Urban agglomeration travel fusion development prediction and evaluation method

Abstract

The invention relates to the technical field of urban agglomeration development prediction, in particular to an urban agglomeration travel fusion development prediction and evaluation method. The method comprises the following steps: establishing an urban agglomeration associated tourism project data model, and obtaining an associated bar of an associated tourism project in combination with tourist consumption content; all the association conditions are weighted, and an association condition weight matching database is established. According to the invention, by determining the development direction of the to-be-developed tourism project in advance, i.e., tourist attractions, travel modes, consumption ranges and the like, and combining with the weight ratio database, the associated city fusion change prediction map is formulated, and the association trend of the to-be-developed tourism project and each tourism project of the urban agglomeration in the region is determined. For example, association degrees between different time periods and corresponding associated tourism projects are obtained according to the change trend of the change prediction map, and travel fusion propaganda is carried out in advance for later operation.

Description

Technical Field

[0001] The present invention relates to the technical field of urban agglomeration development prediction, and in particular to a method for predicting and evaluating the integrated development of transportation and tourism in an urban agglomeration. Background Art

[0002] The integrated development of transportation and tourism in urban agglomerations refers to the deep integration of the tourism industry within the urban agglomeration to achieve resource sharing, complementary advantages, and promote the coordinated development of regional economy and society. This development model can effectively expand domestic demand and promote consumption.

[0003] In the process of integrated development of transportation and tourism in urban agglomerations, their mutually integrated tourism industries have certain connections. For example, adjacent cities are close to each other, which leads to a binding integration of the tourism industries between the two cities. That is, tourists will visit the tourism projects in one city and then the other city. Therefore, there will be correlation conditions between the related tourism projects of the urban agglomeration. However, since the tourism projects to be opened have not been actually operated, it is impossible to determine their integration trend with the tourism projects in other cities, thus missing the best opportunity for publicity and integration.

[0004] In order to address the above problems, a prediction and evaluation method for the integrated development of transportation and tourism in urban agglomerations is urgently needed. Summary of the invention

[0005] The purpose of the present invention is to provide a method for predicting and evaluating the integrated development of transportation and tourism in urban agglomerations to solve the problems raised in the above-mentioned background technology.

[0006] To achieve the above purpose, a prediction and evaluation method for the integrated development of transportation and tourism in urban agglomerations is provided, which includes the following steps:

[0007] S1. Divide the urban agglomeration area, obtain various tourism projects of urban agglomerations in the area, and establish an urban agglomeration tourism project database;

[0008] S2. Collect tourists’ consumption content and obtain related tourism projects Rt between city clusters projects ;

[0009] S3. Establishing the data model of tourism projects associated with urban agglomerations TP model , and combined with the tourist consumption content in S2, obtain the associated conditions As for the associated tourism items conditions ;

[0010] S4. For each associated condition As conditions Empower and establish association conditions As conditions Weighted ratio database;

[0011] S5. Obtain the tourism projects to be developed in the urban agglomeration, determine the development direction of the tourism projects to be developed, and select the ones that meet the association conditions of the urban agglomeration in the region.conditions ;

[0012] S6. Combined with the weighted ratio database, a prediction map of the integration changes of related cities is developed to determine the correlation trend between the tourism projects to be developed and the various tourism projects in the urban agglomerations in the region.

[0013] As a further improvement of the technical solution, the method of dividing the urban agglomeration area in S1 includes the following steps:

[0014] S1.1. Establishing tourism consumption time threshold Ti threshold ;

[0015] S1.2. Obtain the distance Ap between tourist projects in adjacent cities distance , average commuting time Ti average And the average time Ti spent on the game items consume ;

[0016] S1.3. Calculate the travel consumption time Ti travel = Average commuting time Ti average +Average time spent on activitiesTi consume ;

[0017] S1.4. When there is tourism consumption time Ti in adjacent cities travel <Tourism consumption time threshold Ti threshold If there are tourism projects in the city, the adjacent cities will be marked as a group.

[0018] As a further improvement of this technical solution, the associated tourism projects Rt between the city clusters are obtained in S2 projects The method comprises the following steps:

[0019] S2.1. Specify a unit statistical time T0, and count the number of visitors to the tourism projects in the urban agglomeration within the unit statistical time T0. volume ;

[0020] S2.2. Mix of two or more tourist attractions between city clusters within the statistical unit statistical time T0 volume ;

[0021] S2.3. Calculate Mix volume The number of visits to the previous tourist project Pl volume The ratio of , get the mixed play rate Mix rate ;

[0022] S2.4. Establishing a mixed play rate threshold

[0023] When the mixed play rate Mix rate ≥ Mixed play rate threshold When the tourism project played later is related to the tourism project played earlier, Rt projects ;

[0024] When the mixed play rate Mix rate <Mixed play rate threshold When the tourism project played later and the tourism project played earlier are accidental tourism projects Oc projects .

[0025] As a further improvement of this technical solution, the city cluster associated tourism project data model TP is established in S3 model The method comprises the following steps:

[0026] S3.1. Count the related tourism projects Rt of each group in the urban agglomeration projects , and bind the corresponding mixed play rate Mix rate ;

[0027] S3.2, Mix by Mix Rate rate Size determination of each group of related tourism projects Rt projects degree of association;

[0028] S3.3. Create a dynamic change graph to display the Rt of each group of related tourism projects in real time projects The trend of the degree of correlation.

[0029] As a further improvement of the technical solution, the dynamic change graph in S3.3 is established by programming in Python, and the steps of establishing the dynamic change graph are as follows:

[0030] The first step is to install matplotlib and pandas in the Python script;

[0031] Step 2: Import matplotlib.pyplot and pandas into the Python script.

[0032] Step 3: Use pandas to create data;

[0033] Step 4: Create a bar chart using matplotlib;

[0034] Step 5. Create a function to read new data and update the chart, where the horizontal axis is the associated tourism project Rt projects , the vertical axis is the corresponding mixed play rate Mix rate .

[0035] As a further improvement of this technical solution, the association condition As in S3 conditions These include distance between each other, commuting guarantee, interactive promotion of specialty products, complementary advantages and disadvantages, and bundled discounts;

[0036] Wherein, the distance is the distance between related tourism projects;

[0037] The commuting guarantee means that there is a fixed commuting method between the related tourism projects;

[0038] The mutual promotion of special products refers to the promotion of the characteristics of related tourism projects;

[0039] The complementary advantages and disadvantages refer to the existence of attractions with complementary advantages and disadvantages between the related tourism projects;

[0040] The binding discount is a discount on the relevant fees for purchasing related travel items.

[0041] As a further improvement of this technical solution, the various association conditions As in S4 are conditions The method of empowerment includes the following steps:

[0042] S4.1. Establish unit weight Un weight , determine the associated tourism project Rt projects Matching association conditions As conditions ;

[0043] S4.2. For each associated condition As conditions Assign the original weight Or weight , each time you increase the amount of mixed play Mix volume , related tourism projects Rt projects Matching association conditions As conditions Increase the unit weight Un weight ;

[0044] S4.3. Count the associated conditions As in each time period conditions The weight changes and real-time update of the associated conditions As conditions Weighted matching database.

[0045] As a further improvement of this technical solution, the various association conditions As in S4.2 conditions Assign the original weight Or weight The unit weighting algorithm is adopted, and its algorithm formula is as follows:

[0046] Or weight =ρFix weight ×μGr rate ;

[0047]

[0048] Fix weight Each associated condition As conditions Fixed weight, Fix weightis a fixed value, ρ is the associated condition As conditions The degree of influence of Gr rate As the association condition conditions The unit increase of mixed play rate, μ is the mixed play constant, which is positively correlated with the influence degree ρ, T0 is the unit time, For the occurrence of the associated condition As conditions The total amount of mixed play before, The associated condition As appears within the unit time T0 conditions The total amount of mixed play afterwards.

[0049] Compared with the prior art, the present invention has the following beneficial effects:

[0050] In the prediction and evaluation method for the integration of transportation and tourism in this urban agglomeration, by determining in advance the development direction of the tourism projects to be developed, namely tourist attractions, travel patterns and consumption scope, etc., combined with the weight ratio database, a prediction map of the integration changes of related cities is formulated to determine the correlation trend between the tourism projects to be developed and the various tourism projects in the urban agglomerations in the region. For example, the change trend of the change prediction map can obtain the degree of correlation between different time periods and the corresponding related tourism projects, so as to carry out publicity for the integration of transportation and tourism in advance for the later operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 It is the overall process step diagram of the present invention;

[0052] Figure 2 A diagram showing the steps of a method for dividing the scope of urban agglomeration areas according to the present invention;

[0053] Figure 3 For the present invention, the associated tourism projects Rt between the city clusters are obtained projects Method steps diagram;

[0054] Figure 4 The data model TP for establishing the city cluster associated tourism projects of the present invention model Method steps diagram;

[0055] Figure 5 For the present invention, each associated condition As conditions A step-by-step diagram of the empowerment method. DETAILED DESCRIPTION

[0056] The following will be combined with the accompanying drawings in the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0057] See also Figure 1As shown, a prediction and evaluation method for the integrated development of transportation and tourism in urban agglomerations is provided, comprising the following steps:

[0058] S1. Divide the urban agglomeration area, obtain various tourism projects of urban agglomerations in the area, and establish an urban agglomeration tourism project database;

[0059] S2. Collect tourists’ consumption content and obtain related tourism projects Rt between city clusters projects ;

[0060] S3. Establishing the data model of tourism projects associated with urban agglomerations TP model , and combined with the tourist consumption content in S2, obtain the associated conditions As for the associated tourism projects conditions ;

[0061] S4. For each associated condition As conditions Empower and establish association conditions As conditions Weighted ratio database;

[0062] S5. Obtain the tourism projects to be developed in the urban agglomeration, determine the development direction of the tourism projects to be developed, and select the ones that meet the association conditions of the urban agglomeration in the region. conditions ;

[0063] S6. Combined with the weighted ratio database, a prediction map of the integration changes of related cities is developed to determine the correlation trend between the tourism projects to be developed and the various tourism projects in the urban agglomerations in the region.

[0064] When used specifically, in the process of predicting the development of integrated transportation and tourism for tourism projects to be developed, since the tourism integration of urban agglomeration development needs to be established within a certain regional scope, for cities that are far away, it is difficult to integrate their tourism projects due to inconvenient transportation and tourists spending too much time. Even if tourists visit tourism projects in distant cities in a short period of time, it is accidental and has no reference value. Therefore, it is necessary to determine urban agglomerations within a certain regional scope. Therefore, firstly, by dividing the regional scope of urban agglomerations, obtaining various tourism projects of urban agglomerations in the region, and establishing an urban agglomeration tourism project library, that is, specifying the distance range of urban agglomerations in advance, and determining urban agglomerations within this range;

[0065] Since the city clusters that integrate transportation and tourism need to be determined by the number of tourists, in order to determine the related tourism projects in the city clusters, it is necessary to obtain the related tourism projects Rt between the city clusters by collecting tourists’ consumption content. projects That is, in a short period of time, tourists visit a tourist project in a city in the city cluster and then visit a tourist project in another city. It is worth noting that this way of playing is accidental, so it needs to reach a certain probability to be defined as a related tourist project Rt projects, and then establish the city cluster associated tourism project data model TP model , and combined with the tourists' consumption content, obtain the associated conditions of the associated tourism projects As conditions , such as distance, complementary tourist attractions, and promotion of local specialties, are all used as association conditions As con itions, for each associated condition As conditions To empower, the correlation rate Co rate The larger the value, the greater the corresponding weight, which means that the association condition As conditions The higher the degree of influence, the more likely it is that the association condition As conditions Weighted ratio database;

[0066] When undeveloped tourism projects appear in urban agglomerations, in order to predict their future operation and the possibility of integration with other tourism projects in the urban agglomeration, it is necessary to determine in advance the development direction of the undeveloped tourism projects, that is, tourist attractions, travel patterns, and consumption scope, etc. These are all used as references for later predictions. Combined with the weight ratio database, a prediction map of related city integration changes is formulated to determine the correlation trend between the undeveloped tourism projects and various tourism projects in the urban agglomerations in the region. For example, the change trend of the change prediction map obtains the degree of correlation between different time periods and the corresponding related tourism projects, so as to carry out transportation and tourism integration publicity in advance for later operation.

[0067] In addition, the method for dividing the urban agglomeration area in S1 includes the following steps:

[0068] S1.1. Establishing tourism consumption time threshold Ti threshold ;

[0069] S1.2. Obtain the distance Ap between tourist projects in adjacent cities distance , average commuting time Ti average And the average time Ti spent on the game items consume ;

[0070] S1.3. Calculate the travel consumption time Ti travel = Average commuting time Ti average +Average time spent on activitiesTi consume ;

[0071] S1.4. When there is tourism consumption time Ti in adjacent cities travel <Tourism consumption time threshold Ti threshold If there are tourism projects in the city, the adjacent cities will be marked as a group.

[0072] In specific use, in the process of dividing the urban agglomeration area, it is necessary to consider a variety of influencing factors, such as the distance between two tourist projects and commuting time. In order to exclude accidental tourism, it is necessary to first establish a tourism consumption time threshold Ti. threshold , that is, the total time threshold spent in playing two tourist projects, including the commuting time and the time spent on the projects, and then obtain the distance Ap between the tourist projects in adjacent cities distance , average commuting time Ti average And the average time Ti spent on the game items consume At this time, the two adjacent cities cannot be determined as an urban agglomeration, and the tourism consumption time Ti needs to be calculated travel = Average commuting time Ti average +Average time spent on activitiesTi consume , and the tourism consumption time threshold Ti threshold For comparison, when there are two tourist projects in adjacent cities, the tourism consumption time Ti travel Less than the tourism consumption time threshold Ti threshold , it means that the adjacent cities can be determined as an urban agglomeration, otherwise they cannot be considered as an urban agglomeration.

[0073] Furthermore, S2 obtains the related tourism projects Rt between city clusters projects The method comprises the following steps:

[0074] S2.1. Specify a unit statistical time T0, and count the number of visitors to the tourism projects in the urban agglomeration within the unit statistical time T0. volume ;

[0075] S2.2. Mix of two or more tourist attractions between city clusters within the statistical unit statistical time T0 volume ;

[0076] S2.3. Calculate Mix volume The number of visits to the previous tourist project Pl volume The ratio of , get the mixed play rate Mix rate ;

[0077] S2.4. Establishing a mixed play rate threshold

[0078] When the mixed play rate Mix rate ≥ Mixed play rate threshold When the tourism project played later is related to the tourism project played earlier, Rt projects ;

[0079] When the mixed play rate Mix rate <Mixed play rate threshold When the tourism project played later and the tourism project played earlier are accidental tourism projects Oc projects .

[0080] When used specifically, when obtaining the associated tourism projects Rt between city clusters projects In the process, in order to avoid the occurrence of accidental tourism projects, that is, a small number of tourists will not travel across regions because of the correlation between two tourism projects, but because of their own accidental reasons, such as the next tourism project is located in the residential area of ​​their relatives, and the purpose of the trip is mainly to visit relatives. There is neither geographical connection nor actual consumption connection between these tourism projects. This is the accidental tourism mentioned above. Therefore, it is necessary to stipulate the unit statistical time T0, and count the number of visits to the tourism projects of the urban agglomeration within the unit statistical time T0 Pl volume And the amount of mixed play of two or more tourism projects between city clusters volume , through the ticket verification results and real-name ticket purchase data, obtain the number of visitors to various tourism projects in the city cluster within the unit statistical time T0 Pl volume It is worth noting that if more than two tourist attractions are visited within the unit statistical time T0, the tourist attraction visited first shall be the first tourist attraction visited;

[0081] After completing the statistics, calculate the amount of mixed play Mix volume The number of visits to the previous tourist project Pl volume The ratio of , get the mixed play rate Mix rate , that is, the mixed play rate Mix rate = Mix volume / Play VolumePl volume , and finally set the mixed play rate threshold Make a determination;

[0082] When the mixed play rate Mix rate ≥ Mixed play rate threshold When the tourism project played later is related to the tourism project played earlier, Rt projects ;

[0083] When the mixed play rate Mix rate <Mixed play rate threshold When the tourism project played later and the tourism project played earlier are accidental tourism projects Oc projects .

[0084] Furthermore, a data model of city cluster-related tourism projects TP is established in S3 model The method comprises the following steps:

[0085] S3.1. Count the related tourism projects Rt of each group in the urban agglomeration projects , and bind the corresponding mixed play rate Mix rate ;

[0086] S3.2, Mix by Mix Rate rate Size determination of each group of related tourism projects Rt projects degree of association;

[0087] S3.3. Create a dynamic change graph to display the Rt of each group of related tourism projects in real time projects The trend of the degree of correlation.

[0088] In specific use, in order to distinguish each group of related tourism projects Rt projects To determine the degree of association, we first need to count the related tourism projects Rt in each group of city clusters. projects , and bind the corresponding mixed play rate Mix rate The degree of association is proportional to the degree of integration of the two tourism projects, and is calculated according to the Mix rate. rate Size determination of each group of related tourism projects Rt projects The degree of association, mixed play rate Mix rate The larger the value, the more it represents two related tourism projects Rt projects The more tourists travel together, the more vivid the representation needs to be. A dynamic change chart needs to be established to display the related tourism projects Rt of each group in real time. projects The trend of the degree of correlation.

[0089] In S3.3, Python is used to program the dynamic change graph. The steps to create the dynamic change graph are as follows:

[0090] The first step is to install matplotlib and pandas in the Python script;

[0091] Step 2: Import matplotlib.pyplot and pandas in Python script

[0092] Programming language: pip install matplotlib pandas;

[0093] The third step is to use pandas to create data. The data can be static or dynamically read from external data sources, that is, the Rt of each related tourism project. projects Corresponding mixed play rate Mix rate ;

[0094] The programming languages ​​are:

[0095] import matplotlib.pyplot as plt;

[0096] import pandas as pd;

[0097] Step 4: Create a bar graph using matplotlib. The present invention mainly uses the bar function.

[0098] The programming languages ​​are:

[0099] data={'Category':['A','B','C','D','E'];

[0100] 'Values':[10,15,20,25,10]}, where the values ​​are for example only. The specific values ​​are based on the calculated mixed play rate Mix rate The result is final;

[0101] df = pd.DataFrame(data);

[0102] Step 5. Create a function to read new data and update the chart, where the horizontal axis is the associated tourism project Rt projects , the vertical axis is the corresponding mixed play rate Mix rate ;

[0103] The programming languages ​​are:

[0104] def update_data(new_values):

[0105] df['Values'] = new_values;

[0106] ax.clear();

[0107] ax.bar(df['Category'],df['Values']);

[0108] plt.draw().

[0109] Specifically, the association conditions in S3 are conditions These include distance between each other, commuting guarantee, interactive promotion of specialty products, complementary advantages and disadvantages, and bundled discounts;

[0110] Among them, the distance between the related tourism projects is the distance between them;

[0111] Commuting guarantee means that there is a fixed commuting method between the related tourist projects, such as a vehicle that picks up and drops off between two related tourist projects;

[0112] Specialty product mutual promotion is the promotion of the characteristics of related tourism projects. For example, tourists buy specialties of another tourism project in one tourism project, which arouses their interest in visiting the other tourism project.

[0113] Complementary advantages and disadvantages refer to the attractions with complementary advantages and disadvantages among the related tourism projects;

[0114] Bundled discounts provide discounts on related expenses for purchasing associated travel items.

[0115] In addition, S4 has a number of associated conditions As conditions The method of empowerment includes the following steps:

[0116] S4.1. Establish unit weight Un weight , determine the associated tourism project Rt projects Matching association conditions As conditions ;

[0117] S4.2. For each associated condition As conditions Assign the original weight Or weight , each time you increase the amount of mixed play Mix volume , related tourism projects Rt projects Matching association conditions As conditions Increase the unit weight Un weight ;

[0118] S4.3. Count the associated conditions As in each time period conditions The weight changes and real-time update of the associated conditions As conditions Weighted matching database.

[0119] When used specifically, for each associated condition As conditions In the process of empowerment, firstly, the unit weight Un is set weight , determine the associated tourism project Rt projects Matching association conditions As conditions , that is, each group of related tourism projects Rt projects Medium Impact Mix volume The associated conditions of conditions ;

[0120] Then, each association condition As conditions Assign the original weight Or weight , each time you increase the amount of mixed play Mix volume , related tourism projects Rt projects Matching association conditions As conditions Increase the unit weight Un weight , such as the associated tourism project Rt projects Matching association conditions As conditionsTo ensure the distance and commuting time, add a Mix volume , the distance and commuting guarantee will be in the original weight Or weight Add a unit weight Un on the basis weight It is worth noting that the unmatched association condition As conditions In the later stage, policy changes, cooperation between tourism projects, and road construction will become matching related conditions. conditions , and finally count the various associated conditions As in each time period conditions The weight changes and real-time update of the associated conditions As conditions Weighted matching database.

[0121] Furthermore, in S4.2, each of the associated conditions As conditions Assign the original weight Or weight The unit weighting algorithm is adopted, and its algorithm formula is as follows:

[0122] Or weight =ρFix weight ×μGr rate ;

[0123]

[0124] Fix weight Each associated condition As conditions Fixed weight, Fix weight is a fixed value, ρ is the associated condition As conditions The degree of influence, such as the associated condition As conditions For binding discounts, the greater the corresponding discount strength, the higher the impact degree ρ, Gr rate As the associated condition conditions The unit increase of mixed play rate, μ is the mixed play constant, which is positively correlated with the influence degree ρ, T0 is the unit time, For the occurrence of the associated condition As conditions The total amount of mixed play before, The associated condition As appears within the unit time T0 conditions The total amount of mixed play after that, for example, scenic spot A and amusement park B are related tourism projects Rt projects Before the binding discount between scenic spot A and amusement park B appeared, the number of tickets purchased for scenic spot A and amusement park B at the same time during the statistical period was 500, indicating that at this time After the binding discount between scenic spot A and amusement park B appeared, the number of tickets purchased for scenic spot A and amusement park B at the same time during the same statistical period was 1,500, indicating that at this time The corresponding ticket purchase difference is 1000 tickets. The ratio between the calculation and statistical time can be used to obtain the unit increase in mixed play rate Gr rate .

[0125] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments. The above embodiments and descriptions are only preferred examples of the present invention and are not intended to limit the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. A method for predicting and evaluating the integrated development of transportation and tourism in urban agglomerations, characterized in that: The steps include: S1. Divide the urban agglomeration area, obtain various tourism projects of urban agglomerations in the area, and establish an urban agglomeration tourism project database; S2. Collect tourists’ consumption content and obtain related tourism projects Rt between city clusters projects ; Among them, the related tourism project Rt projects The method comprises the following steps: S2.

1. Specify a unit statistical time T0, and count the number of visitors to the tourism projects in the urban agglomeration within the unit statistical time T0. volume ; S2.

2. Mix of two or more tourist attractions between city clusters within the statistical unit statistical time T0 volume ; S2.

3. Calculate Mix volume The number of visits to the previous tourist project Pl volume The ratio of , get the mixed play rate Mix rate ; S2.

4. Establishing mixed play rate threshold When the mixed play rate Mix rate ≥ Mixed play rate threshold When the tourism project played later is related to the tourism project played earlier, Rt projects ; When the mixed play rate Mix rate <Mixed play rate threshold When the tourism project played later and the tourism project played earlier are accidental tourism projects Oc projects ; S3. Establishing the data model of tourism projects associated with urban agglomerations TP model , and combined with the tourist consumption content in S2, obtain the associated conditions As for the associated tourism items conditions ; S4. For each associated condition As conditions Empower and establish association conditions As conditions Weighted ratio database; S5. Obtain the tourism projects to be developed in the urban agglomeration, determine the development direction of the tourism projects to be developed, and select the ones that meet the association conditions of the urban agglomeration in the region. conditions ; S6. Combined with the weighted ratio database, a prediction map of the integration changes of related cities is developed to determine the correlation trend between the tourism projects to be developed and the various tourism projects in the urban agglomerations in the region.

2. The method for predicting and evaluating the integrated development of transportation and tourism in urban agglomerations according to claim 1 is characterized by: The method for dividing the urban agglomeration area in S1 comprises the following steps: S1.

1. Establishing tourism consumption time threshold Ti threshold ; S1.

2. Obtain the distance Ap between tourist projects in adjacent cities distance , average commuting time Ti average And the average time Ti spent on the game items consume ; S1.

3. Calculate the travel consumption time Ti travel = Average commuting time Ti average +Average time spent on activitiesTi consume ; S1.

4. When there is tourism consumption time Ti in adjacent cities travel <Tourism consumption time threshold Ti threshold If there are tourism projects in the city, the adjacent cities will be marked as a group.

3. The method for predicting and evaluating the integrated development of transportation and tourism in urban agglomerations according to claim 1 is characterized by: The data model TP of the city cluster-related tourism projects is established in S3 model The method comprises the following steps: S3.

1. Count the related tourism projects Rt of each group in the urban agglomeration projects , and bind the corresponding mixed play rate Mix rate ; S3.2, Mix by Mix Rate rate Size determination of each group of related tourism projects Rt projects degree of association; S3.

3. Create a dynamic change graph to display the Rt of each group of related tourism projects in real time projects The trend of the degree of correlation.

4. The method for predicting and evaluating the integrated development of transportation and tourism in urban agglomerations according to claim 3 is characterized by: In S3.3, the dynamic change graph is established by programming in Python, and the steps of establishing the dynamic change graph are as follows: The first step is to install matplotlib and pandas in the Python script; Step 2: Import matplotlib.pyplot and pandas into the Python script. Step 3: Use pandas to create data; Step 4: Create a bar chart using matplotlib; Step 5. Create a function to read new data and update the chart, where the horizontal axis is the associated tourism project Rt projects , the vertical axis is the corresponding mixed play rate Mix rate .

5. The method for predicting and evaluating the integrated development of transportation and tourism in urban agglomerations according to claim 3 is characterized by: The association conditions in S3 conditions These include distance between each other, commuting guarantee, interactive promotion of specialty products, complementary advantages and disadvantages, and bundled discounts; Wherein, the distance is the distance between related tourism projects; The commuting guarantee means that there is a fixed commuting method between the related tourism projects; The mutual promotion of special products refers to the promotion of the characteristics of related tourism projects; The complementary advantages and disadvantages refer to the existence of attractions with complementary advantages and disadvantages between the related tourism projects; The binding discount is a discount on the relevant fees for purchasing related travel items.

6. The method for predicting and evaluating the integrated development of transportation and tourism in urban agglomerations according to claim 1 is characterized by: In S4, each of the associated conditions As conditions The method of empowerment includes the following steps: S4.

1. Establish unit weight Un weight , determine the associated tourism project Rt projects Matching association conditions As conditions ; S4.

2. For each associated condition As conditions Assign the original weight Or weight , each time you increase the amount of Mix volume , related tourism projects Rt projects Matching association conditions As conditions Increase the unit weight Un weight ; S4.

3. Count the associated conditions As in each time period conditions The weight changes and real-time update of the associated conditions As conditions Weighted matching database.

7. The method for predicting and evaluating the integrated development of transportation and tourism in urban agglomerations according to claim 6 is characterized by: In S4.2, the various association conditions As conditions Assign the original weight Or weight The unit weighting algorithm is adopted, and its algorithm formula is as follows: Or weight =ρFix weight ×μGr rate ; Fix weight Each associated condition As conditions Fixed weight, Fix weight is a fixed value, ρ is the associated condition As conditions The degree of influence of Gr rate As the associated condition conditions The unit increase of mixed play rate, μ is the mixed play constant, which is positively correlated with the influence degree ρ, T0 is the unit time, For the occurrence of the associated condition As conditions The total amount of mixed play before, The associated condition As appears within the unit time T0 conditions The total amount of mixed play afterwards.

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