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P-Tour: A Personal Navigation System for Tourist

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. ,

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P-Tour: A Personal Navigation System for Tourist

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  1. 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.

  2. Outline of our presentation • Background • Overview of P-Tour • The route search engine • Evaluation • Conclusion • Future works

  3. 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(1) • High performance PDA • Small built-in GPS unit on mobile phone • Wireless LAN, 3G mobile phone, PHS

  4. 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

  5. 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 We propose personal navigation system with these features Background(3) • Many destinations to visit • Navigation system for tour navigation • 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

  6. User inputs: • All destinations and corresponding timezones P-Tour : A Personal Navigation System for Tourism • Tour scheduling • Importance of each destination • Beginning and ending locations of the tour

  7. 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

  8. Server User P-Tour : A Personal Navigation System for Tourism • Tour scheduling Request Schedule Request Schedule • Incremental tour scheduling • Adding new destinations • Changing importance of destinations One step of incremental calculation is performed in a few seconds

  9. Map database Server Route search engine Implemented as a Java servlet Destination database Internet Schedule Request • Client (cell phone or PDA) • Route guidance program • User interface • Implemented with Java MIDlet System overview of P-Tour

  10. Schedule display The entire route Schedule display Arrival/Departure and stay time Moving along the scheduled route When visiting a destination. Remaining stay time/Departure time Route guidance mode

  11. The system warns user • Displays a route to return to the original route • Changes the schedule and route 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 automatically detected using GPS and clock

  12. 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

  13. 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 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

  14. Fitness function Numerical expression of the fitness function is a constant

  15. Value of and the output route Low value leads to detour High value leads to destinations near to the beginning location of the tour to be only selected low medium high It is desirable to set value according to user’s preference Fitness function Setting of value and output route

  16. 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

  17. Route search algorithm Determining visiting order of each destination by genetic algorithm • GA is used to obtain approximate solution for combinatorial optimization problem 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

  18. Overview of Genetic Algorithm Candidate solution Beginning location Ending location National Museum 法隆寺 Horyuji Todaiji Kofukuji Yakusiji GA always retains multiple candidate solutions Candidate solutions are generated randomly

  19. Calculate fitness values and select solutions with relatively high fitness values Randomly selects two solutions, and make a new solution from them Candidate solutions for the next iteration Repeat the iteration until predefined iteration count expires

  20. 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 roadmap 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

  21. Calculation time of route Calculation time of routes between any combination of two destinations are not included Computation time Number of iterations Fitness value • Converges at 50th iteration • Outputs a satisfactory route within 10 seconds

  22. Difference between optimal and output solutions Number Of dest. Fitness value Optimal value Difference (%) Calc. Time (P-Tour) Calc. Time (Optimal) • Difference is about 1% • Sufficient for practical use

  23. Enhancement:multi-objective scheduling • Minimization of multiple fitness functions • Traveling cost • Time Fast, but expensive Kyoto Special Express Slow, but cheap Express Slow and expensive Airplane + train Osaka Find a set of routes which are worth consideration

  24. Enhancement:multi-objective scheduling Satisfaction : 119 Cost : 0 Satisfaction : 154 Cost : 80 Satisfaction : 182 Cost : 2520 • User can choose one schedule from several candidate plans • Actual routes are more intuitive than set of values

  25. Conclusions • We proposed P-tour • Tour scheduling using GA • Timezones can be specified • We evaluated P-tour • Search time is about 10 seconds

  26. 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

  27. Thank you.

  28. GA always retains multiple candidate solutions • Candidate solutions are encoded … Dest. n Dest. 1 Dest. 2 Dest. 3 • Destination ID • Wait time • Stay time • … Overview of route search algorithm

  29. User’s input No. Name Importance Arrival Stay D1 Toshodaiji Tmpl. 5 - 60min D2 Yakusiji Tmpl. 5 - 60min D3 Horyuji Tmpl. 5 <=15:00 >=180min D4 Fujinoki Tomb 5 - 90min D5 Heijo-kyo 5 - 150min D6 Saidaiji Tmpl. 5 - 30min D7 Gakuem-mae 5 <=19:30 >=60min D8 Sarusawa pond 5 - 60min D9 Botanical garden 5 - 45min D10 National Museum 5 - 60min D11 Shin-Yakusiji Tmpl. 5 - 60min D12 Shou-sou-in 5 - 30min D13 Tai-an-ji Tmpl. 5 - 60min D14 Hou-rin-ji Tmpl. 1 - 60min … … … … D30 Hokke-ji Tmpl 1 - 30min • Beginning and ending locations of the tour: • NAIST • Tour begins at: • 9:00am • Tour ends at: • 9:00pm

  30. D13 We changed importance to 10, and recalculated the route D4 Validity of output route INPUT D1～D13 Importance 5 D14～D30 Importance 1 Timezone D3 ≦15:00 D7 ≦19:30 Destinations in output D1, D2, D3, D7, D8, D9 D10, D12 Arrival time D3 14:50 D7 19:10 D12 D10 D7 D9 D2 D8 D1 D3

  31. Validity of output route Before After D4,D13 Importance 10 D5 D12 D10 D7 D7 D9 D2 D2 D13 D8 D1 D1 D3 D4 D3

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