عنوان سمینار آموزشی
Insights on Walkability and Walking in Lisbon with the IAAPE Method
سمینار آموزشی ارزیابی قابلیت پیاده روی در شهر لیسبون پرتقال
Filipe Moura, Instituto Superior Técnico
transportsafety.ir
حمید مرادی
Hamid Moradi
New Tools for Estimating Walking and Bicycling Demand
Track: Sustain
Format: 90 minute panel
Abstract: Walking and bicycling demand estimates can make a stronger case for investing in new facilities and are necessary inputs to important planning tasks. This session presents state-of-the-art tools to predict walking and bicycling demand at varying geographic scales. Tools include: 1) a framework to incorporate walking into regional travel demand models; 2) a method to estimate bicycle and pedestrian traffic based on count data; 3) new mode choice models; and 4) a web-based repository of non-motorized demand analysis tools.
Presenter(s)
Presenter: Patrick Singleton Portland State University
Co-Presenter: J. Richard (Rich) Kuzmyak Renaissance Planning Group
Co-Presenter: Greg Lindsey University of Minnesota, Humphrey School
Co-Presenter: Jeremy Raw Federal Highway Administration
How learning mobility impact people\' life. Europemobility initiative to support individuals and educational organizations to promote mobility initiatives, for further info: www.europemobility.eu
Discovering Meaningful Connections between Resources in the Web of DataLaurens De Vocht
Slides of LDOW2013 presentation, May 14th, Rio De Janeiro, Brazil
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Detecting Food and Activities in Lifelogging ImagesRami Albatal
Presentation by Dr. Rami Albatal and Dr. Bahjat Safadi on using Computer Vision and Artificial Intelligence technologies to process and analyse large Lifelogging archives.
Presentation was given on 20 April 2016 in The Science Gallery of Trinity College Dublin, Ireland.
New Tools for Estimating Walking and Bicycling Demand
Track: Sustain
Format: 90 minute panel
Abstract: Walking and bicycling demand estimates can make a stronger case for investing in new facilities and are necessary inputs to important planning tasks. This session presents state-of-the-art tools to predict walking and bicycling demand at varying geographic scales. Tools include: 1) a framework to incorporate walking into regional travel demand models; 2) a method to estimate bicycle and pedestrian traffic based on count data; 3) new mode choice models; and 4) a web-based repository of non-motorized demand analysis tools.
Presenter(s)
Presenter: Patrick Singleton Portland State University
Co-Presenter: J. Richard (Rich) Kuzmyak Renaissance Planning Group
Co-Presenter: Greg Lindsey University of Minnesota, Humphrey School
Co-Presenter: Jeremy Raw Federal Highway Administration
How learning mobility impact people\' life. Europemobility initiative to support individuals and educational organizations to promote mobility initiatives, for further info: www.europemobility.eu
Discovering Meaningful Connections between Resources in the Web of DataLaurens De Vocht
Slides of LDOW2013 presentation, May 14th, Rio De Janeiro, Brazil
We will show that semantically annotated paths lead to discovering meaningful, non-trivial relations and connections between multiple resources in large online datasets such as the Web of Data. Graph algorithms have always been key in pathfinding applications (e.g., navigation systems). They make optimal use of available computation resources to find paths in structured data. Applying these algorithms to Linked Data can facilitate the resolving of complex queries that involve the semantics of the relations between resources. In this paper, we introduce a new approach for finding paths in Linked Data that takes into account the meaning of the connections and also deals with scalability. An efficient technique combining pre-processing and indexing of datasets is used for finding paths between two resources in largedatasets within a couple of seconds. To demonstrate our approach, we have implemented a testcase using the DBpedia dataset.
Detecting Food and Activities in Lifelogging ImagesRami Albatal
Presentation by Dr. Rami Albatal and Dr. Bahjat Safadi on using Computer Vision and Artificial Intelligence technologies to process and analyse large Lifelogging archives.
Presentation was given on 20 April 2016 in The Science Gallery of Trinity College Dublin, Ireland.
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A trend observed in the last year of opportunistic routing refers to considering social similarity metrics to improve the exchange of data. Social relationships, shared interests, and popularity are examples of such metrics that have been employed successfully: as users interact based on relationships and interests, this information can be used to decide on the best next forwarders of information.
This Thesis work combines the features of today's devices found in the regular urban environment with the current social-awareness trend in the context of opportunistic routing. To achieve this goal, this work was divided into different tasks that map to a set of specific objectives, leading to the following contributions: i) an up-to-date opportunistic routing taxonomy; ii) a universal evaluation framework that aids in devising and testing new routing proposals; iii) three social-aware utility functions that consider the dynamic user behavior and can be easily incorporated to other routing proposals; iv) two opportunistic routing proposals based on the users' daily routines and on the content traversing the network and interest of users in such content; and v) a structure analysis of the social-based network formed based on the approaches devised in this work.
This presentation was given as part of my PhD defense to the Universities of Minho, Aveiro, and Porto, on September 29th, 2014 in University of Aveiro.
For a copy of the thesis: http://copelabs.ulusofona.pt/scicommons/index.php/publications/show/732
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- draft a reference model for analysing and sharing Citizen Science tools and data – with first examples;
- define a high-level roadmap with checkpoints for synchronising already ongoing activities.
More information on the workshop and other presentations can be found here: https://ec.europa.eu/jrc/en/event/workshop/citizen-science-workshop
Presentation I used while defending my thesis on MEILI: Multiple Day Travel Behaviour Data Collection, Automation and Analysis.
Thesis available at: http://kth.diva-portal.org/smash/record.jsf?pid=diva2%3A1204245&dswid=7962
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گزارش تلفات تصادفات جاده ای انگلستان در سال 2016
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http://transportsafety.ir/آمار-سوانح-ترافیکی-انگلستان-2016/
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For a copy of the thesis: http://copelabs.ulusofona.pt/scicommons/index.php/publications/show/732
Lessons from a trial of MEILI a smartphone based semi-automatic activity-trav...Adrian C. Prelipcean
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سمینار آموزشی ارزیابی قابلیت پیاده روی در شهر لیسبون پرتقال
1. INSIGHTS ON WALKABILITY AND WALKING
IN LISBON WITH THE IAAPE METHOD
Filipe Moura
Fulbright Visiting Scholar at PSU
Associate Professor in Transportation
Systems at IST - CERIS
fmoura@tecnico.ulisboa.pt
2. AKNOWLEDGMENTS
• Paulo Cambra (PhD Student at IST)
paulo.cambra@tecnico.ulisboa.pt
• Alexandre Bacelar Gonçalves (Assistant Professor at IST)
alexandre.goncalves@tecnico.ulisboa.pt
3. OUTLINE
1. Lisbon in a nutshell
2. Why studying walking and walkability?
3. IAAPE method
4. The importance of pedestrian networks
5. Some case studies
6. How to validate walkability assessment models?
7. Questions remain regarding IAAPE
8. Technology can help: WALKBOT project
1.Lisbon in a nutshell 1.Walk &
Walkability 1.IAAPE method 1.Ped. networks Case studies Validation 1.Questions WALKBOT project
4. 1. LISBON IN A NUTSHELL
1.Lisbon in a nutshell 1.Walk &
Walkability 1.IAAPE method 1.Ped. networks Case studies Validation 1.Questions WALKBOT project
5. Lisbon Metropolitan Area
Portugal
LISBON
LISBON METRO AREA
AML
18 municipalities
Total Area = 3 015 km2 (1164mi2)
Total Population = 2,82x106 Inhab.
Pop. Density ~ 940 Inhab./km2 (2 400 Inhab./mi2)
1.Lisbon in a nutshell 1.Walk &
Walkability 1.IAAPE method 1.Ped. networks Case studies Validation 1.Questions WALKBOT project
6. 24 parishes
Total Area = 100 km2 (38,61mi2)
Total Population = 504 x103 Inhab.
Pop. Density = 5 040 Inhab./km2
(= 13 053 Inhab./mi2)
1.Lisbon in a nutshell 1.Walk &
Walkability 1.IAAPE method 1.Ped. networks Case studies Validation 1.Questions WALKBOT project
8. LISBON VS. PORTLAND (and metro areas)
LISBON PORTLAND
City Area City Area
Area (km2) 100 3 015 376 17 310
Population (103 inhab) 504 2 817 640 2 425
Density (inhab/km2) 5 040 940 1 702 140
Ageing index (P65+/P19-) 137,9 89,4 54,5 47,8
Car/Transit/Walk/Bike (%) 48/34/17/0,1 55/28/15/0,2 78/4,4/10,4/2,9
Motorization rate (car/household) 1,4 1,8
Av. Ann. Precipitation - inch (mm) 27 (691) 36 (915)
Max/Min Temperature - ºF (ºC) 73 (23) /52 (11) 63 (17,3) / 46 (7,6)
1.Lisbon in a nutshell 1.Walk &
Walkability 1.IAAPE method 1.Ped. networks Case studies Validation 1.Questions WALKBOT project
9. 2. WHY STUDYING WALKING AND WALKABILITY?
1.Lisbon in a nutshell 1.Walk &
Walkability 1.IAAPE method 1.Ped. networks Case studies Validation 1.Questions WALKBOT project
10. Physical Activity
+
Social Interaction
+
Motorized traffic
-
Mental health
Obesity
Overweight
Sedentary
lifestyle
+
-
-
Safety Inclusion
Inequalities-
+ +
Air qualityNoise
Public space
+-
+
Walking
WHY STUDYING WALKING AND WALKABILITY
1.Lisbon in a nutshell 1.Walk &
Walkability 1.IAAPE method 1.Ped. networks Case studies Validation 1.Questions WALKBOT project
Physical l Interaction
Mental health
Obesity
Overweight
Sedentary
lifestyle
Safety Inclusion
Inequalities
Air qualityNoise
Public space
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MMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMoooooooooooooooooooooooooooooottttooriiiiiiiiizzzzzzzzzed trraffic
Overwwwwwwwwwwwwweeeeeeeiiiggggggggggghhhhhhhht
Ai lit
Walking
Social
Environmental
Economic
Benefits
11. WHY STUDYING WALKING AND WALKABILITY
1.Lisbon in a nutshell 1.Walk &
Walkability 1.IAAPE method 1.Ped. networks Case studies Validation 1.Questions WALKBOT project
Source: Paulo Cambra (2012), adapted from Handy (2005) and Schmid (2006)
Walkability
Sociodemographic Profile
Preferences, attitudes
and lifestyles
Alternative means of
transportation
We may postulate
that:
• Perceptions are
context specific (local)
• Perceptions vary from
person to person
• Perceptions of a
person may vary
according to the trip
motive
12. 3. IAAPE – INDICATORS OF ACCESSIBILITY AND
ATTRACTIVENESS OF PEDESTRIAN ENVIRONMENTS
1.Lisbon in a nutshell 1.Walk &
Walkability 1.IAAPE method 1.Ped. networks Case studies Validation 1.Questions WALKBOT project
13. THE IAAPE METHOD
• Objectives
Set of indicators to measure walkability in urban context
Detailed digital pedestrian network
Operational tool to support urban planning
• Why is it different from the others?
Context-Specific (local)
Participatory method to capture context-specific perceptions
Micro-scale analysis based on the detailed pedestrian network
Considers different population segments and different trip motivations
Validation
1.Lisbon in a nutshell 1.Walk &
Walkability 1.IAAPE method 1.Ped. networks Case studies Validation 1.Questions WALKBOT project
14. THE IAAPE METHOD: STRUCTURED BY 7 C’S
1.Lisbon in a nutshell 1.Walk &
Walkability 1.IAAPE method 1.Ped. networks Case studies Validation 1.Questions WALKBOT project
CONNECTIVITY
CONVENIENCE
COMFORT
CONVIVIALITY
CONSPICUOUSNESS
Do I have access to a formal pedestrian network?
Does the network suits me? Is it functional?
Do I have a nice experience?
Does it attrack other people?
Is the built enviroment legible? Do I get the guidance I need?
5 C’s
originally
(Methorst et
al, 2010)
Methorst, R., Monterde, I., Bort, H., Risser, R., Sauter, D., Tight, M., & Walker, J. (2010). COST 358 pedestrians’ quality needs, PQN final report (European Cooperation in Science and
Technology). Cheltenham: Walk21.
COEXISTENCE
COMMITMENT
Do other modes disturb me? Put me into danger?
Do community and decision-makers commit to improving
walkability?
2 C’s
additionally
15. THE IAAPE METHOD
1.Lisbon in a nutshell 1.Walk &
Walkability 1.IAAPE method 1.Ped. networks Case studies Validation 1.Questions WALKBOT project
Source: Moura, F., Cambra, P., & Gonçalves, A. B. (2017). Measuring walkability for distinct pedestrian groups with a participatory assessment method: A case study in
Lisbon. Landscape and Urban Planning, 157, 282-296.
16. IAAPE’S PARTICIPATORY EVALUATION PROCESS
STRUCTURING/SCORING
(Define and weight keypoints/indicators)
Walk score = 42
AGGREGATE7Cs
accordingtoSCORES
COEXISTENCE
COMMITMENT
CONNECTIVITY
CONVENIENCE
COMFORT
CONVIVIALITY
CONSPICUOUSNESS
1.Lisbon in a nutshell 1.Walk &
Walkability 1.IAAPE method 1.Ped. networks Case studies Validation 1.Questions WALKBOT project
KEYPOINT A
KEYPOINT B
KEYPOINT C
KEYPOINT D
KEYPOINT E
KEYPOINT F
KEYPOINT G
130
0.07416
Los B
80%
DATA COLLECTION
(Measure)
130
4
VALUE FUNCTION
50
30
80
40
80
10
20
100
0
Keypoint
///
17. STRUCTURING
“KEY POINTS”/INDICATORS FOR EACH DIMENSION
1.Lisbon in a nutshell 1.Walk &
Walkability 1.IAAPE method 1.Ped. networks Case studies Validation 1.Questions WALKBOT project
C 2
C (...) C 7
1) Distribution “play roles” 2) Selection of “Key points”/ indicators
Final selection:
17 Key points /Indicators
For 7 C’s
18. SCORING: WEIGHTING WITH “DELPHI” METHOD
1.Lisbon in a nutshell 1.Walk &
Walkability 1.IAAPE method 1.Ped. networks Case studies Validation 1.Questions WALKBOT project
1) Group “play role” in round tables 2) Answer the moderator questions
Which of the two
settings do you thing is
more walkable,
A or B?
Clearly A!
or
We couldn´t reach
consensus => skip
The group answer had to be consensual
(discuss until consensus)
19. Pedestrian group: Adults
“SCORING” RESULTS: WEIGHTS BY TRIP MOTIVE
1.Lisbon in a nutshell 1.Walk &
Walkability 1.IAAPE method 1.Ped. networks Case studies Validation 1.Questions WALKBOT project
20. Trip motive: Utilitarian
1.Lisbon in a nutshell 1.Walk &
Walkability 1.IAAPE method 1.Ped. networks Case studies Validation 1.Questions WALKBOT project
RESULTS: WEIGHTS BY PEDESTRIAN GROUP
21. 4. THE IMPORTANCE OF PEDESTRIAN
NETWORKS
1.Lisbon in a nutshell 1.Walk &
Walkability 1.IAAPE method 1.Ped. networks Case studies Validation 1.Questions WALKBOT project
22. DETAILED PEDESTRIAN NETWORKS
1.Lisbon in a nutshell 1.Walk &
Walkability 1.IAAPE method 1.Ped. networks Case studies Validation 1.Questions WALKBOT project
• Walking distance is
widely used in urban and
transportation planning
and analysis
• Where do we actually get
in 5 minutes walking ?
• How appropriate are the
conditions to walk ?
Elementary school,
1km / 15 mins walking
Sports playground,
1km to 2km /
15 to 30 mins walking
High School,
2km / 30 mins
walking
Portuguese Standards for location of public facilities
23. DETAILED PEDESTRIAN NETWORKS
1.Lisbon in a nutshell 1.Walk &
Walkability 1.IAAPE method 1.Ped. networks Case studies Validation 1.Questions WALKBOT project
5 locations in distinct urban settings in Lisbon
Network Analysis
Standard
5 minute buffer
(radius 300m)
Street network
centrelines
Pedestrian
Network
(sidewalk+
crossings)
24. DETAILED PEDESTRIAN NETWORKS
1.Lisbon in a nutshell 1.Walk &
Walkability 1.IAAPE method 1.Ped. networks Case studies Validation 1.Questions WALKBOT project
100
5 minute
buffer
0
Realistic Spatial coverage
50 30
Centreline
Network
Pedestian
Network
Detailed
Pedestian
Network
->
waiting
times
Robust
Pedestian
Network
->
walkability
attributes
Considering
different quality
standards for
walking
- seniors; children;
impaired mobility -
25. 5. CASE STUDIES
1.Lisbon in a nutshell 1.Walk &
Walkability 1.IAAPE method 1.Ped. networks Case studies Validation 1.Questions WALKBOT project
26. TWO ELEMENTARY SCHOOLS IN ARROIOS
1.Lisbon in a nutshell 1.Walk &
Walkability 1.IAAPE method 1.Ped. networks Case studies Validation 1.Questions WALKBOT project
• Less than 60% of the standard circular buffer area
Setting a 300m radius => PEDSHED (1min/s)
27. TWO ELEMENTARY SCHOOLS IN ARROIOS
1.Lisbon in a nutshell 1.Walk &
Walkability 1.IAAPE method 1.Ped. networks Case studies Validation 1.Questions WALKBOT project
Measuring walkability indicates QUALITY of walking:
School A School B
Length of Pedestrian Network by LOS (%)
Walkscores for pedestian group "Children" and trip purpose
"Transportation"
E D C B A
[0; 20%] ]20; 40%] ]40; 60%] ]60; 80%] ]80; 100%]
School A 1% 38% 54% 8% 0%
School B 8% 65% 24% 2% 0%
28. AVENIDAS NOVAS: 3 DIFFERENT POPULATION SEGMENTS
1.Lisbon in a nutshell 1.Walk &
Walkability 1.IAAPE method 1.Ped. networks Case studies Validation 1.Questions WALKBOT project
• Distinct pedestrians -> Distinct Quality Needs
• Different factors are valued differentely
• Same urban space, different Walkability Scores
29. 6. VALIDATION OF IAAPE
1.Lisbon in a nutshell 1.Walk &
Walkability 1.IAAPE method 1.Ped. networks Case studies Validation 1.Questions WALKBOT project
30. WHAT TO VALIDATE IN THE MODEL?
1.Lisbon in a nutshell 1.Walk &
Walkability 1.IAAPE method 1.Ped. networks Case studies Validation 1.Questions WALKBOT project
Uncertainty of data
collection/input
Methodological uncertainty
Calibration
uncertainty
Model/function specification
uncertainty
Sources and types of uncertainty
31. HOW TO VALIDATE THE MODEL?
1.Lisbon in a nutshell 1.Walk &
Walkability 1.IAAPE method 1.Ped. networks Case studies Validation 1.Questions WALKBOT project
• Pedestrian counts
Higher pedestrian flows => Higher walkability scores
• Street surveys
Pedestrians’ perceptions match walkability scores
• Home-based surveys
Respondents’ route choices match routes with higher walkability
scores
• Other models
Consistency with other tools
32. + Walk Score
+ Pedestrian
activity
?
2.600 audited street segments
Sample of 60 street segments used
for validation
60 streets x 6 days (5 weekdays + 1
Saturday) x 5 time periods x 6
counts per period = aprox. 10.000
counts
MORE PEDESTRIANS => MORE WALKABILITY
1.Lisbon in a nutshell 1.Walk &
Walkability 1.IAAPE method 1.Ped. networks Case studies Validation 1.Questions WALKBOT project
33. OUTLIERS CAN BE OUR FRIENDS!
1.Lisbon in a nutshell 1.Walk &
Walkability 1.IAAPE method 1.Ped. networks Case studies Validation 1.Questions WALKBOT project
50
Pedestrianflows
Walkability Scores
34. MORE PEDESTRIANS => MORE WALKABILITY
1.Lisbon in a nutshell 1.Walk &
Walkability 1.IAAPE method 1.Ped. networks Case studies Validation 1.Questions WALKBOT project
• Significant pedestrian flow, with
unsatisfying quality
• Improving walking conditions shifts
these outliers to the right of the graph
Quality
below
expectations
Walkability Scores
35. MORE PEDESTRIANS => MORE WALKABILITY
1.Lisbon in a nutshell 1.Walk &
Walkability 1.IAAPE method 1.Ped. networks Case studies Validation 1.Questions WALKBOT project
• Network is inconsistency + Scarce integration in the
system
• Improving connectivity within the network could raise
pedestrian flow, shifting these outliers up in the graph
• If no action taken, conditions may degrade,
walkability decreases and outliers would shift left
Network is
inconsistency
Walkability Scores
36. MORE PEDESTRIANS => MORE WALKABILITY
1.Lisbon in a nutshell 1.Walk &
Walkability 1.IAAPE method 1.Ped. networks Case studies Validation 1.Questions WALKBOT project
• It is not a matter of pursuing a better model fit.
• It is a matter of aiming to a more coherent
pedestrian network.
Walk Score
Pedestrian
Flow
Walkability Scores
37. PIE LEVELS AND WALKING SHARE
0%
5%
10%
15%
20%
25%
(20,25] (25,31] (31,35] (35,38] (38,41] (41,44] (44,49] (49,57] (57,64] (64,97]
WalkShare
PIE Levels
Source: Orrego, Jaime, "Density Differences: Exploring Built Environment Relationships with Walking Between and Within Metropolitan Areas" (2018).
TREC Friday Seminar Series. 135. https://pdxscholar.library.pdx.edu/trec_seminar/135
50
38. STREETS SURVEYS: PERCEPTIONS MATCH WALKABILITY
1.Lisbon in a nutshell 1.Walk &
Walkability 1.IAAPE method 1.Ped. networks Case studies Validation 1.Questions WALKBOT project
Source: Moura, F., Cambra, P., & Gonçalves, A. B. (2017). Measuring walkability for distinct pedestrian groups with a participatory assessment method: A case
study in Lisbon. Landscape and Urban Planning, 157, 282-296.
• Good match for high
measured/perceived walkability
pairs
• Poor match for low
measured/perceived walkability
pairs
• Problem?
• IAAPE measures everything single
street
• Respondent don’t know every
(worst) street segments
39. 7. QUESTIONS REMAIN?
1.Lisbon in a nutshell 1.Walk &
Walkability 1.IAAPE method 1.Ped. networks Case studies Validation 1.Questions WALKBOT project
40. 7. QUESTIONS REMAIN?
1.Lisbon in a nutshell 1.Walk &
Walkability 1.IAAPE method 1.Ped. networks Case studies Validation 1.Questions WALKBOT project
Does “more pedestrians” mean ALWAYS “more walkable”?
Walkability Score
Pedestrianflow
?
• Do current Walkability Assessment
models capture this effect? For
IAAPE?
E.g., too much conviviality => lower score after
some level?
Measure of Conviviality
Critical threshold (?capacity?)
Value Function
41. 7. QUESTIONS REMAIN?
1.Lisbon in a nutshell 1.Walk &
Walkability 1.IAAPE method 1.Ped. networks Case studies Validation 1.Questions WALKBOT project
• How to avoid all sources of uncertainty ?
• How much “walkable” is enough, when planning ?
Benchmarking => how to define benchmarks?
• Can we use walkability scores to predict demand?
Can walkability scores be a measure of impedance?
What about “cumulative impedance” over a route?
first mile last mile
42. 7. TECHNOLOGY CAN HELP: WALKBOT PROJECT
1.Lisbon in a nutshell 1.Walk &
Walkability 1.IAAPE method 1.Ped. networks Case studies Validation 1.Questions WALKBOT project
43. PROBLEMS AND CHALLENGES
• Big amount of data collection
Pedestrian network configuration, network quality, network
accessibility.
The common way of doing it is manually, with visual scanning and
street audits.
• Automatic or semi-automatic pedestrian network
scanners?
Allow for wider and faster data collection
Potentially more objective and more reliable.
1.Lisbon in a nutshell 1.Walk &
Walkability 1.IAAPE method 1.Ped. networks Case studies Validation 1.Questions WALKBOT project
44. WalkBot
Detailed mapping:
• Sidewalks
• Pedestrian crossings
Walkability indicators:
• Sidewalk width
• Slope
• Obstacles
• Steps
• Risk of slipping (granularity)
• Pavement quality
(irregularities, wholes)
* Sensor Box *
• Imagery
recognition
• Scan laser 3D
• GPS + IMU
• Urban vehicle
(mono-wheeler,
2 wheeler, cart,
Segway, etc.)
Mapping
(digitizing pedestrian network)
Walkability indicators+
WALKBOT: SEMI-AUTOMATIC DATA COLLECTION
WALKBOT project
Thales Innovation
Challenge – 1st Ed.
Research team - IST:
Civil Eng. (CERiS)
Paulo Cambra
Filipe Moura
Alexandre Gonçalves
Robotics (ISR)
Miguel Costa
Manuel Marques
45. FIRST TESTS AND RESULTS
• Effective width:
• Automatic detection up to 5m
distance,
• Error +/- 5cm (2 in.)
1,75m
sidewalkroad Bump
• Risk of slipping:
• Automatic detection of
irregularities, wholes, bumps.
Video recognition - interpretation
46. WALKBOT: HOW IT “SHOULD” WORK IN THE END
Walkability
Indicators
Video recognitionVideo recognition
Video interpretation
47. WALKBOT:
PUTTING IT INTO PRACTICE
• Crowd sourcing
• Involving agents that walk (circulate)
regularly in the built environment
• Automated vehicle?