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17TCS Walk, don’t run? Advancing the state of the practice in pedestrian demand modeling

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Instructors: Kelly Clifton, Portland State University Joe Broach, Portland State University Robert Schneider, University of Wisconsin–Milwaukee Patrick Singleton, Utah State University Jaime Orrego, Portland State University

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17TCS Walk, don’t run? Advancing the state of the practice in pedestrian demand modeling

  1. 1. WALK, DON’T RUN? ADVANCING THE STATE OF THE PRACTICE IN PEDESTRIAN DEMAND MODELING Transportation and Communities Summit September 12, 2017
  2. 2. Dr. Kelly J. Clifton Portland State University 2 Dr. Joseph Broach Portland State University Dr. Patrick Singleton Utah State University Jaime Orrego Onate Portland State University Dr. Robert Schneider University of Wisconsin, Milwaukee
  3. 3. Model of Pedestrian Demand (MoPeD) 3 TAZ = transportation analysis zone PAZ = 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 III All Trips Pedestrian Trips Vehicular Trips
  4. 4. Opportunities & challenges Behavioral research/data/methods Adapted from: Wegener and Fürst, 1999
  5. 5. Decision sequencing: activity, mode, destination; mode, destination, activity; destination, activity, mode Destination choice considerations – choice set generation Willingness to walk Path/route choice considerations Behavioral research 5
  6. 6. Behavioral Research Built environment – Thresholds & nonlinearities – Mixing – Scale Lifestyle questions: – Vehicle ownership & residential location – Attitudes, motivations & values Positive Utility ofTravel – What aspects? – Diminishing returns? Mode feedbacks to trip generation 6
  7. 7. Spatial/Temporal Scale • Depends on output needed for policy/research • Capture variations in the pedestrian built & natural environment • Spatial accuracy • Theory/Behavior 7
  8. 8. Walking Behavior • Passive data sources – Trip-level information – Multi-day – Multi-modal – Destinations – Routes & speeds • But also need… – Motivations & considerations – Barriers – Trips not made 8
  9. 9. Built environment • How & what to represent? • Indices, proxies • Forecasting S.R. Gehrke, & K.J. Clifton. (2016). Toward a spatial- temporal measure of land-use mix. Journal of Transport and Land Use, 9(1):171–186 S.R. Gehrke, & K.J. Clifton. (2014). Operationalizing land use diversity at varying geographic scales and its connection to mode choice: Evidence from Portland, Oregon. Transportation Research Record: Journal of the Transportation Research Board 2453: 128-136. 9
  10. 10. Networks • Network representation • How do we attribute networks? • Feedbacks of travel costs • Do we need to assign trips to a network? Broach, J. P. (2016). Travel mode choice framework incorporating realistic bike and walk routes (Order No. 10061477). Available from Dissertations & Theses @ Portland State University; ProQuest Dissertations & Theses Global. 10
  11. 11. Link to Health Outcomes • Health impact analysis • Total time spent walking + speeds • Physical activity budgets • Crash risk exposure • Pollutant exposure • Feedback into life expectancy Woodcock J, Givoni M, Morgan AS. Health Impact Modelling of Active Travel Visions for England and Wales Using an Integrated Transport and Health Impact Modelling Tool (ITHIM). Barengo NC, ed. PLoS ONE. 2013;8(1):e51462 11
  12. 12. Objectives Understand motives for developing and using pedestrian demand models Share knowledge & experiences Discuss key challenges and opportunities in pedestrian modeling Develop an agenda for improving the state of the research Outcomes Define state of the practice White paper on state of the practice and research needs TRB workshop Discuss next steps for MoPed & other efforts 12
  13. 13. Agenda 830-9 Introductions and framing of the workshop (Clifton) 9-930 Why model pedestrian demand? (Singleton) 930-1030 What are the data needs and opportunities? (Schneider) 1030-945 Break 11AM-12 What is the appropriate scale? (Broach and Orrego) 12-13 Lunch break 13-14 How do we represent networks and attributes? (Broach) 14-1445 How do we forecast inputs? (Clifton) 1445-15 Break 15-1545 How can model outputs link to health impact, safety, and other modeling tools? (Singleton) 1545-16 What are the most important next steps to improve practice? (Clifton) 13
  14. 14. Pedestrian modeling: State-of-the-practice “Walk, don’t run?” Workshop Transportation and Communities Summit Portland, OR — 12 September 2017
  15. 15. Why model pedestrian demand? analyze health & safety impacts utilize new data resources mode shifts air quality & GhG plan for pedestrian investments & non-motorized facilities
  16. 16. Early (regional) pedestrian modeling  1988 Metropolitan Service District (Portland, OR)  Home-based motorized/non-motorized mode split model  1993 Sacramento Association of Governments (SACOG)  Mode choice model w/ separate walking & bicycling modes  Late 1990s  Baltimore, Boston, Chicago, Hampton Roads, Los Angeles, Philadelphia, Portland, Sacramento, San Francisco Bay Area  2005 TRB Special Report 288  54% (35) of large MPOs reported non-motorized modeling
  17. 17. Early pedestrian environment measures  1988 Maryland-National Capital Park and Planning Commission (Washington, DC)  Pedestrian and Bicycle Friendliness Index  1990s Making the Land Use, Transportation, Air Quality Connection (LUTRAQ) (Portland, OR)  Pedestrian Environment Factor (PEF)  Used in Chicago, Hampton Roads, Miami, Philadelphia, Portland, Sacramento, Salt Lake City, …  2000s  Transitioning from subjective indices to objective measures
  18. 18. Pedestrian modeling frameworks
  19. 19. Pedestrian modeling frameworks
  20. 20. State-of-the-practice reviews  Summer 2012 & Summer 2017  Largest 48 US MPOs (> 1,000,000 population)  Reviewed model documentation  2012: 63% model non-motorized travel; 60% in mode choice  2017: 69% model non-motorized travel; 82% in mode choice  Online questionnaires to MPO modelers  2012: 29 responses (60%)  2017: 31 responses (65%)
  21. 21. State-of-the-practice (2012) 1 0 4 4 4 4 4 4 4 4 0 4 0 4 4 0 0 4 0 4 0 4 2 1 4 0 0 0 0 4 0 4 0 2 4 3 0 2 2 2 3 0 0 0 3 3 3 0
  22. 22. State-of-the-practice (2017) 2 0 4 4 4 4 4 4 4 4 0 4 4 4 4 0 0 4 0 4 0 4 4 4 4 4 0 0 0 4 0 4 0 4 4 3 0 2 2 2 3 4 0 0 4 4 4 0
  23. 23. Barriers & challenges  2012  84% (16) Limited travel survey records  58% (11) Limited built environment data  58% (11) Limited modeling resources  47% (9) Limited decision-maker interest  2017  100% (25) Limited survey records of walking (& bicycling)  81% (21) Few resources for data collection  48% (12) Forecasting future scenarios  40% (10) Insufficient information on the built environment  16% (4) Not a priority among decision-makers  4% (1) Not a priority among the public  Other: Regional scale incompatible with short-distance walking
  24. 24. Pedestrian modeling applications  Project prioritization  Scenario planning  Corridor planning  Traffic safety analysis  Health impact assessment  Infrastructure gap analysis Currently Future interest 61% (14) 78% (18) 43% (10) 65% (15) 43% (10) 61% (14) 35% (8) 57% (13) 35% (8) 57% (13) 30% (7) 57% (13)
  25. 25. Pedestrian modeling outputs  Direct transportation outputs  Walk trips generated  Walk trips with origins & destinations  Walk trips with “routes”   Distances walked   Pedestrian miles traveled (PMT)   Minutes of walking   Physical activity levels (METs)  Classified by…  Geographic location  Personal characteristics (socio-demographics)
  26. 26. Questions?  In your expert judgement or opinion…  What uses motivate pedestrian modeling today?  What potential (existing? new?) uses motivate pedestrian modeling in the near/long-term future? Patrick A. Singleton patrick.singleton@usu.edu Singleton, P. A., Totten, J. C., Orrego-Onate, J. P., Schneider, R. J., & Clifton, K. J. (under review). Making strides: State-of-the-practice of pedestrian forecasting in regional travel models.
  27. 27. Data for Pedestrian Demand Modeling Robert Schneider, PhD University of Wisconsin-Milwaukee, Department of Urban Planning Portland State, NITC Workshop – September 2017 1
  28. 28. Data for Pedestrian Demand Modeling Robert Schneider, PhD University of Wisconsin-Milwaukee, Department of Urban Planning Portland State, NITC Workshop – September 2017 2 How many? How safe?
  29. 29. Outline 1) Who is represented in our models? – How do we measure pedestrian activity? – Are there other (better) ways to measure pedestrian activity? 2) What variables do we use to predict? – What explanatory variables are common? – Are there other (better) ways to represent behavior? Different needs in the future? 3) How well do our models work? 4) Are our models valued in practice? 3 Explanatory Variables Validation Dependent Variables Usefulness
  30. 30. 1) Dependent Variables
  31. 31. Pedestrian Data Types Safety (Crashes, injuries, behaviors) User Characteristics (Age, gender) Infrastructure (Facility coverage & quality) Public Opinion (Satisfaction, desires) Exposure/ Volume (Count, mode share) Counts Surveys Household (Phone, mail, internet) Intercept Manual (Data collectors, imagery review) Automated (Continuous counters, imagery processing)
  32. 32. Pedestrian Data Types Safety (Crashes, injuries, behaviors) User Characteristics (Age, gender) Infrastructure (Facility coverage & quality) Public Opinion (Satisfaction, desires) Exposure/ Volume (Count, mode share) Counts Surveys Direct Demand Models Household (Phone, mail, internet) Intercept Manual (Data collectors, imagery review) Automated (Continuous counters, imagery processing) Flow Models
  33. 33. Pedestrian Counts Manual Counts
  34. 34. 8Automated Counts Pedestrian Counts
  35. 35. Google Earth—Tele Atlas 2008 Pedestrian Segment/Screenline Counts
  36. 36. Google Earth—Tele Atlas 2008 Pedestrian Intersection Crossing Counts
  37. 37. Google Earth—Tele Atlas 2008 Pedestrian Intersection Crossing Counts
  38. 38. Google Earth—Tele Atlas 2008 Pedestrian Midblock Crossing Counts
  39. 39. Graphic source: Google Earth, 2008. Counts  Direct Demand Models
  40. 40. Travel Surveys Example: Boulder, CO Region
  41. 41. Surveys  Flow Models Example: Boulder, CO Region
  42. 42. Surveys  Flow Models Source: Clifton, K.J., C.V. Burnier, R.J. Schneider, S. Huang, and M.W. Kang. “Pedestrian Demand Model for Evaluating Pedestrian Risk Exposure,” Prepared by the National Center for Smart Growth Research and Education, University of Maryland for the Maryland SHA, June 2008.
  43. 43. Tour Graphic source: McGuckin, N. & Y. Nakamoto. Trips, Chains, and Tours—Using an Operational Definition, 2004. Available online: http://onlinepubs.trb.org/onlinepubs/archive/conferences/nhts/McGuckin.pdf
  44. 44. Trips Graphic source: McGuckin, N. & Y. Nakamoto. Trips, Chains, and Tours—Using an Operational Definition, 2004. Available online: http://onlinepubs.trb.org/onlinepubs/archive/conferences/nhts/McGuckin.pdf
  45. 45. Non-Home-Based Trips Graphic source: McGuckin, N. & Y. Nakamoto. Trips, Chains, and Tours—Using an Operational Definition, 2004. Available online: http://onlinepubs.trb.org/onlinepubs/archive/conferences/nhts/McGuckin.pdf
  46. 46. Stages Graphic source: McGuckin, N. & Y. Nakamoto. Trips, Chains, and Tours—Using an Operational Definition, 2004. Available online: http://onlinepubs.trb.org/onlinepubs/archive/conferences/nhts/McGuckin.pdf
  47. 47. Challenge: Secondary walk trip stages • The travel survey data used to develop flow models does not often capture secondary walk stages • Even if data were available, could stages be integrated into the structure of flow models? 21
  48. 48. Pedestrian Mode Share on Trips Within 20 Shopping Districts Intercept Survey NHTS Overall 65% 71% Urban core 96% 87% Suburban Main St. 63% 64% Suburban Thoroughfare 30% 52% Suburban Shop. Center 40% 16%
  49. 49. Urban Core Shopping District (Market Street, San Francisco) Intercept Survey Respondent Pedestrian Path Density
  50. 50. Suburban Main Street Shopping District (Burlingame) Intercept Survey Respondent Pedestrian Path Density
  51. 51. Suburban Main Street Shopping District (Richmond) Intercept Survey Respondent Pedestrian Path Density
  52. 52. Are there other ways to measure pedestrian activity? 26
  53. 53. Are there other ways to measure pedestrian activity? 27 Source: USGS, “Earth Explorer,” Available online, https://earthexplorer.usgs.gov/, 2017. Aerial Imagery or Drone (HAV) Counts
  54. 54. Are there other ways to measure pedestrian activity? 28 Source: USGS, “Earth Explorer,” Available online, https://earthexplorer.usgs.gov/, 2017. Aerial Imagery or Drone (HAV) Counts
  55. 55. Are there other ways to measure pedestrian activity? 29 Source: STRAVA Labs. “Strava, 2014 vs. 2015,” Available online, http://labs.strava.com/heatmap/2014-2015.html#6/-120.90000/38.36000/gray/bike, 2016. GPS Traces: STRAVA running routes
  56. 56. Challenge: Temporal variation Data Source: Milwaukee County Parks, 2014-2015. 0.0% 0.2% 0.4% 0.6% 0.8% 1.0% 1.2% 1.4% 1.6% 1.8% 2.0% 0 6 12 18 24 30 36 42 48 54 60 66 72 78 84 90 96 102 108 114 120 126 132 138 144 150 156 162 PercentofWeeklyVolume Hour of the Week Oak Leaf Trail Weekly Volume Pattern (11/4/14 to 11/3/15) M Tu W Th F Sa Su Wed., 4-6 pm 3.13% of week
  57. 57. 2) Explanatory Variables
  58. 58. What explanatory variables are common in our current models? • Trip distance or time • Local environment factors – Population and employment density – Proximity to commercial/retail – Proximity to transit – Pedestrian network connectivity • Socioeconomic factors – HH automobile ownership – HH income 32 These are also mostly convenience factors Convenience
  59. 59. Are there other factors that might influence pedestrian behavior?
  60. 60. 5) Habit (People who choose a particular mode regularly are more likely to consider it as an option in the future) 2) Basic Safety & Security (People seek a mode that they perceive to provide a basic level of safety from traffic collisions and security from crime ) 3) Convenience & Cost (People seek a mode that will get them to an activity using an acceptable amount of time, effort, and money) 4) Enjoyment (People seek a mode that provides personal (e.g., physical, mental, or emotional), social, or environmental benefits) Pedestrian, Bicycle, Transit, or Automobile? 1) Awareness & Availability (People must be aware of the mode and have it available as an option to travel to an activity) Theory of Routine Mode Choice Decisions Situational Tradeoffs SocioeconomicFactors (Explaindifferencesinhowpeoplerespondtoeachstep) Schneider, R.J. “Theory of Routine Mode Choice Decisions: An Operational Framework to Increase Sustainable Transportation,” Transport Policy, Volume 25, pp. 128-137, 2013
  61. 61. 35Singleton, P.A. and K.J. Clifton. “The theory of travel decision-making: A conceptual framework of active travel behavior,” Presented at the Transportation Research Board Annual Meeting, Washington, DC, January 2015. Are there other factors that might influence pedestrian behavior?
  62. 62. • Attractiveness of other modes – Transit service, auto parking pricing & supply, gas prices, even bicycle infrastructure, AVs • Safety and security – Pedestrian network attributes (pedestrian facilities; how hard is it to cross the street?) – Perceived risk of crime • Social norms; Personal preferences/enjoyment 36 Are there other factors that might influence pedestrian behavior?
  63. 63. Pedestrian Network Attribute Example Source: Miranda-Moreno, L.F. and D. Fernandes. “Pedestrian Activity Modeling at Signalized Intersections: Land Use, Urban Form, Weather and Spatio-Temporal Patterns,” Transportation Research Record 2264, pp. 74-82, 2011. Montreal Signalized Intersection Pedestrian Volume Model
  64. 64. Challenge: Measurement detail…how good is good enough?
  65. 65. Challenge: Geographic scalability Source: Urbitran Associates. Pedestrian Flow Modeling for Prototypical Maryland Cities, Prepared for Maryland DOT, 2004.
  66. 66. 3) Validation
  67. 67. How good (or bad) are our models? Source: Clifton, K.J., C.V. Burnier, R.J. Schneider, S. Huang, and M.W. Kang. “Pedestrian Demand Model for Evaluating Pedestrian Risk Exposure,” Prepared by the National Center for Smart Growth Research and Education, University of Maryland for the Maryland SHA, June 2008. Source: Schneider R.J., L.S. Arnold, and D.R. Ragland. “A Pilot Model for Estimating Pedestrian Intersection Crossing Volumes,” Transportation Research Record 2140, pp. 13-26, 2009. Central Baltimore Estimated 24-Hour Pedestrian Crossing Volumes
  68. 68. How good (or bad) are our models? Source: Clifton, K.J., C.V. Burnier, R.J. Schneider, S. Huang, and M.W. Kang. “Pedestrian Demand Model for Evaluating Pedestrian Risk Exposure,” Prepared by the National Center for Smart Growth Research and Education, University of Maryland for the Maryland SHA, June 2008. MoPeD, Version 1: Central Baltimore Case Study
  69. 69. How good (or bad) are our models? Source: National Cooperative Highway Research Program Report 770, “Estimating Bicycling and Walking for Planning and Project Development: A Guidebook.” Authors: Kuzmyak, J.R., J. Walters, M. Bradley, and K.M. Kockelman, Transportation Research Board, Washington, DC, 2014. Santa Monica Pedestrian Intersection Volume Model
  70. 70. Alameda County Pedestrian Volume Model 2009 Observed Volumes vs. 2008 Pilot Model Predictions -5,000 0 5,000 10,000 15,000 20,000 25,000 0 5,000 10,000 15,000 20,000 25,000 30,000 35,000 40,000 WeeklyPedestrianVolumePredictedbyPilotModelin2008 Weekly Pedestrian Volume "Observed" in 2009 Trendlinefor Observed vs. Predicted Data LineRepresenting PerfectPrediction (Observ. = Pred.)
  71. 71. Other Validation Approaches (validation from within same dataset) Source: Hankey, S. and G. Lindsey. “Facility-demand models of peak-period pedestrian and bicycle traffic: A comparison of fully- specified and reduced-form models,” Transportation Research Record: Journal of the Transportation Research Board, 2016. Minneapolis Pedestrian Volume Model Monte Carlo-based random hold-out analysis Portland Metro Pedestrian Destination Choice Model percent correct & probability correct Source: Clifton, K.J., P.A. Singleton, C.D. Muhs, and R.J. Schneider. Development of a Pedestrian Demand Estimation Tool, NITC Report, NITC-RR-677, 2015.
  72. 72. Variation in Pedestrian Volumes • 5 Control Intersections ID # 2008 Weekly Pedestrian Volume based on Counts 2009 Weekly Pedestrian Volume based on Counts Absolute Difference (2009 - 2008) Percent Difference 1 50 315 310 -5 1.6% 2650 15691 16113 422 2.7% 9179 8342 7429 -913 12.3% 9436 105297 88118 -17179 19.5% 499 5186 3448 -1738 50.4% 1) Percent difference is calculated using the smaller number as the base value. If the model value is greater than the actual value, the percent difference is calculated as (2009 - 2008)/2008. If the actual value is greater than the model value, the percent difference is calculated as (2008 -2009)/2009.
  73. 73. • Time of day, weather, etc. (accounted for) • Measurement error • “Unexplainable” variation – Individual sickness, people walking for scenery, store sales, etc. – Not feasible to predict in a planning-level model – Require additional data and cost for small benefit Variation in Pedestrian Volumes
  74. 74. Variation in “Typical” Alameda County Pedestrian Activity Pattern 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 12AM 4AM 8AM 12PM 4PM 8PM 12AM 4AM 8AM 12PM 4PM 8PM 12AM 4AM 8AM 12PM 4PM 8PM 12AM 4AM 8AM 12PM 4PM 8PM 12AM 4AM 8AM 12PM 4PM 8PM 12AM 4AM 8AM 12PM 4PM 8PM 12AM 4AM 8AM 12PM 4PM 8PM 95%ConfidenceIntervalPercentAbove&BelowMeanHourlyValue M T W Th F Sa SuM T W Th F Sa Su 95% of mid-day counts are between 20% above and 20% below the hourly mean
  75. 75. Challenges: External factors; Special circumstances
  76. 76. Other ideas for validation • Transferability: Evaluate the performance of models in many different communities – Can they capture different norms, preferences, and ranges of built and social environments? • Compare the performance of several different types of models in the same study area • Have practitioners and advocates carefully review predicted volumes against their local knowledge
  77. 77. 4) Usefulness
  78. 78. Challenge: How can we do a better job demonstrating the value of our models? • How many? – Is there still a need to make the case that there are high levels of walking activity? • How safe? – With the concept of Vision Zero, is there still as much of a need to identify high-risk locations? • What value do our models add to the practice? • Do practitioners understand the value that our models add?
  79. 79. Source: Hankey, S. and G. Lindsey. “Facility-demand models of peak-period pedestrian and bicycle traffic: A comparison of fully-specified and reduced-form models,” Transportation Research Record: Journal of the Transportation Research Board, 2016. Minneapolis Intersection Pedestrian Volume Model
  80. 80. Pedestrian Index of the Environment Source: Singleton, P.A., C. Muhs, R.J. Schneider, and K.J. Clifton. “Representing Pedestrian Activity in Travel Demand Models: A Framework and Proof-of-Concept,” Portland State University, Working Paper, 2015.
  81. 81. Absolute number of crashes suggests safety problem is in downtown Oakland Oakland Reported Intersection Pedestrian Crashes (1996-2005)
  82. 82. But the highest risk per pedestrian crossing is along major arterial roads Oakland Estimated Intersection Pedestrian Crash Risk (1996-2005)
  83. 83. Model Estimated Walk Commuting Schneider, R.J., L. Hu, and J. Stefanich. “Development of a Neighborhood Commute Mode Share Model Using Nationally-Available Data,” Transportation, 2017
  84. 84. Change in Walk Commuting Schneider, R.J., L. Hu, and J. Stefanich. “Development of a Neighborhood Commute Mode Share Model Using Nationally-Available Data,” Transportation, 2017
  85. 85. Mainline Roadway Intersecting Roadway City Total population within 1/2-mile radius3 Total employment within 1/4-mile radius Total number of commercial retail propertieswithin 1/4-mile radius Presence ofregional transit station within 1/10 mile (Yes =1, No =0) Estimated Pedestrian Crossings in aTypical Week5,6,7 Dr. Martin Luther King Drive Walnut Street Milwaukee 4924 2390 40 0 8830 Dr. Martin Luther King Drive Walnut Street Milwaukee 9848 2390 40 0 13399 Dr. Martin Luther King Drive Walnut Street Milwaukee 9848 4780 40 0 18633 Dr. Martin Luther King Drive Walnut Street Milwaukee 9848 4780 80 0 22569 Notes: 1. This is a pilot model based on a study of weekly pedestrain volumes at 50 intersections in Alameda County, CA. The model has a good fit for the Alameda County study data (adjusted-R2 =0.897). Since the analysis was conducted on 50 intersections in Alameda County, CA, more research is needed to refine the model equation and determine the applicability of the results for other communities. The model equation is: Estimated pedestrian intersection crossings per week = 0.928 * Total population within 0.5-miles of the intersection + 2.19 * Total employment within 0.25-miles of the intersection + 98.4 * Number of commercial retail properties within 0.25-miles of the intersection + 54,600 * Number of regional transit stations within 0.10-miles of the intersection - 4910. Details of the study are provided in two papers: 1) Schneider, R.J., L.S. Arnold, and D.R. Ragland. "Extrapolating Weekly Pedestrian Intersection Crossing Volumes from 2-Hour Manual Counts," Transportation Research Record, 2010, and 2) Schneider R.J., L.S. Arnold, and D.R. Ragland. “A Pilot Model for Estimating Pedestrian Intersection Crossing Volumes,” Transportation Research Record, 2010. 2. The pedestrian volume estimates produced by the model are intended for planning, prioritization, and safety analysis at the community, neighborhood, and corridor levels. Since the model provides rough estimates of pedestrian activity, actual pedestrian counts should be used for site-level safety, design, and engineering analyses. 3. The intersections selected for the study did not include intersections in areas with very low population densities (<50 people per square mile). Therefore, the model is not appropriate for intersections below this density threshold (i.e., the model does not apply if there are fewer than 64 people within a 1/2-mile radius). 4. The study of Alameda County, CA found that land use characteristics are the most important factors for predicting pedestrian activity. Roadway design factors, such as the presence of sidewalks, median crossing islands, curb radii, or pedestrian crossing signals may have minor effects on pedestrian volumes, but they are not as significant for predicting pedestrian activity. However, roadway design factors are critical for pedestrian safety and comfort. Roadways must be designed to accommodate pedestrians of all abilities, regardless of volume. 5. The model output is an estimate of the number of pedestrian crossings during a typical 168-hour week (with an average seasonal volume). Pedestrian crossings are counted each time a pedestrian crosses any leg of the intersection (e.g., one person is counted twice if they cross the east leg and then the south leg of an intersection). Pedestrians do not need to cross completely inside the crosswalk; they are counted if if they cross within 50 feet of the intersection. 6. The model may not perform well in locations close to special attractors, such as amusement parks, waterfronts, sports arenas, regional recreation areas, and major multi-use trails. Pedestrian volumes in these areas tend to be highly variable, with high volumes during certain seasons or during nice weather. Bridges and underpasses may also channel pedestrian activity, so more research may be necessary to adjust volume estimates near these features. Model OutputIntersection Identification Model Inputs 4 Pedestrian Intersection Crossing Volume Model Pilot Model--January 20091,2 Developed by Robert Schneider, Lindsay Arnold, and David Ragland University of California-Berkeley Traffic Safety Center Example Pedestrian Volume Model Application: Dr. Martin Luther King Drive & Walnut Street
  86. 86. Concluding Thoughts • Can we measure pedestrian activity more completely? • Should we be including other explanatory variables? • How accurate are our models? Do they scale geographically and transfer across communities? • Are our models useful to practitioners?
  87. 87. Contact Information Robert J. Schneider, PhD University of Wisconsin-Milwaukee Department of Urban Planning rjschnei@uwm.edu Photo by Transportation Research Board
  88. 88. Jaime Orrego Joe Broach Portland State University TCS 2017 Workshop September 12, 2017 Portland, Oregon What is the appropriate scale?
  89. 89. What is scale? Every transportation analysis requires a unit area for the analysis as we need aggregation. At what level do we sample? GOAL: Balance resolution with practicality -- desire for resolution capturing individual-level behavior, but also interested low sampling error (people don’t behave the same every time) and data realities To what level do we summarize? GOAL: Policy makers need to assess their interventions at a larger scale that assure greater certainty of benefits and fit broader planning goals
  90. 90. Why should we re-consider scale when we model walking? Traditionally transportation planning has addressed problems relating to vehicle demand. This type of travel is more consistent in terms of analysis and has been largely studied in the history of transportation engineering. The frameworks are widely used and accepted. Can we just use the same? Why not? What scale do you work at? Traditional 4-step model (Rethinking How We Get Around Sunnyvale Ria Hutabarat Lo)
  91. 91. Why it is important to get the scale right? •Determines the sensitivity to (smaller) scale changes •Determines limits of the output resolution
  92. 92. Parcel
  93. 93. Fixed Grid
  94. 94. TAZ Traffic Analysis Zone
  95. 95. Census Block Group
  96. 96. Census tracts
  97. 97. Regions
  98. 98. Example: Strategic model – household travel survey Sampling unit to represent population (Census block groups or tracts) Input for strategic model Output in TAZ level
  99. 99. Example: Gerrymandering
  100. 100. What is the appropriate scale? Behavioral perspective Many times there are not environmental boundaries for the definition of zones. People don’t see those boundaries. Thus, we have to avoid edge effects. Our behavioral understanding still is limited. What would you do with finer grained data? Why kinds of attributes are relevant? A BC D E Is the school’s impact on walking behavior the same for A and B? How about B & C? (School by PJ Souders)
  101. 101. How big is (probably) too big? walk trip distances (mi) for ~7500 walk trips TAZ Tract Block Group 0.6 mi 0.7 mi 1.2 mi PAZ 0.05 mi medianzonesizeinPortland observedwalktripdistances
  102. 102. Measurement scale vs. Aggregation scale
  103. 103. Practical implementation for planning • Scope of the projects • Sensitivity policy variables that matter • Compatibility capturing shifts between modes • Computation challenges • Forecast-ability (more to come…)
  104. 104. Example: Metro regional travel model (~2010) Adapting TAZs Compatibility Shifted TAZ “centroids” for walking but lost compatibility with auto... From all good intentions...walk distance was capped at 0.5 mi to improve predictions in larger TAZs. Walk trips out-competed bike and shifted out away from inner Portland!
  105. 105. Planning implementation - MoPeD
  106. 106. Planning implementation - clustering
  107. 107. Planning implementation - clustering
  108. 108. Planning implementation - sliding scale To handle intrazonal trips and edge effects and of larger zones… 1) Sample smaller scale locations 2) Aggregate up to compatible/practical scale
  109. 109. Discussion •
  110. 110. Reality transformed into links, nodes, and attributes
  111. 111. From above 600 walk trips, do pedestrians follow networks?
  112. 112. Three components of a travel network.
  113. 113. Block Face Walkway Roadway
  114. 114. … What were some key issues, challenges, and solutions?
  115. 115. … What were some key issues, challenges, and solutions?
  116. 116. … What were some key issues, challenges, and solutions?
  117. 117. … What were some key issues, challenges, and solutions?
  118. 118. Compatibility or Resolution? http://rbracket.github.io/GISJammerIdeas/papers _presentations/Walkway_Network_Analysis.pdf
  119. 119. Which side of the street is the middle GPS signal on? What to do when sidewalks come and go?
  120. 120. 1 Broach, J., & Dill, J. (2016). Using predicted bicyclist and pedestrian route choice to enhance mode choice models. Transportation Research Record: Journal of the Transportation Research Board, (2564), 52–59. Scan at sub-block scale. Aggregate to centerline links and intersection nodes. Use crossing logic instead of tracking crossings.
  121. 121. 1 Broach, J., & Dill, J. (2016). Using predicted bicyclist and pedestrian route choice to enhance mode choice models. Transportation Research Record: Journal of the Transportation Research Board, (2564), 52–59. Block Face Walkway Roadway commercial land-use distance traffic volume off-street path sidewalks marked crosswalk park turns ped signal residential upslope median refuge downslope substandard street enclosure (design) pre-war construction Attributes tested in pedestrian route and mode choice models. Significant factors (p<0.05) shown in white text, insignificant in gray.
  122. 122. Though project small if measured as zonal change, connectivity value large. Network-based model predicts walking on trip 3.6x more likely with overcrossing, all else equal. Even larger change for round trip.
  123. 123. Why should we care which routes pedestrians are most likely to use?
  124. 124. Many factors from theory (design, land-use) that are difficult to attach to empirical networks “Micro”-design features may be important -- but are they worth measuring for large-scale models? Likely a strong dynamic component to pedestrian travel To date, little traction in regional modeling compared to bicycle network modeling Network model fit: transit > auto > bike > walk -- we’re pushing it here and may need a hybrid network model What’s missing? Thank you! Joe Broach <jbroach@pdx.edu> web.pdx.edu/~jbroach
  125. 125. HOW DO WE FORECAST INPUTS AND DEVELOP SCENARIOS? Walk, don’t run? Advancing the state of the practice in pedestrian demand modeling Transportation and Communities Summit September 12, 2017
  126. 126. Background Models have become much more disaggregate in the representation of travelers and their behaviors. But, it is difficult to forecast model inputs at the SCALE needed: Land use – population and employment Networks Walkability indices/built environment How can are various policies & improvements to the pedestrian environment represented in these forecast or scenario years.These are new considerations for most demand models. 2
  127. 127. Pedestrian analysis zones 3 264 feet = 80 m ≈ 1 minute walk Metro: ~2,000TAZs  ~1.5 million PAZs TAZs PAZs Home-based work trip productions
  128. 128. Land use: Population and employment • Most population, employment and other built environment conditions are forecast at theTAZ or aggregate level • Potential approaches • AllocateTAZ (or other zonal structures) forecasts to pedestrian zones (top- down) • Parcel (or grid cell-level) land use model that forecasts change incrementally (bottom-up) • “Paint” scenarios at corridor or area-wide level • Challenge for regional travel models, integrated models, ABM, as well as pedestrian modeling… 4
  129. 129. Pedestrian Investments & Walkability • How can we represent spatially-explicit policies intended to improve the pedestrian environment in future scenarios? • Land use features – density and mix – suffer the problems discussed earlier • Sidewalks construction, crossing aids, connectivity, etc. pose different challenges. • Many modeling efforts, including MoPeD, do not directly represent detailed features. Rather they proxy overall walkability using an index. • How do we related future improvements & policies to these indices? 5
  130. 130. Walkability measures • Pedestrian Environment Factor (PEF)1 • Walkability Index2 • Walk Opportunities Index3 • Walk Score®4 • Pedestrian Index of the Environment (PIE)5 6 1. Parsons Brinckerhoff Quade and Douglas, Cambridge Systematics, & Calthorpe Associates, 1993. LUTRAQ Volume 4A. http://www.friends.org/resources/reports 2. Frank, Schmid, Sallis, Chapman, & Saelens, 2005. https://doi.org/10.1016/j.amepre.2004.11.001 3. Kuzmyak, Baber, & Savory, 2005. https://doi.org/10.3141/1977-19 4. https://www.walkscore.com 5. Singleton, Schneider, Muhs, & Clifton, 2014. https://trid.trb.org/view.aspx?id=1289281
  131. 131. What is PIE? 7ULI = Urban Living Infrastructure: pedestrian-friendly shopping and service destinations used in daily life. People & job density Transit access Block size Sidewalk extent Comfortable facilities Urban living infrastructure The Pedestrian Index of the Environment (PIE) = ∑ (6 dimensions) weighted by association with walking
  132. 132. Pedestrian Index of the Environment Portland Region PAZ Scale Scored from 20-100
  133. 133. Visualizing PIE 9 100 – Downtown core 80 – Major neighborhood centers Downtown Lloyd District
  134. 134. Visualizing PIE 10 60 – Residential inner-city neighborhoods 70 – Suburban downtowns Laurelhurst Gresham
  135. 135. Visualizing PIE 11 50 – Suburban shopping malls 40 – Suburban neighborhoods/subdivisions
  136. 136. Visualizing PIE 12 20 – Rural, undeveloped, forested 30 – Isolated business and light industry N. Marine Drive
  137. 137. Relating to policies 13 People & job density Block size Sidewalk extent Transit access • Growth in population, housing production and employment • Need to get at right scale • New stops/routes • Frequency • Transit network connectivity • New pedestrian connectivity • No change? • Permeability/Connectivity Construction/investments Connectivity
  138. 138. Other explanatory variables Increasing recognition that subjective attributes matter in behavior Synthetic populations offer opportunities to attribute individual How do we forecast: – Preferences/Attitudes – Perceptions – Culture – Health outcomes Weather/Climate Crime 14
  139. 139. Questions • Top down vs bottom up? • What does it take to move PIE 10 points? How do we unpack these indices? • Is moving from a PIE score 90 to 100 the same input as moving from PIE score 50-60? • Non-linearities? Step functions? Minimum/maximums? • What if there is no change in the attribute (e.g. block size)? • Can we answer the question: what is the impact of X investment? 15
  140. 140. How can model outputs link other tools? “Walk, don’t run?” Workshop Transportation and Communities Summit Portland, OR — 12 September 2017
  141. 141. Why model pedestrian demand? analyze health & safety impacts utilize new data resources mode shifts air quality & GhG plan for pedestrian investments & non-motorized facilities
  142. 142. Pedestrian modeling outputs  Direct transportation outputs  Walk trips generated  Walk trips with origins & destinations  Walk trips with “routes”   Distances walked   Pedestrian miles traveled (PMT)   Minutes of walking   Physical activity levels (METs)  Classified by…  Geographic location  Personal characteristics (socio-demographics)
  143. 143. Air quality & emissions models  Estimates motor vehicle emissions using outputs like…  Vehicle miles traveled  Vehicle speeds  Vehicle fleets  Indirect contributions of pedestrian-enhanced models…  Better estimates of auto use  Mode choice model sensitivity to changes in pedestrian environment
  144. 144. Safety analysis  Crash rate = Crashes ÷ exposure  Highway Safety Manual  Safety performance functions (SPFs)  Estimate expected average crash frequency of a network, facility, or individual site  Crashes = f (exposure, facility characteristics)  Crash modification factors (CMFs)  Calculates expected average crash frequency as a result of geometric or operational modifications to a site that differs from set base conditions  Crashes with treatment = CMFs * Crashes without treatment
  145. 145. Problems for safety analysis  Insufficient data for pedestrian safety analysis  Crash data  Frequency, severity, injury patterns, contributing factors, types  Exposure data  Volume, severity, event information  Traditional data collection problematic  Ethical difficulties with experimentation  Pedestrian volume data rare, difficult to collect http://apps.trb.org/cmsfeed/TRBNetProjectDisplay.asp?ProjectID=4203
  146. 146. Estimating exposure?  Models could estimate # walk trips for…  An area or neighborhood  A corridor  A facility segment  An intersection  Could be used as measure of exposure for…  Pedestrian safety assessment  Crash rates  Safety performance functions Proulx, Frank. (2016) Using Heterogeneous Demand Data Sources as Exposure in a Bicycle Risk Model (dissertation). Berkeley, CA: University of California, Berkeley.
  147. 147. Health impact assessment  Estimating transportation’s health impacts  Injury, morbidity (disease), & mortality (death)  Measured in consistent units  Dollars (using the value of a statistical life (VSL))  Lives (disability-adjusted life years (DALY))  Two widely-used HIA models  Health Economic Assessment Tool (HEAT)  World Health Organization / Europe  http://www.heatwalkingcycling.org/index.php  Integrated Transport and Health Impact Modelling Tool (ITHIM)  Centre for Diet and Activity Research / James Woodcock  http://www.cedar.iph.cam.ac.uk/research/modelling/ithim/
  148. 148. HEAT for walking/bicycling Kahlmeier et al. (2014) HEAT methodology and user guide
  149. 149. HEAT travel data inputs  # people walking (& cycling)  average time spent walking (or cycling)  Duration (minutes/day)  Amount (steps/day) & [walking speed]  Distance (miles/day) & [walking speed]  Trips (trips/day) & [walk trip length] & [walking speed]  Notes about travel data inputs  Adult population only (20–74 years old)  Average walking levels over year
  150. 150. ITHIM tool  Evaluates regional/national…  Scenarios  Comparisons  Interventions  Outputs  Premature deaths  Disability adjusted life-years (DALYs)  Costs  Health impacts  Physical activity  Traffic injuries  Air pollution
  151. 151. ITHIM tool Maizlish (2016)
  152. 152. ITHIM calibration data Maizlish (2016)
  153. 153. ITHIM travel data inputs  Per capita mean daily travel distance (PMT, VMT)  By mode: walk, bicycle, bus, rail, motorcycle, auto driver, auto passenger, truck  By facility type (for VMT): local, arterial, highway  Per capita mean daily travel time  By mode  Distribution of per capita mean daily active travel time (for walking & cycling)  By gender (male, female) & age groups  Or ratio of mean vs 15–29 females & standard deviation  Mean walking speed, mean cycling speed, bus occupants
  154. 154. ITHIM travel data inputs  Physical activity   Distribution of active travel time by gender & age   Walking & cycling speeds  Traffic safety   PMT & VMT by mode & facility type   Injuries & fatalities by striking & victim modes  Air quality   Emissions by pollutant
  155. 155. Questions?  How can travel demand models better link pedestrian outputs to air quality, safety, health, and other analysis tools?  Does the precision (and the accuracy) of pedestrian outputs match what these tools assume or require?  What other post-model analysis could make use of pedestrian modeling outputs? Patrick A. Singleton patrick.singleton@usu.edu

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