New Tools for Estimating Walking and Bicycling Demand
- 1. MoPeD: A Model of Pedestrian Demand forTravel Demand Forecasting Models Pro Walk/Pro Bike/Pro Place –Pittsburgh, PA 09September 2014 Patrick A. Singleton* Kelly J. Clifton, PhD* Christopher D. Muhs* Robert J. Schneider, PhD† * Portland State U. † U. Wisconsin–Milwaukee
- 2. Background Why model pedestrian travel? 2 health & safety new data mode shifts greenhouse gas emissions plan for pedestrian investments & non-motorized facilities Background — Method — Results — Future Work
- 3. •Metro: metropolitan planning organization for Portland, OR •Two research projects Project overview 3 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 pedestrian environment pedestrian demand estimation model Background — Method — Results — Future Work
- 4. Demand modeling 4 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? 1,000trips start here 100trips go to Point 75% walk via Penn and Liberty Question Example Background — Method — Results — Future Work
- 5. Current method 5 Trip Distribution or Destination Choice (TAZ) Mode Choice (TAZ) Trip Assignment Pedestrian Trips All Trips Pedestrian Trips Vehicular Trips TAZ = transportation analysis zone Trip Generation (TAZ) Background — Method — Results — Future Work
- 6. New method 6 TAZ = transportation analysis zone PAZ = pedestrian analysis zone Trip Generation (PAZ) Trip Distribution or Destination Choice (TAZ) Mode Choice (TAZ) Trip Assignment Pedestrian Trips Walk Mode Split (PAZ) Destination Choice (PAZ) I II All Trips Pedestrian Trips Vehicular Trips Background — Method — Results — Future Work
- 7. Pedestrian analysis zones 7 TAZs PAZs Home-based work trip productions 1/20mile = 264feet ≈ 1minute walk Metro: ~2,000TAZs ~1.5million PAZs Background — Method — Results — Future Work
- 8. Pedestrian Index of the Environment (PIE) PIE is a 20–100score total of 6 dimensions, calibrated to observed walking activity: 8 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
- 10. Walk mode split Probability(walk) = f(traveler characteristics, pedestrian environment) 10 I Walk Mode Split (PAZ) Pedestrian Trips Vehicular Trips •Data: 2011OR Household Activity Survey: (4,000walk trips) ÷(50,000trips) = 8% walk •Model: binary logistic regression Background — Method — Results — Future Work
- 11. Results •Household characteristics 11 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 + 1point PIE associated with: Background — Method — Results — Future Work
- 12. Destination choice 12 II Pedestrian Trips Destination Choice (PAZ) Prob(dest. zone) = f(distance, size, pedestrian environment, traveler characteristics) Δ odds of walking to destination + 1mile of distance 75–90%decrease 2x number of retail jobs 10–50% increase + 1point PIE 1–5%increase •Preliminary results: Background — Method — Results — Future Work
- 13. Future work •Continue destination choice modeling •Predict potential pedestrian paths •Refine and verify PIE •Test method in other region(s) Background — Method —Results — Future Work 13
- 14. Questions? Project report/info: http://otrec.us/project/510 http://otrec.us/project/677 Patrick A. Singletonpatrick.singleton@pdx.edu Christopher D. Muhsmuhs@pdx.edu Kelly J. Clifton, PhDkclifton@pdx.edu Robert J. Schneider, PhDrjschnei@uwm.edu 14 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