Lets Not Stare at Smartphones while Walking: Memorable Route Recommendation by Detecting Effective...

LetsNotStareatSmartphoneswhileWalking:MemorableRouteRecommendationby

DetectingEffectiveLandmarks

ShokoWakamiya1,HiroshiKawasaki2,YukikoKawai3,AdamJatowt4,Eiji Aramaki1,Toyokazu Akiyama3

1NaraInstituteofScienceandTechnology2KagoshimaUniversity3KyotoSangyoUniversity4KyotoUniversity

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Sept.16,2016

IfEyeballingMapsonSmartphoneswhileWalking…

Conventionalnavigationsystems• maybeuseful• leadtounsafesituations• cannotbeusedincasewhen– GPSisnotavailableor– usersaren’tabletouseanyelectronicdevices

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Minimuminfo.ispreferredAsmallnumberofnavigationinstructionsispreferred,

asbeingeasytobememorizedandrequiringlownumberofmapreferences

Pro.

Con.

UsingTwoTypesofLandmarksforRouteNavigationItismorenaturalandefficienttomemorizelandmarksthandetailedrouteinstructions– Locallandmarks

Toidentifyusers’locations(e.g., gasstation,postoffice)

– GloballandmarksToindicateapproximatedirections(e.g., high-risebuildings,towers)

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ChristtheRedeemer(globallandmark)

Postbank(locallandmark)

Difficulttousethesetwotypesoflandmarkstogetherduetotheirdifferentcharacteristicssuchasvisibilityandpositionidentification

Local,Global,andLinelandmarkse.g.,“Walktowardthebuilding toamainstreet.Thengostraightalongthestreetandgotowardthetoweratthecornerofthecafe.”

UsingThreeTypesofLandmarksforRouteNavigation

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Destination

building

tower

Iheadtowardthebuildingfreelyuntilamainstreet

Cafe

Goal&Approach:LandmarkNaviToconstructsimpleandintuitivenavigationroutes– Tomeasurelandmark’sutilitybasedontwoaspects:

• Visibilityusing3Dgeographicdata• SocialrecognitionusingSNSdata

– Todetectpoint,area,andlinelandmarks

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http://www.ibe.kagoshima-u.ac.jp/~lmnavi/index_en.html

Building(visiblefromfaraway)

PopularBuilding Crowdedstreet

River

Popularcafe

Convenience store

Point(Local) Area(Global) Line

Outline

• Background• GoalandApproach• Proposedmethod– Landmarkextraction– Routegraphconstruction

• ExperimentsandEvaluation• ConclusionandFuturework

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MeasuringLandmark’sUtility

Road network

p =<pID,name,coordinates,catID,checkins,users>

PlacesfromFoursquare

t =<tweetID,userID,content,timestamp,coordinates>

TweetsfromTwitter

Geographicdata SNSdata 7

VisitpopularityDirectvisibility Indirectvisibility

Buildings

Roads&intersections

Ground

VisitPopularityofPlaces

• Toextractpopularplacesascandidatesofpointandarealandmarks

• Measuredbasedonthenumberofuserscheckedineachplace

8GeographicdistributionofplacesinSanFrancisco

p =<pID,name,coordinates,catID,checkins,users>

Schloss Heidelbergisgreat

I’matSchloss Heidelberg

I’matSchloss Heidelberg VeryniceviewfromSchloss Heidelberg

IndirectVisibilityofPlaces

• Tofindplacesthatmaynotbedirectlyvisiblebutarerecognizedfromfaraway

• Measuredbasedongeographicdistributionoftweetsmentioningaplace

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Schloss Heidelberg

HeidelbergHauptbahnhof

I’mgonna gotoSchloss Heidelberg

I gotaticketforSchloss Heidelberg

signboard

MaybewecanseeSchloss Heidelberg

soon

SchlossHeidelberg

DirectVisibilityofPlaces

• Tofindhigh-risebuildingsthatuserscanseefromfaraway

• Measuredbyanalyzing3DgeographicdataComputingthenumberofcolorpixelsassignedtoeachbuildingintherenderedimage

10Renderedimage

Extractingthetop-Nhighestbuildingsineacharea

ConstructingPopularity/VisibilityMap

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Tocheckwhatcanberecognized/seenfromintersections

Popularitymapbasedonindirectvisibility Visibilitymapbasedondirectvisibility

Toassignalandmarktypebasedonthevaluesofthecomputedindicators

ClassifyingPlacesintoPointandAreaLandmarks

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Visitpopularity Indirectvisibility Directvisibility Landmark Type1 High Low Low Point2 High Low High Area(Geo)3 High High Low Area(SNS+Geo)4 High High High Area (SNS+Geo)5 Low

/Not measuredNot measured High Area(Geo)

6 Low/Not measured

Not measured Low -

VisitPopularityofStreets

Todetectcrowded/popular/famousstreetsaslinelandmarks– Usinggeo-taggedtweets– Findingsequentialcrowdedintersections

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Mappingtonearest

intersections

Geographicdistributionoftweets Intersectionsweightedbytweets

RouteGraphConstruction

Inordertoextractswitchinglocationsoflandmarks,wegeneratevirtualedgesandnodes

Extractedlandmarksinaboundingbox Routegraph

:Pointlandmark :Linelandmark :Arealandmarkwithitsvisiblearea

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ReorientationpointsDirectionindicators

Virtualpath

RouteSearch• Tofindanoptimalroute– Thenumberoflandmarks(thesmaller)– Thevisibleratioofarealandmarks(thehigher)– Thelength(theshorter)

• Methods– GeneticAlgorithm(GA)– DijkstraAlgorithm

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Implementation

• Constructedroutegraphsoftwocities

• Datasets

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SanFrancisco,US(pop: 840K)

Kagoshima,JP(pop: 610K)

SNSdata

Locationinfo. 25,256 383Geo-taggedtweets 0.6M 98K

Geodata

Streets 1,233 61,075Intersections 9,649 30,703Buildings 85,116 66,189

SanFrancisco Kagoshima

HighdirectvisibilityofSakurajimamadelotsofvirtualedges

Demo:LandmarkNavi

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http://www.ibe.kagoshima-u.ac.jp/~lmnavi/index_en.html

Evaluation• Toaimatevaluatinglandmark-basedroutes(LR)• Participantswalkedroutesthattheyrememberedinthevirtualspace(StreetView)orrealspace

• Comparativemethods– VR(LRwithoutindirectvisibility)– GR(GoogleDirections(walkingmode))

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Visitpopularity Indirectvisibility(basedon SNS)

Directvisibility(basedongeo)

LR ✔ ✔ ✔

VR ✔ - ✔

GR - - -

Proposed

EvaluationItems

i)time(min.)Timeneededtoreachthedestination

ii)routeref.Numberoftimesausercheckedtheroutedirectionsonprintedmaterials

iii)self-positionref.Numberoftimesausercheckedtheself-positions

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Self-positionref.inSV

ResultsofKagoshimaEvaluationinVirtualSpace&RealSpace

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� �

• vs.GR:Significantdifferencesinii)inbothspaces• vs.VR: i)isshorterandii)issmaller(Nosignificantdifferences)

(a)VirtualSpace (b)RealSpace

• #routes:18(6routes×3methods)• Distancesofroutes:1.5~2.6km• #navi points:LR(5.0)<VR(5.3)<GR(11.2)• #participants:30students

ResultsofSanFranciscoEvaluationinVirtualSpace

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• vs.GR: Therearesignificantdifferencesinii)andiii)• vs.VR:i)isshorterandii)issmaller(Nosignificantdifferences)

� �

• #routes:9(3routes×3methods)• Distancesofroutes:0.8~2.0km• #navi points:LR(3.0)<VR(4.0)<GR(6.3)• #participants:36students

QualitativeEvaluationParticipantsanswered4questions

q1)Didyouthinkthattheroutedirectionswereeasytorememberwhenyoucheckthemintheprintedmapatfirst?q2)Didyouthinkthatthedirectionsyoumemorizedwerereliable?q3)Didyouthinkthatitiseasytofollowthememorizedroutedirections?q4)Didyouthinkthattheroutedirectionswereuseful asroutenavigation?

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q1(memorability)

q2(reliability)

q3(easiness)

q4(usefulness)

LR 4.5 4.1 3.9 3.9VR 4.2 3.8 3.6 3.8GR 3.1 3.2 2.6 3.2

Discussions

• ExploitingSNSdata–Wecoulddetectusefullandmarksevenifwayfinders couldnotdirectlyseethem

• Generatedroutes– Consistingofasmallnumberoflandmarks(3~5)–Mostadults’ short-termmemoryis7±2

• VirtualspaceevaluationwithSV– AlthoughSVhaslimitations,weobservedsimilarpatternsofresultsinboththespaces

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Conclusions

• Aneffectiveroutesearchthatrecommendseasy-to-rememberandshortroutes– Extractedthreetypesoflandmarks– DemonstratedeffectivecombinationofrealspacedatawiththeoneharvestedandcomputedfromSNS

• Futurework– ToinvestigatepatternsofvisitpopularityandvisibilitydependingonseveralconditionsTime,weather,etc.

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