Impact of Aging Population on Regional Travel Patterns:The San Diego Experience

Impact of Aging Population on Regional Travel Patterns:The San Diego Experience

14th TRB National Transportation Planning Applications Conference, Columbus OH

May 7th, 2013

Wu Sun, Beth Jarosz & Gregor SchroderSan Diego Association of Governments (SANDAG)

BackgroundBackground

Population Aging Activity-Based Travel Demand Model

(ABM) Evaluate Impact of Aging Population on

Travel Patterns Using ABM

3

U.S. Population AgingU.S. Population Aging

0-4

10-14

20-24

30-34

40-44

50-54

60-64

70-74

80-84

0 5,000,000 10,000,000 15,000,000 20,000,000 25,000,000

1970 1980 1990 2000 2010Source: U.S. Census Bureau, decennial census 1970, 1980, 1990, 2000, and 2010

1970 2010

Younger than 30 53% 41%

Age 65 or older 10% 13%

4

In 25 years, Boomers will nearly double the population age 65+

In 25 years, Boomers will nearly double the population age 65+

2010 2015 2020 2025 2030 20350

10,000

20,000

30,000

40,000

50,000

60,000

70,000

80,000

90,000

85+65-84

Po

pu

lati

on

pro

ject

ion

s

Source: U.S. Census Bureau, Projections (2012) ,“Constant International Migration Series”

5

3 sources of change3 sources of change

Life-course Generational Broad social/economic trends

6

Life-course: disability status by ageLife-course: disability status by age

< 5 5 to 17 18 to 34 35 to 64 65 to 74 75+0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

% o

f p

op

ula

tio

n w

ith

dis

abil

-it

y

Source: U.S. Census Bureau, ACS 2011

7

Time of Day: Older Drivers Report Avoiding Certain Driving ConditionsTime of Day: Older Drivers Report

Avoiding Certain Driving Conditions Older drivers likely to

avoid driving:– at night– in bad weather– in heavy traffic

Some avoidance of highway driving

Time-shifting of trips to avoid congested periods

Source: U.S. Centers for Disease Control and Prevention, “New Data on Older Drivers,” April 19, 2011

8

Mode share: Means of Transport to Work by Age (2007-09)

Mode share: Means of Transport to Work by Age (2007-09)

Source: U.S. Census Bureau, ACS 2011

16-19 20-24 25-44 45-54 55-59 60-64 65 +0%

20%

40%

60%

80%

100%

66%71% 76% 79% 79% 79% 76%

15%12%

10% 9% 8% 7%7%

4%6%

6% 4% 4% 4%4%

9%6% 2% 2% 2% 2%

3%

3% 2% 2% 1% 1% 1%1% Home

Other

Walk

Transit

HOV

SOV

9

Aggregate System Effects: Average Daily Miles of Travel

Aggregate System Effects: Average Daily Miles of Travel

1983 1995 2001 20090

10

20

30

40

50

60

16-2021-3536-6566+

Ave

rag

e D

aily

Mil

es o

f P

erso

n

Tra

vel

Sources: U.S. Department of Transportation, Federal Highway Administration, 1983, 1995, 2001, and 2009 National Household Travel Survey.

MethodologyMethodology

Generation of 3 aging scenarios ABM-A travel forecast model sensitive to

socio-demographic changes Generation of a synthetic population

Generation of Aging Scenarios: Data

Generation of Aging Scenarios: Data

2010 Census 2035 Forecast – 3 scenarios

– Base case: derived from SANDAG 2050 Regional Growth Forecast (2010)

– Older population: 2.3% increase in population over age 65, compared with base case, offset by fewer persons age 64 and younger (with most change under age 18)

– Younger population: 2.2% decrease in population over age 65, compared with base case, offset by fewer persons age 64 and younger (with most change under age 18)

Geography:– San Diego County– Unit of analysis: approximately 23,000 census block level geographies

known as Master Geographic Reference Areas (MGRAs)

12

Aging ScenariosAging Scenarios

0-4

10-14

20-24

30-34

40-44

50-54

60-64

70-74

80-84

0 50,000 100,000 150,000 200,000 250,000 300,000

2035 Younger

2035 Older

Source: SANDAG, 2050 Regional Growth Forecast (2010) and alternate age scenarios

Aging ScenariosAging Scenarios

Age 0-17 Age 18-39 Age 40-64 Age 65+0

200,000

400,000

600,000

800,000

1,000,000

1,200,000

1,400,000

YoungerBase CaseOlder

ABM

CVM

TransportationSystem

TransportationPolicy

Traffic Assignment

SystemPerformance

Environmental Impact

EconomicAnalysis

14

Land Use Models

Activity-Based Model (ABM)Activity-Based Model (ABM)

BorderModel

SpecialModels

Why ABM?Why ABM?

• Simulate travel behavior individually

• Detailed temporal & spatial resolutions

• Sensitive to socio-demographic changes

• Increased Sensitivity• Environmental Justice / Social Equity

• Spatial and network changes

• Land use changes

15

Treatment of SpaceTreatment of Space

16

• MGRA (gray lines)

• 21,633 MGRA

• 4,682 TAZs

MGRA: Master Geographic Reference Area (Grey Lines)TAZ: Transportation Analysis Zone (Orange Line)

Treatment of TimeTreatment of Time

TOD in travel demand modeling• 40 departure half-hours (5AM-24PM) by• 40 arrival half-hours (departure-24PM)

TOD in traffic assignment

17

NUMBER DESCRIPTION BEGIN TIME END TIME

1 Early A.M. 3:00 A.M. 5:59 A.M.

2 A.M. Peak 6:00 A.M. 8:59 A.M.

3 Midday 9:00 A.M. 3:29 A.M.

4 P.M. Peak 3:30 P.M. 6:59 P.M.

5 Evening 7:00 P.M. 3:29 A.M.

Treatment of Travel PurposesTreatment of Travel Purposes

TYPE PURPOSE DESCRIPTION CLASSIFICATION

1 Work Working outside the home Mandatory

2 University College + Mandatory

3 High School Grades 9-12 Mandatory

4 Grade School Grades K-8 Mandatory

5 Escorting Pick-up/drop-off passengers Maintenance

6 Shopping Shopping away from home Maintenance

7 Other Maintenance Personal business/services Maintenance

8 Social/Recreational Recreation, visiting friends/family Discretionary

9 Eat Out Eating outside of home. Discretionary

10 Other Discretionary Volunteer work, religious activities Discretionary

18

Treatment of Travel ModesTreatment of Travel Modes

19

Choice

Auto

Drive alone

GP(1)

Pay(2)

Shared ride 2

GP(3)

HOV(4)

Pay(5)

Shared ride 3+

GP(6)

HOV(7)

Pay(8)

Non-motorized

Walk(9)

Bike(10)

Transit

Walk access

Local bus(11)

Express bus(12)

BRT(13)

LRT(14)

Commuter rail(15)

PNR access

Local bus(16)

Express bus(17)

BRT(18)

LRT(19)

Commuter rail(20)

KNR access

Local bus(21)

Express bus(22)

BRT(23)

LRT(24)

Commuter rail(25)

School Bus(26)

Tour Mode

Trip Mode

Treatment of Socio-Demographics

Treatment of Socio-Demographics

Household characteristics– Household size– Household income– Number of workers per household– Number of children in household– Dwelling unit type– Group quarter status

Person characteristics– Age (0-17, 18-24,25-34, 35-49, 50-64, 65-79, 80+ )– Gender– Race

Population Synthesizer (PopSyn)Population Synthesizer (PopSyn)

Synthetic population: – a collection of records that represents

household and person characteristics

Foundation of individual behavioral simulation based model such as ABM

PopSyn InputsPopSyn Inputs

Census and ACS PUMS– Household and person level microdata

Census and ACS summary data– Source for base year control targets– Source for base year validation data

SANDAG estimates and forecasts– Source for future year control targets– 3 aging scenarios

PopSyn OutputsPopSyn Outputs

HHID HH Serial # GeoType GeoZone Version SourceID

…

HH Serial # PUMA Attributes

Household Table

PUMS Person TablePerID HH Serial # Attributes

PUMS Household Table

ResultsResults

Mode choice TOD choice Tour purposes Average tour distance/Daily tour distance VMT (resident households only)

Mode Choice Results:Individual Tours

Mode Choice Results:Individual Tours

38.1%

41.5%

2.1%5.5%

12.8%

37.8%

41.7%

2.3%5.4%

12.8%

38.2%

41.6%

1.9%5.4%

12.9%

Younger Base Line OlderAge Scenarios

Per

cent

age

of T

otal

Tou

rs

Drive Alone

Drive Shared

School Bus

Transit

Walk/Bike

Mode Choice Results:Joint Tours

Mode Choice Results:Joint Tours

86.3%

2%

11.7%

86.6%

2.1%

11.4%

86.1%

2.1%

11.7%

Younger Base Line OlderAge Scenarios

Per

cent

age

of T

otal

Tou

rs

Drive Shared

Transit

Walk/Bike

TOD Choice Results:Individual Tours

TOD Choice Results:Individual Tours

1.7%

41.2%

36.3%

14%

6.8%

1.6%

40.9%

36.6%

14.1%

6.8%

1.6%

40.9%

36.6%

14.1%

6.8%

Younger Base Line OlderAge Scenarios

Per

cent

age

of T

otal

Tou

rs

Early

AM Peak

Midday

PM Peak

Evening

TOD Choice Results:Joint Tours

TOD Choice Results:Joint Tours

1.7%

9.1%

31.9%

34.8%

22.5%

1.6%

9%

33.5%

34.3%

21.6%

1.7%

9.1%

32.9%

34.3%

22.2%

Younger Base Line OlderAge Scenarios

Per

cent

age

of T

otal

Tou

rs

Early

AM Peak

Midday

PM Peak

Evening

Tours by Tour PurposesTours by Tour Purposes

32%

16.4%

3.6%

17.8%

30.2%

30.9%

16.2%

3.8%

18.8%

30.4%

31.6%

15.9%

3.7%

18.3%

30.4%

Younger Base Line OlderAge Scenarios

Per

cent

age

of T

otal

Tou

rs

Work

School

University

Escort

Other

Average Tour Distance:Individual Tours

Average Tour Distance:Individual Tours

Average Tour Distance:Joint Tours

Average Tour Distance:Joint Tours

Average Daily Miles of TravelAverage Daily Miles of Travel

Regional VMT (Resident Households)

Regional VMT (Resident Households)

ConclusionsConclusions

Population aging is a national trend Impact of population on travel patterns Evaluate population aging impact on travel

using ABM Say something about analysis results

here….