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17TCS Incorporating Innovations in Trip Generation into Practice

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Moderator: Kelly Clifton, Portland State University Speakers: Kristi Currans, University of Arizona; Kendra Breiland, Fehr & Peers; David Somers, Los Angeles Department of Transportation

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17TCS Incorporating Innovations in Trip Generation into Practice

  1. 1. Incorporating Innovations in Trip Generation into Practice: Advancing the Evaluation of Transportation Demand for Land Use Development Moderator: Kelly J. Clifton, Professor, CEE PSU
  2. 2. Assessing travel demand for development 2 Caliper Corporation: accessed September 2016 http://www.caliper.com/transmodeler/transmodeler-se-analysis-software.htm
  3. 3. ITE’s Handbook • Historic Data • 550 sites • ~5,000 data points • 172 land uses • Average rates or regressions • Vehicle trip counts • Based on: • Square footage • Employees • Seats • Dwelling units 3
  4. 4. Things are changing Communities want more out of their transportation system Want to plan for all modes Developing new performance measures Collecting new data Realizing some of the limitations of historic practice New research 4 Sam Beebe / Flickr: https://www.flickr.com/photos/sbeebe/5817452248/
  5. 5. Putting People First We have changing goals for our transportation system where people are central TIA methods largely exclude people: characteristics of traveler, socio-demographics of place, market demographics Our vision is to develop a people- oriented approach that reflects these values 5 www.pedbikeimages.org / Ryan Snyder Sustainability Equity Safety EconomyEfficiency Health Accessibility
  6. 6. ITE Manual (Data) vs. New Methods ITE Manual 10th edition (Fall 2017) • Incorporate more PT & MM data New methods • More controls/options for varying built environment • Summary and critique in the Journal of Planning Literature (QR Code)
  7. 7. On-going Data Collections Affordable Housing 20 locations in California (PSU/Caltrans) 60 locations in LA (vehicle only, City of LA) Smart Growth Trip Generation, Phase II 17 Office, 13 housing, 2 “other” locations in California (TTI/Caltrans) TODs 10 TODs across the US (UUtah/NITC) New York DOT 160 sites: residential, office, local retail, hotel, medical offices, supermarkets, restaurants San Francisco Planning Dept. 25 restaurants and market-rate housing Vehicle trips and parking Arlington Mobility Lab Various land uses Vehicle and/or person trips Washington, D.C. DOT 50+ market-rate housing and lodging 7
  8. 8. Panelists Kristina Currans – New Findings Assistant Professor of Planning University of Arizona Kendra Breiland – Practitioner’s Perspective Principal Fehr & Peers (Seattle) David Somers – Agency’s Perspective Transportation Planner City of LADOT
  9. 9. New Findings for Practice Kristina M. Currans Assistant Professor University of Arizona
  10. 10. New Findings NITC Technical Brief Dissertation • Age of Data • Land Use Taxonomy • Converting Vehicle Counts to Person Counts • Median Income and Activity Levels 2
  11. 11. As Age increases by 1%, Vehicle Trip Rates are _% Higher 0 1 2 Free-Standing Discount Superstore Free-Standing Discount Store Shopping Center Convenience Market with Gasoline Pumps Home Improvement Superstore Drive-in Bank High-Turnover (Sit-Down) Restaurant Fast-Food Restaurant with Drive- Through Windows Elasticity (%) 3
  12. 12. Age of data 4 Proportion Date of Observation 15% 10% 5% 0% Age of Data
  13. 13. Age of data 5 2000 – Carsharing Proportion Date of Observation 2010 – Peer-to-Peer Carshare 2005 – Google Transit 2001 – Modern Streetcar 1990s – Internet & Popularized SUVs 1994 – Bikeshare 1981 – LRT 1984 - Minivan ? – AVs 15% 10% 5% 0% 1956 – Federal-Aid Highway Act 2005 – Intermodal Surface Transportation Act (ISTEA)
  14. 14. Land Use Taxonomy (67 Retail & Service Categories) 6
  15. 15. Land Use Taxonomy (67 Retail & Service Categories) Approximate Cost to Replace a Sample of 10 Sites Every 10 Years: $80-100,000 per Category $5.3-6.7 million for 67 categories $240-300,000 for 3 categories 7
  16. 16. Demographics Matter 0 10 20 30 40 50 60 70 80 20 40 60 80 100 EstimatedPersonTripRate Median Income ($10,000) Supermarket Convenience Market 10
  17. 17. For More Information & Background • NITC Technical Brief • Handout during the Summit • Full Report (dissertation) available online (QR Code) 11
  18. 18. Bonus Slides Currans, NITC TCS, 9/11/2017
  19. 19. Case Study: G ITE – Age – Peak – BE – SDC Vehicle Trips per 1,000 Square Feet Average US Impact Fee Dollars per 1,000 Square Feet ITE’s Handbook Adjustment for New Data Only Adjusted for Peak Hour Adjusted for Income Adjusted for Built Environment Case Study: Supermarket 13
  20. 20. Case Study: G ITE – Age – Peak – BE - SDC Vehicle Trips per 1,000 Square Feet Average US Impact Fee Dollars per 1,000 Square Feet Adjusted Estimate Inflated Estimate Case Study: Supermarket 14
  21. 21. Accounting for Equity in Local Travel Data FROM POLICY TO PRACTICE
  22. 22. Transportation policy is evolving CALIFORNIA & LOS ANGELES ARE LEADING THE WAY
  23. 23. PRESENTATION TITLE Outcomes with Current Policies
  24. 24. PRESENTATION TITLE Outcomes with Current Policies
  25. 25. PRESENTATION TITLE Outcomes with Current Policies
  26. 26. California Complete Streets Act California Senate Bill 743 LA Vision Zero Action Plan LA Mobility Plan 2035 LA County Measure M LA Mayor ED 1 Great Streets Sustainable City pLAn
  27. 27. From LOS to VMT LOS measures vehicle capacity, or how many can be moved through our roadways. VMT measures vehicle miles traveled, or how a project impacts overall travel to our destinations. By moving from LOS to VMT, we can evaluate the impacts based on travel distance, encourage development near transit, and promote more diverse travel choices.
  28. 28. Affordable housing & mixed use vehicle trip adjustments Localized trip generation rates & VMT Travel Demand Forecasting (TDF) Model
  29. 29. PRESENTATION TITLE Source: 2013 California Household Travel Survey (CHTS) 20.7 23.4 26.3 32.8 49.3 29 30.2 38.1 45.4 57.9 41.6 41 52.7 64.2 78.7 0 20 40 60 80 Extremely Low Very Low Low Moderate Higher HouseholdVMTPerDay HUD Income Threshold HCD TOD HQTA Non-TOD Affordable Housing’s Low VMT
  30. 30. PRESENTATION TITLEAffordable Housing Data Collection TRIPS AND PARKING Data Collection Factors • Housing Type • Transit Proximity • Retail jobs proximity Sources: City of Los Angeles, SCAG Transit Priority Area = area within ½ mile of a major transit stop; Major Transit Stop = rail transit station or intersection of 2 or more major bus routes with peak service frequency of 15 minute or less
  31. 31. PRESENTATION TITLEAffordable Housing Trip Generation Results TPA Area Housing Type BIN Sample Size Daily Rate (Trips per DU) Inside Family Inside, Family 8 4.16 Inside Seniors Inside, Seniors 5 1.31 Inside Special Needs Inside, Special Needs 4 1.00 Inside Permanent Supportive Inside, Permanent Supportive 3 0.87 Outside Family Outside, Family 6 4.15 Outside Seniors Outside, Seniors 8 1.97 Outside Special Needs Outside, Special Needs 4 1.98 Outside Permanent Supportive Outside, Permanent Supportive 4 1.50
  32. 32. LA Affordable Housing Trip Generation Outside Transit AreasInside Transit Areas 4.16 1.31 1 0.87 4.15 1.97 1.98 1.5 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 DailyTripRate(tripsperdwellingunit) Family Affordable Housing Senior Affordable Housing Permanent Supportive Special Needs Family Affordable Housing Senior Affordable Housing Permanent Supportive Special Needs
  33. 33. 4.16 1.72 1.49 1.23 6.65 4.2 3.44 2.02 2.4 0 1 2 3 4 5 6 7 DailyTripRate(Tripsperdwellingunit) LA Affordable Housing Trip Generation ITE Handbook Trip RatesTrip Rates based on local data Family Affordable Housing Senior Affordable Housing Permanent Supportive Apartment High-Rise Apartment Senior Adult Housing Congregate Care Facility Continuing Retirement Community Special Needs
  34. 34. LA Affordable Housing Parking Rates Outside Transit AreasInside Transit Areas ParkingRateperdwellingunit 0.85 0.44 0.20 0.29 0.82 0.48 0.44 0.43 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 Family Affordable Housing Senior Affordable Housing Permanent Supportive Special Needs Family Affordable Housing Senior Affordable Housing Permanent Supportive Special Needs
  35. 35. LA Affordable Housing Parking Incentives
  36. 36. PRESENTATION TITLE VMT Impact Analysis – VMT Calculator – Full model run for large scale projects Step 1 Step 2 Step 3 Reviewing Projects through a VMT lens Project Screening Apply TDM project mitigation
  37. 37. PRESENTATION TITLE
  38. 38. PRESENTATION TITLE
  39. 39. PRESENTATION TITLE
  40. 40. Mitigation options include TDM Measures as Mitigation Parking management Transit incentives Education and encouragement Commute trip reduction Shared mobility Bicycle infrastructure Neighborhood enhancement
  41. 41. PRESENTATION TITLE
  42. 42. PRESENTATION TITLE
  43. 43. PRESENTATION TITLE 24 See you on the planning.lacity.org ladot.lacity.org buses. trains. streets.streets.
  44. 44. Moving Beyond the Car Case Studies from Seattle and DC Presenter: Kendra Breiland, Fehr & Peers TREC Conference September 11, 2017
  45. 45. I think many communities start with a similar story… Next StepsDDOT MXD+SeattleIntro
  46. 46. Next StepsDDOT MXD+SeattleIntro
  47. 47. Next StepsDDOT MXD+SeattleIntro
  48. 48. Next StepsDDOT MXD+SeattleIntro
  49. 49. Next StepsDDOT MXD+SeattleIntro
  50. 50. Next StepsDDOT MXD+SeattleIntro
  51. 51. Seattle is moving away from vehicle-to-capacity ratios to drive alone mode share targets to increase the people-moving capacity of the transportation network. Next StepsDDOT MXD+SeattleIntro
  52. 52. There’s a recognition that the surrounding urban form impacts a project’s ability to meet its mode share target… Next StepsDDOT MXD+SeattleIntro
  53. 53. The story’s not finished. While the policy has been set, the details are still being worked out: To implement this standard, we need good mode share data – is the city ready to commit to funding regular mode share survey data? What’s appropriate to include in the mitigation menu? How to treat special users, like freight? Next StepsDDOT MXD+SeattleIntro
  54. 54. Custom Trip Generation For The District DDOT MXD+
  55. 55. Limitations of Current Practice General Office BuildingShopping Center Single-Family Detached Housing “Guidance” taken as “Gospel” Next StepsDDOT MXD+SeattleIntro
  56. 56. DDOT MXD+ • Understand trip generation in multi-modal urban areas • Understand the influence of parking supply on trip generation • Accurately estimate non-vehicle trips • Apply a statistically valid model using a functional tool Project Goals Next StepsDDOT MXD+SeattleIntro
  57. 57. DDOT MXD+ Collected data at 62 sites throughout the District: 1. Raw person counts 2. Intercept surveys 3. Parking supply Next StepsDDOT MXD+SeattleIntro
  58. 58. DDOT MXD+ Variable Testing Next StepsDDOT MXD+IssuesIntro
  59. 59. What Influences Trip Generation? One Variable Multiple Context Variables Next StepsDDOT MXD+IssuesIntro
  60. 60. DDOT MXD+ Final Variables • Regression from person trip data • Distance to Metro (ft) • Transit competiveness: Employment share w/in 45 min by Metro vs. 45 min by car • Parking provided per service population • Neighborhood Population Density • Employment w/in 1 mile • Transit service intensity (transit trips/hr/acre) PERSON TRIP MODEL MODE CHOICE MODEL Next StepsDDOT MXD+SeattleIntro
  61. 61. DDOT MXD+ 0 200 400 600 800 1,000 1,200 0 200 400 600 800 1,000 1,200 PREDICTEDTRIPS OBSERVED TRIPS PM Peak Hour - All Person Trips ITE Raw DDOT MXD+ Match Improved Accuracy Next StepsDDOT MXD+IssuesIntro
  62. 62. DDOT MXD+ 0 50 100 150 200 250 300 350 400 450 500 0 50 100 150 200 250 300 350 400 450 500 PREDICTEDTRIPS OBSERVED TRIPS PM Peak Hour - Auto Vehicle Trips ITE Raw DDOT MXD+ Match Improved Accuracy Next StepsDDOT MXD+SeattleIntro
  63. 63. DDOT MXD+ Validation Statistic Auto Vehicle Trips Transit Trips Walk Trips Bike Trips All Person Trips AM Peak Hour The Model Average Model Error 4% 9% 4% 14% 5% R-Squared 0.66 0.63 0.67 0.46 0.67 ITE Average Model Error 129% N/A N/A N/A -40% R-Squared 0.60 N/A N/A N/A 0.66 PM Peak Hour The Model Average Model Error 4% 8% 4% 5% 5% R-Squared 0.79 0.76 0.64 0.65 0.76 ITE Average Model Error 169% N/A N/A N/A -36% R-Squared 0.66 N/A N/A N/A 0.66 Model Validation Next StepsDDOT MXD+IssuesIntro
  64. 64. So Why Does This Matter? • ITE over predicts auto impacts • New model demonstrates value of walk/bike/transit infrastructure • Helps DDOT “right size” mitigations Next StepsDDOT MXD+SeattleIntro
  65. 65. DDOT MXD+ • Public-facing web application tool for developers, agencies, and consultants • Collect more data • Advance the person trip model Next Steps Next StepsDDOT MXD+SeattleIntro

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