RTC

DVRPC 2012-2013 Household Travel SurveyTMIP Peer-ReviewOctober 29, 2014

Ben Gruswitz, AICPOffice of Modeling & Analysis

Sarah MoranOffice of Modeling & Analysis

Delaware Valley Regional Planning Commission

SURVEY BACKGROUND

• 1-day paper diary survey• 10,000 households goal, 9,384 actual

complete good surveys (almost 900,000 HHs contacted)

• 3 day GPS sub-sample (500 HH goal, 380 actual)

• Address based sampling frame• 12 month roughly equal sample, weekdays• State, area-type, HH-size x income, and HH-

size x auto ownership as control variables• Diary data retrieved by either phone, web, or

mail

• Stratified random sample by State & Area Type

• (CBD, urban, suburban, rural, etc.)• Oversample with higher transit propensity• Transit Score - population density, employment

density, & carless households• Quintile Approach – oversample tracts by an

individual county’s top scoring tracts (top 20%)• Incentive program: 0-vehicle; Low income

(<$35,000); and Spanish speaking

SAMPLING PLAN

SAMPLING PLAN

• GPS data was originally desired to correct for under-reporting

• Conclusions so far:• Data is interesting • Can be good check

against diary data• Has it’s own flaws

DATA QUALITY - GPS

GPS SAMPLE

6

Income RangeGPS Sample

$0 to $9,999 10$10,000 to $24,999 37$25,000 to $34,999 26$35,000 to $49,999 47$50,000 to $74,999 92$75,000 to $99,999 71$100,000 to $149,999 74$150,000 to $199,999 37$200,000 to $249,999 14$250,000 or more 18Don't know 1Refused 44Total 471

Household Size

GPS Sample

1 1182 1993 764 575 146 6

10 1Total 471

Area Type

GPS Sample

CBD 9CBD Fringe 1Urban 93Suburban 260Rural 95Open Rural 13Total 471

• Transponders not always on (take a while to connect)

DATA QUALITY - GPS

• Data processing can under-report trips

DATA QUALITY - GPS

DATA PROCESSING

Data Quality Assurance & Control• Verbatim response

recoding• Geocoding• Tour identification and

classification• Missing data and trip

imputation• Formatting• Misaligned fields

Weighting• Data weighted and

expanded to reflect demographics by county and area type

• Household and Person weights

9

QUERY SUMMARY

Pass Passable Fail

Result Data Table DescriptionDelivery

CountDelivery Percent

Contract Standard

Household Are there at least 9,500 complete HHs? 9,502 100.02% 100%Household Are all HH locations geocoded? 9,502 100.02% 100%Household How many HHs provided their income? 8,856 91.98% 90%Person Do # of people in each complete HH = # of complete person records for that HHID? 9,502 100.00% 100%Person Do all complete HHIDs in Household and Person tables match? 9,502 100.00% 100%Person Are household worker counts less than HH size? 9,502 100.00% 100%Person Are all people over 15 employed or unemployed? 17,962 99.82% 100%Person How many work locations of employed people at fixed work sites geocoded? 8,557 93.86% 95%Person Are all people a either a student or non-student? 21,266 99.89% 100%Person Do all students have a school type? 4,228 100.00% 100%Person How many students have coordinates for their school? 4,037 95.48% 95%Vehicle Do # of vehicles in each complete HH = # of complete vehicle records for that HH? 9,502 100.00% 100%Vehicle Do all HHIDs in Vehicle table match Household table HHIDs? 9,502 100.00% 100%Trip Do # of trips in complete households = # of complete trip records? 9,502 100.00% 100%Trip Do all complete HHIDs in Household and Trip tables match? 9,502 100.00% 100%Trip How many HHs have trips with coordinates or have a household size exemption? 5,719 60.19% 100%Trip How many trips are geocoded among non-exempt households? 59,892 93.17% 100%Trip Is there a mode for each trip? (non-exempt) 64,684 94.79% 100%

WHAT’S AN “OTHER” RESPONSE?

Work Status Codes & Categories

1. Retired2. Disabled/on disability status3. Homemaker4. Unemployed but looking for

work5. Unemployed and not looking

for work6. Student7. Volunteer97.Other98.Don't know99.Refused

Person IDWork Status

Work Status - Other

UUUUU 6 <null>

WWWWW 3 <null>

XXXXX 97 Elderly

YYYYY 1 <null>

ZZZZZ 1 <null>

COMPARING RESULTS

• ArcGIS Model Output

• “XY to Line” tool gives point to point distance

• Model created fields to flag records where Google/Bing geographies disagree

GoogleBing

CHANGE IN PERSON TRIP RATE

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Household Trip Rate Person Trip Rate

County Households Population Trip Rate Trips

(HHRate*HH) Trip Rate Trips

(PRate*P)

Burlington

165,620 449,117 7.70 1,274,834 2.73958 1,230,391

Camden

188,861 513,660 7.90 1,491,732 2.92433 1,502,110

Gloucester

104,091 288,187 7.52

783,176 2.93160

844,848

Mercer

130,292 366,442 7.98 1,040,044 3.43389 1,258,322

NJ Counties 588,864 1,617,406 7.79 4,589,786 2.98977 4,835,671

Bucks

229,933 625,485 7.57 1,739,928 3.01902 1,888,352

Chester

183,793 499,548 7.81 1,436,169 2.82283 1,410,137

Delaware

206,021 558,874 7.53 1,550,553 2.91042 1,626,561

Montgomery

308,083 799,886 7.58 2,334,522 2.88910 2,310,950

Philadelphia

580,509 1,525,811 6.29 3,653,757 2.71217 4,138,252

PA Counties 1,508,339 4,009,604 7.10 10,714,929 2.83675 11,374,252

DVRPC Total 2,097,203 5,627,010 7.30 15,304,715 2.88073 16,209,923

CHANGE IN PERSON TRIP RATE

14

Burlin

gton

Camde

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Glouc

este

r

Mer

cer

Bucks

Chest

er

Delaw

are

Mon

tgom

ery

Phila

delp

hia

-1.00

-0.80

-0.60

-0.40

-0.20

0.00

0.20

0.40

0.60

0.80

Motorized Non-Motorized All Trips

Ch

an

ge

in

Tri

p R

ate

CHANGE IN MODE SHARE

Walk Bike Personal Vehicle

Private Transit

Transit School Bus

Other-2.5%

-2.0%

-1.5%

-1.0%

-0.5%

0.0%

0.5%

1.0%

1.5%

2.0%

0.0%0.2%

-2.0%

0.0%

1.6%

-0.3%

0.4%

Ch

an

ge

in

Mo

de

Sh

are

15