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The Bicycle Investment Scenario Analysis ModelA Web-based Sketch Planning Tool for Los Angeles County

2013 TRB Planning Applications Conference

May 6, 2013

Michael Snavely, Cambridge Systematics

Chris Porter, Monique Urban, David Jackson (Cambridge Systematics)Robert Cálix (Los Angeles County Metro)

with

presented by

Agenda

Background / Context

Model Structure

Trip Estimation Models

Application: User Interface

Conclusion / Results

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Background – Cycling in Los Angeles

1897 - First Class I Bike Facility in US: California Cycle-Way (LA – Pasadena)

» Privately funded &: tolled (10¢ per trip)

» Replaced by Red Car prior to completion

» 1951: right-of-way becomes Arroyo Seco Parkway

Bicycling in LA County Today:

» Over 1000 planned bikeway miles

» Cycling visibility

• Mayor’s accident

• Advocacy presence

» Bikesharing (DTLA, SM, LB)

» Countywide demand for active transportation alternatives

3

Background - Policy Context

2008 – 2012: Countywide Congestion Mitigation Fee Pilot Study

» 1/3 of projects submitted by cities (close to $1B) are bicycle related

» No way to estimate benefits

2012: Metro Board calls for tools to estimate impacts

» 1) Sketch-planning tool (zonal)

• Must be web-based

• Accessible to all 89 jurisdictions

» 2) Travel demand model component (network)

• Late 20144

Regional Context – Existing Bicycle Mode Share

5Average bicycle work-trip mode share = 0.75%

Methodology

Methodology – Guiding Principles

Enable easy web-based access for cities

Complete within 1 year (no new data collection)

Impacts should be sensitive to local conditions

To extent possible, estimate impacts of:

» Bikeways,

» Bike Parking Facilities, and

» Bike Sharing Programs

Sketch-level scenario analysis

» Provide “order of magnitude” estimate of benefits

» Include recreational bicycling impacts7

Methodology – Data Sources

American Community Survey (2007-2011)

» Population, Age, Income, Employment (census tract level)

SCAG MPO Data (2008)

» Land Use Types

» SE Forecasts

National Household Travel Survey (2009)

» Cycling Travel Behavior

Metro Facility geodatabases

» Cycling conditions:

• Street Grades, Intersections, Bike Facilities, etc.8

Model ApplicationBikeway trip estimation models» “Local data” stored in census tract (SE data, land use,

facilities)

» Assumption: higher bikeway facility density greater # bike trips

» Neighboring zones’ land use and infrastructure also affect propensity to bike

Off-model research-based adjustments for:

» Workplace parking /showers

» Transit station bicycle parking

» Bikesharing Programs

Results summarized at jurisdiction level for scenario analysis

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Methodology – Bikeway Projects

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SocioeconomicPopulation Jobs Age

Grade

Education

Density

Land Use & Facilities

FixedCensus Tract

Bike Facilities

Car Traffic Cycling Conditions

Income Sex Car Ownership

User-Defined

New Bikeway Type

Project shapefile

Bikeway Attributes

GIS CalculationAreal Factors Influence Project Impact

Project Location

Work / Utilitarian Trip Estimation

Model

Recreation TripEstimation Model

Bike Trip ModelsEstimate change in bike travel

New Annual Bike TripsDue to bikeway investments (year 2035 vs. no build)

Methodology – Bikeway Trip Estimation Models

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Work Trip Model Recreational Trip Model

Data Source ACS (2007 - 2011), LA County

Travel Purpose

Model Type

NHTS (2009) LA & Orange

Work Trips Strictly recreational (enjoyment/exercise)

Logistic regression 2-Step: binary logit + linear regression

New

Facilities

Logistic Regression Model

∆ Work Bike Trips

∆ Other Utilitarian

Trips(4:1)

Loca

l

Fact

or

s

New

Facilities

Binary Logit Model

Loca

l

Fact

or

s

∆ Persons who take at least 1 rec bike trip

Linear Regression Model

∆ # New Recreational

Bike Trips

Benefits Calculation

Work Trip Model

Bicycle Work Trip Model

ParameterWork Trip Model

CoefficientIntercept -4.82Dense Urban (Core, CBD & Urban Business District)

1.15

Other Urban Area 0.92Suburban 0.39Percentage of HH with Zero Vehicles 1.99

% of Roads with Grades Greater than 3% -0.614Mean Travel Time to Work (Drive Alone) -0.05

Miles of Class 1 (off-street) Bicycle Facilities per Sq. Mi.

0.09

Miles of Class 2 & 3 (on-street) Bicycle Facilities per Sq. Mi.

0.13

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Estimated on census tract-level data using 2007-2011 ACS and land use/infrastructure data from LA County

Logistic regression

Trip Purpose Work

Other Utilitaria

nAvg 1-way length (mi) 3.8 2.3

Fraction all trips 20% 80%

Commute days/year

250

Key constants

Work Trip Model Sensitivity Tests

Scenario 1 – Increasing avg bikeway density to 2.5 mi/sq mi would raise bike commute share to 0.9%

Scenario 2 - Increasing to 5.0 mi/sq mi would raise bike commute share to ~1.2%

» Reasonable results

» Comparable to recent national study (Buehler & Pucher, 2012)

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Facility DensityAverage mi/sq mi

Bicycle Mode Share –

Commute

Scenario Class 1Class 2&3 TOTAL Mean

Base 0.18 0.90 1.1 0.76%

Scenario 1 0.5 2.0 2.5 0.89%

Scenario 2 1.0 4.0 5.0 1.16%

Recreational Trip Model

Rec Model - Data Sources and Processing

Data sources

» 2009 NHTS person data (~10,000 L.A., Orange Co. residents)

• Information on bicycling activity in past week

• Sociodemographics and dependent variables

GIS Data processing

» Facility density by tract

» Proximity measures:

• Number and length of facilities within 1, 2, 5, 10 miles

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Binary Logit Model (n ~ 10,000): Propensity to Bicycle for Recreation

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Variable CoefficientConstant -2.52Sex and Age

Female -1.04 Age (continuous): Number of years over 44 -0.044

Education LevelLess than high school (base) -

High school or GED 0.41 Vocational/Associate's 0.54

Graduated college 0.68 Master's, Ph.D., or Professional Degree 0.71

Bicycle FacilitiesDistance to nearest bike trail: < 1 mile 0.20

1-2 miles 0.041 > 2 miles -

Facility density in home zone (miles per sq. mi.): Class 1 0.13

Class 2 & 3 0.056

Linear Regression Model (n ~ 600):Number of Weekly Recreational Bicycling Trips

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Variable Coefficient

Constant 2.92

Sex and Age

Female -0.27

Age -0.029

Additional Years over 44 0.057

Education Level

High School or Associate's 0.50

College Graduate or above 0.79

Household Data

Household Children under 18 -0.12

Household Income ($100,000s) -0.33

Summary of Recreational ModelTwo-stage model

1. Identify individuals that bicycle for recreation

2. Compute the number of trips made by each individual

Key findings

» Demographics – greatest impact

» Bicycle facilities – also significant

Data limitations

» Estimation dataset: NHTS person data (disaggregate)

» Application dataset: census tract (aggregate)

» GIS processing, zonal aggregation19

Application: User Interface

Bicycle Investment Scenario Analysis Model

Results

ResultsBenefits» Shows order-of-magnitude estimates based on best

available data» Sensitivity tests showed reasonable results» For first time, cities can justify funding cycling projects

based on local conditions» Relatively low cost implementation and ease of

calculation

Limitations» Zonal aggregation misses connectivity/network issues» Limited by small sample sizes (e.g. only 600 rec trips)» More research needed to validate ‘off-model’ methods» For sketch purposes only

Opportunities to improve/expand functionality» Next step: census block zonal aggregation» Update pending TDM estimation & data collection results» Add other project types

Q&A

CONTACT

Michael SnavelyCambridge Systematics, Inc.

Los Angeles, CAmsnavely@camsys.com

213-612-7705

Robert CálixMetro

Los Angeles, CAcalixr@metro.net

213-922-5644

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THANKS

Technical Advisory Panel

Jennifer DillJeremy Raw

Bill SteinDavid Ory

Susan HandyThomas Götschi

Peter Furth

“Off-Model” Project Impacts

“Off-Model” Trip Estimation

Fixed-Rail Transit Station Bike Parking

» Use actual fixed-rail transit boardings/alightings by jurisdiction

» New secure parking (lockers or guards):

• 0.5% change in access to transit modeshare)

• i.e. of 5,000 wkday boardings, 25 new trips

» New unsecure parking:

• 0.25% change in modeshare

• i.e. of 5,000 wkday boardings, 13 new trips

Worksite Bicycling Amenities

» User specifies workers with access:

Type of parking Max % change in mode share from

baseline, for affected worksites

Unsecure 9% Secure 16%Secure + showers 22%

“Off-Model” Trip Estimation Example

Citywide Bikesharing Programs

» Deployment Areas: tracts where (pop + jobs)/sq mi > 10,000

» Saturation Level: max 40 bikes / sq mi

» Trips per bike per day: 2

» Prior mode share of bike trips: same (61% drive; 18% active)

» Avg length of bike share trips: same as county avg: 2.0 mi

Source: Toole (2012)

Benefits Estimation

Benefits Estimation (2035 vs. 2035 no build)

Benefit Category Description Performance Measure

MobilityNew Trips New bike trips (work, non-work,

rec) # annual trips by purpose

New BMT New bicycle miles traveled (w, nw, r) # annual BMT by purpose

Roadway Congestion Impacts

Reduction in vehicle delay Reduction in annual VHD

EnvironmentalEnergy Consumption Reduced vehicle fuel consumption

Annual gallons of motor vehicle fuel reduced

GHG Emissions Reduced carbon equivalent emissions Annual lbs of GHG reduced

Air Pollution DamagesReduced cost of air pollution damages (public health, building repair, agriculture, ecosystems)

Annual cost savings as a result of better air quality($)

EconomicHousehold Savings Household vehicle O&M cost

savings Annual O&M savings ($)

Local Economic Value Economic benefit of add’l bike facility access New jobs created

Accessibility & EquityImproved bike access Enhanced bike facility access for

householdsAdd’l HH within 1 mi of bikeways

Improved low-income bike access

Enhanced bike facility access for low-income households

Add’l low-income HH within 1 mi of bikeways

Public Health

Fitness BenefitsReduced heath care & mortality costs due to increased physical activity & health

Annual economic value of public health benefits of added physical activity

Comparison with Recent National Study

Buehler and Pucher (2012)

» City-level data from 90 of the 100 largest U.S. cities

» Evaluated length of bike paths and lanes per capita vs. % commuting by bike

» Elasticity of bike trips with respect to facility density:

• 0.25 for paths, 0.31 for lanes

CS – Metro work trip model elasticity:

» 0.13 to 0.15 from base

» 0.31 for Scenario 1 to Scenario 2

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Number of Recreational Bicycling Trips (Weekly)

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1 2 3 4 5 6 7 8 9 10+0

10

20

30

40

50

Frequency Distribution

Number of Recreational Bicycling Trips in Past Week

Percentageof

RecreationalBicyclistsin LA & Orange

Counties

Key Findings

Independent Variables

Impact of variable on…Propensity to

bicycle for recreation

Typical weekly recreational bicycling

trips

Individual characteristics

Sex --- -

Age ---Additional years over

44 -- +++

Education ++ ++

Household characteristics

Children -

Income -

Bicycle facilities

Density (Access) +

Distance to Nearest ++32

Number of individuals that reported making a recreational bicycling trip in the previous week

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Bicycling Purposes

County of residenceTotal, LA &

OrangeLAOrang

e Other AllDoes not bicycle

6,699 2,62516,15

225,47

6 9,324Utilitarian purposes only 60 28 124 212 88Exercise/recreation only 241 126 570 937 367Both utilitarian and exercise/recreation 157 53 326 536 210Total utilitarian 217 81 450 748 298Total exercise/recreation 398 179 896 1,473 577All

7,157 2,83217,17

227,16

1 9,989

Model SensitivityGreatest impact: Individual characteristics (sex, age, education)

Moderate impact: Bicycle facilities

» Living near trail – greater propensity to bicycle for recreation

» Getting to/from the trail is important too

» No measurable impact on number of weekly trips

Low impact: household characteristics

» Higher income and more children – fewer recreational bicycling trips

» Indicators of a busy lifestyle?

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Interpretation of Model ResultsPropensity to bicycle for recreation

» Sex and age – males more likely; flat rate by age until middle age, then starts to decline

» Education – more educated are more likely

» Bicycle facility variables – trails within 1 mile are preferred; getting to/from the trail is important too

Number of weekly recreational bicycling trips made

» Sex – males ride more

» Age – declines until middle age, then more trips made with age

» Education – more educated ride more

» Income – higher income ride less (too busy?)

» Facility variables – tested but not significant35

Recreational Bike Trip Model

Definition – three criteria:

» Bicycle is used

» Trip purpose is fitness, enjoyment, or both

» Origin and destination are the same

• E.g. , start at home, ride on bicycle trail, ride back home

Benefit impacts:

» Public health only

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NHTS Person Data Set Questionnaire

Questions on bicycling (in the past week)

» How many times did you bicycle?

» For what reasons (select from table)?

Data issues

» Total bike trips split into estimated number by purpose

» Possible confusion of purposes – e.g., reports “exercise” but is destined to the beach or gym

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