17tcs walk, don’t run? advancing the state of the practice in pedestrian demand modeling
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WALK, DON’T RUN? ADVANCING THE STATE OF THE PRACTICE IN
PEDESTRIAN DEMAND MODELING
Transportation and Communities SummitSeptember 12, 2017
Dr. Kelly J. CliftonPortland State University
2
Dr. Joseph BroachPortland State University
Dr. Patrick SingletonUtah State University
Jaime Orrego OnatePortland State University
Dr. Robert SchneiderUniversity of Wisconsin, Milwaukee
Model of Pedestrian Demand (MoPeD)
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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)
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III
All Trips Pedestrian Trips Vehicular Trips
Opportunities & challengesBehavioral research/data/methods
Adapted from: Wegener and Fürst, 1999
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
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Behavioral Research
Built environment– Thresholds & nonlinearities – Mixing– Scale
Lifestyle questions: – Vehicle ownership & residential location– Attitudes, motivations & values
Positive Utility of Travel– What aspects?– Diminishing returns?
Mode feedbacks to trip generation
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Spatial/Temporal Scale
• Depends on output needed for policy/research
• Capture variations in the pedestrian built & natural environment
• Spatial accuracy• Theory/Behavior
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Walking Behavior
• Passive data sources– Trip-level information– Multi-day– Multi-modal– Destinations– Routes & speeds
• But also need…– Motivations &
considerations– Barriers– Trips not made
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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):171–186S.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.
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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.
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Link to Health Outcomes
• Health impact analysis
• Total time spent walking + speeds
• Physical activity budgets
• Crash risk exposure• Pollutant exposure• Feedback into life
expectancyWoodcock 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
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ObjectivesUnderstand 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
OutcomesDefine state of the practice
White paper on state of the practice and research needs
TRB workshop
Discuss next steps for MoPed & other efforts
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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 break13-14 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
Pedestrian modeling: State-of-the-practice
“Walk, don’t run?” WorkshopTransportation and Communities Summit
Portland, OR — 12 September 2017
Why model pedestrian demand?
analyze health & safety impacts
utilize new data resources
mode shifts air quality & GhG
plan for pedestrian investments& non-motorized facilities
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
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
Pedestrian modeling frameworks
Pedestrian modeling frameworks
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%)
State-of-the-practice (2012)
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State-of-the-practice (2017)
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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
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)
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)
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.
Data for Pedestrian Demand Modeling
Robert Schneider, PhDUniversity of Wisconsin-Milwaukee, Department of Urban Planning
Portland State, NITC Workshop – September 2017 1
Data for Pedestrian Demand Modeling
Robert Schneider, PhDUniversity of Wisconsin-Milwaukee, Department of Urban Planning
Portland State, NITC Workshop – September 2017 2
How many?
How safe?
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
1) Dependent Variables
Pedestrian Data TypesSafety
(Crashes, injuries, behaviors)
UserCharacteristics
(Age, gender)
Infrastructure(Facility coverage
& quality)
PublicOpinion
(Satisfaction, desires)
Exposure/ Volume
(Count, mode share)
Counts Surveys
Household(Phone, mail,
internet)Intercept
Manual(Data collectors, imagery review)
Automated(Continuous
counters, imagery processing)
Pedestrian Data TypesSafety
(Crashes, injuries, behaviors)
UserCharacteristics
(Age, gender)
Infrastructure(Facility coverage
& quality)
PublicOpinion
(Satisfaction, desires)
Exposure/ Volume
(Count, mode share)
Counts SurveysDirect
Demand Models
Household(Phone, mail,
internet)Intercept
Manual(Data collectors, imagery review)
Automated(Continuous
counters, imagery processing)
FlowModels
Pedestrian Counts
Manual Counts
8Automated Counts
Pedestrian Counts
Google Earth—Tele Atlas 2008
Pedestrian Segment/Screenline Counts
Google Earth—Tele Atlas 2008
Pedestrian Intersection Crossing Counts
Google Earth—Tele Atlas 2008
Pedestrian Intersection Crossing Counts
Google Earth—Tele Atlas 2008
Pedestrian Midblock Crossing Counts
Graphic source: Google Earth, 2008.
Counts Direct Demand Models
Travel Surveys
Example: Boulder, CO Region
Surveys Flow Models
Example: Boulder, CO Region
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.
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
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
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
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
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
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%
Urban Core Shopping District (Market Street, San Francisco)Intercept Survey Respondent Pedestrian Path Density
Suburban Main Street Shopping District (Burlingame)Intercept Survey Respondent Pedestrian Path Density
Suburban Main Street Shopping District (Richmond)Intercept Survey Respondent Pedestrian Path Density
Are there other ways to measure pedestrian activity?
26
Are there other ways to measure pedestrian activity?
27Source: USGS, “Earth Explorer,” Available online, https://earthexplorer.usgs.gov/, 2017.
Aerial Imagery or Drone (HAV) Counts
Are there other ways to measure pedestrian activity?
28Source: USGS, “Earth Explorer,” Available online, https://earthexplorer.usgs.gov/, 2017.
Aerial Imagery or Drone (HAV) Counts
Are there other ways to measure pedestrian activity?
29Source: 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
Challenge: Temporal variation
Data Source: Milwaukee County Parks, 2014-2015.
0.0%
0.2%
0.4%
0.6%
0.8%
1.0%
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1.6%
1.8%
2.0%0 6 12 18 24 30 36 42 48 54 60 66 72 78 84 90 96 102
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Perc
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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 pm3.13% of week
2) Explanatory Variables
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
Are there other factors that might influence pedestrian behavior?
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
Soci
oeco
nom
ic F
acto
rs(E
xpla
in d
iffer
ence
s in
how
peo
ple
resp
ond
to e
ach
step
)Schneider, R.J. “Theory of Routine Mode Choice Decisions: An Operational Framework to Increase Sustainable Transportation,” Transport Policy, Volume 25, pp. 128-137, 2013
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?
• 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?
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
Challenge: Measurement detail…how good is good enough?
Challenge: Geographic scalability
Source: Urbitran Associates. Pedestrian Flow Modeling for Prototypical Maryland Cities, Prepared for Maryland DOT, 2004.
3) Validation
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
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
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
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
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Weekly Pedestrian Volume "Observed" in 2009
Trendline forObserved vs.
Predicted Data
Line Representing Perfect Prediction (Observ. = Pred.)
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.
Variation in Pedestrian Volumes
• 5 Control Intersections
ID #
2008 Weekly Pedestrian Volume
based on Counts
2009 Weekly Pedestrian Volume
based on CountsAbsolute Difference
(2009 - 2008) Percent Difference1
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.
• 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
Variation in “Typical” Alameda County Pedestrian Activity Pattern
0%
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95% of mid-day counts are between 20% above and 20% below the hourly mean
Challenges: External factors; Special circumstances
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
4) Usefulness
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?
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
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.
Absolute number of crashes suggests safety problem is in
downtown Oakland
Oakland Reported Intersection Pedestrian Crashes (1996-2005)
But the highest risk per pedestrian crossing is along
major arterial roads
Oakland Estimated Intersection Pedestrian Crash Risk (1996-2005)
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
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
Mainline Roadway Intersecting Roadway City
Total population within 1/2-mile radius3
Total employment within 1/4-mile radius
Total number of commercial retail properties within 1/4-mile radius
Presence of regional transit station within 1/10 mile (Yes = 1, No = 0)
Estimated Pedestrian Crossings in a Typical Week5,6,7
Dr. Martin Luther King Drive Walnut Street Milwaukee 4924 2390 40 0 8830Dr. Martin Luther King Drive Walnut Street Milwaukee 9848 2390 40 0 13399Dr. Martin Luther King Drive Walnut Street Milwaukee 9848 4780 40 0 18633Dr. Martin Luther King Drive Walnut Street Milwaukee 9848 4780 80 0 22569Notes : 1. This i s a pi lot model based on a s tudy of weekly pedestra in volumes at 50 intersections in Alameda County, CA. The model has a good fi t for the Alameda County s tudy data
(adjusted-R2=0.897). Since the analys is was conducted on 50 intersections in Alameda County, CA, more research i s needed to refine the model equation and determine the appl icabi l i ty of the resul ts for other communities . The model equation i s : Es timated pedestrian intersection cross ings per week = 0.928 * Tota l population within 0.5-mi les of the intersection + 2.19 * Tota l employment within 0.25-mi les of the intersection + 98.4 * Number of commercia l reta i l properties within 0.25-mi les of the intersection + 54,600 * Number of regional trans i t s tations within 0.10-mi les of the intersection - 4910. Deta i l s of the s tudy are provided in two papers : 1) Schneider, R.J., L.S. Arnold, and D.R. Ragland. "Extrapolating Weekly Pedestrian Intersection Cross ing Volumes from 2-Hour Manual Counts ," Transportation Research Record, 2010, and 2) Schneider R.J., L.S. Arnold, and D.R. Ragland. “A Pi lot Model for Estimating Pedestrian Intersection Cross ing Volumes ,” Transportation Research Record, 2010.2. The pedestrian volume estimates produced by the model are intended for planning, priori ti zation, and safety analys is at the community, neighborhood, and corridor levels . Since the model provides rough estimates of pedestrian activi ty, actua l pedestrian counts should be used for s i te-level safety, des ign, and engineering analyses .3. The intersections selected for the s tudy did not include intersections in areas with very low population dens i ties (<50 people per square mi le). Therefore, the model i s not appropriate for intersections below this dens i ty threshold (i .e., the model does not apply i f there are fewer than 64 people within a 1/2-mi le radius ).4. The s tudy of Alameda County, CA found that land use characteris tics are the most important factors for predicting pedestrian activi ty. Roadway des ign factors , such as the presence of s idewalks , median cross ing i s lands , curb radi i , or pedestrian cross ing s ignals may have minor effects on pedestrian volumes , but they are not as s igni ficant for predicting pedestrian activi ty. However, roadway des ign factors are cri tica l for pedestrian safety and comfort. Roadways must be des igned to accommodate pedestrians of a l l abi l i ties , regardless of volume.5. The model output i s an estimate of the number of pedestrian cross ings during a typica l 168-hour week (with an average seasonal volume). Pedestrian cross ings are counted each time a pedestrian crosses any leg of the intersection (e.g., one person i s counted twice i f they cross the east leg and then the south leg of an intersection). Pedestrians do not need to cross completely ins ide the crosswalk; they are counted i f i f they cross within 50 feet of the intersection.6. The model may not perform wel l in locations close to specia l attractors , such as amusement parks , waterfronts , sports arenas , regional recreation areas , and major multi -use tra i l s . Pedestrian volumes in these areas tend to be highly variable, with high volumes during certa in seasons or during nice weather. Bridges and underpasses may a lso channel pedestrian activi ty, so more research may be necessary to adjust volume estimates near these features .
Model OutputIntersection Identification Model Inputs 4
Pedestrian Intersection Crossing Volume ModelPilot Model--January 20091,2
Developed by Robert Schneider, Lindsay Arnold, and David RaglandUniversity of California-Berkeley Traffic Safety Center
Example Pedestrian Volume Model Application:Dr. Martin Luther King Drive & Walnut Street
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?
Contact Information
Robert J. Schneider, PhDUniversity of Wisconsin-Milwaukee
Department of Urban Planningrjschnei@uwm.edu
Photo by Transportation Research Board
Jaime OrregoJoe BroachPortland State University
TCS 2017 WorkshopSeptember 12, 2017Portland, Oregon
What is the appropriate scale?
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
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)
Why it is important to get the scale right?
• Determines the sensitivity to (smaller) scale changes
• Determines limits of the output resolution
Parcel
Fixed Grid
TAZTraffic Analysis Zone
Census Block Group
Census tracts
Regions
Example: Strategic model – household travel survey
Sampling unit to represent population (Census block groups
or tracts)
Input for strategic model
Output in TAZ level
Example: Gerrymandering
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)
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
med
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zone
siz
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Por
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obse
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wal
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Measurement scale vs. Aggregation scale
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…)
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!
Planning implementation - MoPeD
Planning implementation - clustering
Planning implementation - clustering
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
Discussion •
Reality transformed into links, nodes, and attributes
From above 600 walk trips, do pedestrians follow networks?
Three components of a travel network.
Block Face Walkway Roadway
…
What were some key issues, challenges, and solutions?
…
What were some key issues, challenges, and solutions?
…
What were some key issues, challenges, and solutions?
…
What were some key issues, challenges, and solutions?
Compatibility or Resolution?
http://rbracket.github.io/GISJammerIdeas/papers_presentations/Walkway_Network_Analysis.pdf
Which side of the street is the middle GPS signal on?What to do when sidewalks come and go?
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.
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.
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.
Why should we care which routes pedestrians are most likely to use?
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
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 SummitSeptember 12, 2017
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 employmentNetworksWalkability 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
Pedestrian analysis zones
3
264 feet = 80 m ≈ 1 minute walk
Metro: ~2,000 TAZs ~1.5 million PAZs
TAZs PAZs
Home-based work trip productions
Land use: Population and employment
• Most population, employment and other built environment conditions are forecast at the TAZ or aggregate level
• Potential approaches• Allocate TAZ (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
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
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.2004.11.0013. Kuzmyak, Baber, & Savory, 2005. https://doi.org/10.3141/1977-19 4. https://www.walkscore.com5. Singleton, Schneider, Muhs, & Clifton, 2014. https://trid.trb.org/view.aspx?id=1289281
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
Pedestrian Index of the Environment
Portland Region
PAZ Scale
Scored from 20-100
Visualizing PIE
9
100 – Downtown core
80 – Major neighborhood centers
Downtown
Lloyd District
Visualizing PIE
10
60 – Residential inner-city neighborhoods
70 – Suburban downtowns
Laurelhurst
Gresham
Visualizing PIE
11
50 – Suburban shopping malls
40 – Suburban neighborhoods/subdivisions
Visualizing PIE
12
20 – Rural, undeveloped, forested
30 – Isolated business and light industry
N. Marine Drive
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/investmentsConnectivity
Other explanatory variables
Increasing recognition that subjective attributes matter in behaviorSynthetic populations offer opportunities to attribute individualHow do we forecast:
– Preferences/Attitudes– Perceptions– Culture– Health outcomes
Weather/ClimateCrime
14
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
How can model outputs link other tools?
“Walk, don’t run?” WorkshopTransportation and Communities Summit
Portland, OR — 12 September 2017
Why model pedestrian demand?
analyze health & safety impacts
utilize new data resources
mode shifts air quality & GhG
plan for pedestrian investments& non-motorized facilities
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)
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
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
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
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.
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/
HEAT for walking/bicycling
Kahlmeier et al. (2014) HEAT methodology and user guide
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
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
ITHIM tool
Maizlish (2016)
ITHIM calibration data
Maizlish (2016)
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
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
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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