Route planning method and system based on urban hotspot tourist attractions
By acquiring real-time visitor density data of scenic areas, and dynamically planning alternative routes to avoid high-visit areas, the problem that existing scenic area route planning cannot cope with instantaneous changes in visitor flow is solved, improving tour efficiency and comfort, and achieving load balancing within the scenic area.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- NANTONG ZHONGJIANG TOURISM TECHNOLOGY SERVICE CO LTD
- Filing Date
- 2026-03-09
- Publication Date
- 2026-06-09
AI Technical Summary
Existing scenic area route planning methods rely on static data, which cannot effectively cope with the instantaneous changes in visitor flow distribution within the scenic area. This results in tourists queuing for long periods in popular areas, and the system cannot proactively provide congestion avoidance solutions.
By acquiring real-time visitor density data from multiple sub-areas within the scenic area, it can determine whether the target area exceeds a preset threshold, dynamically plan alternative routes to avoid high-visit areas, and recommend visits to low-visit areas based on user interests and attraction types. The system also monitors and adjusts routes in real time to avoid congestion.
It enables real-time perception and proactive response to the dynamic visitor flow in scenic areas, avoiding wasted time for tourists in congested areas, improving tour efficiency and comfort, and balancing the load pressure within the scenic area.
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Abstract
Description
Technical Field
[0001] This invention relates to the field of scenic area planning methods, specifically to a route planning method and system based on popular urban tourist attractions. Background Technology
[0002] In the field of scenic area management, existing tourist guidance and route planning technologies are mainly based on static maps and fixed recommended routes. These routes are usually provided to tourists through the scenic area's official website, mobile applications, or paper guide maps. These routes are generally pre-set based on static factors such as the popularity of attractions, spatial distance, and walking time. In addition, some scenic areas have introduced GPS-based navigation systems that can provide tourists with basic route guidance from their current location to the target attraction. The core of these systems is to calculate the shortest path or the estimated travel time.
[0003] However, the existing technologies have the following shortcomings in practical applications: existing route planning methods rely heavily on static data and cannot effectively cope with the instantaneous changes in visitor flow distribution within scenic areas. When tourists arrive at a popular sub-area according to a preset route, the area may already be crowded, causing tourists to have to wait in line for a long time. Tourists have no way of knowing this situation before the trip begins. When the visitor flow in the target sub-area exceeds the limit, the system cannot proactively provide effective congestion avoidance solutions. Even if tourists find congestion ahead, they can only find alternative routes on their own. Existing technologies cannot achieve peak shaving and valley filling, or balance the overall spatial load of the scenic area. Therefore, in order to address the shortcomings of existing technologies, this invention provides a route planning method and system based on popular urban tourist attractions to solve the above problems. Summary of the Invention
[0004] To address the shortcomings of existing technologies, this invention provides a route planning method and system based on popular urban tourist attractions, thereby resolving the aforementioned technical problems.
[0005] To achieve the above objectives, the present invention provides a route planning method based on popular urban tourist attractions, comprising the following steps:
[0006] Step S1: Obtain real-time visitor density data for multiple sub-areas within the scenic area;
[0007] Step S2: Obtain the user's current location and at least one target sub-region that the user wishes to access;
[0008] Step S3: Based on the real-time passenger flow density data, determine whether the current passenger flow density of the target sub-area exceeds a preset threshold.
[0009] Step S4: If the preset threshold is exceeded, the route from the current location to the target sub-region is replanned to generate an alternative route that avoids the high-traffic area of the target sub-region.
[0010] Preferably, the real-time passenger flow density data is calculated using the gate counting data of each of the target sub-areas.
[0011] Preferably, the preset threshold is dynamically adjusted based on the scenic area's historical visitor flow data and the current time period.
[0012] Preferably, the alternative route includes guiding the user to at least one low-traffic sub-area associated with the target sub-area, and after visiting the low-traffic sub-area, determining again whether to proceed to the target sub-area based on real-time passenger flow density data.
[0013] Preferably, the low-traffic sub-regions associated with the target sub-region are selected from the scenic area database based on user interests or attraction type similarity.
[0014] Preferably, the alternative route includes guiding users to enter from a non-crowded entrance of the target sub-area, which is an entrance with fewer people currently queuing, determined based on real-time passenger flow density data.
[0015] Preferably, the route planning method further includes: monitoring the passenger flow density of sub-areas within a predetermined range ahead of the user in real time as the user moves along the planned route; if the passenger flow density of the sub-area ahead exceeds a threshold, dynamically adjusting the current route to avoid the sub-area.
[0016] Preferably, the dynamic adjustment of the current route includes pushing prompts to the user and recommending alternative attractions with nearby visitor density below a threshold as temporary stops.
[0017] Preferably, the route planning method also includes: predicting the trend of passenger flow density change in each sub-area within a future time period based on historical passenger flow data and current real-time passenger flow data, and adjusting the planned route in advance based on the prediction results.
[0018] A route planning system based on popular urban tourist attractions, used to implement the aforementioned route planning method, includes:
[0019] The data acquisition module is used to obtain real-time visitor density data for multiple sub-areas within the scenic area;
[0020] The user interaction module is used to obtain the user's location and at least one target sub-region that the user wishes to access.
[0021] The route planning module is used to plan and generate tour routes based on the real-time passenger flow density data and user location;
[0022] The display module is used to show the route to the user.
[0023] The present invention also discloses
[0024] The technical effects and advantages of this invention are as follows:
[0025] 1. This route planning method based on popular urban tourist attractions obtains real-time visitor density data for multiple sub-areas within the scenic area, acquires the user's current location and desired target sub-area, determines whether the visitor density of the target sub-area exceeds a preset threshold, and replans alternative routes to avoid high-visit areas when the threshold is exceeded. This achieves real-time perception and proactive response to the dynamic visitor flow situation in the scenic area. Compared with existing technologies that rely on static maps or only calculate paths based on distance, this solution ensures that users know the true congestion situation of the target sub-area before being guided there. This effectively avoids wasted time and decreased experience caused by users blindly heading to congested areas, improves tour efficiency and comfort, and solves the problem of overcrowding.
[0026] 2. This route planning method based on popular urban tourist attractions guides users to visit low-traffic sub-areas associated with the target sub-area first by using alternative routes. This achieves staggered peak hours and precise distribution of passenger flow. When the target sub-area is crowded, the system does not simply detour or cancel the trip, but intelligently recommends alternative attractions that the user may be interested in and that are sparsely populated, guiding the user to visit them first. The user can then visit the target sub-area after the passenger flow has decreased. This design avoids users waiting in vain outside the crowded area and guides the instantaneous peak passenger flow to other areas within the scenic area through diversion methods, effectively balancing the load pressure of each sub-area within the scenic area. Attached Figure Description
[0027] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0028] Figure 1 This is a flowchart of the overall process of the present invention;
[0029] Figure 2 This is the logic diagram for passenger flow density judgment and replanning in this invention;
[0030] Figure 3 The flowchart for dynamically adjusting the route in this invention;
[0031] Figure 4 This is a system architecture diagram of the present invention. Detailed Implementation
[0032] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.
[0033] This embodiment discloses a route planning method based on popular urban tourist attractions, according to the appendix. Figure 1 To be continued Figure 4 As shown, it includes the following steps:
[0034] Step S1: Obtain real-time visitor density data for multiple sub-areas within the scenic area;
[0035] Step S2: Obtain the user's current location and at least one target sub-region that the user wishes to access;
[0036] Step S3: Based on real-time passenger flow density data, determine whether the current passenger flow density of the target sub-area exceeds a preset threshold.
[0037] Step S4: If the preset threshold is exceeded, the route from the current location to the target sub-area is replanned to generate an alternative route that avoids the high-traffic area of the target sub-area.
[0038] Furthermore, in step S1, the real-time passenger flow density data is calculated using the gate counting data set in each target sub-area. Specifically, each sub-area has an intelligent gate system deployed at its entrance and exit. When tourists pass through, the gate automatically records the number of people passing through and uploads the data to the scenic area data center in real time. The data center calculates the real-time passenger flow density using an integral algorithm based on parameters such as the area of each sub-area and the instantaneous capacity of tourists. The unit is people / square meter. In addition, to further improve the data accuracy, Wi-Fi probes, intelligent video surveillance analysis, and mobile base station positioning data can be integrated to correct and supplement the gate data, thereby obtaining a more accurate passenger flow distribution heat map.
[0039] Furthermore, in step S3, the preset threshold is dynamically adjusted based on the scenic area's historical visitor flow data and the current time period. The scenic area's data center stores long-term accumulated historical visitor flow data. Through big data analysis, benchmark values for visitor flow density in different time periods can be established, such as holidays, weekdays, peak seasons, off-seasons, and daily peak and off-peak hours. The threshold setting module will dynamically generate a reasonable upper limit value for visitor flow density by combining the current date, weather conditions, and the real-time influx speed of people. For example, during Golden Week, the threshold will be raised accordingly, but it still needs to be ensured to be within the scenic area's carrying capacity; while on weekday evenings, the threshold will be lowered to improve the comfort of tourists.
[0040] Furthermore, in step S4, if it is determined that the passenger flow density of the target sub-area exceeds the threshold, the route planning module will generate an alternative route. The alternative route includes guiding the user to at least one low-passenger-flow sub-area associated with the target sub-area first, and after visiting the low-passenger-flow sub-area, determining again whether to go to the target sub-area based on real-time passenger flow density data. The low-passenger-flow sub-areas associated with the target sub-area are selected from the scenic area database based on the user's interests or attraction type similarity.
[0041] Specifically disclosed, the alternative routes include guiding users to enter from the non-crowded entrance of the target sub-area. Major attractions in the scenic area usually have multiple entrances. The system monitors the number of people queuing and the average waiting time at each entrance in real time to determine the entrance with the fewest people queuing and the shortest waiting time as the non-crowded entrance. When users approach the target sub-area, the alternative routes will guide them to bypass the main entrance with dense crowds and enter quickly through the non-crowded entrance, thereby saving queuing time and achieving diversion.
[0042] Specifically disclosed, the route planning method also includes a dynamic adjustment mechanism: as the user moves along the planned route, the system monitors the user's location in real time using GPS or indoor positioning technology, and continuously scans the real-time passenger flow density of sub-areas within a predetermined range ahead of the user, such as within a 50-meter or 100-meter radius. If the passenger flow density of the sub-area ahead exceeds a threshold, the dynamic adjustment process is immediately triggered to recalculate the current route to avoid the suddenly congested sub-area. Dynamically adjusting the current route includes pushing prompts to the user's mobile terminal and recommending nearby alternative attractions with passenger flow density below the threshold as temporary tourist spots, guiding the user to change their itinerary in time and avoid getting stuck in congestion.
[0043] It is worth emphasizing that this method also uses historical passenger flow data and current real-time passenger flow data, combined with time series analysis and machine learning models, to predict the passenger flow density change trend of each sub-area in the future. When the route planning module is initially planning or dynamically adjusting, it will refer to this prediction result and prioritize visiting those areas that are currently comfortable but may be congested in the future.
[0044] It should be particularly emphasized that this embodiment also discloses a route planning system based on popular urban tourist attractions, used to implement the above method, according to the appendix. Figure 4 The system architecture diagram shown includes:
[0045] The data acquisition module connects to sensing devices such as turnstiles, cameras, and Wi-Fi probes within the scenic area to obtain real-time visitor density data for multiple sub-areas within the scenic area.
[0046] The user interaction module, usually an app or mini-program on a visitor's mobile phone, is used to obtain the user's location and at least one target sub-area they wish to visit.
[0047] The route planning module, as the core processing unit of the system, is deployed on a cloud server or local data center. It is used to plan and generate optimized tour routes based on real-time passenger flow density data, forecast data, and user location and preferences.
[0048] The display module is used to show routes and related prompts to users in the form of maps, text or voice on the user interface. The modules communicate with each other through a wireless network and work together.
[0049] Example 1: This example uses a tourist's visit to a large theme park as an example, combined with the attached... Figure 1 To be continued Figure 4 Detailed workflow description: The workflow is as follows:
[0050] Tourist A opens the scenic area's official app and enters "Roller Coaster Theme Area" as the desired destination. The app's user interaction module determines that the tourist's current location is at the scenic area entrance. Simultaneously, the data collection module acquires real-time visitor flow data for each sub-area. The system determines that the current visitor density in the "Roller Coaster Theme Area" has reached 0.8 people / square meter, exceeding the historical threshold of 0.6 people / square meter for the current time period. Therefore, the route planning module initiates a replanning process. Figure 2As shown in the logic, the system retrieves visitor A's interests and preferences, and combines this with the similarity of attraction types. It selects the "Virtual Shooting Range," which belongs to the same dynamic experience category as the "Roller Coaster" but currently has a low visitor density of only 0.3 people / square meter, as a related low-visit sub-area. The generated alternative route guides visitor A to the "Virtual Shooting Range" first. After visitor A finishes at the "Virtual Shooting Range," the system again determines that the visitor density of the "Roller Coaster Theme Area" has dropped to 0.5 people / square meter. Therefore, it pushes a prompt to visitor A through the display module: "The visitor flow in the roller coaster area has decreased. Do you want to go now?" After visitor A confirms, the system plans a specific route from the "Virtual Shooting Range" to the less crowded entrance of the "Roller Coaster Theme Area," and monitors the area ahead in real time during the journey to ensure a smooth path and ultimately a successful visit.
[0051] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.
Claims
1. A route planning method based on popular urban tourist attractions, characterized in that, Includes the following steps: Step S1: Obtain real-time visitor density data for multiple sub-areas within the scenic area; Step S2: Obtain the user's current location and at least one target sub-region that the user wishes to access; Step S3: Based on the real-time passenger flow density data, determine whether the current passenger flow density of the target sub-area exceeds a preset threshold. Step S4: If the preset threshold is exceeded, the route from the current location to the target sub-region is replanned to generate an alternative route that avoids the high-traffic area of the target sub-region.
2. The route planning method based on popular urban tourist attractions according to claim 1, characterized in that, The real-time passenger flow density data is calculated using the gate counting data of each of the target sub-areas.
3. The route planning method based on popular urban tourist attractions according to claim 1, characterized in that, The preset threshold is dynamically adjusted based on the scenic area's historical visitor flow data and the current time period.
4. The route planning method based on popular urban tourist attractions according to claim 1, characterized in that, The alternative route includes guiding the user to at least one low-traffic sub-area associated with the target sub-area, and after visiting the low-traffic sub-area, determining again whether to proceed to the target sub-area based on real-time passenger flow density data.
5. The route planning method based on popular urban tourist attractions according to claim 4, characterized in that, Low-traffic sub-regions associated with the target sub-region are selected from the scenic area database based on user interests or attraction type similarity.
6. The route planning method based on popular urban tourist attractions according to claim 1, characterized in that, The alternative route includes guiding users to enter from a non-crowded entrance to the target sub-area, which is an entrance with fewer people currently queuing, determined based on real-time passenger flow density data.
7. The route planning method based on popular urban tourist attractions according to claim 1, characterized in that, The route planning method also includes: monitoring the passenger flow density of sub-areas within a predetermined range ahead of the user in real time as the user moves along the planned route; if the passenger flow density of the sub-area ahead exceeds a threshold, dynamically adjusting the current route to avoid that sub-area.
8. The route planning method based on popular urban tourist attractions according to claim 7, characterized in that, The dynamic adjustment of the current route includes pushing prompts to users and recommending alternative attractions with nearby visitor density below a threshold as temporary stops.
9. The route planning method based on popular urban tourist attractions according to claim 1, characterized in that, The route planning method also includes: predicting the trend of passenger flow density changes in each sub-region over a future period based on historical passenger flow data and current real-time passenger flow data, and adjusting the planned routes in advance based on the prediction results.
10. A route planning system based on popular urban tourist attractions, used to implement the method described in any one of claims 1-9, characterized in that, include: The data acquisition module is used to obtain real-time visitor density data for multiple sub-areas within the scenic area; The user interaction module is used to obtain the user's location and at least one target sub-region that the user wishes to access. The route planning module is used to plan and generate tour routes based on the real-time passenger flow density data and user location; The display module is used to show the route to the user.