Tourism time optimization method based on big data

Abstract

The invention relates to the field of tourism planning, and discloses a big data-based tourism time optimization method, which comprises the following steps of: firstly, collecting estimated tourism information of tourists, traffic road information and historical tourism data of each tourist attraction; a tourist playing estimation model is established through historical tourism data of each tourist attraction, and the step of establishing the tourist playing estimation model comprises establishing a congestion degree-playing curve graph and establishing a traffic time influence curve graph; analyzing and optimizing tourist travel time according to the congestion degree-travel curve graph and the traffic time influence curve graph to obtain a first tourist time optimization plan; adding travel process alternating time optimization on the basis of the first tourist time optimization plan to obtain a second tourist time optimization plan; the problems that in the prior art, tourism plans are prone to being affected, many tourists in holidays and festivals waste a lot of unnecessary time, and macroscopic time planning regulation and control are not conducted on the tourists are solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of tourism planning, and particularly relates to a tourism time optimization method based on big data. BACKGROUND

[0002] With the increasing of people's living standards, people begin to gradually enrich their lives through travel, but because people have less free time in their daily life, they will make good travel plans before traveling.

[0003] In the actual process of tourism, various problems will be encountered during the trip, and various reasons will affect the tourist's travel plan, especially the estimated duration of the trip, which may even affect the time plan of a series of things arranged by the tourist, and the tourism experience is not good, so it is very necessary to avoid the impact of tourists on their travel time, optimize the travel time of tourists during holidays, regulate the travel time of all tourists, realize the optimization of the overall travel time of tourists, and achieve the best tourism experience. SUMMARY

[0004] The present application aims to provide a tourism time optimization method based on big data to solve the problems of easy influence of the existing tourism plan, waste of a lot of unnecessary time due to many tourists during holidays, and lack of macro time planning and regulation of tourists.

[0005] In order to achieve the above-mentioned purpose, the present application provides the following method:

[0006] The present application provides a tourism time optimization method based on big data, which is:

[0007] S1: collecting estimated travel information of tourists, traffic road information and historical tourism data of each tourist attraction;

[0008] S2: establishing a tourist play estimation model through the historical tourism data of each tourist attraction, the step of establishing the tourist play estimation model including establishing a crowded degree-play curve and establishing a traffic time influence curve;

[0009] S3: analyzing and optimizing the travel time of tourists according to the crowded degree-play curve and the traffic time influence curve to obtain a first tourist time optimization plan;

[0010] S4: adding tourism process alternating time optimization on the basis of the first tourist time optimization plan to obtain a second tourist time optimization plan.

[0011] Preferably, the estimated travel information of the tourists includes: estimated travel mode of the tourists, estimated travel time period of the tourists and estimated number of the tourists; the traffic road information includes: vehicle flow in traffic road vehicle saturation, vehicle flow speed in traffic road different saturation; the historical travel data of each tourist attraction includes: historical tourist number fluctuation data of the scenic spot, travel time of the tourists, geographical position information of the scenic spot, actual area of the tourists in the scenic spot, flow speed of the tourists in the scenic spot and operation time period of the tourist attraction.

[0012] Preferably, before the step of establishing the tourist travel estimation model through the historical travel data of each tourist attraction, the method further includes: calculating the historical same period tourist annual growth rate of the scenic spot through the historical same period tourist number fluctuation data of the scenic spot; the historical same period tourist number fluctuation data of the scenic spot includes: historical same period tourist number, historical same period scenic spot size and historical same period ticket price; the historical same period tourist annual growth rate of the scenic spot is:

[0013] n = b / a * x2 / x1;

[0014] Wherein, a is the historical same period tourist number of the previous year, b is the historical same period tourist number of the next year, x1 is the ticket price of the previous year and x2 is the ticket price of the next year.

[0015] Preferably, the step of establishing the congestion-degree-travel curve includes: estimating the tourist number through the historical same period tourist number fluctuation data of the scenic spot corresponding to the estimated travel time period of the tourists and the historical same period tourist annual growth rate of the scenic spot; calculating the estimated tourist travel congestion degree through the estimated tourist number and the actual area of the tourists in the scenic spot; simulating the estimated tourist travel congestion degree through big data, simulating the travel process of different number of people, obtaining the congestion-degree-travel time curve of the influence of different number of people on the travel time of the tourists.

[0016] Preferably, the step of calculating the estimated tourist travel congestion degree through the estimated tourist number and the actual area of the tourists in the scenic spot includes: estimating the comfortable flow area of the tourists through the flow speed of the tourists in the scenic spot; calculating the total estimated travel area of the tourists through the estimated tourist number and the comfortable flow area of the tourists; calculating the proportion of the total estimated travel area of the tourists in the actual area of the tourists in the scenic spot, obtaining the estimated tourist travel congestion degree.

[0017] Preferably, the step of simulating the estimated tourist play congestion degree by big data to simulate the play process of different number of people to obtain the congestion-time curve of the influence of different number of people on the play time of tourists includes: simulating the play process of different number of people by simulating the estimated tourist play congestion degree by big data, simulating multiple times with a difference of 5% of the estimated tourist play congestion degree, and recording the play time of each simulation; and using the play time of each simulation and the corresponding estimated tourist play congestion degree to establish the congestion-time curve.

[0018] Preferably, the step of establishing a traffic time influence curve includes: planning a tourist travel traffic scheme based on the estimated travel mode of the tourist, the estimated play time period of the tourist, and the estimated number of tourists; statistically analyzing other tourist travel traffic schemes of the same period as the tourist travel traffic scheme by big data; obtaining the influence degree of the tourist travel traffic by comparing the number of the tourist travel traffic schemes of the same period with the vehicle saturation ratio when the traffic road is saturated; and establishing a traffic time influence curve based on the vehicle flow speed at different saturation degrees of the traffic road and the influence degree of the tourist travel traffic.

[0019] Preferably, the step of analyzing and optimizing the tourist travel time based on the congestion-time curve and the traffic time influence curve to obtain a first tourist time optimization plan includes: obtaining the corresponding tourist play time of the optimal tourist play congestion degree based on the congestion-time curve, and planning within the estimated play time period of the tourist to make the total play time of the tourist in all the estimated play time periods of the tourist the shortest, thereby obtaining a tourist scenic spot travel time optimization scheme; quantitatively increasing or decreasing the vehicle saturation degree of the traffic road based on the traffic time influence curve to monitor the influence of the tourist travel time; and comprehensively considering the influence of the tourist travel time of all tourists to make the influence of the tourist travel time tend to be average on the basis of meeting the estimated play time period of the tourist, thereby obtaining a tourist traffic time optimization scheme; and integrating the tourist scenic spot travel time optimization scheme and the tourist traffic time optimization scheme into a time optimization scheme of the entire travel process to obtain the first tourist time optimization plan.

[0020] Preferably, the step of optimizing the alternating time of the travel process includes: segmenting the tourist travel time according to the operation time period of the tourist scenic spot, and planning the optimal travel time of the tourist by combining the estimated time of the tourist arriving at the scenic spot; arranging the best time of the tourist arriving at the scenic spot and the best time of the tourist leaving the scenic spot according to the estimated play time period of the tourist, and optimizing the alternating time of the tourist travel process except the travel time and the traffic time based on the optimal travel time of the tourist, so as to make the alternating time the shortest and realize the optimization of the alternating time of the travel process.

[0021] Preferably, on the basis of the first tourist time optimization plan, a tourism process alternating time optimization is added to obtain a second tourist time optimization plan, including: on the basis of the first tourist time optimization plan, integrating the tourism process alternating time optimization to obtain a complete tourism time optimization plan; calculating the complete tourism time optimization plan of all tourists, and classifying the complete tourism time optimization plan of the tourists according to whether the tourist estimated play time period of the tourists is the same; calculating the average value of the classified complete tourism time optimization plan; if the complete tourism time optimization plan of the tourists is not less than 1.25 times the average value of the complete tourism time optimization plan, returning to step S2; if the complete tourism time optimization plan of the tourists is less than 1.25 times the average value of the complete tourism time optimization plan, obtaining a second tourist time optimization plan.

[0022] The beneficial effects of the present application are embodied in that: the present application collects various information of tourists, various information of traffic roads and various information of various tourist attractions, establishes a tourist play estimation model, the tourist play estimation model includes a crowdedness-play curve and a traffic time influence curve, considers the time consumption of each step in the tourist process, and performs data macroscopic analysis and control on the basis of data in two aspects, reduces the tourist time consumption to the minimum, and the tourism experience is more, which can avoid the influence on the tourist time when there are many tourists during holidays, optimizes and controls the tourist time of the tourists, optimizes the tourist time of all tourists, and realizes the optimization of the overall tourist time of the tourists. BRIEF DESCRIPTION OF DRAWINGS

[0023] In order to more clearly illustrate the technical solutions in the specific embodiments or the prior art, the following will briefly introduce the drawings needed to be used in the specific embodiments or the prior art description. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, each element or part is not necessarily drawn according to the actual proportion.

[0024] Figure 1 is a flowchart of a tourism time optimization method based on big data provided by an embodiment of the present application.

[0025] Figure 2 is a flowchart of a step of establishing a crowdedness-play curve provided by an embodiment of the present application.

[0026] Figure 3 is a flowchart of a step of performing tourism time optimization provided by an embodiment of the present application. DETAILED DESCRIPTION

[0027] To enable those skilled in the art to better understand the present invention, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0028] The terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this invention are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, apparatus, product, or end that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or ends.

[0029] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0030] In the actual process of traveling, various problems and reasons may arise that affect one's travel plans, especially the estimated duration of the trip, and even disrupt the schedule of a series of other things, resulting in a poor travel experience. Therefore, it is essential to prevent tourists from having their travel time affected by travel restrictions, and to optimize and adjust the travel time of all tourists during holidays to achieve the best overall travel time and the best travel experience.

[0031] The present invention aims to provide a tourism time optimization method based on big data to solve the problems in the existing technology that tourism plans are easily affected, that a large number of tourists during holidays lead to a lot of unnecessary time wasted, and that there is no macro-level time planning and control for tourists.

[0032] This invention provides a method for optimizing travel time based on big data, as described in the following embodiments. Figure 1 , Figure 2 and Figure 3 As shown, it includes the following steps:

[0033] S1: Collect estimated tourist information, transportation information, and historical tourist data for various tourist attractions.

[0034] In this embodiment of the invention, the estimated tourism information for tourists includes: the estimated travel mode, the estimated time period for tourists to visit, and the estimated number of tourists; the traffic information includes: the traffic volume when the traffic road is saturated, and the traffic speed at different saturation levels; the historical tourism data for each tourist attraction includes: historical fluctuation data of the number of tourists in the scenic area, the length of time tourists stay, the geographical location information of the scenic area, the actual area visited by tourists, the speed of tourists within the scenic area, and the operating time period of the tourist attraction.

[0035] S2: Establish a tourist visitation prediction model based on historical tourism data of various tourist attractions. The steps to establish the tourist visitation prediction model include establishing a congestion-visit curve and a traffic time impact curve.

[0036] In this embodiment of the invention, before establishing a tourist visit prediction model based on historical tourism data from various tourist attractions, the method further includes: calculating the average annual growth rate of tourists in the scenic area during the same historical period using historical tourist number fluctuation data; the historical tourist number fluctuation data includes: the number of tourists during the same historical period, the scale of the scenic area during the same historical period, and the ticket price standard during the same historical period; the average annual growth rate of tourists in the scenic area during the same historical period is:

[0037] n = b / a*x² / x¹;

[0038] Where 'a' represents the number of tourists visiting the scenic area in the same period of the previous year, 'b' represents the number of tourists visiting the scenic area in the same period of the following year, 'x1' represents the ticket price of the scenic area in the same period of the previous year, and 'x2' represents the ticket price of the scenic area in the same period of the following year; the number of tourists is estimated by using historical tourist number fluctuation data corresponding to the estimated visitor time period and the historical average annual growth rate of tourists in the scenic area; the estimated tourist congestion is calculated by comparing the estimated tourist number with the actual area of ​​the scenic area; big data simulation is used to simulate the estimated tourist congestion, simulating the visitor process of different numbers of people, and obtaining a congestion-visit time curve showing the impact of different numbers of people on the visitor time; the steps of calculating the estimated tourist congestion by comparing the estimated tourist number with the actual area of ​​the scenic area include: estimating the personal comfortable circulation area of ​​tourists based on the tourist flow speed within the scenic area; and calculating the estimated tourist congestion using the estimated tourist number and the personal comfortable circulation area. The project calculates the estimated total area of ​​visitors to be visited, representing a percentage of the actual area visited by visitors, thus determining the estimated crowding level. Big data simulations are used to model the crowding level, simulating different numbers of visitors at multiple simulations with a 5% difference in estimated crowding level, recording the duration of each simulation. A crowding level-visit time curve is created using the simulated visit time and the corresponding estimated crowding level. Traffic plans are developed based on visitors' estimated travel methods, estimated visit time periods, and estimated visitor numbers. Big data analysis is used to compare other visitors' traffic plans during the same period. The impact of visitor traffic on road saturation is determined by comparing the number of traffic plans with the road saturation level. Finally, a traffic time impact curve is created by comparing vehicle flow speeds at different road saturation levels with the impact of visitor traffic on road saturation.

[0039] S3: Analyze and optimize tourist travel time based on the congestion-play curve and the traffic time impact curve to obtain the first tourist time optimization plan.

[0040] In this embodiment of the invention, the optimal tourist congestion level and corresponding tourist play time are obtained based on the congestion-play curve. Planning is then carried out within the tourists' estimated play time period to minimize the total tourist play time across all estimated play time periods, resulting in an optimized tourist attraction time plan. Based on the traffic time impact curve, the saturation of traffic roads is quantitatively increased or decreased to monitor the impact on tourist play time. The impact on tourist play time for all tourists is then integrated to ensure that the impact on tourist play time is averaged while still meeting the tourists' estimated play time periods, resulting in an optimized tourist traffic time plan. Finally, the optimized tourist attraction time plan and the optimized tourist traffic time plan are integrated into a time optimization plan for the entire tourism process, resulting in the first optimized tourist time plan.

[0041] S4: Based on the first tourist time optimization plan, add the optimization of the alternation time of the tourism process to obtain the second tourist time optimization plan.

[0042] In this embodiment of the invention, tourist travel time is segmented according to the operating hours of the tourist attraction, and overall control planning is carried out in combination with the estimated arrival time of tourists to the attraction to plan the optimal travel time for tourists; based on the estimated travel time of tourists, the best arrival time and best departure time of tourists to the attraction are sorted out, and the alternation time during the tourist travel process, excluding travel time and transportation time, is optimized according to the optimal travel time of tourists to minimize the alternation time and achieve the optimization of the alternation time of the travel process; based on the first tourist time optimization plan, the optimization of the alternation time of the travel process is integrated to obtain the complete tourist time optimization plan; the complete tourist time optimization plan of all tourists is calculated, and the complete tourist time optimization plans of tourists are classified according to whether the estimated travel time of tourists are the same; the average value of the classified complete tourist time optimization plans is calculated; if the complete tourist time optimization plan of tourists is not less than 1.25 times the average value of the complete tourist time optimization plan, then return to step S2; if the complete tourist time optimization plan of tourists is less than 1.25 times the average value of the complete tourist time optimization plan, then the second tourist time optimization plan is obtained.

[0043] The beneficial effects of this invention are reflected in the following: By collecting various information about tourists, transportation routes, and tourist attractions, this invention establishes a tourist travel prediction model. The tourist travel prediction model includes a congestion-travel curve and a traffic time impact curve, taking into account the time consumption of each step in the tourist's travel process. Based on the data from these two aspects, macro-analysis and regulation are performed to minimize the tourist's travel time consumption, improve the tourist experience, and avoid the impact of large tourist numbers on travel time during holidays. This optimizes and regulates the travel time of all tourists, achieving the optimal overall travel time for tourists.

[0044] The above descriptions are merely embodiments of the present invention. Commonly known technical solutions or characteristics are not described in detail here. It should be noted that those skilled in the art can make various modifications and improvements without departing from the present invention, and these should also be considered within the scope of protection of the present invention. These modifications and improvements will not affect the effectiveness of the present invention or the practicality of the patent. The scope of protection claimed in this application should be determined by the content of its claims, and the specific embodiments described in the specification can be used to interpret the content of the claims.

Claims

1. A method for optimizing travel time based on big data, characterized in that, The method includes: S1: Collect estimated tourist information, traffic information, and historical tourist data for various tourist attractions; S2: Based on the historical tourism data of each tourist attraction, establish a tourist visitation prediction model. The steps of establishing the tourist visitation prediction model include establishing a congestion-visit curve and a traffic time impact curve. S3: Analyze and optimize tourist travel time based on the congestion-play curve and the traffic time impact curve to obtain the first tourist time optimization plan; S4: Based on the first tourist time optimization plan, add the optimization of the alternation time of the tourism process to obtain the second tourist time optimization plan.

2. The method for optimizing travel time based on big data according to claim 1, characterized in that: The estimated travel information for tourists includes: the estimated mode of travel, the estimated duration of the trip, and the estimated number of tourists. The traffic information includes: vehicle flow when the traffic road is saturated, and vehicle flow speed at different saturation levels. Historical tourism data for each tourist attraction includes: historical fluctuations in visitor numbers, duration of visitor stay, geographical location information, actual area visited by visitors, visitor flow rate within the attraction, and operating hours of the tourist attraction.

3. The method for optimizing travel time based on big data according to claim 2, characterized in that: Before the step of establishing a tourist visitation prediction model based on the historical tourism data of each tourist attraction, the method further includes: calculating the average annual growth rate of tourists in the same period of history based on the historical tourist number fluctuation data of the scenic spot. The historical visitor number fluctuation data for the scenic area includes: the number of visitors in the same period in history, the size of the scenic area in the same period in history, and the ticket price standard in the same period in history. The average annual growth rate of tourists visiting the scenic area during the same historical period was: n = b / a*x² / x¹; Where a represents the number of visitors to the scenic area in the same period of the previous year, b represents the number of visitors to the scenic area in the same period of the following year, x1 represents the ticket price of the scenic area in the same period of the previous year, and x2 represents the ticket price of the scenic area in the same period of the following year.

4. The method for optimizing travel time based on big data according to claim 3, characterized in that, The steps for establishing the congestion-play curve include: The number of tourists is estimated by using the historical tourist number fluctuation data of the scenic spot during the same period corresponding to the tourists' estimated visit time and the average annual growth rate of tourists in the scenic spot during the same period in history. The estimated number of tourists is used to calculate the estimated tourist congestion level by comparing it with the actual area of ​​the scenic spot. By using big data to simulate the estimated tourist congestion, the process of playing with different numbers of people was simulated, and a congestion-play time curve was obtained to show the impact of different numbers of people on the tourist play time.

5. The method for optimizing travel time based on big data according to claim 4, characterized in that, The step of calculating the estimated tourist congestion level by comparing the estimated number of tourists with the actual area of ​​the scenic area visited by tourists includes: The estimated number of tourists is used to estimate the individual comfortable circulation area for each tourist based on the tourist circulation speed within the scenic area. The estimated total play area for tourists is calculated by using the estimated number of tourists and the individual comfortable circulation area for each tourist. The estimated tourist congestion level is obtained by calculating the proportion of the estimated total area to be visited by tourists to the actual area visited by tourists in the scenic area.

6. The method for optimizing travel time based on big data according to claim 4, characterized in that, The step of simulating the estimated tourist congestion using big data, simulating the visitor experience with different numbers of people, and obtaining a congestion-visit time curve showing the impact of different numbers of people on visitor time includes: The estimated tourist congestion level was simulated using big data. The process of playing with different numbers of people was simulated. Multiple simulations were performed with a 5% difference in the estimated tourist congestion level, and the playing time of each simulation was recorded. A congestion-playtime curve is created using the playtime from each simulation and the corresponding estimated visitor congestion.

7. The method for optimizing travel time based on big data according to claim 3, characterized in that, The steps for establishing the traffic time impact curve include: Based on the tourists' estimated travel methods, estimated travel time periods, and estimated number of tourists, a travel transportation plan is developed for the tourists. The other tourist travel transportation plans during the same period as the tourist travel transportation plan were analyzed using big data statistics. The impact of tourist travel on traffic can be obtained by comparing the number of tourist travel transportation options with the vehicle saturation rate when the traffic roads are saturated. By analyzing the vehicle flow speed at different saturation levels of the traffic roads and the degree of traffic impact on tourist travel, a traffic time impact curve is established.

8. The method for optimizing travel time based on big data according to claim 7, characterized in that, The step of analyzing and optimizing tourist travel time based on the congestion-play curve and the traffic time impact curve to obtain the first tourist time optimization plan includes: Based on the crowding-play curve, the corresponding play time for tourists with the optimal play crowding is obtained, and planning is carried out within the estimated play time period for tourists to minimize the total play time for tourists within all the estimated play time periods, thus obtaining an optimized tourist visit time scheme for the scenic area. Based on the aforementioned traffic time impact curve, quantitative adjustments are made to the traffic road vehicle saturation to monitor the impact on tourists' travel duration. By taking into account the impact of all tourists' travel duration, and making the impact of the travel duration on a more average basis while meeting the tourists' estimated travel time, an optimized travel time solution for tourists is obtained. The tourist scenic spot travel time optimization scheme and the tourist transportation time optimization scheme are integrated into a time optimization scheme for the entire tourism process, resulting in the first tourist time optimization plan.

9. A method for optimizing travel time based on big data according to claim 8, characterized in that, The steps for optimizing the alternation time of the tourism process include: Based on the operating hours of the tourist attractions, the tourist time is planned in segments, and the overall adjustment and planning is carried out in combination with the estimated time of tourists' arrival at the attractions, so as to plan the optimal tourist time for tourists. Based on the tourists' estimated travel time, the optimal arrival and departure times of the scenic area are compiled. Then, based on the tourists' planned optimal travel time, the alternation time during the tourist's travel process, excluding travel time and transportation time, is optimized to minimize the alternation time and achieve the optimization of the alternation time in the travel process.

10. A method for optimizing travel time based on big data according to claim 9, characterized in that, The step of adding tourism process alternation time optimization to the first tourist time optimization plan to obtain the second tourist time optimization plan includes: Based on the first tourist time optimization plan, the optimization of alternating time in the tourism process is integrated to obtain a complete tourist time optimization plan; Calculate the complete travel time optimization plan for all tourists, and categorize the tourists' complete travel time optimization plans according to whether the tourists' estimated travel time periods are the same. Calculate the average value of the categorized complete tourism time optimization plan; If the tourist's optimized full travel time is not less than 1.25 times the average value of the optimized full travel time, then return to step S2; If the tourist's optimized full travel time is less than 1.25 times the average value of the optimized full travel time, then a second optimized tourist time plan is obtained.

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