the bicycle investment scenario analysis model
DESCRIPTION
The Bicycle Investment Scenario Analysis Model. A Web-based Sketch Planning Tool for Los Angeles County. 2013 TRB Planning Applications Conference. May 6 , 2013. Michael Snavely, Cambridge Systematics. presented by. with. Chris Porter, Monique Urban, David Jackson ( Cambridge Systematics) - PowerPoint PPT PresentationTRANSCRIPT
Transportation leadership you can trust.
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 SystematicsChris 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
2
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 citiesComplete within 1 year (no new data collection)Impacts should be sensitive to local conditionsTo 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 impacts
7
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
9
Methodology – Bikeway Projects
10
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
ModelRecreation Trip
Estimation ModelBike Trip Models
Estimate 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
Facto
rs
New Facilities
Binary Logit Model
Loca
l
Facto
rs
∆ 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
13
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 2.3Fraction 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)14
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
17
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
18
Variable Coefficient
Constant 2.92Sex 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 DataHousehold Children under 18 -0.12
Household Income ($100,000s) -0.33
Summary of Recreational ModelTwo-stage model1. Identify individuals that bicycle for recreation2. 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
calculationLimitations» 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, [email protected]
213-612-7705
Robert CálixMetro
Los Angeles, [email protected]
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 reducedGHG 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 HealthFitness Benefits
Reduced 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
tripsIndividual characteristics
Sex --- -Age ---
Additional years over 44 -- +++
Education ++ ++Household characteristics
Children -Income -
Bicycle facilitiesDensity (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 tooNumber 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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