Transcript Document

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 Schroder
San Diego Association of Governments (SANDAG)
Background
 Population Aging
 Activity-Based Travel Demand Model
(ABM)
 Evaluate Impact of Aging Population on
Travel Patterns Using ABM
U.S. Population Aging
1970 2010
85+
80-84
75-79
70-74
65-69
60-64
55-59
50-54
45-49
40-44
35-39
30-34
25-29
20-24
15-19
10-14
5-9
0-4
0
5,000,000
1970
10,000,000
1980
Younger than 30
53%
41%
Age 65 or older
10%
13%
15,000,000
1990
2000
20,000,000
25,000,000
2010
Source: U.S. Census Bureau, decennial census 1970, 1980, 1990, 2000, and 2010
3
In 25 years, Boomers will nearly
double the population age 65+
Population projections
90,000
80,000
85+
70,000
65-84
60,000
50,000
40,000
30,000
20,000
10,000
0
2010
2015
2020
2025
2030
2035
Source: U.S. Census Bureau, Projections (2012) ,“Constant International Migration Series”
4
3 sources of change
 Life-course
 Generational
 Broad social/economic trends
5
% of population with
disability
Life-course: disability status by age
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
<5
5 to 17
18 to 34 35 to 64 65 to 74
75+
Source: U.S. Census Bureau, ACS 2011
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Time 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
7
Mode share: Means of Transport to
Work by Age (2007-09)
100%
80%
3%
9%
4%
15%
2%
6%
6%
2%
2%
6%
10%
1%
2%
4%
9%
1%
2%
4%
8%
1%
2%
4%
7%
12%
1%
3%
4%
7%
Home
Other
Walk
Transit
HOV
SOV
60%
40%
66%
71%
76%
79%
79%
79%
76%
20%
0%
16-19 20-24 25-44 45-54 55-59 60-64
65 +
Source: U.S. Census Bureau, ACS 2011
8
Aggregate System Effects: Average
Daily Miles of Travel
Average Daily Miles of Person
Travel
60
50
40
16-20
21-35
36-65
66+
30
20
10
0
1983
1995
2001
2009
Sources: U.S. Department of Transportation, Federal Highway Administration, 1983, 1995, 2001, and 2009 National
Household Travel Survey.
9
Methodology
 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
 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)
Aging Scenarios
85+
80-84
75-79
70-74
65-69
60-64
55-59
50-54
45-49
40-44
35-39
30-34
25-29
20-24
15-19
10-14
5-9
0-4
2035 Younger
2035 Older
0
50,000
100,000
150,000
200,000
250,000
300,000
Source: SANDAG, 2050 Regional Growth Forecast (2010) and alternate age scenarios
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Aging Scenarios
1,400,000
1,200,000
1,000,000
800,000
Younger
Base Case
Older
600,000
400,000
200,000
0
Age 0-17
Age 18-39
Age 40-64
Age 65+
Activity-Based Model (ABM)
Transportation
Policy
Transportation
System
Land Use
Models
ABM
Border
Model
Special
Models
Environmental
Impact
Traffic Assignment
System
Performance
CVM
Economic
Analysis
14
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 Space
• MGRA (gray lines)
• 21,633 MGRA
• 4,682 TAZs
MGRA: Master Geographic Reference Area (Grey Lines)
TAZ: Transportation Analysis Zone (Orange Line)
16
Treatment of Time
TOD in travel demand modeling
• 40 departure half-hours (5AM-24PM) by
• 40 arrival half-hours (departure-24PM)
TOD in traffic assignment
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.
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Treatment of Travel Purposes
TYPE
PURPOSE
DESCRIPTION
CLASSIFICATION
1
Work
Working outside the home
Mandatory
2
3
4
5
University
College +
Mandatory
High School
Grades 9-12
Mandatory
Grade School
Grades K-8
Mandatory
Escorting
Pick-up/drop-off passengers
Maintenance
6
7
Shopping
Shopping away from home
Maintenance
Other Maintenance
Personal business/services
Maintenance
8
Social/Recreational
Recreation, visiting friends/family
Discretionary
Eat Out
Eating outside of home.
Discretionary
Other Discretionary
Volunteer work, religious activities
Discretionary
9
10
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Treatment of Travel Modes
Choice
Nonmotorized
Auto
Drive
alone
Shared
ride 2
Shared
ride 3+
Walk(9)
Transit
Walk
access
PNR
access
School
Bus(26)
KNR
access
Local
bus(11)
Local
bus(16)
Local
bus(21)
HOV(7)
Express
bus(12)
Express
bus(17)
Express
bus(22)
Pay(8)
BRT(13)
BRT(18)
BRT(23)
LRT(14)
LRT(19)
LRT(24)
Commuter
rail(15)
Commuter
rail(20)
Commuter
rail(25)
GP(1)
GP(3)
GP(6)
Pay(2)
HOV(4)
Pay(5)
Bike(10)
Tour Mode
Trip Mode
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Treatment of SocioDemographics
 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)
 Synthetic population:
– a collection of records that represents
household and person characteristics
 Foundation of individual behavioral
simulation based model such as ABM
PopSyn 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 Outputs
HHIDHousehold
HH SerialTable
#
GeoType
GeoZone
Version
SourceID
…
HH Serial # PUMA
Attributes
PUMS Household Table
PerID HH Serial #
Attributes
PUMS Person Table
Results
 Mode choice
 TOD choice
 Tour purposes
 Average tour distance/Daily tour distance
 VMT (resident households only)
Percentage of Total Tours
Mode Choice Results:
Individual Tours
12.8%
12.8%
12.9%
5.5%
2.1%
5.4%
2.3%
5.4%
1.9%
41.5%
41.7%
41.6%
Drive Alone
Drive Shared
School Bus
Transit
Walk/Bike
38.1%
37.8%
38.2%
Younger
Base Line
Older
Age Scenarios
Percentage of Total Tours
Mode Choice Results:
Joint Tours
11.7%
11.4%
11.7%
2%
2.1%
2.1%
Drive Shared
Transit
86.3%
86.6%
86.1%
Younger
Base Line
Older
Age Scenarios
Walk/Bike
Percentage of Total Tours
TOD Choice Results:
Individual Tours
6.8%
6.8%
6.8%
14%
14.1%
14.1%
36.3%
36.6%
36.6%
Early
AM Peak
Midday
PM Peak
Evening
41.2%
40.9%
40.9%
1.7%
1.6%
1.6%
Younger
Base Line
Older
Age Scenarios
Percentage of Total Tours
TOD Choice Results:
Joint Tours
22.5%
21.6%
22.2%
34.8%
34.3%
34.3%
Early
AM Peak
Midday
PM Peak
Evening
31.9%
33.5%
32.9%
9.1%
1.7%
9%
1.6%
9.1%
1.7%
Younger
Base Line
Older
Age Scenarios
Percentage of Total Tours
Tours by Tour Purposes
30.2%
30.4%
30.4%
17.8%
18.8%
18.3%
3.8%
3.7%
Work
School
3.6%
University
Escort
16.4%
16.2%
15.9%
32%
30.9%
31.6%
Younger
Base Line
Older
Age Scenarios
Other
Average Tour Distance:
Individual Tours
Average Tour Distance:
Joint Tours
Average Daily Miles of Travel
Regional VMT
(Resident Households)
Conclusions
 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….