MEILI is a travel diary collection, annotation and automation system that was created to improve upon the limitations of previous attempts. It implements mobility collectors for both Android and iOS, improves the user interface based on feedback, splits the system into well-defined modules, and changes the data model from point-based to period-based. The presentation evaluates MEILI's performance based on a case study with over 170 users, analyzing the distribution and response times of create, read, update and delete operations. It identifies read operations as a bottleneck and discusses lessons learned regarding user studies, usability, and applying artificial intelligence in transportation research.

1. MEILI: a travel diary collection, annotation
and automation system
A. C. Prelipcean1
, G. Gid´ofalvi1
, Y. Susilo2
1Division of Geoinformatics, Dept. of Urban Planning and Environment
2Dept. of Transportation Science
KTH Royal Institute of Technology
acpr@kth.se
@Adi Prelipcean
adrianprelipcean.github.io
01 July 2016

2. Outline
This presentation will be about:
1. Travel behaviour
2. Travel diaries and travel diary collection methods
3. Towards an automated travel diary collection system
– First implementation
– Lessons learned
4. MEILI: a travel diary collection, annotation and
automation system
– Improvements over previous attempt
– MEILI architecture and operation flow
– A brief case study
5. Summary and conclusions
2

3. Travel behaviour
How do we use travel behaviour?
Some of the main reasons for analyzing travel behaviour are:
to investigate the reasons and mechanisms that underlie
an individual’s travel decision making process,
3

4. Travel behaviour
How do we use travel behaviour?
Some of the main reasons for analyzing travel behaviour are:
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. Travel behaviour
How do we use travel behaviour?
Some of the main reasons for analyzing travel behaviour are:
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. (Activity) Travel diaries
What are they?
A way of summarizing where, why and how a user traveled
during a defined time frame by specifying:
The destination of a trip
Img: http://soarministries.com/hp_wordpress/wp-content/uploads/2011/08/Destinations-Icon.jpg 4

7. (Activity) Travel diaries
What are they?
A way of summarizing where, why and how a user traveled
during a defined time frame by specifying:
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. (Activity) Travel diaries
What are they?
A way of summarizing where, why and how a user traveled
during a defined time frame by specifying:
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. (Activity) Travel diaries
How to collect them?
Traditionally - Users declare what they have done in a
survey, e.g., PP or CATI
Img: http://www.schoolsurveyexperts.co.uk/i/photos/paper_survey.jpg
5

10. (Activity) Travel diaries
How to collect them?
Traditionally - Users declare what they have done in a
survey, e.g., PP or CATI
New methods - E.g., GPS collection + Web and Mobile
GIS based interaction
5

11. Towards an automated travel diary collection
system
Considerations on an initial attemp
use smartphones to collect GPS data fused with
accelerometer readings
display the collected data via a web interface and let users
annotate their data into travel diaries
store the annotations in a database
6

12. Towards an automated travel diary collection
system
Implementation of the initial attempt
implemented Mobility Collector on Android (Prelipcean et
al. 2014)
7

13. Towards an automated travel diary collection
system
Implementation of the initial attempt
implemented Mobility Collector on Android (Prelipcean et
al. 2014)
implemented a Web Interface for basic user interaction
7

14. Towards an automated travel diary collection
system
Implementation of the initial attempt
implemented Mobility Collector on Android (Prelipcean et
al. 2014)
implemented a Web Interface for basic user interaction
stored the annotations in the database in a point-based
model
7

15. Towards an automated travel diary collection
system
Implementation of the initial attempt
implemented Mobility Collector on Android (Prelipcean et
al. 2014)
implemented a Web Interface for basic user interaction
stored the annotations in the database in a point-based
model
trialed the system on 30 users (working in
transportation)
7

16. Initial attempt
Lessons learned
Mobility Collector is battery efficient
Android only implementation restricts the user pool in
iOS predominant markets (such as Sweden)
users wanted more freedom to interact with their data in
the web interface
difficult to extract trips and triplegs from a point-based
model
difficult to improve the system due to the lack of isolated
functional components
8

17. MEILI
Improvements over the previous attempt
implemented Mobility Collector for iOS
9

18. MEILI
Improvements over the previous attempt
implemented Mobility Collector for iOS
improved the UI / UX based on user feedback and
discussions with UI / UX experts
9

19. MEILI
Improvements over the previous attempt
implemented Mobility Collector for iOS
improved the UI / UX based on user feedback and
discussions with UI / UX experts
9

20. MEILI
Improvements over the previous attempt
implemented Mobility Collector for iOS
improved the UI / UX based on user feedback and
discussions with UI / UX experts
split the system into well defined functional modules
9

21. MEILI
Improvements over the previous attempt
implemented Mobility Collector for iOS
improved the UI / UX based on user feedback and
discussions with UI / UX experts
split the system into well defined functional modules
changed the data model from a point-based model into a
period-based model
9

22. MEILI
Improvements over the previous attempt
implemented Mobility Collector for iOS
improved the UI / UX based on user feedback and
discussions with UI / UX experts
split the system into well defined functional modules
changed the data model from a point-based model into a
period-based model
complemented the web interface operations:
– Create - insertion of trips, triplegs, locations, POIs
– Read - pagination operations between consecutive trips
– Update - update of trips, triplegs, locations, POIs
– Delete - deletion of trips, triplegs, locations, POIs
9

23. MEILI
Architecture and operation flow
10

24. MEILI
Implementation details
Mobility Collector - module for collecting trajectories
fused with accelerometer readings (Android, iOS)
Travel Diary - module for each user to annotate her
collected data into travel diaries (various HTML libraries,
NodeJS, ExpressJS)
Database - module for storing the data collected by
Mobility Collector and the annotations performed via the
Travel Diary (PostgreSQL, PostGIS)
API - module that securely connects the Database
module to the Travel Diary module (NodeJS, ExpressJS)
AI - middleware module that extracts travel diary
semantics from trajectories (custom code)
11

25. MEILI
Case study overview
171 users used MEILI for at least one day
51 users used MEILI for at least one week
collected 2142 trips and 5961 triplegs
schema of 16 different travel modes
schema of 13 different purposes
POI set of 21953 entries in the database
transportation POI set of 6610 entries in the database
12

26. MEILI
Overview of the performed CRUD operations
the two peaks correspond to emails sent to users
13

27. MEILI
Overview of the performed CRUD operations
the two peaks correspond to emails sent to users
the two off-peaks correspond to weekends and lack of data to
annotate
13

28. MEILI
Overview of the performed CRUD operations
the two peaks correspond to emails sent to users
the two off-peaks correspond to weekends and lack of data to
annotate
there is little variability in the distribution of CRUD operations 13

29. MEILI
Distribution of CRUD operations in relation to the time of day
8PM-12AM
4PM-8PM
12PM-4PM
8AM-12PM
4AM-8AM
12AM-4AM
M
ondayTuesday
W
ednesdayThursday
FridaySaturday
Sunday
42% 19% 9% 32% 0% 0% 47%
38% 2% 12% 12% 13% 100% 40%
15% 16% 40% 28% 5% 0% 9%
3% 44% 34% 3% 81% 0% 0%
0% 13% 3% 2% 0% 0% 0%
0% 3% 0% 20% 0% 0% 2%
0
20
40
60
80
100
People interact most with MEILI
– Tuesday to Friday during morning and noon
– Saturday to Monday during evening and night
14

30. MEILI
Response time for operations - Create and Read
0
200
400
600
800
1000
1200
1400
1600
Previous(14.7%
)
N
ext(85.3%
)
ExecutionTime(ms)
Read Operations in MEILI
Operation performed
often
Execution Time
CUD
Non-indexed
Indexed
0
5
10
15
20
D
estination(0.1%
)
Location(0.2%
)PO
I(1.8%
)
Purpose(6.9%
)
Trip(21.7%
)
Transition(34.6%
)
Tripleg(34.6%
)
ExecutionTime(ms)
Create Operations in MEILI
Operations performed
often
Non-indexed
Indexed
Read operations are expensive
Indexing reduces the execution time by an order of
magnitude
Even after indexing, read operations are a bottleneck
15

31. MEILI
Response time for operations - Update and Delete
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6 Trip(44.5%
)
Tripleg(55.5%
)
ExecutionTime(ms)
Delete Operations in MEILI
Both operations are performed
often
Non-indexed
Indexed
0
0.5
1
1.5
2
Location(0.3%
)
PO
I(1.4%
)
Trip(48.6%
)Tripleg(49.7%
)
ExecutionTime(ms)
Update Operations in MEILI
Operations performed
often
Non-indexed
Indexed
Both types of operations are cheap before indexing
Indexing reduces the execution time to nanoseconds
16

32. MEILI
Lessons learned
it is difficult to organize case studies that overlap release
dates of different smartphone OSes
getting feedback from a specific non-representative user
group can be damaging
UX is influenced by the ease of information access in the
MEILI Travel Diary and by the waiting time for each
operation
Read operations (e.g., pagination) are a bottleneck
significant improvements needed for making MEILI Travel
Diary bug-free and intuitive
the methodology for Artificial Intelligence applied in
Transportation Science is underdeveloped
17

33. Summary
introduced MEILI, an open source travel diary collection,
annotation and automation system.
designed MEILI’s architecture in a modular way to isolate
the development process to each module
trialed MEILI for 9 days in Stockholm and studied the
distribution of operations performed by users to identify
peak and off-peak periods
measured the execution times of the MEILI operations to
identify bottlenecks and reduced their impact by applying
various indexing structures
provided a set of valuable lessons learned during multiple
case studies of applying MEILI to collect travel diaries
18

34. Acknowledgements and References
Acknowledgments
This work was partly supported by Trafikverket (Swedish
Transport Administration) under Grant “TRV 2014/10422”.
References
source code for MEILI https://github.com/Badger-MEILI
Mobility Collector - Prelipcean, A. C., Gid´ofalvi, G., & Susilo, Y. O.
(2014). Mobility collector. Journal of Location Based Services,
8(4), 229-255.
a framework for the comparison of travel diary collection systems -
Prelipcean, A. C., Gid´ofalvi, G., & Susilo, Y. O. (2015).
Comparative framework for activity-travel diary collection systems.
In Models and Technologies for Intelligent Transportation Systems
(MT-ITS), 2015 International Conference on (pp. 251-258). IEEE.
on AI performance measures relevant to travel diaries - Prelipcean,
A. C., Gid´ofalvi, G., & Susilo, Y. O. (2016). Measures of transport
mode segmentation of trajectories. International Journal of
Geographical Information Science, 30(9), 1763-1784.
19

35. Thank you for your attention!
Questions and Discussions
Adrian C. Prelipcean
Phd Student
Division of Geoinformatics
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.