Central Lane MPO Regional Bicycle Count Program:Making Use of the Data

Presentation Outline

• Why collect bicycle traffic counts?

• How we count people riding bikes

• What are we learning?

• How are we applying what we are learning to safety planning?

• Modeling validation

• Monitor changes over time

• System function

• Greenhouse gas analysis

• Health analysis

• Transportation safety

Why collect bicycle traffic counts

• Started in 2012

• 6 portable Eco Counter sensors (one permanent counter)

• Data collected from on-street and off-street locations

• Portable equipment deployed for 8 days at a time

• Data collected year round

• Paid staff now collect data

How we count people riding bikes

How we count people riding bikes

What are we learning?• Comparing year-to-

year observations from short-term counts is not always straight forward

• Temperature and precipitation (and other things) need to be accounted for in comparisons

• Relationships with background conditions and infrastructure

• Can we calculate Bike Miles Traveled using just short-term counts?

No Bike Facility Shared Lane Bike Blvd Bike Lane Off-street Path MeanPath - - - - 461 461Local 338 - 430 1120 - 629Collector 69 285 164 782 - 325Minor Arterial - - - 293 - 293Major Arterial - - - - - -Mean 204 285 297 732 461 -

Mean Daily Bicycle Volume by Street class and Bike Facility Type

Relevant Literature

• Past research have established relationships between bicycle volume and:• Daily conditions (Numerous)

• Infrastructure characteristics (Lindsey, Han, Wilson & Yang, 2006; Reynolds, Wolch, Byrne, Chou& Feng, 2007)

• Built environment – (Dill, 2009; Guo, Bhat & Copperman, 2007; Haynes & Andrzejewski, 2010; Jones et al., 2010)

• Bicycle demand models – (Hankey el al., 2012; Suzuki et al. 2014; Fehr and Peers, many aggregate models)

What are we learning?

What are we learning?

Daily Bicycle Volume

Daily Conditions• Temperatu

re• Day of

week • University

in session

Infrastructure

• Functional classification

• Bicycle facility type

+

Accessibility

• Population

• Jobs+=

Network Density

• Arterials• On

street facilities

+

• Facility Demand Model

• Negative binomial regression

• Predicts daily bike volume for entire network

Variable EstimateStandard Error Z value Pr(>|z|) Sig. Code

(Intercept) 1.55508 2.85E-01 5.462 4.71E-08 ***

Maximum daily temperature 0.01591 0.00259 6.141 8.21E-10 *** ContinuousUniversity of Oregon in session 0.17918 0.07675 2.335 0.019561 *

Dummy (1 = UO in Session)

Is a weekday 0.36327 0.04958 7.327 2.36E-13 *** Dummy (1 = Weekday)

Is a collector street (Reference - Local Street) -1.06899 0.09897 -10.801 2.00E-16 *** Categorical Is a minor arterial street -1.08890 0.19714 -5.524 3.32E-08 *** Categorical

Is a bike lane 0.99675 0.12585 7.92 2.37E-15 *** Dummy Variable

Is a bike route (Designated bike blvd. or has SLM) 0.42943 0.11802 3.639 2.74E-04 *** Dummy Variable

Access To UO students within 0.5 Miles 0.00037 0.00008 4.778 1.77E-06 *** Continuous (TAZ)Access To population within 1.5 Miles 0.00007 0.00001 9.109 2.00E-16 *** Continuous (TAZ)

Access to major retail employment within 1.5 miles 0.00056 0.00011 5.132 2.86E-07 *** Continuous (TAZ)

Link Connections within 0.125 Miles 0.00248 0.00076 3.268 1.08E-03 ** ContinuousOn Street Bike Facilities within 0.125 miles 0.00015 0.00003 5.404 6.51E-08 *** ContinuousMinor Arterial Streets within 0.125 Miles -0.00027 0.00003 -9.233 2E-16 *** Continuous

Null Deviance 1629.17Degrees of Freedom 279 Min -3.4624 0 ***Residual Deviance 286.78 1Q -0.8386 0.001 **Degrees of Freedom 266 Median -0.2023 0.01 *AIC: 3284.6 3Q 0.3914 0.05 .Theta: 6.685 Max 4.0535 0.1Standard Error 0.5782 x log-likelihood: -3254.622

N = 280

Model 1 On-street Locations Not near UO

Significance CodesDeviance Residuals

Variable typeWeather and Temporal Variables

Infrastructure- Street

Accessibility

Network Density

Infrastructure- Bicycle facility

Number of unique locations = 18

What are we learning?

How Are We Applying What We Are Learning?1. Bicycle crashes seem small

compared to motorized crashes

2. Intuition would say bike crashes are disproportionate when considering exposure

3. Let’s use our Bike Miles Traveled estimates to normalize bicycle crashes

How Are We Applying What We Are Learning?1. Crash Rates = Crashes Miles of

Travel

2. Bicycle Crash Rates 2- 4 times higher than motorized crash rates

3. Crash rates using miles of travel normalize for exposure and assess risk

4. Average of 2012-2014 crash conditions to smooth out year-to-year variation

How Are We Applying What We Are Learning?1. Hierarchy of safety related

to functional classification

2. Hierarchy applies to motorized travel and bikes

3. Injury risk on minor arterials 17 times higher than local streets

How Are We Applying What We Are Learning?1. Raw crash data can be

misleading

How Are We Applying What We Are Learning?1. Bike lanes offer significant

protection, especially on arterials

2. Injury rate reduced by 78%

3. Arterials with bike lanes are more dangerous than the most facility types for motorized travel

How Are We Applying What We Are Learning?1. Findings consistent with existing

literature

2. Other research used travel diary to establish exposure

Beck, Dellinger, and O’neil (2007)

Teschke et al. (2013)

Limitations• Margin of Error +/- 25%

• Not accounting for demographic differences in population

• Resolution of BMT results not suitable for link level analysis (not yet)

Things to think about • How to communicate these

kinds of result?

• What are the questions you’re trying to answer?

• Does the data give enough resolution to the question?

• How to store data – Portland State U. Bike/Ped database

Questions?

• CLMPO bike count data available at:http://www.thempo.org/356/Bicycle-Counts

• Or Google: “CLMPO Bicycle Counts”

Josh Roll - jroll@lcog.orgSenior PlannerLane Council of Governments541-682-2454