The presentation discusses the visualization of activity sequences to enhance understanding of travel behavior, utilizing travel diaries as the primary data source. Various visualization techniques, including chord diagrams, timelines, Sankey diagrams, and network topologies, are evaluated for their effectiveness in representing activity sequences and their complexities. The author concludes that network topology offers the most comprehensive overview while other methods serve specific purposes depending on the nature of the data being analyzed.

1. Detecting and visualizing the stability of
activity chains with longest common
purpose subsequences
A. C. Prelipcean1
, Y. Susilo1
, G. Gid´ofalvi2
1Department of Transportation Science, KTH
2Division of Geoinformatics, KTH
acpr@kth.se
adrianprelipcean.github.io
@Adi Prelipcean
09 April 2017

2. Outline
This presentation will be about:
1. Sequences of activities for the study of travel behaviour
2. Data collected for visualizing activity sequences
3. Different ways of visualizing activity sequences
– Chord diagrams
– Timelines
– Sankey diagrams
– Topological networks
4. Summary and conclusions
2

3. Visualizing activity sequences
Why do we want to visualize activity sequences?
Visualizing activity sequences gives us better insights into
travel behaviour, which we use:
to investigate the reasons and mechanisms that underlie
an individual’s travel decision making process,
3

4. Visualizing activity sequences
Why do we want to visualize activity sequences?
Visualizing activity sequences gives us better insights into
travel behaviour, which we use:
to investigate the reasons and mechanisms that underlie
an individual’s travel decision making process,
to predict the effect of implementing new transportation
policies or changing the transportation infrastructure, or
3

5. Visualizing activity sequences
Why do we want to visualize activity sequences?
Visualizing activity sequences gives us better insights into
travel behaviour, which we use:
to investigate the reasons and mechanisms that underlie
an individual’s travel decision making process,
to predict the effect of implementing new transportation
policies or changing the transportation infrastructure, or
to understand the dynamic of transportation movement
within study areas.
3

6. Data collection for visualizing activity sequences
Which is the main source of travel data?
Travel diaries summarize where, why and how a user traveled
during a defined time frame:
The destination of a trip
Img: http://soarministries.com/hp_wordpress/wp-content/uploads/2011/08/Destinations-Icon.jpg 4

7. Data collection for visualizing activity sequences
Which is the main source of travel data?
Travel diaries summarize where, why and how a user traveled
during a defined time frame:
The destination of a trip
The trip’s purpose
Img: https://cdn2.vox-cdn.com/thumbor/93Yaxs7y3Tb8tzFfppyRsSn_yN8=/1020x0/cdn0.vox-cdn.com/ 4

8. Data collection for visualizing activity sequences
Which is the main source of travel data?
Travel diaries summarize where, why and how a user traveled
during a defined time frame:
The destination of a trip
The trip’s purpose
The means of transportation, i.e., trip legs
Img: https://d3ui957tjb5bqd.cloudfront.net/images/screenshots/products/4/42/42990/ 4

9. MEILI
An open source activity travel diary collection system
The data for this case study was collected with MEILI
Meili Components: GPS collection + Web and Mobile
GIS based interaction + Artificial Intelligence / Machine
Learning
Open source – https://github.com/Badger-MEILI/
5

10. Data collected by MEILI
6

11. How to visualize the sequences of activities?
It is intuitive to notice simple schedule
patterns but the complexity increases
with the number of days and activities
7

12. How to visualize the sequences of activities?
It is intuitive to notice simple schedule
patterns but the complexity increases
with the number of days and activities
A visualization should:
– Emphasize at least one
dimension of the studied
phenomenon
– Be easy to understand
independent of expertise
– Ideally, it should convey a
message without assuming any
need for interaction
7

13. Chord diagrams
8

14. Chord diagrams
An altered pie chart with
overlapped arcs that represent
the relationship between slices
9

15. Chord diagrams
An altered pie chart with
overlapped arcs that represent
the relationship between slices
Can easily notice the difference
in the share of activities
between weekdays and
weekends
9

16. Chord diagrams
An altered pie chart with
overlapped arcs that represent
the relationship between slices
Can easily notice the difference
in the share of activities
between weekdays and
weekends
9

17. Chord diagrams
An altered pie chart with
overlapped arcs that represent
the relationship between slices
Can easily notice the difference
in the share of activities
between weekdays and
weekends
It is easy to interact with the
chord diagram for further
exploration of OD activity
shares
9

18. Timeline
A common representation of a schedule
10

19. Timeline
A common representation of a schedule
Can be color coded to hint at pattern repetition
10

20. Timeline
A common representation of a schedule
Can be color coded to hint at pattern repetition
It represents the time budget for each activity
10

21. Timeline
A common representation of a schedule
Can be color coded to hint at pattern repetition
It represents the time budget for each activity
It is difficult to visualize sequences that do not overlap in time
10

22. Timeline
A common representation of a schedule
Can be color coded to hint at pattern repetition
It represents the time budget for each activity
It is difficult to visualize sequences that do not overlap in time
Can be used to visualize schedules up to a week, since it clutters
with more info
10

23. Sankey diagram
Traditionally, Sankey diagrams are used to visualize flows
https://www.ifu.com/knowtheflow/2013/from-data-to-knowledge-the-power-of-elegant-sankey-diagrams/ 11

24. Sankey diagram
Traditionally, Sankey diagrams are used to visualize flows
The distribution of time allocated per activity can be interpreted as
a flow
Represents time using discrete units (of 1 hour)
11

25. Sankey diagram
Traditionally, Sankey diagrams are used to visualize flows
The distribution of time allocated per activity can be interpreted as
a flow
Represents time using discrete units (of 1 hour)
Shows the hours of days where the schedule is constant across days
11

26. Sankey diagram
Traditionally, Sankey diagrams are used to visualize flows
The distribution of time allocated per activity can be interpreted as
a flow
Represents time using discrete units (of 1 hour)
Shows the hours of days where the schedule is constant across days
Shows the hours of days where the schedule varies between days
11

27. Sankey diagram
Traditionally, Sankey diagrams are used to visualize flows
The distribution of time allocated per activity can be interpreted as
a flow
Represents time using discrete units (of 1 hour)
Shows the hours of days where the schedule is constant across days
Shows the hours of days where the schedule varies between days
A compromise that allows for an easy visualization of sequences of
activities over a long period of time while still offering some
information regarding the time of activities
11

28. Network topology
12

29. Network topology
Focuses exclusively on sequences (no
time information)
Can be color coded to represent the
frequency of OD activity pairs
13

30. Network topology
Focuses exclusively on sequences (no
time information)
Can be color coded to represent the
frequency of OD activity pairs
Can easily clutter if visualizing
multiple OD pairs
13

31. Network topology
Focuses exclusively on sequences (no
time information)
Can be color coded to represent the
frequency of OD activity pairs
Can easily clutter if visualizing
multiple OD pairs
However, the visualization can be
isolated to only show:
– irregular OD activity pairs
(home is a very centric place
and activity, which is why it is
not irregularly linked with any
other activity)
13

32. Network topology
Focuses exclusively on sequences (no
time information)
Can be color coded to represent the
frequency of OD activity pairs
Can easily clutter if visualizing
multiple OD pairs
However, the visualization can be
isolated to only show:
– irregular OD activity pairs
– regular OD activity pairs (notice
that home is not connected to
work, shopping, pickup/dropoff
because home is centric to those
activities)
13

33. Network topology
Focuses exclusively on sequences (no
time information)
Can be color coded to represent the
frequency of OD activity pairs
Can easily clutter if visualizing
multiple OD pairs
However, the visualization can be
isolated to only show:
– irregular OD activity pairs
– regular OD activity pairs
– frequent OD activity pairs
(everything revolves around 3
centric activities: private, home
and work)
13

34. Network topology
Focuses exclusively on sequences (no
time information)
Can be color coded to represent the
frequency of OD activity pairs
Can easily clutter if visualizing
multiple OD pairs
However, the visualization can be
isolated to only show:
– irregular OD activity pairs
– regular OD activity pairs
– frequent OD activity pairs
– Longest Common Subsequences
(the longest subsequence of
activities that always occur in
the same order is home →
pickup/dropoff → work →
private → home)
13

35. Summary
the interpretation of visualizations is usually subjective,
which makes it difficult to propose the best way to
visualize activity sequences.
in the main author’s view, the network topology
visualization offers a more robust overview of sequences
Sankey diagrams come as a close second for sequence viz
for the cases when the activity start / end time are needed
timelines are useful for comparing different days to get an
indicator of stability across day of week and hour of day,
but they become overloaded with information fast
chord diagrams are good to visualize Origin / Destination
activity matrices, but they only reveal frequency and not
sequences
14

36. References
source code for this presentation available at https://github.
com/Badger-MEILI/Visualizing-Activity-Sequences
source code for the MEILI family available at
https://github.com/Badger-MEILI
paper on sequential stability of travel behaviour
– A. C. Prelipcean, Y. Susilo, G. Gid´ofalvi. Longest common subsequences:
Identifying the stability of individuals’ travel patterns. Submitted to
Transportation Research Part B.
papers on travel diary data collection with MEILI
– A. C. Prelipcean, G. Gid´ofalvi, and Y. Susilo. 2014. “Mobility Collector”,
in the Journal of Location Based Services, Volume 8, Issue 4, pages
229-255, DOI: 10.1080/17489725.2014.973917
– A. C. Prelipcean, G. Gid´ofalvi, and Y. Susilo. 2016. “Measures of
transport mode segmentation of trajectories”, in the International
Journal of Geographical Information Science, Volume 30, Issue 9, pages
1763-1784, DOI: 10.1080/13658816.2015.1137297. [link]
– A. C. Prelipcean, G. Gid´ofalvi, and Y. Susilo. “MEILI: an activity travel
diary collection, annotation and automation system” submitted to
Journal of Urban Technology
15

37. Inspiration
Chord Diagrams based on http://www.delimited.io/blog/
2013/12/8/chord-diagrams-in-d3 and
https://bl.ocks.org/mbostock/4062006
Timeline based on https://developers.google.com/chart/
interactive/docs/gallery/timeline
Sankey Diagrams based on
https://bost.ocks.org/mike/sankey/
Network Topology based on
http://bl.ocks.org/d3noob/5155181 and
http://bl.ocks.org/mbostock/1153292
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38. Thank you for your attention!
Questions and Discussions
Adrian C. Prelipcean
CTO, Airmee
PhD Student in Transport Science
KTH, Royal Institute of Technology
http://adrianprelipcean.github.io/
acpr@kth.se
@Adi Prelipcean
This work is licensed under a Creative Commons Attribution-ShareAlike 3.0 Unported License.