CN113886699A - Travel and trip integrated planning method considering travel demands and preferences of user - Google Patents

Travel and trip integrated planning method considering travel demands and preferences of user Download PDF

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CN113886699A
CN113886699A CN202111187161.2A CN202111187161A CN113886699A CN 113886699 A CN113886699 A CN 113886699A CN 202111187161 A CN202111187161 A CN 202111187161A CN 113886699 A CN113886699 A CN 113886699A
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姚恩建
王月
薛飞
杨扬
郇宁
洪俊意
王晓雯
李秋晗
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Beijing Jiaotong University
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Abstract

The invention provides a tourism and trip integrated planning method and system considering user requirements and preferences, which comprises the following steps: acquiring travel demands of users, and constructing a travel dynamic and static database; extracting user travel preference according to the user travel information and the travel behavior model; and arranging the sequence of the travel interest points and the traffic modes among the interest points by combining the travel demands of the users, the travel dynamic and static database and the travel preferences of the users to generate a travel and travel integrated planning scheme. The planning scheme is applied to travel reservation and provides integrated travel service for users. Aiming at the increasing travel personalized demand, the invention considers the convenience of the user as service when going out on one hand and the bearing capacity of scenic spots and road networks on the other hand, guides tourists, relieves the problem of travel congestion and promotes the fusion development of travel and traffic industries.

Description

Travel and trip integrated planning method considering travel demands and preferences of user
Technical Field
The invention relates to the technical field of computer application, in particular to a travel and trip integrated planning method considering travel demands and preferences of a user.
Background
In recent years, with the vigorous development of social economy, the travel demands of the national people are increasing day by day, and the travel and related industries become new growth points of the national economy.
The free movement becomes a new fashion of tourism, however, due to lack of local knowledge, the locations of the scenic spots, the tourism and the connection mode between the scenic spots need to consume excessive extra energy, which causes troubles to the travelers. In addition, from the perspective of local travel markets and traffic systems, the phenomenon of travel of scenic spots and road networks is gradually known and suffered by people due to limited bearing capacity, the phenomenon of travel being crowded on the scenic spots and blocked on the roads, and meanwhile, the messy and multi-source information and high travel planning cost greatly reduce the unavailable travel experience.
The ever-increasing travel demands and the demands for the quality and individuation of the travel promote the acceleration of the innovation and construction process of the industry, and how to carry out the development with high quality and high efficiency while ensuring the high-speed development becomes a new challenge facing the travel and traffic industry.
Therefore, the invention is urgently needed to invent a tourism and trip integrated planning system which integrates multi-channel data and is based on personalized needs of tourists, so that the gap of the value needs of the tourists is filled, the service level of enterprises is improved, the joint development of the industry is promoted, and the construction of tourism cities is assisted.
Disclosure of Invention
The embodiment of the invention provides a travel and trip integrated planning method considering the travel demands and preferences of a user, so as to provide an integrated customized travel scheme meeting the personalized demands of the user.
In order to achieve the purpose, the invention adopts the following technical scheme.
A travel and travel integrated planning method considering travel demands and preferences of a user comprises the following steps:
acquiring travel demands of users, and constructing a travel dynamic and static database;
extracting user travel preference according to the user travel information and the travel behavior model;
and arranging the sequence of the travel interest points and the traffic modes among the interest points by combining the travel demands of the users, the travel dynamic and static database and the travel preferences of the users to generate a travel and travel integrated planning scheme.
Preferably, the obtaining of the travel demand of the user and the constructing of the travel dynamic and static database include:
acquiring user travel demands, wherein the user travel demands comprise travel destinations, travel time lengths, travel times and travel budgets;
collecting each piece of tourist interest point information of a travelling destination of a user, wherein the tourist interest point information comprises each scenic spot category, scenic spot grade, ticket price information, opening time and recommended playing time; the positions of all restaurants around the scenic spot, the types of the restaurants, the restaurant evaluation and the per-person consumption; the positions of all hotels, the prices of all hotels and the evaluation of all hotels form a comprehensive interest point set by the information of all tourist interest points;
acquiring traffic travel attribute information among travel interest points in the comprehensive interest points, wherein the traffic travel attribute information comprises traffic dynamic information, basic travel time of a traffic mode, basic travel cost, transfer times, walking distance, departure frequency, waiting time, reliability, comfortableness, bus information around the interest points and matched parking spaces;
acquiring dynamic information of tourism interest points in the comprehensive interest points, wherein the dynamic information of the interest points is the real-time condition of the interest points and can reflect the attraction of the interest points in different time periods;
acquiring weather conditions of a trip date;
and composing the attribute of the tourist interest points, the attribute of the travel among the tourist interest points, the dynamic information of the interest points and the traffic, and the real-time weather condition into a dynamic and static database of the travel.
Preferably, the extracting of the user travel preference according to the user travel information and the travel behavior model comprises:
the method comprises the steps that a user fills in a travel questionnaire during registration to obtain user travel information, the user travel information comprises personal attributes, travel records and travel wishes, a travel behavior model of the user is constructed, based on the user travel information, the travel behavior model is used for calculating and quantifying the user's preference degrees for travel interest points with different attributes and traffic modes during travel, and the user's preference degrees for the travel interest points with different attributes and the traffic modes are used as user travel preferences;
updating the user travel preferences at set time intervals.
Preferably, the constructing of the travel behavior model of the user includes:
determining influence factors influencing travel of a user, wherein the influence factors comprise interest point attributes, travel attributes, weather factors and user personal attributes, and the user personal attributes comprise age, gender, occupation, monthly income and annual travel times;
constructing a latent variable of a user travelling, wherein the latent variable is alphanThe travel preference which is difficult to be directly observed in travel is represented, and the calculation formula of the latent variable is as follows:
αn＝γazn+ζn,ζn～N(0,σζ)
in the formula, znA vector of personal attribute variables representing user n; gamma rayaRepresenting a vector of coefficients to be determined; zetanRepresenting random variables, assuming that the random variables obey normal distribution, mean 0, standard deviation σζ1, for capturing random parts in the latent variable;
capturing the relation between the latent variable and the measurement index, wherein the measurement index is the answer of the travel attitude question of the user, and the relational expression between the latent variable and the measurement index is as follows:
Figure BDA0003299732720000031
Figure BDA0003299732720000032
in the formula (I), the compound is shown in the specification,
Figure BDA0003299732720000033
representing that the respondent n has a continuous latent variable for the s question; gamma rays,cAnd gammas,mRespectively representing the corresponding intercept and coefficient; v iss,nRepresents a random variable assumed to follow a normal distribution, with a mean of 0 and a standard deviation of σs。Is,nShowing the answer result of the s-th question of the n-th traveler, js,iIndicating the ith answer value, τ, in the s-th questions,iRepresentation corresponds to js,iIn which τ is defineds,i≥τs,i-1；
Constructing a utility function by adopting a potential classification method, and selecting the utility U of the alternative c if the user n belongs to different user types to different degreesn,cComprises the following steps:
Figure BDA0003299732720000034
in the formula, pinqThe calculation method is that the membership degree of the user n to the q-th group of people is as follows:
Figure BDA0003299732720000035
in the formula, znA vector representing the personal attribute factor components; gamma rayqRepresenting a vector of coefficients to be determined; deltaqFixing items for classification; q is the number of user types;
Vc,qselection of alternative c for class q usersThe utility is obtained by the following calculation method:
Vc,q＝β0+βs,qzs+βt,qzt
in the formula, beta0As a selected anchor, betas,qFeature parameter vector, z, of point of interest related variables for class q userssVectors formed by point of interest attribute factors, betat,qCharacteristic parameter vector for traffic-related variables of class q users, ztA vector composed of traffic attribute factors. Particularly, the influence of the weather factors is embodied in the attribute factors of the interest points and the traffic attribute factors, and the cross influence of the weather attributes, the interest points and the traffic attributes exists;
determining values of parameters beta and gamma when the selection probability of all known users reaches the maximum through the travel records, the wish survey and the personal attribute information of the existing users and a maximum likelihood method;
utility function Un,cThe travel preference of the user n for the interest point c and the traffic mode reaching the interest point c contains the interest point attribute factors, the traffic attribute factors and the weather factors, and the preference information of the user for each attribute factor is reflected through the characteristic parameter vector.
Preferably, the step of arranging the sequence of the points of interest of the tour and the transportation modes among the points of interest by combining the user travel demand, the dynamic and static database of the tour and the user travel preference to generate the integrated planning scheme of the tour and the trip comprises the following steps:
the objective function is set as:
Figure BDA0003299732720000041
Figure BDA0003299732720000042
Figure BDA0003299732720000043
wherein i represents a starting point interest point, j represents an end point interest point, m represents a traffic mode, and k represents an influence factor; n represents a point of interest set; m represents a traffic mode set; k represents a set of influencing factors;
z is an objective function, represents the whole-course utility of travel and consists of the utility of each single journey;
Figure BDA0003299732720000044
the decision variables represent whether interest points and traffic modes are selected or not;
Figure BDA0003299732720000045
representing the utility which is obtained by the travel preference of the user and stays at the interest point j in the traffic mode m from the interest point i to the interest point j, namely the single-trip utility; beta is akA characteristic parameter being a factor k;
Figure BDA0003299732720000046
characteristic attributes of the interest point j and the arrival mode m thereof;
and establishing a path constraint of the dynamic time window by combining the actual condition of the travel and the limiting conditions in the user request, wherein the path constraint comprises the following components according to the type of the constraint:
the method comprises the following steps of (1) interest point constraint, namely establishing path constraint of a tourist playing route of a dynamic time window based on a VRP model, ensuring that a user does not repeatedly pass through selectable interest points, and returning to a starting point without special requirements;
the single constraint requires that each selectable interest point except the starting point and the ending point go to the place at most once, only one of all the interest points i can be reached for any one interest point j, and only one traffic mode can be used, and the calculation mode is as follows:
Figure BDA0003299732720000051
when the starting point and the ending point are the same point, the closed-loop constraint is calculated as follows:
Figure BDA0003299732720000052
if the user requests to go to the specified interest point j, all the interest points i must be reachable, and only one traffic mode can be used, the interest point constraint is calculated as follows:
Figure BDA0003299732720000053
when the travel mode required by the user request has transportation means limitation or requires to use a special transportation means, constructing a corresponding transportation means M set;
and (3) time window constraint: when in use
Figure BDA0003299732720000054
In the dynamic time window, the service time and the route travel time are real-time considering queuing, congestion and control, and the next interest point is determined by the start time, the stay time and the route travel time of the previous interest point, and the calculation mode is as follows:
Figure BDA0003299732720000055
Figure BDA0003299732720000056
in the formula, tiIs the arrival time of the point of interest i; svtiObtaining the normal stay time of the interest point i according to the suggested stay time of the interest point and the travel preference of the user; wt. ofi(ti) For the additional time of the point of interest i due to special circumstances,
Figure BDA0003299732720000057
the road travel time from the interest point i to the interest point j changes along with the road condition, and the dynamic property of the road travel time is reflected; b is a positive number to control whether a journey from the point of interest i to the point of interest j exists;
time constraints and cost constraints: calculating the whole consumed time according to the time window, wherein the whole consumed time cannot exceed the preset time upper limit requested by the user; and calculating the whole-course cost according to the interest points and the traffic mode, wherein the whole-course cost cannot exceed the upper limit of the preset cost requested by the user.
Figure BDA0003299732720000061
Figure BDA0003299732720000062
Where the total elapsed time is the base dwell time svt for each point of interestiWaiting time wti(ti) And time of travel between points of interest
Figure BDA0003299732720000063
Summing; the whole day cost is paid by the interest point CiAnd expenditure on travel
Figure BDA0003299732720000064
The two parts are jointly formed.
And solving to obtain a solution which maximizes the objective function as a recommendation scheme, wherein the solution result comprises the arrangement results of the selection, the sequence arrangement and the travel mode of the travel interest points, and outputting an integrated planning scheme of the interest points and the traffic mode, wherein the planning scheme comprises the integrated planning scheme of the selection, the sequence and the traffic travel mode among the interest points.
Preferably, the objective function considers the interest point attribute and the traffic mode attribute as influence factors, and the influence factor weight is obtained by the travel preference of the user.
According to the technical scheme provided by the embodiment of the invention, the tourism preference of the user is analyzed, the attributes of each interest point in the tourism and the traffic attributes among the interest points are fully considered, and the integrated customized tourism scheme which meets the individual requirements of the user and is provided for interest point recommendation, play sequence arrangement, a travel mode and path planning is provided for the user.
Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a processing flow chart of a travel and travel integrated planning method considering user travel needs and preferences according to an embodiment of the present invention;
fig. 2 is a user preference modeling model architecture diagram of the travel and travel integrated planning method considering user travel needs and preferences according to the embodiment of the present invention.
Fig. 3 is a user travel latent variable measurement index system of a travel and travel integrated planning method considering user travel needs and preferences according to an embodiment of the present invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the accompanying drawings are illustrative only for the purpose of explaining the present invention, and are not to be construed as limiting the present invention.
As used herein, the singular forms "a", "an", "the" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. It will be understood that when an element is referred to as being "connected" or "coupled" to another element, it can be directly connected or coupled to the other element or intervening elements may also be present. Further, "connected" or "coupled" as used herein may include wirelessly connected or coupled. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.
It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
For the convenience of understanding the embodiments of the present invention, the following description will be further explained by taking several specific embodiments as examples in conjunction with the drawings, and the embodiments are not to be construed as limiting the embodiments of the present invention.
The processing flow of the travel and trip integrated planning method considering the travel demands and preferences of the user disclosed by the embodiment of the invention is shown in fig. 1, and comprises the following processing steps:
step S1: and acquiring the travel demands of the users, and constructing a travel dynamic and static database.
S11: and acquiring the travel requirement of the user.
The user initiates a travel planning request to the system, in which the user provides travel destinations (single city, surrounding areas), travel durations (half day trip, multiple day trip), travel times, and travel budgets. Optionally, the user specifies an intended location and a travel mode, such as a specified accommodation location and a necessary scene of play, and the specified travel mode is a self-driving travel.
S12: and constructing a travel static database.
The method comprises the steps of obtaining attributes of the tourist interest points, wherein the attributes of the tourist interest points comprise the type, the position, the opening time, the consumption price and the attributes of the environment capable of reflecting the characteristics of the interest points. Collecting information of each tourist interest point of a destination, and collecting categories (natural scenery, scenic spots and historical sites, museum and old residences, park and paradise, large business circles, feature street meetings, religious temples, architecture humanity), scenic spot grades (1-5A), scenic spot evaluation values, heat (search times and play heat), ticket price information, open time and recommended play time of each tourist; collecting the positions of restaurants, types of restaurants, restaurant evaluations and per-person consumption around the scenic spot; and collecting positions of all hotels at the destination, hotel prices and hotel evaluations to form a comprehensive interest point set as an alternative interest point set of the destination. Preferably, the point of interest attribute is obtained by integrating the network information resources of the travel trip type, the travel strategy type and the map navigation type.
The method comprises the steps of obtaining traffic travel attributes, and collecting basic travel time, basic travel cost, transfer times, walking distance, departure frequency, waiting time, reliability, comfort, bus information around the points of interest and matched parking spaces of selectable traffic modes among the points of interest. The basic travel time and the basic travel cost are travel time and travel cost without considering congestion and queuing.
S13: and acquiring dynamic information of travel. .
And acquiring dynamic information of the interest points. The dynamic information of the interest points is the real-time condition of the interest points and can reflect the attraction of the interest points in different time periods. Preferably, the point-of-interest dynamic information includes real-time congestion (less, moderate, more), queue wait time, current-limiting measures, temporary closure, special events, and discount promotion dynamic information. Preferably, the dynamic information of the interest point is acquired through the real-time information collection published by the interest point, and the dynamic information of the interest point is acquired through the cooperation and butt joint with the interest point.
And acquiring traffic dynamic information. The traffic dynamic information is the traffic conditions among the points of interest, and the travel experience is influenced by the congestion condition possibly caused by the influence of the travel time period and the accidental accidents. Preferably, the traffic dynamic information includes temporary traffic control, future travel time prediction, waiting time of transportation mode, and travel cost floating condition. Preferably, the dynamic traffic information between the points of interest is acquired through a map navigation API interface, and a real-time traffic travel scheme is acquired, so that a control area is avoided, and a congested route and a congested time period are avoided.
And acquiring the weather condition of the travel date. Preferably, the weather conditions include time-shared high temperature, rain and snow, and haze conditions.
And composing the attribute of the tourist interest points, the attribute of the travel among the tourist interest points, the dynamic information of the interest points and the traffic, and the real-time weather condition into a dynamic and static database of the travel.
Step S2: and extracting the travel preference of the user according to the user information and the travel behavior model. And constructing a travel behavior model, calculating and quantifying the love degree of the user to travel interest points with different attributes and traffic ways in travel through user information, and taking the love degree as the travel preference of the user.
S21: user travel information is obtained. The user acquires personal attributes, travel records, and travel willingness at registration by filling out a travel questionnaire. Personal attributes of the user are obtained, including age, gender, occupation, monthly income, and number of annual trips. Optionally, the user fills in travel records of the last year, including sightseeing, traffic, accommodation type, travel expense; the user fills out a travel wish survey: the user selects the most desirable travel option from the set attractions and transportation modes.
S22: fig. 2 is a user preference modeling model architecture diagram of a travel and travel integrated planning method considering user travel needs and preferences, which is provided by an embodiment of the present invention, and as shown in fig. 2, in a method for extracting user preferences, the present invention provides a behavior modeling manner, which can identify and quantify factors affecting user selected behaviors, and is a modeling manner commonly used in behaviourology.
S221: determining the influencing factor. Considering the influence factors influencing the travel of the user, the method further comprises the following steps: point of interest attribute and travel attribute in S12, point of interest in S13, traffic and weather dynamic information, user travel information in S21. The user personal attributes include age, gender, occupation, monthly income, and number of annual trips. The above-described influencing factors act on the utility function of S222.
And (4) constructing a travel latent variable and representing the influence of the user travel attitude.
Construction of travel latent variable alphanThe travel preference which is difficult to be directly observed in travel is represented by the following calculation formula of latent variables:
αn＝γazn+ζn,ζn～N(0,σζ)
in the formula, znA vector of personal attribute variables representing user n; gamma rayaRepresenting a vector of coefficients to be determined; zetanRepresenting random variables, assuming that the random variables obey normal distribution, mean 0, standard deviation σζIs 1, is used to capture the random portion of the latent variable.
Referring to fig. 3, the embodiment uses the tourism timeliness, tourism economy, sight spot specificity, sight spot recommendation, travel experience and environmental awareness as 6 latent variables.
Capturing a relation between the latent variable and a measurement index, wherein the measurement index is a travel attitude question answer of the user. In the implementation case, 23 attitude-related questions are designed to correspond to the 6 latent variables respectively, 5-level quantity table type questions are adopted, and in order to avoid nonlinearity of an answer result, a sequence logit model is adopted for description.
The relational expression between the latent variable and the measurement index is as follows:
Figure BDA0003299732720000091
Figure BDA0003299732720000101
in the formula (I), the compound is shown in the specification,
Figure BDA0003299732720000102
representing that the respondent n has a continuous latent variable for the s question; gamma rays,cAnd gammas,mRespectively representing the corresponding intercept and coefficient; v iss,nRepresents a random variable assumed to follow a normal distribution, with a mean of 0 and a standard deviation of σs。Is,nShowing the answer result of the s-th question of the n-th traveler, js,iIndicating the ith answer value, τ, in the s-th questions,iRepresentation corresponds to js,iIn which τ is defineds,i≥τs,i-1。
And obtaining measurement results of 6 latent variables and personal attribute variables through 23 measurement indexes, wherein the measurement results are used as the travel attitude of the user, and the latent variables and the personal attribute variables are commonly used for constructing the utility function.
S222: and constructing a utility function. Different decision modes of different types of users for travel exist, and a potential classification method is adopted to construct a utility function under the condition of considering user heterogeneity. If the user n belongs to different user types to different extents, the utility U of the alternative c is selectedn,cComprises the following steps:
Figure BDA0003299732720000103
in the formula, pinqThe calculation method is that the membership degree of the user n to the q-th group of people is as follows:
Figure BDA0003299732720000104
in the formula, znA vector representing the personal attribute factor components; gamma rayqRepresenting a vector of coefficients to be determined; deltaqFixing items for classification; q is the number of user types;
Vc,qacquisition of alternative c for class q user selectionThe utility is calculated in the following way:
Vc,q＝β0+βs,qzs+βt,qzt
in the formula, beta0As a selected anchor, betas,qFeature parameter vector, z, of point of interest related variables for class q userssVectors formed by point of interest attribute factors, betat,qCharacteristic parameter vector for traffic-related variables of class q users, ztA vector composed of traffic attribute factors. Particularly, the influence of the weather factors is embodied in the attribute factors of the interest points and the traffic attribute factors, and the cross influence of the weather attributes, the interest points and the traffic attributes exists;
preferably, the partial interest points and the traffic attribute variables change along with the change of the external environment, and the values in different time periods are different.
S223: determining values of parameters beta and gamma when the selection probability of all known users reaches the maximum through the travel records, the wish survey and the personal attribute information of the existing users and a maximum likelihood method;
utility function Un,cThe travel preference of the user n for the interest point c and the traffic mode reaching the interest point c contains the interest point attribute factors, the traffic attribute factors and the weather factors, and the preference information of the user for each attribute factor is reflected through the characteristic parameter vector. The utility function is used for calculating the travel objective function in S31.
S23: user preferences are extracted and updated periodically based on the user travel information.
S231: and for the user who uses for the first time, according to the user travel information filled in during registration, applying the travel behavior model to obtain quantitative travel user preference.
S232: and updating the calibration result of the travel behavior model to the user through the use record of the user and updating the user preference by taking one month as a period.
Step S3: and planning a travel scheme.
And considering the dynamic and static travel data and the travel preference of the user, considering the interest point selection and the traffic arrangement among the interest points, integrally planning the interest point selection, the play sequence and the traffic mode of the user, and constructing a travel and traffic integrated travel scheme. The integrated travel planning further comprises the following steps:
s31: setting an objective function, comprehensively considering the factors from the interest points, the travel, the weather and the user individuals in the S1 and the S2, wherein the objective function is as follows:
Figure BDA0003299732720000111
Figure BDA0003299732720000112
Figure BDA0003299732720000113
wherein i represents a starting point interest point, j represents an end point interest point, m represents a traffic mode, and k represents an influence factor; n represents a point of interest set; m represents a traffic mode set; k represents a set of influencing factors.
Z is an objective function, represents the whole-course utility of travel and consists of the utility of each single journey;
Figure BDA0003299732720000121
and the decision variables represent whether the interest points and the transportation modes are selected or not.
Figure BDA0003299732720000122
The utility representing the traffic mode m from the interest point i to the interest point j and staying at the interest point j, namely the single-trip utility, is obtained by the travel preference of the S2 user; beta is akA characteristic parameter being a factor k;
Figure BDA0003299732720000123
is the point of interest j and its arrivalCharacteristic properties of the formula m.
S32: generating constraint, combining the actual condition of travel and the limiting condition in the user request, establishing the path constraint of the dynamic time window, and further comprising the following steps according to the type of the constraint:
s321: and (4) interest point constraint, namely establishing path constraint of a tourist playing Route of a dynamic time window based on a VRP (video Route Problem) model, and ensuring that a user does not repeatedly pass through selectable interest points and returns to a starting point without special requirements.
The single constraint requires that each selectable interest point go to once at most (except the starting point and the ending point), only one of all the interest points i can be reached for any one interest point j, and only one traffic mode can be used, and the calculation mode is as follows:
Figure BDA0003299732720000124
preferably, the closed-loop constraint is omitted if the departure point and the end point are not at the same position, and the travel plan is guaranteed to be a closed loop if the departure point and the end point are at the same position, and the constraint calculation method is as follows:
Figure BDA0003299732720000125
optionally, an interest point constraint is specified, if a user request requires to go to a specified interest point j, all interest points i must be reachable, and only one traffic mode can be used, and the calculation mode is as follows:
Figure BDA0003299732720000126
optionally, the designated transportation mode, the travel mode required in the user request has transportation restriction or requires to use a special transportation, and the corresponding transportation mode M set is constructed.
S322: a time window constraint. When in use
Figure BDA0003299732720000127
The interest point j is determined by the start time of the interest point i, the service time and the journey time. Under the dynamic time window, the service time and the road travel time are real-time considering queuing, congestion and control, and can deal with delay and emergency. The next interest point is determined by the starting time, the residence time and the route travel time of the previous interest point, and the calculation method is as follows:
Figure BDA0003299732720000128
Figure BDA0003299732720000131
in the formula, tiIs the arrival time of the point of interest i; svtiObtaining the normal stay time of the interest point i according to the suggested stay time of the interest point and the travel preference of the user; wt. ofi(ti) Additional time for point of interest i due to special circumstances, such as congestion queuing and current limiting;
Figure BDA0003299732720000132
the road travel time from the interest point i to the interest point j changes along with the road condition, and the dynamic property of the road travel time is reflected; b is a large positive number to control whether a trip from point of interest i to point of interest j exists.
S323: time constraints and cost constraints. Calculating the whole consumed time according to the time window, wherein the whole consumed time cannot exceed the preset time upper limit requested by the user; and calculating the whole-course cost according to the interest points and the traffic mode, wherein the whole-course cost cannot exceed the upper limit of the preset cost requested by the user.
Figure BDA0003299732720000133
Figure BDA0003299732720000134
Where the total elapsed time is the base dwell time svt for each point of interestiWaiting time wti(ti) And time of travel between points of interest
Figure BDA0003299732720000135
Summing; the whole day cost is paid by the interest point CiAnd expenditure on travel
Figure BDA0003299732720000136
The two parts are jointly formed.
S33: and (5) forming a scheme. And solving to obtain a solution which maximizes the objective function and other larger feasible solutions as recommendation schemes, wherein the solution results comprise the arrangement results of the selection, the sequence arrangement and the travel mode of the travel interest points, and outputting an integrated planning scheme of the interest points and the traffic mode, wherein the planning scheme comprises the integrated planning scheme of the selection, the sequence and the traffic travel mode among the interest points. And feeding back the recommended scheme to the user, determining a final travel scheme by the user, and storing a selection result in a user travel information record.
Preferably, the planning scheme can be used for travel reservation, reserved travel is realized according to the sequence and time arrangement of each interest point in the planning scheme, unnecessary waiting time of a user is further reduced, and travel service quality is improved.
In summary, the integrated planning scheme for travel and trip generated by the method of the embodiment of the invention can be applied to travel and trip reservation, and provides integrated travel service for users. Aiming at the increasing travel personalized demand, the invention considers the convenience of the user as service when going out on one hand and the bearing capacity of scenic spots and road networks on the other hand, guides tourists, relieves the problem of travel congestion and promotes the fusion development of travel and traffic industries.
Those of ordinary skill in the art will understand that: the figures are merely schematic representations of one embodiment, and the blocks or flow diagrams in the figures are not necessarily required to practice the present invention.
From the above description of the embodiments, it is clear to those skilled in the art that the present invention can be implemented by software plus necessary general hardware platform. Based on such understanding, the technical solutions of the present invention may be embodied in the form of a software product, which may be stored in a storage medium, such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method according to the embodiments or some parts of the embodiments.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for apparatus or system embodiments, since they are substantially similar to method embodiments, they are described in relative terms, as long as they are described in partial descriptions of method embodiments. The above-described embodiments of the apparatus and system are merely illustrative, and the units described as separate parts may or may not be physically separate, and the parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims (6)

1. A travel and travel integrated planning method considering travel demands and preferences of a user is characterized by comprising the following steps:
acquiring travel demands of users, and constructing a travel dynamic and static database;
extracting user travel preference according to the user travel information and the travel behavior model;
and arranging the sequence of the travel interest points and the traffic modes among the interest points by combining the travel demands of the users, the travel dynamic and static database and the travel preferences of the users to generate a travel and travel integrated planning scheme.
2. The method of claim 1, wherein the obtaining of the travel demands of the user and the constructing of the travel dynamic and static database comprise:
acquiring user travel demands, wherein the user travel demands comprise travel destinations, travel time lengths, travel times and travel budgets;
collecting each piece of tourist interest point information of a travelling destination of a user, wherein the tourist interest point information comprises each scenic spot category, scenic spot grade, ticket price information, opening time and recommended playing time; the positions of all restaurants around the scenic spot, the types of the restaurants, the restaurant evaluation and the per-person consumption; the positions of all hotels, the prices of all hotels and the evaluation of all hotels form a comprehensive interest point set by the information of all tourist interest points;
acquiring traffic travel attribute information among travel interest points in the comprehensive interest points, wherein the traffic travel attribute information comprises traffic dynamic information, travel time of a traffic mode, travel cost, transfer times, walking distance, departure frequency, waiting time, reliability, comfort, bus information around the interest points and matched parking spaces;
acquiring dynamic information of tourism interest points in the comprehensive interest points, wherein the dynamic information of the interest points is the real-time condition of the interest points and can reflect the attraction of the interest points in different time periods;
acquiring weather conditions of a trip date;
and composing the attribute of the tourist interest points, the attribute of the travel among the tourist interest points, the dynamic information of the interest points and the traffic, and the real-time weather condition into a dynamic and static database of the travel.
3. The method of claim 1, wherein the extracting the user travel preference according to the user travel information and the travel behavior model comprises:
the method comprises the steps that a user fills in a travel questionnaire during registration to obtain user travel information, the user travel information comprises personal attributes, travel records and travel wishes, a travel behavior model of the user is constructed, based on the user travel information, the travel behavior model is used for calculating and quantifying the user's preference degrees for travel interest points with different attributes and traffic modes during travel, and the user's preference degrees for the travel interest points with different attributes and the traffic modes are used as user travel preferences;
updating the user travel preferences at set time intervals.
4. The method of claim 3, wherein the constructing of the travel behavior model of the user comprises:
determining influence factors influencing travel of a user, wherein the influence factors comprise interest point attributes, travel attributes, weather factors and user personal attributes, and the user personal attributes comprise age, gender, occupation, monthly income and annual travel times;
constructing a latent variable of a user travelling, wherein the latent variable is alphanThe travel preference which is difficult to be directly observed in travel is represented, and the calculation formula of the latent variable is as follows:
αn＝γazn+ζn,ζn～N(0,σζ)
in the formula, znA vector of personal attribute variables representing user n; gamma rayaRepresenting a vector of coefficients to be determined; zetanRepresenting random variables, assuming that the random variables obey normal distribution, mean 0, standard deviation σζ1, for capturing random parts in the latent variable;
capturing the relation between the latent variable and the measurement index, wherein the measurement index is the answer of the travel attitude question of the user, and the relational expression between the latent variable and the measurement index is as follows:
Figure FDA0003299732710000021
Figure FDA0003299732710000022
in the formula (I), the compound is shown in the specification,
Figure FDA0003299732710000023
representing that the respondent n has a continuous latent variable for the s question; gamma rays,cAnd gammas,mRespectively representing the corresponding intercept and coefficient; v iss,nRepresents a random variable assumed to follow a normal distribution, with a mean of 0 and a standard deviation of σs。Is,nShowing the answer result of the s-th question of the n-th traveler, js,iIndicating the ith answer value, τ, in the s-th questions,iRepresentation corresponds to js,iIn which τ is defineds,i≥τs,i-1；
Constructing a utility function by adopting a potential classification method, and selecting the utility U of the alternative c if the user n belongs to different user types to different degreesn,cComprises the following steps:
Figure FDA0003299732710000031
in the formula, pinqThe calculation method is that the membership degree of the user n to the q-th group of people is as follows:
Figure FDA0003299732710000032
in the formula，znA vector representing the personal attribute factor components; gamma rayqRepresenting a vector of coefficients to be determined; deltaqFixing items for classification; q is the number of user types;
Vc,qselecting the alternative item c for the q-th class of users to obtain the utility by the following calculation method:
Vc,q＝β0+βs,qzs+βt,qzt
in the formula, beta0As a selected anchor, betas,qFeature parameter vector, z, of point of interest related variables for class q userssVectors formed by point of interest attribute factors, betat,qCharacteristic parameter vector for traffic-related variables of class q users, ztA vector composed of traffic attribute factors. Particularly, the influence of the weather factors is embodied in the attribute factors of the interest points and the traffic attribute factors, and the cross influence of the weather attributes, the interest points and the traffic attributes exists;
determining values of parameters beta and gamma when the selection probability of all known users reaches the maximum through the travel records, the wish survey and the personal attribute information of the existing users and a maximum likelihood method;
utility function Un,cThe travel preference of the user n for the interest point c and the traffic mode reaching the interest point c contains the interest point attribute factors, the traffic attribute factors and the weather factors, and the preference information of the user for each attribute factor is reflected through the characteristic parameter vector.
5. The method according to any one of claims 1 to 4, wherein the step of arranging the sequence of the points of interest of the tour and the transportation modes among the points of interest by combining the user travel demand, the dynamic and static database of the tour and the user travel preference to generate the integrated plan scheme of tour and travel comprises the following steps:
the objective function is set as:
Figure FDA0003299732710000033
Figure FDA0003299732710000034
Figure FDA0003299732710000041
wherein i represents a starting point interest point, j represents an end point interest point, m represents a traffic mode, and k represents an influence factor; n represents a point of interest set; m represents a traffic mode set; k represents a set of influencing factors;
z is an objective function, represents the whole-course utility of travel and consists of the utility of each single journey;
Figure FDA0003299732710000042
the decision variables represent whether interest points and traffic modes are selected or not;
Figure FDA0003299732710000043
representing the utility which is obtained by the travel preference of the user and stays at the interest point j in the traffic mode m from the interest point i to the interest point j, namely the single-trip utility; beta is akA characteristic parameter being a factor k;
Figure FDA0003299732710000044
characteristic attributes of the interest point j and the arrival mode m thereof;
and establishing a path constraint of the dynamic time window by combining the actual condition of the travel and the limiting conditions in the user request, wherein the path constraint comprises the following components according to the type of the constraint:
the method comprises the following steps of (1) interest point constraint, namely establishing path constraint of a tourist playing route of a dynamic time window based on a VRP model, ensuring that a user does not repeatedly pass through selectable interest points, and returning to a starting point without special requirements;
the single constraint requires that each selectable interest point except the starting point and the ending point go to the place at most once, only one of all the interest points i can be reached for any one interest point j, and only one traffic mode can be used, and the calculation mode is as follows:
Figure FDA0003299732710000045
when the starting point and the ending point are the same point, the closed-loop constraint is calculated as follows:
Figure FDA0003299732710000046
if the user requests to go to the specified interest point j, all the interest points i must be reachable, and only one traffic mode can be used, the interest point constraint is calculated as follows:
Figure FDA0003299732710000047
when the travel mode required by the user request has transportation means limitation or requires to use a special transportation means, constructing a corresponding transportation means M set;
and (3) time window constraint: when in use
Figure FDA0003299732710000048
In the dynamic time window, the service time and the route travel time are real-time considering queuing, congestion and control, and the next interest point is determined by the start time, the stay time and the route travel time of the previous interest point, and the calculation mode is as follows:
Figure FDA0003299732710000051
Figure FDA0003299732710000052
in the formula, tiIs the arrival time of the point of interest i; svtiObtaining the normal stay time of the interest point i according to the suggested stay time of the interest point and the travel preference of the user; wt. ofi(ti) For the additional time of the point of interest i due to special circumstances,
Figure FDA0003299732710000053
the road travel time from the interest point i to the interest point j changes along with the road condition, and the dynamic property of the road travel time is reflected; b is a positive number to control whether a journey from the point of interest i to the point of interest j exists;
time constraints and cost constraints: calculating the whole consumed time according to the time window, wherein the whole consumed time cannot exceed the preset time upper limit requested by the user; and calculating the whole-course cost according to the interest points and the traffic mode, wherein the whole-course cost cannot exceed the upper limit of the preset cost requested by the user.
Figure FDA0003299732710000054
Figure FDA0003299732710000055
Where the total elapsed time is the base dwell time svt for each point of interestiWaiting time wti(ti) And time of travel between points of interest
Figure FDA0003299732710000056
Summing; the whole day cost is paid by the interest point CiAnd expenditure on travel
Figure FDA0003299732710000057
The two parts are jointly formed.
And solving to obtain a solution which maximizes the objective function as a recommendation scheme, wherein the solution result comprises the arrangement results of the selection, the sequence arrangement and the travel mode of the travel interest points, and outputting an integrated planning scheme of the interest points and the traffic mode, wherein the planning scheme comprises the integrated planning scheme of the selection, the sequence and the traffic travel mode among the interest points.
6. The method of claim 5, wherein the objective function considers the point of interest attribute and the transportation attribute as influencing factors, and the influencing factor weight is obtained by the user travel preference.
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