Study and Implementation of Tourist Spot Navigation System

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Study and Implementation of Tourist Spot
Navigation System

• Feng Xu, Junping Du
• Beijing University of Posts and
  Telecommunications
• Sept 11, 2009

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Route navigation between two tourism spots

• Route navigation between two tourism spots
• Route planning among multi-spot
• Navigation based on voice
• Conclusion

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Route navigation between two tourism spots

• The navigation between two tourist spots is the
  route navigation from tourists current location
  to their destination.
• Actually, this navigation is a shortest route
  problem.
• Taking into account the rationality and
  selectivity of this route, the system must give
  the first kth shortest path selection for the
  tourists.

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Route navigation between two tourism spots

• Data process and storage
• The algorithm must take into account the number
  of the tourist.
• Take into account the carrying capacity of
  tourism spot and the number of current visitors.
• Compare the capacity of tourism spot and the
  number of current visitors, then decide how to
  select or reject the tourist spot.

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Route navigation between two tourism spots

• Reduce the routes curvature
• If the routes curvature is too large, insert a
  temporary node c to reduce the routes
  curvature.
• The system designs the route 2 and 3 to come
  close to the actual route.

c
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Route navigation between two tourism spots

• Algorithm
• How to get the k shortest paths between spot a
  and spot b.
• There are many classical algorithms to calculate
  the shortest route between two spots.
• In this paper, we transform a graph to binary
  tree.

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Route navigation between two tourism spots

• Each node represented a tourism spot.
• Fig. b is the depth-first spanning tree of fig.
  a.
• Fig. c is the width-first spanning tree of fig.
  a.

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Route navigation between two tourism spots

• According to binary tree traversal methods, we
  get two routes from V1 to V8

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Route planning among multi-spots

• Route planning among multi-spots is a TSP
  (traveling salesman problem) .
• There are many classic algorithms to solve it,
  such as ant colony algorithm, neural network
  algorithm and so on.
• Our system uses the ant colony algorithm to plan
  the route among the multi-spots.

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Route planning among multi-spots

• Ant Colony Algorithm
• Ant Colony Algorithm studies artificial systems
  that take inspiration from the behavior of real
  ant colonies and which are used to solve discrete
  optimization problems
• Real ants are capable of finding shortest path
  from a food source to the nest without using
  visual cues
• Also, they are capable of adapting to changes in
  the environment, for example finding a new
  shortest path once the old one is no longer
  feasible due to a new obstacle

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Route planning among multi-spots

• It is well known that the main means used by ants
  to form and maintain the line is a pheromone
  trail.
• Ants deposit a certain amount of pheromone while
  walking, and each ant probabilistically prefers
  to follow a direction rich in pheromone rather
  than a poorer one.

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• Once the obstacle has appeared, we can expect
  half the ants to choose to turn right and the
  other half to turn left.
• The shorter path will receive a higher amount of
  pheromone in the time unit and this will in turn
  cause a higher number of ants to choose the
  shorter path.
• Due to this positive feedback process, very soon
  all the ants will choose the shorter path .

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Route planning among multi-spots

• We denote the residual strength of pheromones
  which the ants leave on the path between city i
  and city j at the moment t as .
• we denote the probability which the ant k moves
  from city i to city j at the moment t as .

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Route planning among multi-spots

• is illuminating information which ants
  move from city i to city j , which equals to
  1/dij.
• Tablek is a collection of city which the ant k
  can go next and it will change dynamically with
  the movement of the kth ant.
• Pheromones will fade away gradually, we define
  as the extent of its fade.

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Route planning among multi-spots

• The algorithm uses the pseudo-random probability
  to determine which city ants will go.
• According to this rule, the algorithm produces a
  random number between zero and one whenever the
  ants choose the next city.
• We determine the direction of ants movement in
  combination with this random number and the
  following formula

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Route planning among multi-spots

• In the formula, q is a random number between zero
  and one and q0 is a parameter between zero and
  one.
• After a period of time, the ants have traveled
  all the cities.
• Then the system updates the pheromones on every
  path according to the following formula

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Navigation based on voice

• Define the effective range
• The spot T has two boundaries which are R1 and
  R2.
• If D (the distance between tourist and the spot
  T) is less than R2, take into account of the
  boundary effect, the tourist may wander at the
  boundary of spot T.

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Navigation based on voice

• If D (the distance between tourist and the spot
  T) is less than R1,the system plays introduction
  voice.
• When the tourist leaves the spot T, the system
  needs to calculate the direction angle

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Navigation based on voice

• Build binary sort tree
• System builds the index of tourism spots to
  improve the response speed of system.
• We get the sequence of intervals of X coordinate
  and the sequence of intervals of Y coordinate
  after the regional division.
• Then we build the binary sort tree about the
  sequence of intervals of X coordinate and the
  sequence of intervals of Y coordinate.

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Navigation based on voice

• If the binary sort tree has been set up, the
  system only needs to search the binary sort tree
  once when tourists enter the scenic spot at
  first.
• Then the system compares the current (i, j) with
  the interval (xi, xi 1) and interval (yj, yj 1)
  to obtain the next region which the tourist will
  go.

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Navigation based on voice

• Implementation the algorithm
• After processing the data, the system uses the
  binary sort tree to obtain the region Aij which
  the tourist stays.
• It calculates the distance between tourist
  current location and the target spot.
• According to the distance, the system knows which
  spot the tourist is in.

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Navigation based on voice

• It includes data processing thread and voice
  playing thread.
• The main function of the data processing thread
  is to calculate the distance between the place of
  the tourist and the spot around to make sure the
  tourist spot and so on.
• When the voice play thread obtains the tourist
  spot, it plays this spots voice automatically.

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Route navigation between two tourism spots
Route planning among multi-spots
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Conclusions

• We use the Mapx Mobile to implement the
  navigation system on Windows Mobile 6.
• The system works well and meets the needs of the
  tourists.
• Meanwhile, the system fills up the gap of tourist
  attractions navigation, greatly facilitates the
  tourists and promotes the popularity of tourist
  attractions information.

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thanks