Guest presentation by Professor Kelly Clifton, Portland State University, USA. https://www.pdx.edu/profile/kelly-clifton-0 www.its.leeds.ac.uk/about/events/seminar-series There have long been calls for better pedestrian planning tools within travel demand models, as they have been slow to incorporate the large body of research connecting the built environment and walking behaviours. Most regional travel demand forecasting performed in practice in the US uses four-step travel demand models, despite advances in the development and implementation of activity-based travel demand models. This paper introduces a framework that facilitates the abilities of four-step regional travel models to better represent walking activity, allowing metropolitan planning organizations (MPOs) to implement these advances with minimal changes to existing modelling systems. Specifically, the framework first changes the spatial unit from transportation analysis zones (TAZs) to a finer-grained geography better suited to modelling pedestrian trips. The MPO’s existing trip generation models are applied at this spatial unit for all trips. Then, a walk mode choice model is used to identify the subset of all trips made by walking. Trips by other modes are aggregated to the TAZ level and proceed through the remaining steps in the MPO’s four-step model. The walk trips are distributed to destinations using a choice modelling approach, thus identifying pedestrian trip origins and destinations. In this presentation, we give an overview of this framework and provide a proof-of-concept application to demonstrate its successful operation using data from the Portland, Oregon, region. Opportunities for future work include more research on the potential routes between origins and destinations for walk trips, application of the framework in another region, and developing ways the research could be implemented in activity-based modelling systems. This viewpoint is seen through a lens of unrealized activities and travel, including unmet activities due to transport disadvantage, time poverty, lack of knowledge, or current inability to conceptualize them. In this work in progress, new data and methods are required and some ideas of how to capture latent activities and travel will be discussed.

1. Development of a Pedestrian
Demand EstimationTool
Kelly J Clifton, PhD
Framework and Methods

2. Outline
• Background
• Project, methods,
zones, & data
• Pedestrian index of
the environment (PIE)
• I:Trip generation
• II:Walk mode split
• III: Pedestrian destination choice
• Conclusions & future work
2
Adapted from: http://www.flickr.com/photos/takomabibelot/3223617185
Background – Project – PIE – Walk Model – DC Model – Conclusion

3. BACKGROUND

4. Background
4
Why model pedestrian travel?
health & safety
new data
mode shifts
greenhouse
gas emissions
plan for pedestrian investments
& non-motorized facilities

5. Background
Background – Project – PIE – Walk Model – DC Model – Conclusion 5
1. Generation
2. Distribution
3. Mode choice
4. Assignment
Trip-based
model sequence
How do travel models estimate walking?
Source: Singleton, P. A., & Clifton, K. J. (2013). Pedestrians in regional travel demand forecasting models: State-of-the-practice.
• Among 48 large MPOs in US:
– 38% did not estimate walking
– 33% estimated non-motorized
(walking + bicycling) travel
– 29% estimated walking
• Lacking pedestrian built
environment measures & small
spatial units

6. Background
• Walking behavior data
– improved travel surveys, pedestrian count data collection
• Built environment data
– archived spatial datasets, GIS processing
• Travel demand models
– smaller zones, complete networks, computer power
• Walking behavior research
– more knowledge and studies
Background – Project – PIE – Walk Model – DC Model – Conclusion 6
What are some opportunities?

7. PROJECT, METHODS, ZONES & DATA

8. Project overview
• Partnered with Metro: metropolitan
planning organization for Portland, OR
• Two research projects
• Improve representation of pedestrian
environment in current 4-step method
Background – Project – PIE – Walk Model – DC Model – Conclusion 8

9. Current 4-step method
9
Trip Distribution or
Destination Choice (TAZ)
Mode Choice (TAZ)
Trip Assignment
PedestrianTrips
AllTrips PedestrianTrips VehicularTrips
TAZ = transportation analysis zone
Trip Generation (TAZ)
Background – Project – PIE – Walk Model – DC Model – Conclusion

10. New MoPeD method
10
TAZ = transportation analysis zone
PAZ = pedestrian analysis zone
Trip Generation (PAZ)
Trip Distribution or
Destination Choice (TAZ)
Mode Choice (TAZ)
Trip AssignmentPedestrianTrips
Walk Mode Split (PAZ)
Destination Choice (PAZ)
I
III
AllTrips PedestrianTrips VehicularTrips
Background – Project – PIE – Walk Model – DC Model – Conclusion

11. Utilizes spatially fine-grained archived
information on the built environment
MoPeD Contributions
11Background – Project – PIE – Walk Model – DC Model – Conclusion
Operates at a smaller spatial scale, more
relevant to pedestrians (PAZ)
Incorporates knowledge of influences on
pedestrian travel behavior
Designed to work with regional travel
demand model or as standalone tool

12. Pedestrian analysis zones
Background – Project – PIE – Walk Model – DC Model – Conclusion 12
264 feet = 80 m ≈ 1 minute walk
Metro: ~2,000TAZs  ~1.5 million PAZs
TAZs PAZs
Home-based work trip productions

13. Travel survey data
• Oregon Household Activity Survey (OHAS)
– Household-based survey
– One-day travel diary
• Portland region dataset (2011)
– 6,100 households
– 13,400 people
– 56,000 trips ÷ 4,500 walk trips
≈ 8% walk mode share
Background – Project – PIE – Walk Model – DC Model – Conclusion 13

14. PEDESTRIAN INDEX OF THE
ENVIRONMENT (PIE)
14

15. Pedestrian environment
Pedestrian Index of the Environment (PIE)
20–100 score = calibrated ∑(6 dimensions)
Background – Project – PIE – Walk Model – DC Model – Conclusion 15
ULI = 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

17. Visualizing PIE
17
100 – Downtown core
80 – Major neighborhood centers
Downtown
Lloyd District
Background – Project – PIE – Walk Model – DC Model – Conclusion

18. Visualizing PIE
18
70 – Suburban downtowns
60 – Residential inner-city neighborhoods
Laurelhurst
Gresham
Background – Project – PIE – Walk Model – DC Model – Conclusion

19. Visualizing PIE
19
50 – Suburban shopping malls
40 – Suburban neighborhoods/subdivisions
Clackamas Town Center
Aloha
Background – Project – PIE – Walk Model – DC Model – Conclusion

20. Visualizing PIE
20
30 – Isolated business and light industry
20 – Rural, undeveloped, forested
Forest Park
N. Marine Drive
Background – Project – PIE – Walk Model – DC Model – Conclusion

21. I. TRIP GENERATION
21

22. 22
TAZ = transportation analysis zone
PAZ = pedestrian analysis zone
Trip Generation (PAZ)
Trip Distribution or
Destination Choice (TAZ)
Mode Choice (TAZ)
Trip AssignmentPedestrianTrips
Walk Mode Split (PAZ)
Destination Choice (PAZ)
I
III
AllTrips PedestrianTrips VehicularTrips
Background – Project – PIE – Walk Model – DC Model – Conclusion
Trip Generation

23. Trip Generation
23Background – Project – PIE – Walk Model – DC Model – Conclusion
Metro currently has 8 trip production models applied to
~2,000TAZs:
– HBW – Home-based work;
– HBshop – Home-based shopping;
– HBrec – Home-based recreation;
– HBoth – Home-based other (excludes school and college);
– NHBW – Non-home-based work;
– NHBNW – Non-home-based non-work;
– HBcoll – Home-based college; and
– HBsch – Home-based school.
After testing for scalability, we applied the same models
to our pedestrian scale ~1.5M PAZs

24. 24

25. II. WALK MODE SPLIT
25

26. 26
TAZ = transportation analysis zone
PAZ = pedestrian analysis zone
Trip Generation (PAZ)
Trip Distribution or
Destination Choice (TAZ)
Mode Choice (TAZ)
Trip AssignmentPedestrianTrips
Walk Mode Split (PAZ)
Destination Choice (PAZ)
I
III
AllTrips PedestrianTrips VehicularTrips
Background – Project – PIE – Walk Model – DC Model – Conclusion
Walk mode split

27. Walk mode split
Prob(walk) = f(traveler characteristics, PIE)
Data: 2011 OHAS, Production trip ends,
90% sample
Method: binary logit model
Spatial unit: pedestrian analysis zone (PAZ)
Background – Project – PIE – Walk Model – DC Model – Conclusion 27
Walk Mode Split (PAZ)
PedestrianTrips
VehicularTrips
II

28. 28
Walk mode split modelsII
Background – Project – PIE – Walk Model – DC Model – Conclusion
Traveler characteristics: Household size, income, age, # of
workers, # children, # vehicles
Built environment: PIE

29. Walk model results
29
II
Background – Project – PIE – Walk Model – DC Model – Conclusion
Traveler characteristics:
+ positively related to walking – negatively related to walking
number of children in HH age of household head
HH vehicle ownership
Ped. Environment: ∆ odds of choosing to walk
+ 10 points PIE 43% increase (HBW)
54% increase (HBNW)
67% increase (NHB)
Pseudo R2 0.137 (HBNW) – 0.253 (NHB)

30. Mode SplitValidation
Model
HBW HBO NHB
Observed Walk Mode Share 2.9% 9.4% 6.7%
Predicted Walk Mode Share 3.0% 9.5% 8.6%
30
1. Apply the final model equations to trips in the validation
sample (10% of data) and calculate the walk probability for
each trip;
2. Average the probabilities to get the predicted walk mode
share of trip ends (called sample enumeration)
Background – Project – PIE – Walk Model – DC Model – Conclusion

31. 31Background – Project – PIE – Walk Model – DC Model – Conclusion
Walk model application

32. 32
TAZ = transportation analysis zone
PAZ = pedestrian analysis zone
Trip Generation (PAZ)
Trip Distribution or
Destination Choice (TAZ)
Mode Choice (TAZ)
Trip AssignmentPedestrianTrips
Walk Mode Split (PAZ)
Destination Choice (PAZ)
I
III
AllTrips PedestrianTrips VehicularTrips
Walk mode split
Background – Project – PIE – Walk Model – DC Model – Conclusion

33. III. DESTINATION CHOICE
33

34. 34
TAZ = transportation analysis zone
PAZ = pedestrian analysis zone
Trip Generation (PAZ)
Trip Distribution or
Destination Choice (TAZ)
Mode Choice (TAZ)
Trip AssignmentPedestrianTrips
Walk Mode Split (PAZ)
Destination Choice (PAZ)
I
III
AllTrips PedestrianTrips VehicularTrips
Background – Project – PIE – Walk Model – DC Model – Conclusion
Destination choice

35. PedestrianTrips
Destination Choice (PAZ)
Prob(dest.) = function of…
– network distance
– size / # of destinations
– pedestrian environment
– traveler characteristics
Data: 2011 OHAS
Method: multinomial logit model
Spatial unit: super-pedestrian analysis zone
Six trip types: home-based: work (HBW),
shopping (HBS),
recreation (HBR), &
other (HBO);
non-home-based: work (NHBW) and
non-work (NHBNW)
Destination choice
35
III
Background – Project – PIE – Walk Model – DC Model – Conclusion

36. 36
Destination choice
Background – Project – PIE – Walk Model – DC Model – Conclusion

37. Destination Choice
superPAZ:
– a grid of
5 × 5 = 25 PAZs
Choice set generation:
– Random sample of 10 superPAZs within 3 miles
– 99% of OHAS walk trips < 3 miles (4.8 km)
Background – Project – PIE – Walk Model – DC Model – Conclusion 37
III

38. Destination Choice
38Background – Project – PIE – Walk Model – DC Model – Conclusion
Key variables
Impedance Size
Pedestrian
supports
Pedestrian
barriers
Traveler
attributes
Additional variables
network distance btw. zones employment by category, households
PIE, parks slope, freeway, industrial LUs
III
auto own., children

39. Destination Choice
39
III
Impedance ∆ odds of walking to destination
+ 1 mile of distance
by auto own.:
by children:
by children:
76–86% decrease (*)
-62% (no), -74% (yes) (HBW)
-78% (no), -83% (yes) (HBR)
-78% (no), -90% (yes) (HBS)
Size ∆ odds of walking to destination
2 × # destinations
minimum:
maximum:
28–42% increase (†)
4% increase (HBR)
88% increase (HBS)
† Except for HBR and HBS.
Background – Project – PIE – Walk Model – DC Model – Conclusion
* Except for HBW, HBR, and HBS.

40. Destination Choice
40
III
Ped. supports ∆ odds of walking to destination
+ 10 points PIE: 16–34% increase (*)
presence of park: 58% increase (HBR)
Ped. Barriers ∆ odds of walking to destination
+ 1° mean slope: 14–35% decrease (2,3,4)
presence of freeway: 64% decrease (2)
+ 1% industrial jobs: 33–82% decrease (1,2,3,4)
Pseudo R2 0.416 (HBR) – 0.680 (HBS)
Background – Project – PIE – Walk Model – DC Model – Conclusion
* Except for HBS and HBR.
1 HBW, 2 HBS, 3 HBO, 4 NHBW.

41. Destination Choice
Background – Project – PIE – Walk Model – DC Model – Conclusion 41
III

42. Destination Choice
Background – Project – PIE – Walk Model – DC Model – Conclusion 42
III
PIE = 75 PIE = 85

43. Destination choice
43Background – Project – PIE – Walk Model – DC Model – Conclusion
ModelValidation – % Correct Destination

44. Destination Choice
44Background – Project – PIE – Walk Model – DC Model – Conclusion
ModelValidation – Avg. Distance Walked

45. 45Background – Project – PIE – Walk Model – DC Model – Conclusion
Destination Choice

46. CONCLUSIONS & FUTURE WORK
46

47. Conclusions
• Nests within current model but can be used alone
• Pedestrian scale analysis (PAZs)
• Pedestrian-relevant variables (PIE)
• One of the first studies to examine pedestrian
destination choice in modeling framework
• Highlights policy relevant
variables: distance, size,
pedestrian supports &
barriers
Background – Project – PIE – Walk Model – DC Model – Conclusion 47

48. Future work
Before application:
• Relate PIE more explicitly to policy changes
• Forecasting inputs
• Test method in other area(s)/regions
– Examine relationships in other contexts
– Assess PIE’s transferability
• Provide agency guidance for
implementation
Background – Project – PIE – Walk Model – DC Model – Conclusion 48

49. Future work
Research & Model Improvements:
Trip Generation
– Multinomial Logit model
• Destination Choice
– Allocate from superPAZ to PAZ level
– Explore non-linear effects & other interactions
• Route choices or potential pathways
– Need fundamental research to improve
understanding
49Background – Project – PIE – Walk Model – DC Model – Conclusion

50. Questions?
Project info & reports:
http://trec.pdx.edu/research/project/510
http://trec.pdx.edu/research/project/677
Kelly J. Clifton, PhD kclifton@pdx.edu
Patrick A. Singleton Portland State University
Christopher Muhs DKS & Associates
Robert Schneider, PhD Univ. Wisconsin–Milwaukee
50Background – Project – PIE – Walk Model – DC Model – Conclusion