, cell phones)

Restructured (simplified) daily activity pattern: Compressed work week with no other out-of-

home activities on regular workday

Compressed shopping and discretionary activities on (extended) weekends

60


NYMTC, June 8, 2011

Perceived Time by Congestion Levels

U=b×Time+c×Cost

U=b1×FFTime+b2×Delay+c×Cost

b2 / b1 ≈ 1.5-2.0

Every minute spend in congestion conditions is perceived as 1.5-2.0 min of free driving!

Proxy for travel time reliability:

Loses significance if reliability is incorporated directly

Useful for simple models that cannot incorporate reliability directly

61


NYMTC, June 8, 2011

Impact of Income on Sensitivity to Cost (NY)

U=b×Time+c×Cost

U=b×Time+c×(Cost / Ince)

e ≈ 0.5-0.7

VOT grows with income (constant elasticity)

Commuting VOT range:

62

Household Income VOT

$12,500 $5/hour

$25,000 $8/hour

$50,000 $15/hour

$75,000 $22/hour

$100,000 $27/hour

$150,000 $39/hour

$200,000 $46/hour


NYMTC, June 8, 2011

Impact of Car Occupancy (NY) U=b×Time+c×Cost

U=b×Time+c×(Cost / Occf) f ≈ 0.6-0.8

VOT grows with occupancy but not linearly: Less cost sharing for intra-household carpools

Almost proportional cost sharing for inter-household carpools

Typical cost sharing: SOV=1.00

HOV2=0.57

HOV3=0.41

63


NYMTC, June 8, 2011

Cost Sharing Parameter for HOV

64

Intra-household

Inter-household

With

child

ren

Adults

only

1.0

0.0


NYMTC, June 8, 2011

Combined Income-Occupancy Effects

Captured by: Constants in mode choice framework Explicit modeling of joint travel in advanced ABMs

Low-income workers have more opportunities to form inter-household carpools: Fixed schedules Residential clusters Job clusters

Mitigates equity concerns regarding pricing: Cost is shared Low-income workers can switch from HOV or transit High-income workers can only switch to transit

65


NYMTC, June 8, 2011

Distributed Value of Time (SFCTA)

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

0.18

$- $5 $10 $15 $20 $25 $30

Value of T ime ($/Hour)

Pro

ba

bil

ity

De

ns

ity

Income $0-30kIncome $30-60k

Income $60-100kIncome $100k+



NYMTC, June 8, 2011

Activity Types

67

TYPE PURPOSE DESCRIPTION CLASSIFICATION ELIGIBILITY

1 Work Working at regular workplace or work-related activities outside the home.

Mandatory Workers and students

2 University College + Mandatory Age 18+

3 High School Grades 9-12 Mandatory Age 14-17

4 Grade School Grades K-8 Mandatory Age 5-13

5 Escorting Pick-up/drop-off passengers (auto trips only).

Maintenance Age 16+

6 Shopping Shopping away from home. Maintenance 5+ (if joint travel, all persons)

7 Other Maintenance Personal business/services, and medical appointments.

Maintenance 5+ (if joint travel, all persons)

8 Social/Recreational Recreation, visiting friends/family.

Discretionary 5+ (if joint travel, all persons)

9 Eat Out Eating outside of home. Discretionary 5+ (if joint travel, all persons)

10 Other Discretionary Volunteer work, religious activities.

Discretionary 5+ (if joint travel, all persons)

Markets

Extended Population Synthesis (MAG)


Markets

Pop

ulat

ion

Syn

thes

isU

sual

wor

kpla

ceU

sual

Sch

ool

1.1. Core

HH

population

and GQ

2.1.1-2.1.2

2.1.3-2.1.4

Major universities

Permanent Seasonal

Students in dorms

(1-person)

Students in rent apt (1-person)

Stock for rent

Students control

1.2.1. Fixed on-campus

1.2.2. Off-campus choice

Enr

ollm

ent

Em

ploy

men

t

1.3. Seasonal residents

1.4.1. Visitors for business

(1-person)

1.4.2. Visitors for recreation

1.5. Transient population

Hotels occupancy

Duration of stay

Duration of stay

2.1.2. Agriculture & construction

jobs

2.1.2. Business meeting location

Conventional

components

New endogenous components

New exogenous components

Integration of Special Events (MAG)


Markets

Treatment of Space

Level of spatial resolution: TAZ (3,000-4,000)

MGRA (20,000-30,000)

Parcel (1,000,000)

Calculation of LOS: Predetermined Origin and Destination

catchment areas

On-fly path building


Transit

Fine-Grain LOS (1=Pre-fixed)


Origin 2 TAZ/TAP

Destination 1 TAZ/TAP

Origin 1 TAZ/TAP

Origin 3 TAZ/TAP



2

3

1

5

6

4

8

9

7

2

3

1

5

6

4

8

9

7

Access EgressMain In-Vehicle

Transit

Fine-Grain LOS (2=on Fly)


Origin 2 Stop

Destination 1 Stop

Origin 1 Stop

Origin 3 Stop

Destination 2 Stop

Destination 3 Stop

2

3

1

5

6

4

8

9

7

2

3

1

5

6

4

8

9

7

Access EgressStop-to-Stop LOS

Transit

Transit Tour Modes

Main transit modes classification: Bus (local & express)

Subway, LRT, ferry (w/bus)

Commuter rail (w/subway & bus)

Access/egress modes classification: Walk/bike

K&R (including taxi)

P&R


Transit

Transit Nest StructuresTransit

Bus

Walk P&R K&R

Subway, LRT, ferry

Walk P&R K&R

Commuter rail

Walk P&R K&R


Transit

Walk

Bus Subway Rail

P&R

Bus Subway Rail

K&R

Bus Subway Rail

Transit

Advanced Cross-Nested Structure


Walk to bus

P&R bus

K&R bus

Walk to subway

P&R subway

K&R subway

Walk to rail

P&R rail

K&R rail

Bus Subway Rail Walk P&R K&R

Competition between main modes

Competition between access modes

Transit

Transit

Transit Crowding Function


Crowding Factor

Voltr

1.00

0 Seat Cap

Fcap

Fseat

MaxCon

Transit

Activity Analysis – Why Essential?

Substitution between in-home and out-of-home activities: Telecommuting

Teleshopping

Compressed work schedules

Impact of one activity on other activities through time-space constraints Interdependence between work and non-work trip rates

Interdependence between activity & travel of household members


Commuting

General Framework for Workplace Choice


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

Commuting

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

Commuting

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


Variable Coefficient t-stat

Constants General -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

Commuting

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


Commuting

Taxonomy of Work Arrangements

Arrangement Normal Alternative

Job type Full time (30+ hours) Part-time (≤29 hours)

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, otherNYMTC, June 8, 2011 82

Commuting

Work Arrangements:Sequence of Choices


Person & household chacteristics

Occupation

Job type:

1=full-time, 2=part-time

Schedule flexibility:

1=no, 2=some, 3=free

Workplace

Home Outside home

Workplace Location

1/7 2/7 3/7 4/7 5/7 6/7 7/7

General commuting frequency

(days at work)

Telecommuting frequency

6=No5=Once a

year

4=Few

times a

year

3=once a

month or

more

2=once a

week or

more

1=almost

every day

Number of jobs: 1, 2+

1. Main work

arrangements

2. Commuting

frequency &

flexibility

Commuting


Aggregation Bias Example: Parking Cost

Auto Share

0%

20%

40%

60%

80%

100%

0 4 8 12 16 20

Parking Cost, $

Before:50% - no charge

50% - $16

Average - $8

After:50% - $4

50% - $20

Average - $12

Shift:From 70% to 30% by

average

From 52.5% to 47.5% structural

Parking


Parking Demand

Travel-related attributes (endogenous to ABM): Tour/trip destination

Arrival time

Planned activity duration (parking space occupied)

Person-related attributes: Person socio-economic characteristics

Travel party size

Willingness to pay versus parking search and/or walk associated with remote parking

Eligibility for free / subsidized parking

Parking


Parking Supply in each Zone

Free parking:

Capacity

Paid parking:

Capacity

Rate:

Daily (for long parking)

Hourly (for short parking)

Parking


Parking Equilibrium ModelIndividual Demand for Parking

Parking supply

Demand-supply equilibrium

Person characteristics and willingness to pay

Free parking eligibility

Travel purpose, destination, and arrival time

Duration of activity

Travel party

Free and paid parking capacity

Parking rate (daily/hourly)

Location and distance from the destination

Parking location choice

Parking search time

Parking occupancy and residual availability

Impact on

destination choice

and schedule

corrections

Impact of parking

scarcity on rate

(price mechanism)

Parking

Examples of Estimated Models

Included in Columbus, OH (MORPC) and San-Diego (SANDAG) ABMs

Based on Household Travel Survey and complementary Parking Survey in downtown

Choice models applied for each tour: Parking provision for downtown destinations (free

on-site, off-site reimbursement, no provision)

Reimbursement amount

Parking lot location for downtown destinations


Parking

Parking Location Choice

In most applied models, parking lot is assumed in the destination zone

In reality, parking constraints and congestion may lead to remote parking


Origin zone

Destination zone

Parking zone

Intended auto trip

Actual auto trip Walk

Parking

Parking Locations (San Diego)


Parking

Parking Location Choice

-6 -4 -2 0 2

Parking cost, cents

Walk distance, miles

Log of parking capacity

Auto time, min

Same area type as destination

CBD

Free, non-work

Free, work

Paid, non-work

Paid, work


Parking

Car Type Choice


Car type

ABM-DTA Integration Dilemma


ABM-DTA

Microsimulation ABM

Microsimulation DTA

List of

individual

trips

Individual

trajectories

for the

current list of

trips

LOS for

the other

potential

trips?

New Approach (SHRP 2 L04)


Microsimulation ABM

Microsimulation DTA

List of

individual

trips

Individual

trajectories

for the

current list of

trips

Consolidation of individual

schedules (inner loop for

departure / arrival time

corrections)

Sample of alternative origins,

destinations, and departure times

Individual

trajectories

for potential

trips

ABM-DTA

Temporal equilibrium to achieve individual schedule consistency

Individual Schedule Consistency


0 24

Activity i=0 Activity i=1 Activity i=2

Trip i=1 Trip i=2 Trip i=3

Activity i=3

Departure

Arrival

Duration

Travel

id

iT

i

i

Schedule

i

ABM-DTA

Conclusions

Tremendous progress in ABM since 2002 when BPM 1.0 was created

Combination of most advanced features of existing ABMs is good starting point to specify BPM 2.0

Integration of advanced features and addressing unique NY conditions in computationally efficient way is still challenge


State of the Art & Practice in ABM: How Can We Better...

Documents

Transcript of State of the Art & Practice in ABM: How Can We Better...