MEILI: a travel diary collection, annotation

and automation system

A. C. Prelipcean1, G. Gidofalvi1, Y. Susilo2

1Division of Geoinformatics, Dept. of Urban Planning and Environment2Dept. of Transportation Science

KTH Royal Institute of Technology

acpr@kth.se

@Adi Prelipcean

adrianprelipcean.github.io

01 July 2016

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 andautomation system

– Improvements over previous attempt– MEILI architecture and operation flow– A brief case study

5. Summary and conclusions

2

Travel behaviour

How do we use travel behaviour?

Some of the main reasons for analyzing travel behaviour are:

I to investigate the reasons and mechanisms that underliean individual’s travel decision making process,

I to predict the effect of implementing new transportationpolicies or changing the transportation infrastructure, or

I to understand the dynamic of transportation movementwithin study areas.

3

Travel behaviour

How do we use travel behaviour?

Some of the main reasons for analyzing travel behaviour are:

I to investigate the reasons and mechanisms that underliean individual’s travel decision making process,

I to predict the effect of implementing new transportationpolicies or changing the transportation infrastructure, or

I to understand the dynamic of transportation movementwithin study areas.

3

Travel behaviour

How do we use travel behaviour?

Some of the main reasons for analyzing travel behaviour are:

I to investigate the reasons and mechanisms that underliean individual’s travel decision making process,

I to predict the effect of implementing new transportationpolicies or changing the transportation infrastructure, or

I to understand the dynamic of transportation movementwithin study areas.

3

(Activity) Travel diaries

What are they?

A way of summarizing where, why and how a user traveledduring a defined time frame by specifying:

I The destination of a trip

I The trip’s purposeI The means of transportation, i.e., trip legs

Img: http://soarministries.com/hp_wordpress/wp-content/uploads/2011/08/Destinations-Icon.jpg 4

(Activity) Travel diaries

What are they?

A way of summarizing where, why and how a user traveledduring a defined time frame by specifying:

I The destination of a tripI The trip’s purpose

I The means of transportation, i.e., trip legs

Img: https://cdn2.vox-cdn.com/thumbor/93Yaxs7y3Tb8tzFfppyRsSn_yN8=/1020x0/cdn0.vox-cdn.com/uploads/chorus_asset/file/2509782/confused_man.0.jpg

4

(Activity) Travel diaries

What are they?

A way of summarizing where, why and how a user traveledduring a defined time frame by specifying:

I The destination of a tripI The trip’s purposeI The means of transportation, i.e., trip legs

Img: https://d3ui957tjb5bqd.cloudfront.net/images/screenshots/products/4/42/42990/white-transportation-icons-300x200.jpg

4

(Activity) Travel diaries

How to collect them?

I Traditionally - Users declare what they have done in asurvey, e.g., PP or CATI

I New methods - E.g., GPS collection + Web and MobileGIS based interaction

Img: http://www.schoolsurveyexperts.co.uk/i/photos/paper_survey.jpg

5

(Activity) Travel diaries

How to collect them?

I Traditionally - Users declare what they have done in asurvey, e.g., PP or CATI

I New methods - E.g., GPS collection + Web and MobileGIS based interaction

5

Towards an automated travel diary collection

system

Considerations on an initial attemp

I use smartphones to collect GPS data fused withaccelerometer readings

I display the collected data via a web interface and let usersannotate their data into travel diaries

I store the annotations in a database

6

Towards an automated travel diary collection

system

Implementation of the initial attemptI implemented Mobility Collector on Android (Prelipcean et

al. 2014)

I implemented a Web Interface for basic user interactionI stored the annotations in the database in a point-based

modelI trialed the system on 30 users (working in

transportation)

7

Towards an automated travel diary collection

system

Implementation of the initial attemptI implemented Mobility Collector on Android (Prelipcean et

al. 2014)I implemented a Web Interface for basic user interaction

I stored the annotations in the database in a point-basedmodel

I trialed the system on 30 users (working intransportation)

7

Towards an automated travel diary collection

system

Implementation of the initial attempt

I implemented Mobility Collector on Android (Prelipcean etal. 2014)

I implemented a Web Interface for basic user interaction

I stored the annotations in the database in a point-basedmodel

I trialed the system on 30 users (working intransportation)

7

Towards an automated travel diary collection

system

Implementation of the initial attempt

I implemented Mobility Collector on Android (Prelipcean etal. 2014)

I implemented a Web Interface for basic user interaction

I stored the annotations in the database in a point-basedmodel

I trialed the system on 30 users (working intransportation)

7

Initial attempt

Lessons learned

I Mobility Collector is battery efficient

I Android only implementation restricts the user pool iniOS predominant markets (such as Sweden)

I users wanted more freedom to interact with their data inthe web interface

I difficult to extract trips and triplegs from a point-basedmodel

I difficult to improve the system due to the lack of isolatedfunctional components

8

MEILI

Improvements over the previous attempt

I implemented Mobility Collector for iOS

I improved the UI / UX based on user feedback anddiscussions with UI / UX experts

I split the system into well defined functional modules

I changed the data model from a point-based model into aperiod-based model

I 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

MEILI

Improvements over the previous attempt

I implemented Mobility Collector for iOSI improved the UI / UX based on user feedback and

discussions with UI / UX experts

I split the system into well defined functional modulesI changed the data model from a point-based model into a

period-based modelI 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

MEILI

Improvements over the previous attempt

I implemented Mobility Collector for iOSI improved the UI / UX based on user feedback and

discussions with UI / UX experts

I split the system into well defined functional modulesI changed the data model from a point-based model into a

period-based modelI 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

MEILI

Improvements over the previous attempt

I implemented Mobility Collector for iOS

I improved the UI / UX based on user feedback anddiscussions with UI / UX experts

I split the system into well defined functional modules

I changed the data model from a point-based model into aperiod-based model

I 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

MEILI

Improvements over the previous attempt

I implemented Mobility Collector for iOS

I improved the UI / UX based on user feedback anddiscussions with UI / UX experts

I split the system into well defined functional modules

I changed the data model from a point-based model into aperiod-based model

I 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

MEILI

Improvements over the previous attempt

I implemented Mobility Collector for iOS

I improved the UI / UX based on user feedback anddiscussions with UI / UX experts

I split the system into well defined functional modules

I changed the data model from a point-based model into aperiod-based model

I 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

MEILI

Architecture and operation flow

10

MEILI

Implementation details

I Mobility Collector - module for collecting trajectoriesfused with accelerometer readings (Android, iOS)

I Travel Diary - module for each user to annotate hercollected data into travel diaries (various HTML libraries,NodeJS, ExpressJS)

I Database - module for storing the data collected byMobility Collector and the annotations performed via theTravel Diary (PostgreSQL, PostGIS)

I API - module that securely connects the Databasemodule to the Travel Diary module (NodeJS, ExpressJS)

I AI - middleware module that extracts travel diarysemantics from trajectories (custom code)

11

MEILI

Case study overview

I 171 users used MEILI for at least one day

I 51 users used MEILI for at least one week

I collected 2142 trips and 5961 triplegs

I schema of 16 different travel modes

I schema of 13 different purposes

I POI set of 21953 entries in the database

I transportation POI set of 6610 entries in the database

12

MEILI

Overview of the performed CRUD operations

I the two peaks correspond to emails sent to users

I the two off-peaks correspond to weekends and lack of data toannotate

I there is little variability in the distribution of CRUD operations

13

MEILI

Overview of the performed CRUD operations

I the two peaks correspond to emails sent to usersI the two off-peaks correspond to weekends and lack of data to

annotate

I there is little variability in the distribution of CRUD operations

13

MEILI

Overview of the performed CRUD operations

I the two peaks correspond to emails sent to usersI the two off-peaks correspond to weekends and lack of data to

annotateI there is little variability in the distribution of CRUD operations 13

MEILI

Distribution of CRUD operations in relation to the time of day

8PM-12AM

4PM-8PM

12PM-4PM

8AM-12PM

4AM-8AM

12AM-4AM

Mon

day

Tuesd

ay

Wed

nesd

ay

Thurs

day

Friday

Satur

day

Sunda

y

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

I People interact most with MEILI– Tuesday to Friday during morning and noon– Saturday to Monday during evening and night

14

MEILI

Response time for operations - Create and Read

0

200

400

600

800

1000

1200

1400

1600

Previou

s(14

.7%

)

Nex

t(85.

3%)

Exe

cu

tio

n T

ime

(m

s)

Read Operations in MEILI

Operation performed often

Execution Time CUD

Non-indexedIndexed

0

5

10

15

20

Des

tinat

ion(

0.1%

)

Loca

tion(

0.2%

)

POI(1

.8%

)

Purpo

se(6

.9%

)

Trip(2

1.7%

)

Trans

ition

(34.

6%)

Tripleg(

34.6

%)

Exe

cu

tio

n T

ime

(m

s)

Create Operations in MEILI

Operations performed often

Non-indexedIndexed

I Read operations are expensive

I Indexing reduces the execution time by an order ofmagnitude

I Even after indexing, read operations are a bottleneck

15

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(4

4.5%

)

Tripleg(

55.5

%)

Exe

cu

tio

n T

ime

(m

s)

Delete Operations in MEILI

Both operations are performed often

Non-indexedIndexed

0

0.5

1

1.5

2

Loca

tion(

0.3%

)

POI(1

.4%

)

Trip(4

8.6%

)

Tripleg(

49.7

%)

Exe

cu

tio

n T

ime

(m

s)

Update Operations in MEILI

Operations performed often

Non-indexedIndexed

I Both types of operations are cheap before indexing

I Indexing reduces the execution time to nanoseconds

16

MEILI

Lessons learned

I it is difficult to organize case studies that overlap releasedates of different smartphone OSes

I getting feedback from a specific non-representative usergroup can be damaging

I UX is influenced by the ease of information access in theMEILI Travel Diary and by the waiting time for eachoperation

I Read operations (e.g., pagination) are a bottleneck

I significant improvements needed for making MEILI TravelDiary bug-free and intuitive

I the methodology for Artificial Intelligence applied inTransportation Science is underdeveloped

17

SummaryI introduced MEILI, an open source travel diary collection,

annotation and automation system.

I designed MEILI’s architecture in a modular way to isolatethe development process to each module

I trialed MEILI for 9 days in Stockholm and studied thedistribution of operations performed by users to identifypeak and off-peak periods

I measured the execution times of the MEILI operations toidentify bottlenecks and reduced their impact by applyingvarious indexing structures

I provided a set of valuable lessons learned during multiplecase studies of applying MEILI to collect travel diaries

18

Acknowledgements and References

AcknowledgmentsThis work was partly supported by Trafikverket (SwedishTransport Administration) under Grant “TRV 2014/10422”.

ReferencesI source code for MEILI https://github.com/Badger-MEILII Mobility Collector - Prelipcean, A. C., Gidofalvi, G., & Susilo, Y. O.

(2014). Mobility collector. Journal of Location Based Services,8(4), 229-255.

I a framework for the comparison of travel diary collection systems -Prelipcean, A. C., Gidofalvi, 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.

I on AI performance measures relevant to travel diaries - Prelipcean,A. C., Gidofalvi, G., & Susilo, Y. O. (2016). Measures of transportmode segmentation of trajectories. International Journal ofGeographical Information Science, 30(9), 1763-1784.

19

Thank you for your attention!Questions and Discussions

Adrian C. PrelipceanPhd StudentDivision of GeoinformaticsKTH, Royal Institute of Technologyhttp://adrianprelipcean.github.io/acpr@kth.se@Adi Prelipcean

This work is licensed under a Creative Commons Attribution-ShareAlike 3.0 Unported License.