new tools for estimating walking and bicycling demand
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MoPeD: A Model of Pedestrian Demand forTravel Demand Forecasting Models
Pro Walk/Pro Bike/Pro Place – Pittsburgh, PA
09 September 2014
Patrick A. Singleton*
Kelly J. Clifton, PhD *
Christopher D. Muhs*
Robert J. Schneider, PhD†
* Portland State U. † U. Wisconsin–Milwaukee
Background
Why model pedestrian travel?
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health & safety
new data
mode shifts
greenhouse gas emissions
plan for pedestrian investments& non-motorized facilities
Background — Method — Results — Future Work
• Metro: metropolitan planning organization for Portland, OR
• Two research projects
Project overview
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Image: Citizens for a Better Environment and the Environmental Defense Fund. In: Beimborn, E., & Kennedy, R. (1996). Inside the blackbox: Making transportation models work for livable communities. https://www4.uwm.edu/cuts/blackbox/blackbox.pdf
pedestrianenvironment
pedestrian demand estimation model
Background — Method — Results — Future Work
Demand modeling
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1. Generation
2. Distribution
3. Mode Choice
4. Assignment
Trip-Based Model Sequence
How many trips?
Where do they go?
What travel mode?
What route?
1,000 trips start here
100 trips go to Point
75% walk
via Penn and Liberty
Question Example
Background — Method — Results — Future Work
Current method
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Trip Distribution or Destination Choice (TAZ)
Mode Choice (TAZ)
Trip Assignment
Pedestrian Trips
All Trips Pedestrian Trips Vehicular Trips
TAZ = transportation analysis zoneTrip Generation (TAZ)
Background — Method — Results — Future Work
New method
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TAZ = transportation analysis zonePAZ = pedestrian analysis zone
Trip Generation (PAZ)
Trip Distribution or Destination Choice (TAZ)
Mode Choice (TAZ)
Trip AssignmentPedestrian Trips
Walk Mode Split (PAZ)
Destination Choice (PAZ)
I
II
All Trips Pedestrian Trips Vehicular Trips
Background — Method — Results — Future Work
Pedestrian analysis zones
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TAZs PAZs
Home-based work trip productions
1/20 mile = 264 feet ≈ 1 minute walk
Metro: ~2,000 TAZs ~1.5 million PAZs
Background — Method — Results — Future Work
Pedestrian Index of the Environment (PIE)PIE is a 20–100 score total of 6 dimensions, calibrated to observed walking activity:
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ULI = Urban Living Infrastructure: pedestrian-friendly shopping and service destinations used in daily life.
Pedestrian environment
People and job density
Transit access
Block size
Sidewalk extent
Comfortable facilities
Urban living infrastructure
Background — Method — Results — Future Work
Walk mode split
Probability(walk) = f(traveler characteristics, pedestrian environment)
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I
Walk Mode Split (PAZ)
Pedestrian Trips
Vehicular Trips
• Data: 2011 OR Household Activity Survey: (4,000 walk trips) ÷ (50,000 trips) = 8% walk
• Model: binary logistic regression
Background — Method — Results — Future Work
Results
• Household characteristics
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I
+ positively related to walking – negatively related to walking
number of children age of household
vehicle ownership
3.6%
4.4%
5.4%
0% 2% 4% 6%
Increase in odds of walking
home–work trips
home–other trips
other–other trips
• Pedestrian environment+ positively related to walking
+ 1 point PIE
associated with:
Background — Method — Results — Future Work
Destination choice
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II
Pedestrian Trips
Destination Choice (PAZ) Prob(dest. zone) =f(distance, size,
pedestrian environment, traveler characteristics)
∆ odds of walking to destination
+ 1 mile of distance 75–90% decrease
2 x number of retail jobs 10–50% increase
+ 1 point PIE 1–5% increase
• Preliminary results:
Background — Method — Results — Future Work
Future work
• Continue destination choice modeling
• Predict potential pedestrian paths
• Refine and verify PIE
• Test method in other region(s)
13Background — Method — Results — Future Work
Questions?
Project report/info:http://otrec.us/project/510
http://otrec.us/project/677
Patrick A. Singleton [email protected]
Christopher D. Muhs [email protected]
Kelly J. Clifton, PhD [email protected]
Robert J. Schneider, PhD [email protected]
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Source: Clifton, K. J., Singleton, P. A., Muhs, C. D., Schneider, R. J., and Lagerwey, P. (2014). Improving the representation of the pedestrian environment in travel demand models: Phase I report (OTREC-RR-510).
Background — Method — Results — Future Work