tracking daily mobilities: gps based bicycle data collection, processing, and analysis snapshots
DESCRIPTION
Introduction by Organizers Seraphim Alvanides1, Godwin Yeboah1, Stefan Van der Spek2, Nico de Weghe3 1Northumbria University, UK; 2TU-Delft, Netherlands; 3Ghent University, Belgium Topic: "Tracking daily mobilities: GPS based bicycle data collection, processing, and analysis snapshots"TRANSCRIPT
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Cycling Data Challenge Workshop - CDC2013
Pre-Workshop of 16th AGILE Conference 2013
Leuven – Belgium.
Tuesday 14th May 2013
“Bisschopskamer” room at Faculty Club
Alvanides1, Yeboah1, Van der Spek2, de Weghe3
Northumbria University1; TU Delft2; Ghent University3
WELCOME
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Cycling Data Challenge Workshop - CDC2013 Pre-Workshop of 16th AGILE Conference 2013
Alvanides1, Yeboah2, Van der Spek3, de Weghe4
Northumbria University1,2; TU Delft3; Ghent University4
INTRODUCTION
TRACKING DAILY MOBILITIES: GPS BASED BICYCLE DATA
COLLECTION, PROCESSING, AND ANALYSIS SNAPSHOTS
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Overview
House keeping
Brief background of project
Data collection and sample characteristics
Challenges in data collection
Challenges in data processing
Remarks and the rest of the programme
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Yeboah & Alvanides, Northumbria University
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House keeping 4
Internet (see paper in circulation)
Exits
Fire alarm
Where to go for coffee
Where to go for lunch
Gents/Ladies
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Aim of presentation 5
To provide evidence on methods used for data collection,
processing, and some analysis
To share challenges faced during the data collection and
processing phase
To set the scene for subsequent presentations
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Strands: Suggestions and demands from
literature (Why Cycling?)
There is demand for sustainable ways of living due to
traffic congestion, population growth, climate change, low physical activity, health related issues (e.g., obesity & non-communicable diseases), sedentary lifestyles etc.
Cycling as active transport
one of the solutions to sustainable ways of living
Calls for research to focus on understanding cycling through:
investigation and knowledge discovery of cyclist’s perception and actual route choice experiences and preferences
integrated research methods which recent technological advancements may permit (e.g. GPS+GIS+GISc+ABMS)
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Yeboah & Alvanides, Northumbria University
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Why primary data collection?
Secondary data is aggregated or not detailed
enough (e.g. census data; surveys; more recently DfT)
Lack of “detailed quality data” limits this research.
To make available new scientific data on actual and
revealed route choice preferences of utility cyclists
within the research area; not existing previously.
To enable further research towards understanding
constraints and enablers for cycling; especially in
relation to transport and (indirectly) “well-being”.
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Yeboah & Alvanides, Northumbria University
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Choosing study area:
Analysing UK Census 2001 & 2011 8
0
10
20
30
40
50
60
70
80
90
100
0 20 40 60 80 100
Cum
ula
tive %
of
base
(to
tal)
act
ivity
(NE E
ngla
nd 2
011
Censu
s as
base
)
Cumulative % of activity (Travel to Work by Bike across NE England )
Lorenz Curve for Travel to Work by Bike – Census 2011
Travel to work by Bike
Index of Dissimilarity (IoD)= 11
Note: Census 2001 IoD = 5 North Tyneside
Newcastle upon Tyne
South Tyneside
Rest of North East
Gateshead
Sunderland
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Choosing study area:
Analysing Tyne & Wear Household Travel Survey 9
From 2003 to 2011
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Data collection / methodological issues
/ Further work
Godwin Yeboah, Northumbria University
STUDY AREA
Area:
in & around
Newcastle upon
Tyne
Background map: Google Maps 2012
HOME
WORK/SCHOOL
STUDY AREA
LEGEND
Overview
Slide 10
Yeboah & Alvanides, Northumbria University
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Fieldwork planning 11
Extensive piloting of
survey instruments
with 7 participants
Evaluated 4 GPS
devices: i-gotU GT-600;
Atmel BTT08; Canmore
GT-750 (L); and Qstarz
BT-Q1000XT (selected)
Screening
Data processing
&
further analysis
Stepwise flow
(main survey)
Stepwise flow
(during testing)
Recruitment
Data collection
Planning and
Preparation
Invitation
Yeboah & Alvanides, Northumbria University
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Tracked sample size
This work (Northumbria project within Tyneside conurbation):
One wave: October-November 2011
118 initially agreed to participate
In the end: 81 participants out of 111utility cyclists
79 used in this presentation
Lessons learnt from other related work such as:
UK National Travel Survey (NTS) GPS Feasibility study (DfT)
The fieldwork was done in two waves; 66 adults in one wave (October-November) and 68 adults in the second wave (January-March). In all 96 adults were interviewed face-to-face across the two waves for the NTS study.
TU Deft project in the town of Almere
15 families initially agreed to participate. However, in the end, 40 participants out of 13 families from three neighbourhoods participated in the study by carrying GPS devices for one week.
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Yeboah & Alvanides, Northumbria University
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Space-Time-Cube (STC) based GPS data
processing workflow 13
Yeboah & Alvanides, Northumbria University
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Example of visual inspection:
GPS raw data (left) & processed data (right) 14
Visual inspection of GPS raw data
Processed/ refined data
Yeboah & Alvanides, Northumbria University
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Space-Time-Cube applicability/usability cycle
15
GAP
Yeboah & Alvanides, Northumbria University
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Gender against number of cycle trips and
distance (km) travelled 16
Gender No. Over one week period per person
Female distance value is weighted to control for gender
TRIPS KM
(weighted)
Average
KM / TRIP
Average
KM /
PERSON
MIN / MAX
(trip)
Female 27 319 2137.4 6.7 79.2 0.25 km /
13 km
Male 52 622 3373.0 5.4 64.9 0.12 km /
36 km
Total 79 941 5510.4 5.9 69.8
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Trips, gender & annual household income
17
31%
9%
19% 15%
46%
14%
45%
21%
77%
23%
65%
35%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
High Income(Distance)
Low Income(Distance)
High Income(Trip)
Low Income(Trip)
Female (f) Male (m) All (f+m)
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Cycle trips share per employment status
18
59%
7% 16%
9% 10%
0%10%20%30%40%50%60%70%
Participants' cycle trips (%)
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Reported travel mode by participants - t. diary
19
43%
29%
1%
5% 2%
20%
1% 0%
5%
10%
15%
20%
25%
30%
35%
40%
45%
Bike Walk Taxi Train Bus Car Other
Num
ber
of
Tri
ps
(%)
(100%
= 2
432)
Travel mode by Participants (Travel Diary)
Trip (%)
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Challenges in data collection 20
Planning considerations
device procurement timing, size, cost, customer support
Sample, survey response, spatial distribution of trajectories
Device features
Battery life and the means to charge/re-charge
Accuracy
Memory for storing logged points
Fix time. The faster the better. Mostly <=35 seconds
Software for GPS device
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GPS Logged Points 21
2 3
787641
1623132
4808 34 20 11 15
Points
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Challenges in data processing 22
Non-algorithmic approach
Space Time Cube usage is limited; Travel diary needed
Convenient for small to medium datasets
Algorithmic approach
Quality assessments
how reliable is the data without extra information?
Non-availability of generic algorithmic tools
Tool 1: Must know Java + MATSim + Eclipse
http://sourceforge.net/projects/posdap/
Tool 2: Must know Java + need to conform to Copenhagen study
https://github.com/bsnizek/JMapMatching
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Our case: Network route generation
23
Papinski, D. & D. M. Scott (2011) A GIS-based toolkit for route choice analysis. Journal of Transport Geography, 19, 434-442.
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Our case: An example of generated
Home-to-Work Network constrained routes 24
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Remarks and the rest of the programme
Res. design: implemented in few published cycling studies
No significant differences between gender and use of
cycling “corridors”
Reasonable use of current cycling network (more than half
of trips take place within 20m buffer around cycling
paths). Network data from Newcastle City Council used.
However, need to improve cycling network for the 1/3 of
trips taking place “off” the network => Policy implications
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Rest of the programme
Let’s go through the workshop programme
Possible discussions during breaks or sessions
Keynote presentations
Methods and findings arising from presenters’ presentation
Your reasons for attending the workshop
New ideas emanating from discussions
Organizers intend to take pictures during the presentations
and discussions.
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Yeboah & Alvanides, Northumbria University
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MOVE-COST:
Funded CDC2013 Workshop
CHOROCHRONOS:
Provided secure platform for the bike data management
AGILE2013 TEAM:
Accepted and facilitated this workshop
ALL CONTRIBUTORS:
Organizers, presenters, attendees
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Yeboah & Alvanides, Northumbria University
Please keep questions for the morning open discussion
Acknowledgements
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Other information:
About presenter and supervision team
PhD Student:
• Blog: http://godwinyeboah.blogspot.com/
• YouTube Channel: http://www.youtube.com/SpatialScience
• Twitter: http://twitter.com/#!/godwinyeboah
Supervision team:
• Dr. Seraphim Alvanides
http://www.northumbria.ac.uk/sd/academic/bne/study/aec/
acestaff/seraphimalvanides
• Dr. Emine Mine Thompson
http://www.northumbria.ac.uk/sd/academic/bne/study/aec/
acestaff/eminethompson
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Yeboah & Alvanides, Northumbria University