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P-Tour: A Personal Navigation System for Tourist. Atsushi Maruyama Xanavi Informatics , Naoki Shibata Shiga University , Yoshihiro Murata Nara Institute of Sci. and Tech. , Keiichi Yasumoto Nara Institute of Sci. and Tech. , - PowerPoint PPT Presentation
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P-Tour: A Personal Navigation System for Tourist
Atsushi Maruyama Xanavi Informatics ,
Naoki Shibata Shiga University ,Yoshihiro Murata Nara Institute of Sci. and Tech.,Keiichi Yasumoto Nara Institute of Sci. and Tech.,Minoru Ito Nara Institute of Sci. and Tech.
Outline of our presentation
1. Background2. Overview of P-Tour3. The route search engine4. Evaluation5. Conclusion6. Future works
Background(1) High performance PDA Small built-in GPS unit on mobile phone Wireless LAN, 3G mobile phone, PHS
Navigation system on mobile phone or PDA
Navigation service on mobile phone : EZ Navi Walk on mobile phone by au kddi
• Built-in GPS unit on mobile phones• Route search between two locations• Guidance by voice, text, etc.
Background(2)
Existing navigation systems– Car navigation system– Personal navigation service by au kddi– Etc.
Functions are limited
–Route guidance between two locations
Inadequate for tour navigation
Background(3) Navigation system for tour navigation
•Many destinations to visit
•Each destination may have business hours, appointed time, etc.
•User may wish visiting destinations as many as possible
•If it is impossible to visit all destinations, system should choose part of destinations to visit
•Importance value can be specified for each destination•Timezone of visits can be specified for each destination•Guidance function includes time schedule management
•Many destinations to visit
•If it is impossible to visit all destinations, system should choose part of destinations to visit
•User may wish visiting destinations as many as possible
•Each destination may have business hours, appointed time, etc.
We propose personal navigation system with these features
P-Tour : A Personal Navigation System for Tourism
Tour scheduling
•Beginning and ending locations of the tour
•Importance of each destination
– User inputs: •All destinations and corresponding timezones
P-Tour : A Personal Navigation System for Tourism
Tour scheduling– Pre-calculation is performed within 10 sec.
– System outputs:• Route with arrival/departure time for each
destination
P-Tour : A Personal Navigation System for Tourism
Tour scheduling
Request
Schedule
Request
Schedule
User Server
Incremental tour scheduling• Adding new destinations• Changing importance of destinations One step of incremental calculation is performed in a
few seconds
System overview of P-Tour
Client (cell phone or PDA)•Route guidance program•User interface
Implemented with Java MIDlet
Internet
ServerRoute search engineImplemented as a Java servlet
Map database
Destinationdatabase
RequestSchedule
Route guidance mode
Schedule display
The entire route
Moving along the scheduled route
When visiting a destination.
Remaining stay time/Departure time
Schedule display
Arrival/Departureand stay time
Automatic recalculation of schedule
When user goes into wrong route When user’s moving speed is too slow When user stays at a destination too long
These situations are automaticallydetected using GPS and clock
The system warns user Displays a route to return to the original route Changes the schedule and route
Requirements for the route search engine
Fast enough for the incremental scheduling– 10 seconds for the initial calculation– A few seconds for a recalculation
The output route should maximize user’s satisfaction– Fitness function converts route to
satisfaction rate
Fitness function Output route should include important
destinations as many as possible
Each destination should satisfy a corresponding time restriction
Output route should an efficient route without detours
Importance values of included destinations are added to the fitness value
Importance values are only added if each of destinations satisfies the restriction
Total distance of user’s movement is subtracted from the fitness value
Fitness functionNumerical expression of the fitness function
otherwise
nsrestrictio its satisfiesand route in the included is if
nsdestinatio all
0
1)(
of valueImportance)(
)movement of distance total(
)()(
valueFitness
d
d
dS
ddI
dIdS
is a constant
Fitness functionSetting of value and output route
Value of and the output route
low medium high
Low value leads to detourHigh value leads to destinations near to the beginning location of the tour to be only selected
It is desirable to set value according to user’s preference
Route search algorithm Route between all combinations of
two destinations are calculated– A* algorithm is used
• In our experiment, moving speed is assumed to be 30km/h for usual road, 60km/h for express ways
– same as car navigation system• Moving speed can be obtained from map or
other data sources– Routes between known destinations are
calculated beforehand• Routes to/from newly entered destinations
are calculated extempore
Route search algorithm
Determining visiting order of each destination by genetic algorithm
Advantages of using GA
GA always retains multiple candidate solutions
•It is always possible to return approximate solutions
•User can choose preferred solution from them
GA is used to obtain approximate solution for combinatorial optimization problem
Overview of Genetic Algorithm
Candidate solutions are generated randomly
NationalMuseum
法隆寺HoryujiTodaiji Kofukuji Yakusiji
Beg
innin
glo
catio
n
En
din
glo
catio
n
Candidate solution
GA always retains multiple candidate solutions
Randomly selects two solutions, and make a new solution from themCandidate
solutions for the next iteration
Calculate fitness values and select solutions with relatively high fitness values
Repeat the iteration until predefined iteration count expires
Evaluation of our system Equipments/settings
– Server HW: A personal computer with Pentium4 2.4GHz– Server SW: Linux(Debian), Java Servlet, Tomcat 4.2– Map data format: Navigation System Researchers’ association digital r
oadmap format– Navigation area: North Nara– Moving method : by car– GA iterations(generations) : 100– Settings of constants : γ=1
Things evaluated– Validity of output route– Time to calculate routes– Difference between optimal and output solutions
Calculation time of route
Calculation time of routes between any combination of two destinations are not included
•Converges at 50th iteration•Outputs a satisfactory route within 10 seconds
Fitnessvalue
Computation time
Number ofiterations
Difference between optimal and output solutions
Difference is about 1% Sufficient for practical use
NumberOf dest.
Fitnessvalue
Optimalvalue
Difference(%)
Calc. Time(P-Tour)
Calc. Time(Optimal)
Enhancement:multi-objective scheduling
Minimization of multiple fitness functions– Traveling cost– Time
Osaka
Kyoto
Express
Special Express
Fast, but expensive
Airplane + train
Slow and expensive
Slow, but cheap
Find a set of routes which are worth consideration
• User can choose one schedule from several candidate plans• Actual routes are more intuitive than set of values
Enhancement:multi-objective scheduling
Satisfaction : 119Cost : 0
Satisfaction : 154Cost : 80
Satisfaction : 182Cost : 2520
Conclusions
We proposed P-tour– Tour scheduling using GA– Timezones can be specified
We evaluated P-tour– Search time is about 10 seconds
Ongoing works Supporting tour using multiple
transportation methods – Car, train, bus, walking, etc.– Appropriate route can be selected using
multi-objective scheduling Improvement of user interface
The route automatically change when context changes• When it begins to rain, user may want to visit
indoor exhibition• Group of users can break up and get together
using P-Tour
Thank you.
Overview of route search algorithm
•Destination ID•Wait time•Stay time•…
GA always retains multiple candidate solutions Candidate solutions are encoded
Dest. 1 Dest. 2 Dest. 3 Dest. n…
User’s input
Beginning and ending locations of the tour: NAIST
Tour begins at: 9:00amTour ends at: 9:00pm
No. Name Importance Arrival Stay
D1 Toshodaiji Tmpl. 5 - 60minD2 Yakusiji Tmpl. 5 - 60minD3 Horyuji Tmpl. 5 <=15:00 >=180minD4 Fujinoki Tomb 5 - 90minD5 Heijo-kyo 5 - 150minD6 Saidaiji Tmpl. 5 - 30minD7 Gakuem-mae 5 <=19:30 >=60minD8 Sarusawa pond 5 - 60minD9 Botanical garden 5 - 45minD10 National Museum 5 - 60minD11 Shin-Yakusiji Tmpl. 5 - 60minD12 Shou-sou-in 5 - 30minD13 Tai-an-ji Tmpl. 5 - 60minD14 Hou-rin-ji Tmpl. 1 - 60min… … … …D30 Hokke-ji Tmpl 1 - 30min
Validity of output route
D3
D1D2
D7 D12D9
D8
D10
D1 ~ D13 Importance 5D14 ~ D30 Importance 1Timezone D3 ≦15:00 D7 ≦19:30
Destinations in outputD1, D2, D3, D7, D8, D9D10, D12
Arrival timeD3 14:50D7 19:10
INPUT
D4
D13
We changed importance to 10, and recalculated the route
Validity of output route
D3
D1D2
D7 D12D9
D8
D10
D1D2
D3D4
D13
D5D7
Before After D4,D13 Importance 10