tracking daily mobilities: gps based bicycle data collection, processing, and analysis snapshots

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

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

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

3

Yeboah & Alvanides, Northumbria University

House keeping 4

Internet (see paper in circulation)

Exits

Fire alarm

Where to go for coffee

Where to go for lunch

Gents/Ladies

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

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)

6


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”.

7


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

Choosing study area:

Analysing Tyne & Wear Household Travel Survey 9

From 2003 to 2011

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


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


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.

12


Space-Time-Cube (STC) based GPS data

processing workflow 13


Example of visual inspection:

GPS raw data (left) & processed data (right) 14

Visual inspection of GPS raw data

Processed/ refined data


Space-Time-Cube applicability/usability cycle

15

GAP


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

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)

Cycle trips share per employment status

18

59%

7% 16%

9% 10%

0%10%20%30%40%50%60%70%

Participants' cycle trips (%)

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 (%)

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

GPS Logged Points 21

2 3

787641

1623132

4808 34 20 11 15

Points

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

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.

Our case: An example of generated

Home-to-Work Network constrained routes 24

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

25


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.

26


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

27


Please keep questions for the morning open discussion

Acknowledgements

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

28


http://godwinyeboah.blogspot.com/

http://www.youtube.com/SpatialScience



http://twitter.com/

http://twitter.com/

http://www.northumbria.ac.uk/sd/academic/bne/study/aec/acestaff/seraphimalvanides

http://www.northumbria.ac.uk/sd/academic/bne/study/aec/acestaff/seraphimalvanides

http://www.northumbria.ac.uk/sd/academic/bne/study/aec/acestaff/eminethompson



tracking daily mobilities: gps based bicycle data collection, processing, and analysis snapshots

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