Automated and Adaptive Activity-Travel Survey
using Online Interaction with Travelers

Toru SEO
Postdoctoral Researcher, Asakura Lab., Tokyo Institute of Technology
Joint work with Dr. Kusakabe and Prof. Asakura
The 7th International BinN Seminar @ UTokyo
20160925

Introduction

Methodology

Empirical Validation

Conclusion

Travel behavior survey2

 Travel behavior data is essential for activity/travel
behavior modelingtraveler id

trip idorigindestination

mode

departure time ⋯1

1

home

office

train

8:30

1

2

officeshopwalk

17:00

2

1

home

restaurantcar

11:30⋮ Actual activity/travel data is often collected byconducting a survey– realistic compared to stated preference (virtual) dataIntroduction

Methodology

Empirical Validation

Conclusion

Conventional survey methods

Questionary survey
 Travel data is collected by paper- or web-based questionary

 Limitations:– burden for survey participants: where did you go? when? how?
why? with whom? etc.
– inaccurate due to incomplete memory→Long-term, large-scale, and accurate data collection is difficult(panel attrition, fatigue)

3

Introduction

Methodology

Empirical Validation

Conclusion

Conventional survey methods
Time4source: Asakura and Hato (2004)

staying = some activity
The activity type is unknown

West ↔ East

GPS data collection
 accurate spatiotemporal data
 Activity type (trip purpose) is missing

movingIntroduction

Methodology

Empirical Validation

Conclusion

Conventional survey methods

5Timesurvey sheet

web/app interface

staying+The activity type is asked byquestionary survey

West ↔ East

GPS data collection + questionary survey
 accurate spatiotemporal data relatively accurate activity data
– GPS log can help memory recalling burden for participants– Extensive manual input is still mandatory

Introduction

Methodology

Empirical Validation

Conclusion

Existing advanced survey methods
The place is shop↓
The activity
must be shopping

Timeland use/facility info.
𝑒 𝑉𝑖𝑗𝑒staying+Existing behavior model predicts
that he will go shopping↓
It must be shopping

𝑉𝑗calibrated model

6

West ↔ East

GPS data collection + imputation based on a priori info.
 Automatic imputation based on a priori information (eg: Wolf et al., 2001;
Shen and Stopher, 2013; Gong et al., 2013)– land use– behavior model calibrated by using existing data Collected data may have several limitation– Traveler heterogeneity is ignored– The data is not suitable for behavior modeling purposesIntroduction

Methodology

Empirical Validation

Conclusion

Existing advanced survey methods

7

Time

staying

calibrated model
based on preliminarysurvey+

During the preliminary survey,
he went there for shopping↓
It must be shoppingWest ↔ East

GPS data collection + imputation based on offline info.
(Kim, Ben-Akiva, et al., 2014, 2015)

 Automatic imputation based on preliminary survey– Data is collected by preliminary survey for the sameparticipants– Traveler heterogeneity can be captured

Introduction

Methodology

Empirical Validation

Conclusion

Proposed survey methodTime

If there is no clue,
the system asks a question↓
answer: shopping

online updatemodel

8

staying+

According to the up-to-date model,
it seems shopping↓
I must be shoppingWest ↔ East

GPS data collection + imputation based on online info. (Kusakabe et al., 2015; Seo et al., 2016)
 Automatic imputation based on data collected from previous online interaction– online interaction: the survey system will ask a question automatically anddynamically

Possible features of the proposed method:–––––

Reducing frequency of questions
Keeping quality of data highConsidering traveler heterogeneity
Adaptive tracking of behavioral change
Automatic process (No need of manual control by survey administrator)

Introduction

Methodology

Empirical Validation

Conclusion

Illustration of methods
traveler

9
GPS + online info.

Manually
collected

GPS + a priori info.

AutomaticallyestimatedManually collected+
AutomaticallyestimatedLearning process
can differ

Learning process can track behavioral change
GPS + offline info.

Manually
collected
Questionary survey

AutomaticallyestimatedGPS survey

Manuallycollected

AutomaticallyestimatedtimeActivity detected
Notation

Collect activity situation
dataProcess
Input byparticipant[Estimation] Estimate activity
type using estimation model

Decision[Estimation] Calculate
estimation confidence[Interaction]
Determine whether ask aquestion or notNoStore the estimation result

Yes[Interaction] Ask activity type

[Interaction] Participant
answers the activity type𝑐 = argmax𝑐 𝑃(𝑐|𝑌)𝑐 = argmax𝑐
𝑘𝑐1 , 𝑌1 , 𝑐2 , 𝑌2 , … 𝑐𝑖 , 𝑌𝑖 , …

[Learning] Update estimation
model based on the answer
Store the answer

Methodology

End of routine for thisactivity

𝑃 𝑥𝑘 𝑐 𝑃(𝑐)𝑃(𝑐 |𝑌)Introduction

Methodology

Empirical Validation

Conclusion

Concept11

 The proposed method estimates activity type
 The system always measures activity situation using standard sensors– date, time, location
 The system detects occurrence of an activity– move-or-stay identification
 The system can ask a question about activity to the survey participants

What are you doing now?
• date• time• location

Are you working now?

Introduction

Methodology

Empirical Validation

Conclusion

Concept12

 The system can ask a question, if the system cannot estimate the activity
 The system learns a traveler-specific behavior pattern The system runs automaticallyDay 1, 9am, home to office

What are you doing?
Day 2, 9am,
home to officeThis must be
commuting.

Commuting!
Good. It stops
bothering me.

Day 3, 9am, home to somewhere else

What are you doing?
Enjoying my vacation!

12

Introduction

Methodology

Empirical Validation

Conclusion

Overview

13

Detect activity 𝑖 of traveler 𝑗 Activity detection phase– Detects the survey participant staying
somewhere to do unknown activity– out of scope of this study

Collect activity situation
data automatically[Estimation] Estimate activity
type using estimation model Estimation phase– Estimates the activity type using an
traveler-specific estimation model[Estimation] Calculate
estimation confidence[Interaction]
Determine whether aska question or notNo

 Interaction phase– Will ask activity type to the survey
participant, if the estimation is notconfident– Will not ask the question, if theestimation is confident

Yes[Interaction] Ask activity type
[Interaction] Participant
answers actual activity type[Learning] Update estimation
model based on the answer
Store the answer
End of routine for thisactivity

Store theestimation
result Learning phase– Updates the estimation model basedon the participant’s answer

Introduction

Methodology

Empirical Validation

Conclusion

Estimation phase
Detect activity 𝑖 of traveler 𝑗 Activity type estimation problem:𝑐 = argmax𝑐 𝑃(𝑐|𝑌)
where

Collect activity situation
data automatically[Estimation] Estimate activity
type using estimation model– activity type: 𝑐eg: work, leisure[Estimation] Calculate
estimation confidence[Interaction]
Determine whether ask
a question or not– activity situation: 𝑌 = (𝑥1 , 𝑥2 , … )No[Interaction] Ask activity type𝑐 = argmax𝑐[Interaction] Participantanswers actual activity type𝑃 𝑥𝑘 𝑐 𝑃(𝑐)𝑘[Learning] Update estimation
model based on the answer

End of routine for thisactivity

eg: time: 𝑥1 , location: 𝑥2

 Naive Bayes assumption:

YesStore the answer

14Store theestimation
result 𝑃(𝑥𝑘 |𝑐) and 𝑃(𝑐) are calculated
based on historical data
= learning

Introduction

Methodology

Empirical Validation

Conclusion

Learning phase
Detect activity 𝑖 of traveler 𝑗15
 Historical data:

Collect activity situation
data automatically

𝐻traveler,time =𝑐1 , 𝑌1 , 𝑐2 , 𝑌2 , … 𝑐𝑖 , 𝑌𝑖 , …

– traveler-specific– dynamically updated[Estimation] Estimate activity
type using estimation model[Estimation] Calculate
estimation confidence[Interaction]
Determine whether ask
a question or notNoYes

 𝑃(𝑥𝑘 |𝑐) and 𝑃(𝑐) can be easily
calculated based on thehistorical data

[Interaction] Ask activity type
[Interaction] Participant
answers actual activity type[Learning] Update estimation
model based on the answer
Store the answer
End of routine for thisactivity

Store theestimation
result How to collect the historicaldata?
→ online interaction between
the system and participant

Introduction

Methodology

Empirical Validation

Conclusion

Interaction phase
Detect activity 𝑖 of traveler 𝑗Collect activity situation
data automatically[Estimation] Estimate activity
type using estimation modelNo

[Interaction] Ask activity type[Interaction] Participant
answers actual activity type[Learning] Update estimation
model based on the answer

End of routine for thisactivity– If estimation confidence is high enough, theestimation result will be stored as a survey
result– Otherwise, the system will ask the actual
activity to the survey participant

The answer is stored to historical data for learning, as
well as a survey result

Estimation confidence:𝑃(𝑐 |𝑌)– probability of performing activity 𝑐 undersituation 𝑌– easily calculated based on historical dataYes

Store the answer

Interaction:•[Estimation] Calculate
estimation confidence[Interaction]
Determine whether ask
a question or not

16

The system will ask question with certain
probability 𝑝𝑞 so that expected error rate will
be equal to a given acceptable error rate 𝑝𝑎– 𝑝𝑎 is given by the survey plannerStore theestimation
resulteg: 5%

Introduction

Methodology

Empirical Validation

Conclusion

Summary17

estimationlaborsaving
automatic
imputation
without asking

questionasked
– It makes long-term survey easier

composition ratio

The survey admin will be happy if Wrong issmall
The survey participants will be happy if
Question is small

accurate

correct
efficient

wrong

Question

Correct

Wrong≃ 𝑝𝑎 

The acceptable error rate 𝑝𝑎 is given byadmin– quality control– trade-off: Wrong↓ ⇔ Question↑

Estimation model keeps being updated
during the entire survey period, for eachtravelers (=online)– At the initial stage, the estimation model isdumb•

Question will be frequent•

Question will decrease; Correct will increase– As the survey progresses, the estimation
model will become accurate

Wrong

Wrong

sudden behavioral change(eg: vacation)

day– Long-term behavioral change can betracked– Traveler heterogeneity can be captured

composition ratio

Empirical Validation

Question
CorrectWrongIntroduction

Methodology

Empirical Validation

Conclusion

Data19 The proposed method is validated by using existing travel
survey data
– collected by GPS + questionary survey

 Validation procedure
1. Suppose that the survey data is true (ground truth)
2. Emulate the proposed method
3. Compare the estimation result with the ground truth
Date

Dec 17-30, 2007
• duration: 2 weeks

Location

Matsuyama city, Japan

Number of participants

92Number of trips4120• 3.5 trips/person/day

Activity type(trip purpose)

commuting, returning home, business,
shopping, food/leisure, others

Activity situation

weekday dummy, arrival time, location

Introduction

Methodology

Empirical Validation

Conclusion

Result20



For weekdays, Question decreases and Correct increases as time progresses

Wrong is almost constant at 5%Commuting and returning home trips were easier to be estimated– 37% Correct on the 12th day– Number of Question was almost halved– equal to the given acceptable error rate 𝑝𝑎

composition ratioweekend/holiday

Question
59%
Correct

37%

Wrong
5%

Conclusion

Introduction

Methodology

Empirical Validation

Conclusion

Achievements

22 Travel behavior data collection method is proposed– for long-term and large-scale survey
 Features:– reduced frequency of questions– guaranteed quality of data
– considers traveler heterogeneity– adaptive tracking of behavioral change– automatic process (no need of manual control by survey administrator)
 Methodology:– machine learning based on online interaction between the survey systemand participant
 The proposed method is validated using existing travel data
– It almost halved the frequency of questions on the 12th day• It may be reduced further if the survey period is longer– The error ratio was 5% as intended

Introduction

Methodology

Empirical Validation

References

Conclusion

23

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