Impact of Aging Population on Regional Travel Patterns: The San Diego Experience 14th TRB National Transportation Planning Applications Conference, Columbus.

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Presentation transcript:

Impact of Aging Population on Regional Travel Patterns: The San Diego Experience 14th TRB National Transportation Planning Applications Conference, Columbus OH May 7 th, 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 3 Source: U.S. Census Bureau, decennial census 1970, 1980, 1990, 2000, and Younger than 3053%41% Age 65 or older10%13%

In 25 years, Boomers will nearly double the population age Source: U.S. Census Bureau, Projections (2012),“Constant International Migration Series”

3 sources of change  Life-course  Generational  Broad social/economic trends 5

Life-course: disability status by age 6 Source: U.S. Census Bureau, ACS 2011

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

Mode share: Means of Transport to Work by Age ( ) 8 Source: U.S. Census Bureau, ACS 2011

Aggregate System Effects: Average Daily Miles of Travel 9 Sources: U.S. Department of Transportation, Federal Highway Administration, 1983, 1995, 2001, and 2009 National Household Travel Survey.

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

Aging Scenarios

ABM CVM Transportation System Transportation Policy Traffic Assignment System Performance Environmental Impact Economic Analysis 14 Land Use Models Activity-Based Model (ABM) Border Model Special Models

Why ABM? Simulate travel behavior individuallySimulate travel behavior individually Detailed temporal & spatial resolutionsDetailed temporal & spatial resolutions Sensitive to socio-demographic changesSensitive to socio-demographic changes Increased SensitivityIncreased Sensitivity Environmental Justice / Social EquityEnvironmental Justice / Social Equity Spatial and network changesSpatial and network changes Land use changesLand use changes 15

Treatment 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 Time TOD in travel demand modeling 40 departure half-hours (5AM-24PM) by40 departure half-hours (5AM-24PM) by 40 arrival half-hours (departure-24PM)40 arrival half-hours (departure-24PM) TOD in traffic assignment 17 NUMBERDESCRIPTION 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. 3Midday 9:00 A.M. 3:29 A.M. 4 P.M. Peak 3:30 P.M. 6:59 P.M. 5Evening 7:00 P.M. 3:29 A.M.

Treatment of Travel Purposes TYPEPURPOSEDESCRIPTIONCLASSIFICATION 1 WorkWorking outside the homeMandatory 2 UniversityCollege +Mandatory 3 High SchoolGrades 9-12Mandatory 4 Grade SchoolGrades K-8Mandatory 5 EscortingPick-up/drop-off passengersMaintenance 6 ShoppingShopping away from homeMaintenance 7 Other MaintenancePersonal business/servicesMaintenance 8 Social/RecreationalRecreation, visiting friends/familyDiscretionary 9 Eat OutEating outside of home.Discretionary 10 Other DiscretionaryVolunteer work, religious activitiesDiscretionary 18

Treatment 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  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 HHIDHH Serial #GeoTypeGeoZone VersionSourceID … HH Serial #PUMAAttributes Household Table PUMS Person Table PerIDHH Serial #Attributes PUMS Household Table

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

Mode Choice Results: Individual Tours

Mode Choice Results: Joint Tours

TOD Choice Results: Individual Tours

TOD Choice Results: Joint Tours

Tours by Tour Purposes

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….