CN103402173B - A kind of travel time optimization method for tourist attractions - Google Patents

A kind of travel time optimization method for tourist attractions Download PDF

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CN103402173B
CN103402173B CN201310301447.8A CN201310301447A CN103402173B CN 103402173 B CN103402173 B CN 103402173B CN 201310301447 A CN201310301447 A CN 201310301447A CN 103402173 B CN103402173 B CN 103402173B
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particle
visitor
sight spot
sight
location
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CN103402173A (en
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张勇
徐小龙
陈玲
黄杰
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Hefei University of Technology
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Hefei University of Technology
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Abstract

The invention discloses a kind of travel time optimization method for tourist attractions, having designed a kind of the shortest travel time method that is applicable to tourist attractions helps visitor and realizes high efficiency sight spot visit mode, the more sight spot of playing within the identical time, the method is treated in optimizing visit sight spot required visit total time and is also made the reasonable and balanced of the service loads such as scape intra zone traffic, hotel, food and drink, shop realizing visitor. This algorithm combines particle cluster algorithm and genetic algorithm, the problem of the physical distance minimum in all cities of traversal of common traveling salesman problem discussion is improved, adopt the size of temporal adaptation degree value to determine the renewal of particle and the iteration of population, wherein the time of temporal adaptation degree value is jointly to be determined by play required stand-by period of each sight spot of playing under required time and real time status of the physical distance between sight spot, each sight spot. This algorithm can be better in conjunction with actual, for visitor provides better path Push Service.

Description

A kind of travel time optimization method for tourist attractions
Technical field
The present invention relates to travel information service field, be specially a kind of travel time for tourist attractionsOptimization method.
Background technology
Improving constantly of contemporary people's living standard, people also improve the requirement of quality of life thereupon,People constantly strengthen the demand of cultural tour aspect in recent years, simultaneously in tourism process to elder generationThe requirement of entering computerized information science and technology is also progressively soaring. Present Internet of Things, indoor and outdoor are mixedThe structure, the exploitation of user's handheld terminal software, the 3D that close location, GIS geographic information map playIt is ripe that the technology such as equipment or have substantially been tending towards, and these technology application are fused to cultural tourIn the middle of reality, show the unification of culture, tourism and science and technology.
In the planning of existing sight spot, routing technique comprises bionical colony intelligence optimized algorithm---ant group algorithm, simulated annealing etc., there is following weak point in these methods. Ant group algorithm parameter is more, search time that generally need to be longer, be again to there will be stagnation behavior, search certain journeyThe solution that after degree, all individualities are found is in full accord, can not further search for solution space, be unfavorable for finding better solution. Simulated annealing due to the initial temperature of having relatively high expectations,Slow rate of temperature fall, lower final temperature, and enough at each temperature sampling repeatedly, because ofThis its convergence rate is slow, and the time of implementation is long, algorithm performance and parameter sensitivity relevant with initial value etc.Shortcoming. If temperature-fall period is too fast in addition, probably can not get globally optimal solution.
Existing sight spot path planning algorithm is the TSP problem based on basic mostly, and what draw is physical distanceThe shortest path, what only have little part consideration is to reduce visiting time, raising visit efficiencyProblem, with actual conditions in conjunction with defective tightness.
Summary of the invention
The object of this invention is to provide a kind of travel time optimization method for tourist attractions, to solveThe problem that prior art exists.
In order to achieve the above object, the technical solution adopted in the present invention is:
For a travel time optimization method for tourist attractions, be every visitor who enters tourist attractionsBeing equipped with a handheld terminal that integrates the functions such as Wi-Fi, GPS location, multimedia establishesStandby, many places AP WAP is set in tourist attractions, controls AP access location-server ACController arranges the positioning service that receives hand-held terminal device, AP WAP signal simultaneouslyDevice, presets figure place in location-serverAccording to storehouse, information management platform, it is characterized in that: comprise the following steps:
(1), visitor is while entering tourist attraction, hand-held terminal device reads AP WAP aroundRSSI signal strength values, and the RSSI signal reading is sent to location-server, positioning serviceThe RSSI calculated signals that in device, location database reads according to hand-held terminal device obtains visitor placeTourist attraction position, simultaneously in location-server information management platform to visitor's positional information andVisitor's quantity at current each sight spot place is carried out dynamic statistics in real time and is upgraded;
(2), visitor selects request signal, path by handheld terminal to location-server transmit pathSelect request signal by after multiple AP WAPs read near visitor, send out by AC controllerDeliver to location-server;
(3), location-server receives after visitor's Path selection request signal, according to information managementThe information that platform statistics is upgraded, carries out travel time based on Hybrid Particle Swarm and optimizes computing,Operation method is as follows:
(a), for each sight spot in tourist attraction, according to the two dimension at each sight spot in location databaseCoordinate data, introduces the coordinate figure in length and breadth at sight spot, and unit is rice, to determine hybrid particle swarm calculationThe position data at each sight spot in method feasible solution, then to all sight spots, in particular cases forPart visitor independently selects to go sight-seeing sight spot, carries out integer coding; In described Hybrid Particle SwarmFeasible solution by each particle corresponding to a suggestion travel path, the dimension of feasible solution is swum exactlyThe number n at the not autonomous all sight spots selected of visitor or the autonomous sight spot of selecting of visitor, feasible solution is everyThe order of individual element is exactly the order in the path, visit sight spot of suggestion;
(b), initiation parameter, initialized parameter is included in Hybrid Particle Swarm computing to be neededIterations, population number, people's walking speed v, particle initial position, wherein iterationsNMax, population number indiNum, people's walking speed v are given in algorithm initialization programValue, can respective settings be nMax=200, indiNum=500, and v=100m/min, as for particleInitial position is whole by random 1 to n this n producing of randperm (n) function in matlab programNumber is upset the Serial No. obtaining at random;
(c), calculate according to the following formula the temporal adaptation degree value fitness (k) of k particle:
fitness ( k ) = ( Σ i , j = 1 n path ij ) / v + Σ i = 1 n ( waittim e i k + playtime i k ) - - - ( 1 )
∵ waitttime i k = playtime i k · ( num i k - 1 ) - - - ( 2 )
According to formula (1), (2), obtain the temporal adaptation degree value fitness's (k) of k particleFormula is:
. fitness ( k ) = ( Σ i , j = 1 n path i , j ) / v + Σ i = 1 n playtime i k · num i k - - - ( 3 )
In formula (1), (2), (3), n be visitor not independently select all sight spots orThe number at the autonomous sight spot of selecting of visitor, pathi,jFor visitor not independently select all sight spots orThe autonomous sight spot i selecting of visitor, the distance between j, playtimeikSwim when for k particleVisitor plays the required time at i place, sight spot, waittimeikVisitor when for k particleAt sight spot, i place waits in line required time, numikSight spot while playing when for k particleThe number of queuing up in i place, wherein, number is obtained by the real-time location to visitor by location-server；
(d), particle is carried out to interlace operation: particle by and the individual extreme value of particle this particle to orderThe front optimal solution Pbest oneself finding and the colony's extreme value particle colony that all particles form is to orderBefore the optimal solution Gbest that finds intersect to upgrade, cross method adopts integer interior extrapolation method, concrete asUnder:
First, use random function to produce two random numbers between 1 to n in program, these are two years oldNumber is just corresponding to two positions that needs intersect in particle;
Then, individual to particle and particle extreme value or particle and particle colony extreme value are intersected, produceIf new particle in exist repeat sight spot; adjust, the method for adjustment is by scenic spotDo not have the sight spot occurring to insert in the middle of the sequence of new particle, obtain new particle;
Finally, calculate the temporal adaptation degree value of new particle, and by this value and particle individuality extreme value, particleTemporal adaptation degree value corresponding to colony's extreme value compares, if better, less, acceptsNew particle, enters next step (e), otherwise, retain original particle constant, enter nextStep (e);
(e), particle is carried out to mutation operation: adopt two positions of inside particles are exchanged,Specific as follows:
First, use random function to produce two random numbers between 1 to n in program, these are two years oldNumber is just corresponding to two positions that need to further make a variation in the last particle of selecting of previous stepPut;
Then, the sight spot order of two positions that produce is exchanged, thereby produce after mutation operationNew particle;
Finally, calculate the temporal adaptation degree value of new particle after variation, again and by individual to this value and particleBody extreme value, temporal adaptation degree value corresponding to particle colony extreme value compare, if better,Less, accept new particle, enter next step (f), otherwise, retain the particle before variationConstant, enter next step (f);
(f), according to the temporal adaptation degree value of the particle intersecting, finally obtain after mutation operation withIndividual extreme value and colony's extreme value comparative result, upgrade optimal value, obtains more excellent particleIndividual optimal value Pbest and the optimal value Gbest of particle colony;
(g), judge stop condition: whether iterations reaches maximum, reach and stop computing,The suggestion travel path of output time optimum on hand-held terminal device; Otherwise carry out next round meterCalculate.
Advantage of the present invention is: the present invention is directed to particle cluster algorithm and be easy to be absorbed in local optimum and heredity calculationMethod is easy to occur phenomenon precocious and that stagnate, just particle swarm optimization algorithm (ParticleSwaRmOptimization, i.e. PSO) and genetic algorithm (GeneticAlgorithm, i.e. GA) twoThe Hybrid Particle Swarm that person is combined into, has abandoned passing through in traditional particle cluster algorithmFollow the tracks of extreme value and upgrade the method for particle position, but introduced intersection and the change in genetic algorithmETTHER-OR operation, by particle with the intersection of the individual extreme value of particle and particle colony extreme value and particle fromThe mode of body variation is searched for optimal solution, brings into play two kinds of algorithms advantage separately, well realizesThe planning computing of sight spot travel path. In addition, the present invention can realize tight with actual conditionsClose combination: arithmetic result is selectively swum sight spot according to personal like by visitor on the one handLook at, under real time status each sight spot wait for visitor's number of playing number, the visit of each sight spot is requiredThe length of time determines jointly. Solve on the other hand because visitor's skewness causes scenic spotInterior each sight spot, the vehicles, hotel, dining room, shop etc. do not obtain the problem of balanced application。
Brief description of the drawings
Fig. 1 is the scenic spot culture platform block diagram based on Internet of Things.
Fig. 2 Hybrid Particle Swarm realizes sight spot visiting time optimization method flow chart.
The time-optimized method system block diagram of Fig. 3 tourist attractions.
Detailed description of the invention
As shown in Figure 1. Scenic spot culture platform based on Internet of Things comprises basic ability module, handheld terminalSoftware module, WEB website module, location service information management platform module, this platform is particleColony optimization algorithm carries out the basis of data calculating.
1. basic ability module mainly comprises that handheld terminal, Wi-Fi label, RFID label, GPS are fixedPosition, wireless network submodule, comprise architecture submodule in addition. This module is for realizingThe mixed positioning mode of chamber GPS, indoor WLAN WLAN, makes up gps signal and is easily builtThing blocks and cannot realize indoor, the pinpoint defect of underpass, to determine visitor's positionPut information.
2. handheld terminal software module mainly comprises that map represents, terminal positioning, sight spot are selected, markSign reading submodule, comprise that in addition navigate in sight spot search, electronic chart, scenic spot, voice are broadcastPut, showing advertisement, visitor space submodule. This module client software provides sight spot for visitorThe functions such as multimedia messages, travel-line planning, intelligent guide, scenic spot information inquiry. AllowVisitor carries out sight spot selection so that server info management is flatPlatform provides personalized optimal path selection suggestion by Hybrid Particle Swarm for it, will simultaneouslyVisitor's real-time dynamic information is delivered to Center For Information Management to carry out data statistics renewal.
3.WEB website module mainly comprises that map represents, function of search submodule, comprises in addition tripVisitor's feedback, merchant advertisement, voice play function submodule. This module and wireless network and informationInteraction data information between management platform.
4. location service information management platform mainly comprises location service information management and statistical functionModule, comprises management, storage, calculates three class servers. This module mainly realizes map, markThe location service information data of label, position, sight spot, each sight spot visitor's number, visitor positionManagement, adds up information such as merchant advertisement, user, scenic spot, flow, access, incomesUpgrade. Management server adopts many supervisors, avoids fault. Storage server group: forStorage cluster file system, comprises that visitor has gone sight-seeing and not travel path information and each scenic spotIn the database such as word, sound, picture, video at each sight spot. Calculation server group: calculateServer is the demand that solves visitor's application, as according to the data of storage server, is tripWhat visitor offered suggestions browses route, corresponding multimedia messages and out of Memory is provided.
As shown in Figure 2. Above-mentioned for providing the technology of the travel path that efficiency is high, visitor asks for solvingTopic, the invention provides a kind of the shortest travel time method that is applicable to tourist attractions, comprise asLower step:
1. program starts, and first carries out algorithm parameter and initializes setting: set iterations nMax,Population number indiNum, the position of initialization particle, the speed v of mankind's walking is approximately 5 ~ 7kM/h, gets 6km/h here, i.e. 100m/min. In algorithm routine, need to import in location-serverThe data of the sight spot two-dimensional coordinate (x, y) of record and playing in sight spot required time and server moreUnder new real time status in pending visitor's numbers of playing such as sight spots.
2. individual coding: the individual coding of particle adopts integer coding mode, and each particle represents experienceAll sight spots of crossing. As being 1 → 6 → 5 → 3 → 2 → 4, certain particle Output rusults represents this grainThe time-optimized route of optimum suggestion that son finds is first to play from sight spot 1, then plays to sight spot 6, the like. The scenic area and scenic spot of setting in the present invention after the autonomous selection of visitor is total to n, itPosition introduced by two-dimensional coordinate data, the coordinate figure in length and breadth at each sight spot. Through tripThe location variable of the scenic area and scenic spot after the autonomous selection of visitor is citys, and sight spot i carries out real number volume successivelyCode, i=1,2,3 ..., n. In the present invention, set the play change of required time of sight spot that visitor selectsAmount is for playtime, and the i required time of playing in sight spot is playtimei。
3. calculate k particle temporal adaptation degree value fitness (k): the degree of particle temporal adaptation here valueBe expressed as needed total time of traverse path, calculate by 3 formulas.
4. pair particle carries out interlace operation: particle passes through and the individual extreme value Pbest of particle and colony's extreme valueGbest intersects to upgrade, and cross method adopts integer interior extrapolation method. Specific as follows:
(a), in program, use random function to produce two random numbers between 1 to n, these are two years oldNumber is just corresponding to two positions that needs intersect in particle;
(b), individual to particle and particle extreme value or particle and particle colony extreme value are intersected, produceIf new particle in exist repeat sight spot; adjust, the method for adjustment is by scenic spotDo not have the sight spot occurring to insert in the middle of the sequence of new particle, obtain new particle;
(c), calculate the temporal adaptation degree functional value of new particle, and by this value and particle individuality extreme value,Temporal adaptation degree value corresponding to particle colony extreme value compares, if better, less,Accept new particle, enter next step (5), otherwise, retain original particle constant, enterNext step (5).
5. pair particle carries out mutation operation: adopt two positions of inside particles are exchanged, toolBody is as follows:
(a), in program, use random function to produce two random numbers between 1 to n, these are two years oldNumber is just corresponding to two positions that need to further make a variation in the last particle of selecting of previous stepPut;
(b) the sight spot order of two positions, to generation exchanges, thereby produces after mutation operationNew particle;
(c), calculate the temporal adaptation degree functional value of new particle after variation, again and by this value and grainThe individual extreme value of son, temporal adaptation degree value corresponding to particle colony extreme value compare, if better, less, accept new particle, enter next step (6), otherwise, retain before variationParticle is constant, enters next step (6).
6. according to fitness value and the individual utmost point to the particle finally obtaining after intersection, mutation operationThe comparative result of value and colony's extreme value, upgrades optimal value, obtains more excellent particle individualityOptimal value Pbest and the optimal value Gbest of particle colony;
8. judge stop condition: whether iterations reaches nMax, reach and stop iteration, at handHold the suggestion travel path of output time optimum in terminal; Otherwise, proceed of future generation calculating, proceed crossover and mutation operation and find optimal feasible solution.
As shown in Figure 3. The time-optimized method system block diagram of tourist attractions comprises DBM, clothesBusiness device real-time statistics lastest imformation module, visitor's terminal request information module, hybrid particle swarm are calculatedThe time-optimized path module of the time-optimized module of method and handheld terminal suggestion.
Database module stores have sight spot attribute (word, audio frequency, introductory video) information,Businessman's (shop, hotel, food and drink) information in GIS geographic information map information, scenic spot in scenic spotAnd traffic route letterBreath.
2. server real-time statistics lastest imformation part comprises the real-time positioning information to visitor, each scapeThe flow of the people information of point, businessman, road.
3. visitor's handheld terminal solicited message module is that visitor is prior according to individual to server transmissionGoing and finding out what's going on of sight spot wanted to trip in conjunction with the purposive selection of the hobby of self or settingThe relevant information at the sight spot of looking at.
4. all information of the above-mentioned three aspects: of the time-optimized module synthesis of Hybrid Particle Swarm, pass throughThe described time-optimized algorithm of hybrid particle swarm is realized the selection of time optimal travel path before, make this paths meet that path physical distance is short, visitor's stand-by period is few, meet visitor's scenic focal pointThe requirement of the each sight spot of selection, the balance visitor density of point.
5. the time-optimized path module of handheld terminal suggestion is accepted location-server in WLANThe operation result of feedback, is presented on the sight spot path planning after time-optimized hand-held end of visitorOn end, the path that visitor shows according to handheld terminal can be gone sight-seeing efficiently.
The present invention adopts the Hybrid Particle Swarm of PSO and GA combination to form a kind of tourist attractions that are applicable toThe shortest travel time method. PSO is by JamesKennedy and RussellEb in nineteen ninety-fiveErhart proposes, and this algorithm the convergence speed is fast, and parameter is few, is easy to realize, in continuous optimizationIn problem and discrete optimization problems of device, all show good effect, particularly its natural integer is compiledCode feature is suitable for processing real optimization problem. Genetic algorithm directly operates structure objects,There is not the restriction of differentiate and continuous; There is inherent Implicit Parallelism and better overallOptimizing ability. Hybrid Particle Swarm is exactly to retain the advantage that PSO convergence itself is fast, parameter is fewTime, crossover operator and the mutation operator of also having introduced GA produce new particle to PSO, pass throughRelatively the size of the temporal adaptation degree functional value of new particle and old particle is carried out the renewal of particle,Thereby make the performance of Hybrid Particle Swarm better.
Finally explanation: above embodiment is only for technical scheme of the present invention is described, but not to itRestriction; Although invention is elaborated with reference to previous embodiment, those skilled in the artBe to be understood that: its technical scheme that still can record previous embodiment is modified, orPerson is equal to replacement to part technical characterictic wherein; And these amendments or replacement do not make technologyThe essence of scheme departs from the spirit and scope of embodiment of the present invention technical scheme.

Claims (1)

1. for the travel time optimization methods of tourist attractions, for every visitor who enters tourist attractions joinsA standby hand-held terminal device that integrate functions such as Wi-Fi, GPS location, multimedia, is travellingThe AC controller of many places AP WAP, control AP access location-server is set, simultaneously in sight spotThe location-server that receives hand-held terminal device, AP WAP signal is set, default in location-serverLocation database, information management platform, is characterized in that: comprise the following steps:
(1), visitor is while entering tourist attraction, hand-held terminal device reads the RSSI of AP WAP aroundSignal strength values, and the RSSI signal reading is sent to location-server, locator data in location-serverThe RSSI calculated signals that read according to hand-held terminal device in storehouse obtains tourist attraction position, visitor place, simultaneously fixedIn the server of position, information management platform carries out visitor's quantity at visitor's positional information and current each sight spot placeDynamic statistics is upgraded in real time;
(2), visitor by handheld terminal to location-server transmit path select request signal, Path selection pleaseAsk signal by after multiple AP WAPs read near visitor, be sent to positioning service by AC controllerDevice;
(3), location-server receives after visitor's Path selection request signal, unite according to information management platformThe information that meter upgrades, carries out travel time based on Hybrid Particle Swarm and optimizes computing, and operation method is as follows:
(a), for each sight spot in tourist attraction, according to the two-dimensional coordinate number at each sight spot in location databaseAccording to, introducing the coordinate figure in length and breadth at sight spot, unit is rice, to determine each scape in Hybrid Particle Swarm feasible solutionThe position data of point, then to all sight spots, in particular cases independently selects to go sight-seeing sight spot for part visitor,Carry out integer coding; In described Hybrid Particle Swarm by each particle corresponding to a suggestion travel path canRow is separated, and the dimension of feasible solution is exactly all sight spots of independently not selecting of visitor or the autonomous sight spot of selecting of visitorNumber n, the order of the each element of feasible solution is exactly the order in the path, visit sight spot of suggestion;
(b), initiation parameter, initialized parameter is included in the iteration needing in Hybrid Particle Swarm computingNumber of times, population number, people's walking speed v, particle initial position, wherein iterations nMax, population numberOrder indiNum, people's walking speed v are specified values in algorithm initialization program, can respective settings beNMax=200, indiNum=500, v=100m/min, is by matlab program as for the initial position of particleRandom 1 to n this n integer producing of middle randperm (n) function is upset the Serial No. obtaining at random;
(c), calculate according to the following formula the temporal adaptation degree value fitness (k) of k particle:
f i t n e s s ( k ) = ( Σ i , j = 1 n path i j ) / v + Σ i = 1 n ( waittime i k + playtime i k ) - - - ( 1 )
· · · waittime i k = playtime i k · ( num i k - 1 ) - - - ( 2 )
According to formula (1), (2), the formula that obtains the temporal adaptation degree value fitness (k) of k particle is:
. f i t n e s s ( k ) = ( Σ i , j = 1 n path i j ) / v + Σ i = 1 n playtime i k · num i k - - - ( 3 )
In formula (1), (2), (3), n be visitor not independently select all sight spots or visitor from main separationThe number at the sight spot of selecting, pathi,jFor the visitor sight spot that independently all sight spots of selection or visitor independently do not selectI, the distance between j, playtimeikWhen for k particle visitor i place, sight spot play required timeBetween, waittimeikVisitor waits in line the required time at i place, sight spot, num when for k particleikThe number that while playing when for k particle, queue up in i place, sight spot, wherein, number is passed through by location-serverReal-time location to visitor obtains;
(d), particle is carried out to interlace operation: particle by and the individual extreme value of particle this particle up till now oneselfThe optimal solution that the optimal solution Pbest finding and the colony's extreme value particle colony that all particles form finds up till nowGbest intersects to upgrade, and cross method adopts integer interior extrapolation method, specific as follows:
First, in program, use random function to produce two random numbers between 1 to n, this two numberJust corresponding to two positions that needs intersect in particle;
Then, individual to particle and particle extreme value or particle and particle colony extreme value are intersected, the new grain of generationIf there is the sight spot of repeating in son, to adjust, the method for adjustment is that the sight spot that does not have in scenic spot to occur is insertedEnter in the middle of the sequence of new particle, obtain new particle;
Finally, calculate the temporal adaptation degree value of new particle, and by this value and particle individuality extreme value, the particle colony utmost pointTemporal adaptation degree value corresponding to value compares, if better, less, accepts new particle, enters downOne step (e), otherwise, retain original particle constant, enter next step (e);
(e), particle is carried out to mutation operation: adopt two positions of inside particles exchanged, concrete asUnder:
First, in program, use random function to produce two random numbers between 1 to n, this two numberJust corresponding to two positions that need to further make a variation in the last particle of selecting of previous step;
Then, the sight spot order of two positions that produce is exchanged, thereby produce the new grain after mutation operationSon;
Finally, calculate the temporal adaptation degree value of new particle after variation, again and by this value and particle individuality extreme value,Temporal adaptation degree value corresponding to particle colony extreme value compares, if better, less, accepts new grainSon, enters next step (f), otherwise the particle before reservation variation is constant, enters next step (f);
(f), according to temporal adaptation degree value and the individual utmost point to the particle finally obtaining after intersection, mutation operationValue and colony's extreme value comparative result, upgrade optimal value, obtains i.e. this particle of the individual extreme value of more excellent particleThe optimal solution Pbest oneself finding up till now and the colony's extreme value particle colony that all particles form looks for up till nowThe optimal solution Gbest arriving;
(g), judge stop condition: whether iterations reaches maximum, reaches and stops computing, hand-heldThe suggestion travel path of output time optimum on terminal device; Otherwise carry out next round calculating.
CN201310301447.8A 2013-07-18 2013-07-18 A kind of travel time optimization method for tourist attractions Expired - Fee Related CN103402173B (en)

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