7th ACM SIGSPATIAL Workshop on Locationbased Recommendations,Geosocial Networks and Geoadvertising

Adaptable Data-Driven Geofences
for Notifying Points of Interest
Using Touristsʼ GPS Trajectories
Iori Sasaki, Masatoshi Arikawa, Min Lu,
Ryo Sato, and Tomihiro Utsumi
Akita University, Japan1

DJ Walkers delivers continuous and natural

regional stories, following the footsteps of tourists.
User location
Referring

SpatialContext
ModelFeature
Knowledge

PromptingVisual Assistant
Direction todestinations

Large
Language
ModelAppearance
display

Realization of the mobile app: DJ Walkers• Geofencing with circular geofences

for recognizing user-feature relations• Red: nearby geofencing
Two types:• Blue: staying geofencing

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Target of this presentation: POI notifiers

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It sends messages or alerts whenusers are near POIs or passing bythem.
It encourages users to discoverand visit POIs.

Case study of Geofence Design for POI Notifiers
Where should we set a geofence for good POI notifiers?

5Case study of Geofence Design for POI Notifiers

6Answer?:
A larger geofence,
which would covera more extensive area

Problem of location relevanceA precise geofence based onheuristic estimations of
tourist flows

Problem of user coverage rate

Requirements for good POI notifiers

7To avoid notification about places that are unnecessarily
far from current user locations:
Minimize the distances fromthe boundaries of geofences toPOIs.
To avoid setting geofences in places where few users crossor cannot enter due to walls and rivers:
Maximize the number ofcovered users.

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Our Challenge: Geofence Design
Reliability

Inappropriate
activation/Inactivation

Scalability

Labor-intensive task

Data-driven geofence design that can autonomouslyoptimize location-based services.

Genetic Algorithm – a meta heuristic technique𝑖!"#$9

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Demonstration of our solutions

𝒓𝒔𝒄𝒂𝒏 = 𝟕𝟓[𝒎]B: Kogetsu Lake

C: Yojiro-Inari Shrine

A: Arts Theater

Key Points before detailed explanations

11In POI notifiers, geofences that are set manually cannot
be optimized from the two perspectives: distances ofnotification-to-POI (location relevance) and user coverage
rates.
Our solution using a Genetic Algorithm enables us to reset
their parameters, which leads to better recommendation
experiences for many city walkers.

Genetic Algorithm – a meta heuristic technique𝑖!"#$12

Formulation for good POI notifiers

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For geofence individual 𝑖 = (𝑥, 𝑦, 𝑟),𝑚𝑖𝑛𝑖𝑚𝑖𝑧𝑒 𝐹(𝑖) = 𝑓(𝑖) + 𝑔(𝑖)• location relevance for POI
𝑑𝑖𝑠𝑡 𝑝𝑜𝑖. 𝑥𝑦, 𝑖. 𝑥𝑦 + 𝑖. 𝑟 + 𝑑𝑖𝑠𝑡 𝑝𝑜𝑖. 𝑥𝑦 − 𝑖. 𝑟
𝑓 𝑖 =2• penalty: user coverage rate𝑔 𝑖 = 𝜇 𝑚𝑎𝑥(0, 𝑐𝑟()*)+ − 𝑐𝑟(𝑖))𝜇: penalty coefficient (= 2000)
𝑐𝑟!"#"$ : desirable user coverage rate

Preparation: Synthetic GPS trajectories

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1. Prepare road networks from OSM:

Senshu Park (Japan)・Edges are weighted by their lengths [m]Start

2. Cost scaling:for edge in network.edges:
edge.weight = random.uniform(from edge.weight/2 to edge.weight*2)

3. Route selection: Dijkstra Algorithm

Goal↑ Shortest route by default (red)

4. Generate trajectory data
・Recording interval [s]: 8.0
・Walking speed [m/s]:
𝑟𝑎𝑛𝑑𝑜𝑚. 𝑛𝑜𝑟𝑚𝑎𝑙 𝜇 = 1.3, 𝜎 = 0.2
・GPS Noise [m]:𝑟𝑎𝑛𝑑𝑜𝑚. 𝑛𝑜𝑟𝑚𝑎𝑙 𝜇 = 𝑝%&'( , 𝜎 = 5.0

16Preparation: target POIs
Different point patterns within the scanning range 𝑟=>?@ .
600 trajectories
𝒓𝒔𝒄𝒂𝒏 = 𝟕𝟓[𝒎]B: Kogetsu Lakeby Ippukucho (Wikipedia)

C: Yojiro-Inari Shrine

A: Arts TheaterPerformance Stability of the Genetic Algorithm

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Examining the variability of solutions
as the user coverage rate 𝑐𝑟()*)+ increases.
• 30 trials for each parameter• 150 trajectories within the scanning range(A: 150, B: 210, C: 565 trajectories in the dataset)* In this presentation, the same parameters
have been applied for the Genetic Algorithm.
Crossover probability = 0.5
Mutation probability = 0.1
Number of generations = 150
Population size = 300penalty coefficient = 2000

Performance Improvement: Manual vs. Our solution
A larger geofence,

Our Solution

which would covera more extensive area𝑖 = (𝑝𝑜𝑖. 𝑥, 𝑝𝑜𝑖. 𝑦, 75𝑚)

scanning range: 𝑟&)*+ = 75𝑚
desirable user coverage rate:𝑐𝑟,-.-/ = 1.0

vs.

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Performance Improvement: Manual vs. Our solution
Examining the scalability of the algorithm
as the input data increases.
• For training: 10 – 300 trajectories• User coverage rate: 𝑐𝑟!"#"$ = 1.0• CGF by manual (Scanning range): 𝐶 = (𝑝𝑜𝑖. 𝑥, 𝑝𝑜𝑖. 𝑦, 75)
For test: 200 trajectories(1) User coverage rate

𝑪m75(2) Average distance ofnotification-to-POI

𝑑3𝑑4

𝒕𝒓𝒂𝒋_𝒏𝒖𝒎(𝒊𝒏: 𝒊𝒃𝒆𝒔𝒕 )=𝒕𝒓𝒂𝒋_𝒏𝒖𝒎 (𝒊𝒏: 𝑪)∑𝒏𝒌&𝟏 𝒅𝒌=𝒏

trackingdataResults• The simpler the point pattern, the more stable it is.
• As the user coverage rate increases,
the output becomes slightly more unstable.
• Increasing the training data size contributes to the improvementof the user coverage rate.
• The more complex point patterns the scanning range has, (2)becomes sensitive to changes in the number of training data.

19Summary
The accumulation of tourists’ GPS trajectory data canempower us to find better geofence parameters thanmanually settings from the two perspectives: the user
coverage rate and location relevance.
(GPS logs know the tendency of tourists’ flow and thetechnical limitations better than humans...?)
Geofencing is widely used in the tourism business. The
strategy of geofence design depends on the purpose anddomain of the service. In the future, further discussion on
systematizing autonomous optimizations in geofencing
technology (not only for POI notifiers) are needed.

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