Building a Bicycle Data Fusion Model for Oregon

Sirisha Kothuri, Ph.D.

3/11/2020

Agenda

• Introduction• Nonmotorized Traffic Monitoring Programs• Emerging Data Sources• Research

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Why count?

If we don’t count it, it doesn’t count!!

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Why measure walking and biking?

• Funding & policy decisions• To show change over time• Facility design• Planning (short-term, long-term, regional…)

• Economic impact• Public health• Safety

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How do we measure?• Surveys

• National• Regional• Local

• GPS• Counts

• Permanent• Short duration

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What?

People actually bike here?

Yes! 200 per day

Credit: K. Nordback6

What?People actually walk here?

Credit: K. Nordback7

What are counts not good for?

• Studying trip purpose • Demographics

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Credit: K. Nordback

Traffic Monitoring Programs• Required by FHWA (MAP21):

• all urban and rural principal arterial roadways• all intermodal connector roadways • the strategic defense highway network

• Historically used to allocate federal funds to state DOTs.

• Municipalities • Planning• Signal timing

9Credit: K. Nordback

State Traffic Monitoring• Permanent counters

• Inductive loops

• Short duration counters• Pneumatic tubes

10Pic Credit: MetroCount

Oregon’s Traffic Monitoring Program

11Source: ODOT

Oregon Short Duration Counts

12Credit: K. Nordback

AADT

Credit: K. Nordback13

AADT to Estimate VMT

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Sum (AADT X Segment Length) over network to compute Vehicle Miles Traveled (VMT)

Source: ODOT

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Resources

AADBT Estimation

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• Day-of-week, month-of-year (19 factors)• Day-of-week-of-month• Disaggregate, day-of-year• Seasonal factors• K (design-hour) factors• Statistical modeling approaches (weather, correction

factors)

Minimizing Error

• Using daily factors that are disaggregated by weather conditions;

• Conducting short-duration counts during months with higher traffic volumes (e.g., April–October;) for at least seven days;

• If counts must be limited to 24 h, plan for monitoring Tuesdays through Thursdays, even at sites with high weekend volumes.

• Using monthly rather than seasonal factors;

• Using weather-based regression equations to correct factors;

• Using day-of-year rather than traditional factors; and

• Imputing missing values using methods that account for weather and information from similar, nearby sites.

• Plan for at least four counters per factor group for bicycles and five or more counters for pedestrians.

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Trends

• Innovation in Counting Technologies

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Multiple Emerging Data Sources

• Mobile device crowdsensing • Micro-mobility services • Smarter signalized intersection sensors• Ped/bike exposure and near-miss from connected

cars

• How to make sense?? What is “good enough”?

19Slide Credit: S. Turner

Key Takeaways

1. No single golden data goose 2. Swimming in multiple data sources

• Some public, some private• Integrating disparate sources• Reconciling differences and bias • Understand what is being measured and how • Ask vendors questions, independent eval

3. Keep installing and maintaining accurate permanent counters • Foundation for testing or calibrating any crowdsource method

4. Understand privacy issues and be proactivehttps://www.nytimes.com/interactive/2019/12/19/opinion/

location-tracking-cell-phone.html

20Slide Credit: S. Turner

Trends

21Source: Proulx and Pozdnukhov, 2017

• Data Fusion

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Bike Counts

Automated Manual

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Third-Party Data

Opt-in / Apps Passive/Background Sharing

+ +

Exploring Data Fusion Techniques to Derive Bicycle Volumes on a Network

Scope• What third-party bicycle volume and route data are

available to augment conventional count data, and how have they been used to date?

• How can we evaluate these emerging data sources for factors such as accuracy, coverage, completeness, and representativeness?

• How can we combine emerging third-party and conventional data sources to estimate bicycle volumes across entire networks?

• What relative marginal value do different data sources and model forms add in predicting bicycle volumes relative to acquisition and processing costs?

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Third-party Bicycle User Data: Overview

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Study Location (Country)

User data Demographic(vs. actual)

Count correspondence1

(count data)Peer-reviewed journal articles

Boss et al. (2018) Ottawa (CA) Strava

78% male (68%)Noted age 25-44 over-represented

(11 perm., single month 1 yr apart)1-30% SRr=0.76-0.96

Sample row from full table in Lit Review

22 studies reviewed using 3rd party user data• 11 academic, 11 agency/report/white papers• 13 US, 9 international• Vast majority included Strava Metro data

• Others: Ride Report & other smartphone apps• Not all focused on evaluation, but most reported basic

validation stats

• User data demographics generally limited to age, gender, and type of cycling activity (e.g., commuting vs. recreational riding)

• Information on key equity-related variables such as income and race/ethnicity lacking

• Even where user data available, lack of comparison data on the composition of cyclists in a city or region

• Strava (from reviewed US/Canadian studies): 75-84% male, 25-44 over-represented; 31% 35-44 (single study, Atlanta, 71% recreational (single study, Atlanta)

• National cyclist comparisons: 51%-60% male, 18-21% 35-44

Third-party Bicycle User Data: Demographics

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• Sampling rate is often reported as: third party count / full count

• Most often calculated from automated counters, but sometimes from annualized short-duration counts

• Strava sampling rates typically reported as • < 10% of all cyclists• Most commonly ~1-5%• Maximum 30%, typically along busy trails or at urban

chokepoints (e.g. bridges)• Variable spatially, even within region/city

Third-party Bicycle User Data: Sampling rate

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• Usually reported as either a bi-variate, linear correlation (r) or as an R^2 from a simple multiple regression function

• Overall correlation across all sites typically high (r=0.6-0.9)• Wide variation by site (some sites have even shown negative

correlation with third-party data)• Sampling rate does not necessarily predict correlation; e.g.,

in Austin Ride Report demonstrated comparable correlation results despite a much lower sampling rate

• Specific correlation not always well-described (e.g. across sites or over time at same site), and simple correlation sensitive to outliers such as high volume count locations, so high r’s should be interpreted with caution

Third-party Bicycle User Data: Correlation with Counts

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Anticipated Outcomes• A review of existing bicycle volume estimation methods

and a catalog of potential third-party data sources• Evaluation of emerging third-party bicycle data sources

as inputs into network-wide volume estimation• Demonstration of the development, application, and

validation of models that combine multiple data sources• Comparison of the relative value of different data

sources and modeling techniques in bicycle volume estimation

• Documentation and packaging of methods developed including making scripts available on GitHub

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Contact Information

Sirisha Kothuri, Ph.D.Portland State UniversityEmail: skothuri@pdx.eduPhone: 503.725.4208

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Thank You!