transportation mode annotation of tourist gps trajectories under environmental constraints

ENTER 2015 Research Track Slide Number 1

Transportation Mode Annotation of Tourist GPS

Trajectories under Environmental Constraints

Hidekazu Kasahara, Mikihiko Mori,

Masayuki Mukunoki, and Michihiko Minoh

Kyoto University, Japan

[email protected]

http://www.mm.media.kyoto-u.ac.jp/en/members/hidekazu-

kasahara


Agenda

• Motivation

• Purpose

• Previous Work

• Problems

• Proposed Method

• Experiment & Evaluation

• Discussion

• Future Work & Conclusion


Motivation

• DMOs want to know the tourists’ activities.

– Policy making / destination marketing strategy. • Optimum deployment of transportation.

• Outdoor ads / direction boards.

Spot B

Spot ABus

1,000 tourists

Taxi

10 tourists

Ads on bus is more effective

than those on taxi in this route.

Accurate transportation usage

statistics is necessary for DMOs

for the decision making.

The traditional stats is costly.


Purpose of Study

• Annotating transportation mode from tourists’

GPS trajectory (x, y, t).

• Modeling the tourists activity as a chain of

transportation modes.

• Transportation usage statistics is calculated from

these chains of transportation modes.

• In addition, tourists’ personal preferences can be

explored from the chains.

Walk 60 min. Bus 20 min.Bus 20 min.Walk Walk

5 min.Train 20 min.


Scope of Study

• Tourists move by foot and public transportation.

– Train, route bus and taxi as public transportation.

– Tour bus, bicycle, motor-bike and hire car are out of

scope.

• Destinations that include numerous locations of

interest, such as Milano.

• Only GPS.

– Acceleration meter is out of scope.


Previous Work

• Approaches using tourist moving physical features.

– Speed: Decision tree (Aoki 2008, Zheng 2010).

– Baysean network (Stenneth 2011).

– Hierarchical CRF (Liao 2007).

– SVM (Bolbol 2012).

– Acceleration: Adaptive boosting (Hemminki 2013).

– Regression analysis (Furukawa 2014).

• Approach using environmental factors.

– Assumption on typical tourist travel patterns (Stopher 2008).

– Bus stops and road network (Liao 2007).

– Land utilization situation (Yan 2013).


Transportation Mode Estimation

using Tourist Speed

Yellow points shows

deceleration at bus stops

and crossings.

Station

Temple

Castle

Train

Golden Pagoda

Yellow: Under 1km/hour

Black : 1-60 km/hour

Red : Over 60 km/hour

These decelerations are

estimated as walk / retain

modes using by speed.

GPS Trajectory

1km


Annotation using

Environmental Constraints

bus bus train train train bus

p1p2 p3

p4p5 p6

Estimate using Tourist Features

(Speed)

• In physical space, there are

some environmental constraints

restrict transportation usage.

– Environmental constraints.


Annotation using


Environmental Constraints :

All 6 Points Are on a Bus Route

Not on a railway.


(Speed)




– Environmental constraints.bus bus train train train bus

p1p2 p3

p4p5 p6


Annotation using


Environmental Constraints :

All 6 Points Are on a Bus Route

Not on a railway.


(Speed)Inconsistency




– Environmental constraints.

• In the case when the estimation

with tourist moving features is

failed, there is regional

inconsistency with

environmental constraints.

bus bus train train train bus

p1p2 p3

p4p5 p6


Problem

• The system failed to estimate car and train

as retain or walk modes when they

decelerate or stop in case the system use

only tourist features (speed).

• Examples of deceleration:

– Route bus decelerates at all bus stop along the

route.

– Train decelerates at stations on the railways.


Proposed Method

• Using the environmental constraints,

decrease the inconsistency between the

estimate using tourist features (speed) and

environmental constraints.

• Annotate the transportation modes with

the least inconsistency in total.

• We evaluate the proposed methods

compared with human labeling.


Proposed Method

Output : GPS Trajectory Consists of (x, y, time, speed)

• Environmental

constraints

• SpeedOutput: Fragmented Segments

Output: Merged Segments with the least inconsistency in total

• Interleave

• Continuity

• Filtering

• Speed calculation

Technique

��

��

��

��

Preprocess

Stage 1

Stage 2


Preprocess & Stage #1(x, y, time) : GPS raw data

(x, y, time, speed)

Filtering & speed calculation

On Train Route

On Bus Route

On Motorway

On Pedestrian area

(x, y, time, speed, mode, segment)

speed > sp_train

Speed Environmental Constraints

sp_walk < speed

speed > sp_walk

and

and

…Stop frequency


#2 Stage: Segment Merger

• Interleave Assumption:

Tourists do not change

“train to car” or

“car to train” directly.

• Continuity Assumption:

Tourists typically do not

change the transportation

mode over a short period.

Bus

Taxi

Walk

Bus

Never Directly

Connected

Too short to change


Experiment

• Dataset

– 16 persons’ GPS trajectory data from students

and teachers who travelled.

• Destination : Kyoto

• 160,130 points in total.

• GPS measures location per 1 second.

• From 8:00 to 18:00.


Screening Value

• sp_walk = 2 metres per second.

– The screening value between walk and bus/taxi.

• sp_train = 15 metres per second.

– The screening value between bus/taxi and train.

• �� = 10%

– The screening value of stop frequency between bus

and taxi. (Route bus stops more frequent than taxi.)

• Interleave = 240 seconds

– If the length of segment is under 240 seconds, the

segment is merged to next or before segment


Environmental Constraints• Data is supplied as shape files.

Bus Route

(Gov. data)

Train Route(COTS)

Motorway(COTS)

Pedestrian Zone

(Gov. data)


Results of the Experiment

Bus Taxi Train WalkTotal

count

Bus82.8%

(15,601)

17.5%

(1,413)

0.0%

(0)

4.5%

(5,817) (22,831)

Taxi6.1%

(1,158)

60.1%

(4,847)

10.6%

(529)

0.3%

(371)(6,905)

Train0.2%

(30)

0.3%

(21)

77.3%

(3,869)

1.3%

(1,620)(5,540)

Walk10.9%

(2,048)

22.1%

(1,779)

12.2%

(609)

93.9%

(120,418)(124,854)

Total

count

100.0%

(18,837)

100.0%

(8,060)

100.0%

(5,007)

100.0%

(128,226)

90.4%

(160,130)

Estimate

Actual


Comparison


Proposed 82.8% 60.1% 77.3% 93.9% 90.4%

SVM

*1) 88.9 % 78.9% 67.1% 67.7% 75.2%

Speed without

environmental

constraints

*2)*3)

44.3% 2.5% 49.2% 99.1% 86.2%

Method

Mode

*1) Result of 5-fold cross validation on the same data set

*2) Bus route constraints are used for bus / taxi estimation.

*3) The universe is biased to “walk” mode.


Discussion

• Automatic estimation

– The screening values are given by human in the

proposed method.

• Generalization ability is low.

• These values should be decided automatically.

– Stochastic function.

• Function can be estimated

from training data.

• Stochastic function varies

in different environments 0%

20%

40%

60%

80%

100%

Mode Probability Function

Walk Tran Taxi Bus


Discussion

• Context among primitive components

– Transportation modes consist of three primitive

components; “stable speed move”, “acceleration

(deceleration)” and “stop.”

– We call context to the relationship between

consecutive segments of each primitive component.

– We should consider the context for estimation.

– We have two assumptions of context in this study and

give some screening values. However, these should be

automatically decided by using training data.


Discussion

• Distinction of bus and taxi is difficult

– Only using speed, this system cannot distinguish

between taxi and bus accurately.

• Other environmental constraints is necessary.

– Other environmental constraints is necessary.

• Other environmental constraints

– Traffic confusion.

– Bus stop location.


Conclusion & Future Work

• We proposed a new GPS semantic annotation method

using environmental constraints based on two

assumptions of tourist behaviour.

• The results indicate the high accuracy, 90%.

• Making model based on stochastic logic.

– Improving generalization capability of the method.

• By using other environmental constraints, we try to

improve the performance in future.


Appendix


Transportation Mode Annotation

using Tourist Features

Annotation using

tourist’s speed

is valid for retain /

walk modes.

Golden Pagoda

Blue : 10-60km/hour

Yellow: Under 1km/hour

Green : 1-10 km/hour

In this area, system

estimated retain /

walk / bus modes.

GPS Trajectory


Transportation Mode Estimation

from GPS Trajectory

Walk 60 min. Bus 20 min.

Chain of Transportation

Modes Bus 20 min.

Bus

GPS TrajectoryWalk

Temple


What’s school trip?School trip is one of the biggest group tours in Japan

• School trip is important for DMOs and travel agents• 10% of all stayed tourists in Kyoto 2012 is school trip

students

• The number of students who participated school trip in

2012 is 3.4 million

• Participation rate of students is high• 94.4% of junior high schools /75.5% of senior high schools

Unit : million


Escort-teachers

Field HQ (HOTEL)

School staff

School(Home)

School master

Real-time monitoring

Current position

Smartphone(Group leader carries)

Trajectory

Tablet/PC

- GPS & Wi-Fi positioning

Overview of safety ensuring systemETSS (Educational Tour Support System)

Student groups

Information Sharing among All Related Persons

Group Leader(Student. Trained before trip.)

- Mail & Voice

Safety ensuring

No NavigationFor education

System

Tablet


Screenshot of student phone

Schoolmaster

Homeroom

teacher

Voice & MailDisaster MapNormal Map

School NameNow 1 hr All

Eva

cu

atio

n A

re

a

Ob

serv

ing

Att

racti

on

s

Current Position Evacuation Area MapNormal MapNormal Map


Evacuation mapNetwork DisconnectedNetwork Connected


Safety ensuring by mail

No Problem

Injured

Illness

Stray

Lost Matter

Late

Other

Mr. A lost his way in Ginkaku-ji

Send Mail

Title : SafetyConfirmation

Are you all right?Tell me your status.

No Problem

In Trouble

Safety ConfirmStatus Report

In case of

trouble


Outline of service specification

Before trip� Planning in advance

- Display the observing attractions on the map.

During trip

Normal

Situation

� Monitoring the students position.

- Track real-time students’ position. (per 1 second)

- Send students’ position to the server. (per 30 seconds)

- Store students’ trajectories in the remote server. (Tokyo)

� Visualization of student trajectories.

- Indicate students’ current position and moving history.

Disaster

Situaion

� Visualization of evacuation areas.

- Display the evacuation areas near position of students/teachers.

- Keep map display in case of the wireless network disconnection.

� Voice & mail communication

- Broadcast confirmation mail to students from teachers.

- VoIP call among the permitted users.


GPS data

Date&Time Latitude Longitude Accuracy Provider BatteryLevel

2013/12/13 8:16 34.66693997 135.4960799 0GPS 86

2013/12/13 8:16 34.66884434 135.4969159 37GPS 86

2013/12/13 8:16 34.66915648 135.4967177 52GPS 86

2013/12/13 8:16 34.66908064 135.4967123 57GPS 86

2013/12/13 8:16 34.66900876 135.4968523 47GPS 86

2013/12/13 8:16 34.6692168 135.4962441 48GPS 86

2013/12/13 8:16 34.668968 135.496233 52GPS 86

2013/12/13 8:16 34.66880131 135.4963071 52GPS 86

2013/12/13 8:16 34.66868704 135.4963497 49GPS 86

2013/12/13 8:16 34.66827182 135.4963885 47GPS 86

2013/12/13 8:16 34.66789418 135.4962628 35GPS 86

2013/12/13 8:16 34.66777089 135.4962316 32GPS 86

2013/12/13 8:17 34.66764224 135.4961837 26GPS 86

2013/12/13 8:17 34.66750577 135.4961392 21GPS 86

2013/12/13 8:17 34.66743723 135.49611 21GPS 86

2013/12/13 8:17 34.66739099 135.496083 20GPS 86

2013/12/13 8:17 34.66731841 135.4960461 19GPS 86

2013/12/13 8:17 34.66729817 135.4960326 19GPS 86

2013/12/13 8:17 34.6673011 135.4960286 18GPS 86

2013/12/13 8:17 34.66731857 135.4960296 18GPS 86

2013/12/13 8:17 34.66732602 135.4960259 18GPS 86

2013/12/13 8:17 34.66732767 135.4960237 15GPS 86

2013/12/13 8:17 34.66732837 135.4960193 14GPS 86

2013/12/13 8:17 34.66732553 135.496015 14GPS 86

2013/12/13 8:17 34.66732211 135.4960096 14GPS 86

2013/12/13 8:17 34.66732243 135.4960029 14GPS 86

2013/12/13 8:17 34.66732497 135.4959998 14GPS 86

2013/12/13 8:17 34.66733029 135.4959973 14GPS 86

2013/12/13 8:17 34.66734217 135.4959961 14GPS 86


Comparison Experiment

without Environmental Constraints


count

Bus44.3%

(8,353)

41.8%

(3,372)

1.7%

(83)

0.2%

(212) (22,831)

Taxi1.6%

(303)

2.5%

(198)

19.3%

(964)

0.3%

(333)(6,905)

Train0%

(0)

0.3%

(21)

49.2%

(2,463)

0.5%

(598)(5,540)

Walk54.0%

(10,181)

55.4%

(4,469)

29.9%

(1,497)

99.1%

(127,083)(124,854)

Total

count

100.0%

(18,837)

100.0%

(8,060)

100.0%

(5,007)

100.0%

(128,226)

86.2%

(160,130)

Estimate

Actual

*) Bus route constraints are used for bus / taxi estimation.

*) The universe is biased to “walk” mode.