Transcript P-Tour: A Personal Navigation System for Tourist Atsushi Maruyama Yoshihiro Murata

P-Tour: A Personal Navigation System
for Tourist
Atsushi Maruyama
Naoki Shibata
Yoshihiro Murata
Keiichi Yasumoto
Minoru Ito
Xanavi Informatics
,
,
Nara Institute of Sci. and Tech.,
Nara Institute of Sci. and Tech.,
Shiga University
Nara Institute of Sci. and Tech.
Outline of our presentation
1.
2.
3.
4.
5.
6.
Background
Overview of P-Tour
The route search engine
Evaluation
Conclusion
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
We propose personal navigation system with these features
•Importance value can be specified for each destination
•Timezone of visits can be specified for each destination
•Guidance function includes time schedule management
P-Tour : A Personal Navigation
System for Tourism

Tour scheduling
– User inputs:
•All destinations and corresponding timezones
•Importance of each destination
•Beginning and ending locations of the tour
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
Server
Map database
Route search engine
Implemented as a Java servlet
Destination
database
Internet
Client (cell phone or PDA)
•Route guidance program
•User interface
Implemented with Java MIDlet
Route guidance mode
Schedule display
Schedule display
The entire route
Arrival/Departure
and stay time
Moving along
the scheduled
route
When visiting a
destination.
Remaining stay
time/Departure
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 automatically
detected 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
Importance values of included destinations
are added to the fitness value


Each destination should satisfy a
corresponding time restriction
Importance values are only added if each of
destinations satisfies the restriction
Output route should an efficient route
without detours
Total distance of user’s movement is
subtracted from the fitness value
Fitness function
Numerical expression of the fitness function
Fitness value 
 S (d ) I (d )
all destinations d
  ( total distance of movement )
I (d )  Importance value of d
1
S (d )  
0
if d is includedin the route and
satisfies its restrictions
otherwise

is a constant
Fitness function
 value and output route
Value of  and the output route
Setting of
Low

low
It is desirable to set

value
to detour
High leads
value
leads to
destinations near to the
beginning location of the
tour medium
to be only selected

high
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
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
Overview of Genetic Algorithm
Candidate solution
Kofukuji
Yakusiji
National
Museum
法隆寺
Horyuji
Ending
location
Beginning
location
Todaiji
GA always retains multiple candidate solutions
Candidate
solutions are
generated
randomly
Calculate fitness values and
select solutions with
relatively high fitness values
Candidate
solutions
for the next
iteration
Randomly selects two
solutions, and make a new
solution from them
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 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
Calculation time of route
Number of Computation
time
iterations
Fitness
value
•Converges at 50th iteration
•Outputs a satisfactory route
within 10 seconds
Calculation time of routes between any combination of two
destinations are not included
Difference between optimal and
output solutions
Number
Of dest.
Fitness Optimal Difference
value
value
(%)
Calc. Time
(P-Tour)
Difference is about 1%
 Sufficient for practical use

Calc. Time
(Optimal)
Enhancement:
multi-objective scheduling

Minimization of multiple fitness functions
– Traveling cost
– Time
Fast, but expensive
Special Express
Kyoto
Slow, but cheap
Express
Slow and expensive
Osaka
Airplane + train
Find a set of routes which are worth consideration
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
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


GA always retains multiple candidate solutions
Candidate solutions are encoded
Dest. 1 Dest. 2 Dest. 3
•Destination ID
•Wait time
•Stay time
•…
…
Dest.
n
User’s input
Beginning
and ending
locations of
the tour:
NAIST
Tour begins
at:
9:00am
Tour ends
at:
9:00pm
No. Name
D1
D2
D3
D4
D5
D6
D7
D8
D9
D10
D11
D12
D13
D14
…
D30
Toshodaiji Tmpl.
Yakusiji Tmpl.
Horyuji Tmpl.
Fujinoki Tomb
Heijo-kyo
Saidaiji Tmpl.
Gakuem-mae
Sarusawa pond
Botanical garden
National Museum
Shin-Yakusiji Tmpl.
Shou-sou-in
Tai-an-ji Tmpl.
Hou-rin-ji Tmpl.
Hokke-ji Tmpl
Importance Arrival
5
5
5
5
5
5
5
5
5
5
5
5
5
1
…
1
Stay
60min
60min
<=15:00 >=180min
90min
150min
30min
<=19:30 >=60min
60min
45min
60min
60min
30min
60min
60min
…
…
30min
Validity of output route
INPUT
D1～D13 Importance 5
D14～D30 Importance 1
Timezone D3 ≦15:00
D7 ≦19:30
D7
D2
D1
D10 D12 Destinations in output
D9 D1, D2, D3, D7, D8, D9
D8
D13
D3
D4
D10, D12
Arrival time
D3 14:50
D7 19:10
We changed
importance to 10, and
recalculated the route
Validity of output route
Before
After D4,D13 Importance 10
D7
D2
D1
D3
D10 D12
D9
D5
D7
D2
D1
D8
D4 D3
D13