Public transit network supply and demand matching strategy determination method, device and equipment

Abstract

The invention discloses a public transit network supply and demand matching strategy determination method, device and equipment, and relates to the technical field of public transit network planning and optimization. The method comprises the steps of performing label construction on user travel demand data according to a preset travel label rule to obtain a travel demand data set with a label; determining a high-demand travel data group by combining grid regions obtained by dividing the region to be optimized, and further determining a public transit network supply and demand matching result; in the case of mismatching, determining travel demand path data in combination with third-party path planning data; and in combination with current public transit network data, the travel demand coincidence degree is determined, and finally a public transit network supply and demand matching strategy is determined. According to the scheme, the travel demand coincidence degree is determined under the condition that the user travel demand is not matched with the current public transit network, and then the public transit network supply and demand matching strategy is determined for network optimization, so that the matching degree of the public transit network and the user travel demand is improved, and the demand of the user for the public transit travel efficiency is met.

Description

Technical Field

[0001] This application relates to the field of public transportation technology, and more particularly to the field of bus network planning and optimization technology, specifically to a method, apparatus and equipment for determining a bus network supply and demand matching strategy. Background Technology

[0002] With the acceleration of urbanization, urban population and travel demand continue to grow. As a core component of urban public transportation, the rationality of the planning of the public transport network directly affects citizens' travel efficiency and satisfaction.

[0003] In existing technologies, network optimization mainly relies on passively collecting travel data from existing public transport users. This makes it difficult to capture the potential travel needs of people who do not use public transport services, and it is also impossible to accurately diagnose the matching gaps in the network in terms of spatial coverage, time fit, and capacity configuration. As a result, it cannot respond to the dynamic changes in citizens' travel needs in real time. Summary of the Invention

[0004] This application provides a method, apparatus, and equipment for determining the supply and demand matching strategy of a public transport network, so as to improve the matching degree between the public transport network and users' travel needs, save users' travel time by public transport, and meet users' needs for the efficiency of public transport travel.

[0005] According to one aspect of this application, a method for determining a public transport network supply and demand matching strategy is provided, the method comprising: Acquire user travel demand data and areas to be optimized, and construct labels for the user travel demand data according to preset travel label rules to obtain a labeled travel demand dataset; The region to be optimized is divided according to a preset division size to obtain at least two grid regions. Based on the at least two grid regions and the labeled travel demand dataset, at least two high-demand travel data groups are determined. Each high-demand travel data group includes at least the origin grid location, the destination grid location, the demand quantity, and the travel time distribution data. Obtain current bus network data, and determine the bus network supply and demand matching result based on the current bus network data, the starting point grid position, the ending point grid position, the demand quantity, and the travel time distribution data; For each high-demand travel data group, if the supply and demand matching result of the public transport network is mismatched, the travel demand route data is determined based on the starting grid position, the ending grid position, and third-party route planning data. Based on the travel demand path data and the existing public transport network data, the degree of overlap in travel demand is determined, and based on the degree of overlap in travel demand and the travel demand path data, a supply and demand matching strategy for the public transport network is determined.

[0006] According to another aspect of this application, a device for determining a public transport network supply and demand matching strategy is provided, the device comprising: The travel demand dataset determination module is used to acquire user travel demand data and areas to be optimized, and to construct labels for the user travel demand data according to preset travel label rules to obtain a labeled travel demand dataset. The high-demand travel data group determination module is used to divide the area to be optimized according to a preset division size to obtain at least two grid areas, and determine at least two high-demand travel data groups based on the at least two grid areas and the labeled travel demand dataset; wherein each high-demand travel data group includes at least the origin grid location, the destination grid location, the demand quantity, and the travel time distribution data; The bus network supply and demand matching result determination module is used to acquire current bus network data and determine the bus network supply and demand matching result based on the current bus network data, the starting grid position, the ending grid position, the demand quantity, and the travel time distribution data. The travel demand route data determination module is used to determine travel demand route data for each high-demand travel data group, in the case that the supply and demand matching result of the public transport network is mismatched, based on the starting grid position, the ending grid position, and third-party route planning data. The public transport network supply and demand matching strategy determination module is used to determine the degree of overlap of travel demand based on the travel demand path data and the current public transport network data, and to determine the public transport network supply and demand matching strategy based on the degree of overlap of travel demand and the travel demand path data.

[0007] According to another aspect of this application, an electronic device is provided, the electronic device comprising: One or more processors; Memory, used to store one or more programs; When one or more programs are executed by one or more processors, the one or more processors implement any of the bus network supply and demand matching strategy determination methods provided in the embodiments of this application.

[0008] According to another aspect of this application, a computer-readable storage medium is provided, on which a computer program is stored, which, when executed by a processor, implements any of the methods for determining the supply and demand matching strategy of a public transport network provided in the embodiments of this application.

[0009] According to another aspect of this application, a computer program product is provided, including a computer program that, when executed by a processor, implements any of the methods for determining the supply and demand matching strategy of a public transportation network provided in the embodiments of this application.

[0010] This application obtains user travel demand data and the area to be optimized, and constructs a labeled travel demand dataset by labeling the user travel demand data according to preset travel labeling rules. The area to be optimized is divided according to a preset partitioning size to obtain at least two grid regions. Based on these at least two grid regions and the labeled travel demand dataset, at least two high-demand travel data groups are identified. Each high-demand travel data group includes at least the origin grid location, destination grid location, demand quantity, and travel time distribution data. Current public transport network data is obtained, and the public transport network supply-demand matching result is determined based on the current public transport network data, origin grid location, destination grid location, demand quantity, and travel time distribution data. For each high-demand travel data group, if the public transport network supply-demand matching result is mismatched, travel demand path data is determined based on the origin grid location, destination grid location, and third-party route planning data. The travel demand overlap is determined based on the travel demand path data and the current public transport network data, and a public transport network supply-demand matching strategy is determined based on the travel demand overlap and travel demand path data. The above-mentioned scheme matches user travel demand with the existing public transport network. In cases of mismatch, it determines the degree of overlap in travel demand and then, by combining travel demand path data, determines the supply and demand matching strategy for the public transport network, optimizes and adjusts the network, improves the matching degree between the public transport network and user travel demand, saves users' travel time, meets users' demand for public transport efficiency, improves citizens' travel experience, and also enhances the efficiency and scientific nature of public transport network planning. Attached Figure Description

[0011] Figure 1 This is a flowchart of a method for determining a public transport network supply and demand matching strategy according to Embodiment 1 of this application; Figure 2 This is a flowchart of a method for determining a public transport network supply and demand matching strategy according to Embodiment 2 of this application; Figure 3 This is a schematic diagram of a device for determining the supply and demand matching strategy of a public transport network according to Embodiment 3 of this application; Figure 4 This is a schematic diagram of the structure of an electronic device that implements the method for determining the supply and demand matching strategy of the public transport network in Embodiment 4 of this application. Detailed Implementation

[0012] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0013] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0014] Furthermore, it should be noted that the collection, storage, use, processing, transmission, provision, and disclosure of user travel demand data and other related data involved in the technical solution of this application have all been authorized by users and have undergone relevant de-identification processing, comply with the provisions of relevant laws and regulations, and do not violate public order and good morals.

[0015] Example 1 Figure 1 This is a flowchart illustrating a method for determining a public transport network supply and demand matching strategy according to Embodiment 1 of this application. This embodiment is applicable to situations requiring precise matching and optimization of public transport network supply and demand. It can be executed by a public transport network supply and demand matching strategy determination device, which can be implemented in hardware and / or software and can be configured in a computer device, such as a server. Figure 1 As shown, the method includes: S110. Obtain user travel demand data and areas to be optimized, and construct labels for the user travel demand data according to preset travel label rules to obtain a labeled travel demand dataset.

[0016] User travel demand data was primarily obtained through questionnaire surveys. By combining proactive questionnaire surveys with stratified sampling, existing public transport users were covered while potential travel demands were captured, overcoming the limitations of traditional passive data collection and ensuring the representativeness and comprehensiveness of user travel demand data. The collected questionnaire survey results were first processed, including outlier identification, intelligent missing value imputation (e.g., calculating commuting origin and destination based on residence and workplace), and logical consistency checks, before user travel demand data was obtained. Questionnaires could be distributed to users via online links and mini-programs to determine their travel needs. Areas requiring optimization are geographical regions that need public transport network analysis and improvement. Pre-defined travel labeling rules are pre-set classification standards and judgment logic for processing user travel demand data.

[0017] Optionally, the user travel demand data includes origin location data, destination location data, and departure time data. Correspondingly, according to preset travel labeling rules, the user travel demand data is tagged to obtain a tagged travel demand dataset, including: constructing a travel loop label for the origin location data according to preset travel loop labeling rules; constructing a travel destination label for the destination location data according to preset travel destination labeling rules; constructing a travel distance label for the origin and destination location data according to preset travel distance labeling rules; and constructing a travel time label for the departure time data according to preset travel time period labeling rules. The travel loop label, travel destination label, travel distance label, and travel time period label are then associated with the corresponding user travel demand data to obtain a tagged travel demand dataset.

[0018] The preset travel ring label rules can be based on a city's coordinate origin, dividing the city into inner, middle, and outer rings according to radius. Preset travel purpose label rules can determine the travel purpose based on the type of destination location; for example, an office building as the destination indicates commuting, a hospital as the destination indicates medical treatment, etc. Preset travel distance label rules can calculate and classify the straight-line distance between the origin and destination; for example, a straight-line distance less than 3km indicates a short distance; a straight-line distance greater than or equal to 3km and less than or equal to 10km indicates a medium distance; a straight-line distance greater than 10km indicates a long distance. Preset travel time label rules can determine the travel time based on departure time; for example, 7-9 am is the morning peak, 5-7 pm is the evening peak, etc. Travel ring labels can include categories such as within the middle ring (42%) and outside the middle ring (58%). Travel purpose labels can include categories such as commuting (72%), going to school (12%), seeking medical treatment (6%), and shopping / leisure (10%). Travel distance labels can include short distance (18%), medium distance (75%), long distance (7%), etc. Travel time labels can include morning peak 7-9 am (48%), evening peak 17-19 pm (35%), other off-peak times (12%), after 21 pm (5%), etc.

[0019] User travel demand data is tagged to create a labeled travel demand dataset. This dataset quantifies and generates tags for various travel zones (inside / outside the inner ring road, etc.), travel purpose (commuting, going to school, medical treatment, etc.), travel distance (short / medium / long distance, etc.), and travel time (morning peak, evening peak, nighttime, etc.). This process transforms raw user travel demand data into standardized, computable, aggregable, and interpretable tags, providing a structured data foundation for subsequent rasterized demand aggregation, supply-demand matching diagnosis, and optimization strategy generation. This significantly improves the accuracy and efficiency of public transport network optimization.

[0020] S120. Divide the area to be optimized according to the preset division size to obtain at least two grid areas, and determine at least two high-demand travel data groups based on the at least two grid areas and the labeled travel demand dataset; wherein each high-demand travel data group includes at least the starting grid position, the ending grid position, the demand quantity, and the travel time distribution data.

[0021] The preset division size can be determined according to public transportation industry standards and actual conditions. This application embodiment does not impose specific limitations on this; for example, it can be 300 meters × 300 meters. Specifically, it can be that the area to be optimized is divided into a 300-meter × 300-meter grid with a certain city coordinate origin as the center.

[0022] Optionally, based on the at least two raster regions and the labeled travel demand dataset, at least two high-demand travel data groups are determined, including: determining the starting raster and ending raster corresponding to each travel demand data based on the at least two raster regions and the starting and ending location data in the labeled travel demand dataset; aggregating the labeled travel demand dataset according to the starting raster, the ending raster, and a preset travel demand aggregation rule to obtain the number of travel demands for each travel demand data group; and determining the travel demand data groups whose number of travel demands is greater than or equal to a preset travel demand number threshold as high-demand travel data groups.

[0023] The preset travel demand aggregation rule can be to divide labeled travel demand datasets within the same grid where the starting direction error is less than or equal to 30° and the ending direction error is less than or equal to 30° into a single travel demand data group. The preset number of travel demands can be manually preset based on actual conditions or experience. This application embodiment does not specifically limit this; for example, the preset number of travel demands can be 10.

[0024] Specifically, the origin and destination location data in the labeled travel demand dataset can be mapped to corresponding raster areas according to geographic coordinate mapping rules to determine the origin and destination raster corresponding to each travel demand data. Based on the origin raster, destination raster, and preset travel demand aggregation rules, the labeled travel demand dataset is aggregated to obtain the number of travel demands for each travel demand data group. Travel demand data groups with a number of travel demands greater than or equal to a preset threshold are identified as high-demand travel data groups.

[0025] S130. Obtain current bus network data, and determine the bus network supply and demand matching result based on the current bus network data, the starting point grid position, the ending point grid position, the demand quantity, and the travel time distribution data.

[0026] The current public transport network data can be the basic information of the public transport system currently in operation. The public transport network supply and demand matching result refers to the quantitative assessment output of the degree of fit between supply and demand, which can include matching and mismatch.

[0027] Optionally, the supply-demand matching result of the bus network is determined based on the existing bus network data, the starting point grid location, the ending point grid location, the demand quantity, and the travel time distribution data. This includes: determining station location data, operation scheduling data, capacity allocation data, and route direction data based on the existing bus network data; determining the spatial matching degree of supply and demand based on the station location data, the starting point grid location, and the ending point grid location; determining the temporal matching degree of supply and demand based on the operation scheduling data, the travel time distribution data, and the demand quantity; determining the capacity matching degree of supply and demand based on the capacity allocation data, the demand quantity, and the travel time distribution data; determining the service quality matching degree of supply and demand based on a preset service quality scoring rule table, the station location data, the operation scheduling data, the route direction data, the starting point grid location, the ending point grid location, and the travel time distribution data; and determining the supply-demand matching result of the bus network based on the spatial matching degree of supply and demand, the temporal matching degree of supply and demand, the capacity matching degree of supply and demand, the service quality matching degree of supply and demand, and a preset weight value.

[0028] Specifically, the starting point coverage score can be calculated based on the station location data and the starting point grid location. When the nearest bus stop is less than or equal to 500 meters from the starting point grid location, the starting point coverage score is 100 points, indicating that the starting point is covered (i.e., there is a bus stop at the starting point). When the nearest bus stop is greater than 500 meters from the starting point grid location, the starting point coverage score is 0 points, indicating that the starting point is not covered (i.e., there is no bus stop at the starting point). Similarly, the ending point coverage score can be calculated based on the station location data and the ending point grid location. When the nearest bus stop is less than or equal to 500 meters from the ending point grid location, the ending point coverage score is 100 points. A score of 0 indicates that the destination is covered, meaning there is a stop at the destination. When the nearest bus stop is more than 500 meters away from the destination grid position, the destination coverage score is 0, indicating that the destination is not covered, meaning there is no stop at the destination. Then, based on the starting grid position, the ending grid position, and combined with third-party route planning data, the route overlap score can be calculated. Based on the starting grid coverage score, the ending grid coverage score, the route overlap score, and their preset weights, the supply and demand spatial matching degree can be calculated. The specific calculation formula is: Supply and demand spatial matching degree = Starting grid coverage score × 0.4 + Ending grid coverage score × 0.4 + Route overlap score × 0.2.

[0029] The service period coverage score can be calculated based on the route operating time data and travel time distribution data in the operation scheduling data. For example, if the peak demand is 7-9 am and the existing route operating time is 6:00-21:00, the coverage rate is 100%, which is a score of 100. The departure frequency matching score can be calculated based on the departure frequency data and demand quantity in the operation scheduling data. The departure frequency matching degree can be determined by the ratio of the target departure interval required by the demand quantity to the actual departure interval. For example, if the peak departure interval is 15 minutes and the demand is 85 passengers / day, an interval of 8-10 minutes is required. The matching degree is 60% based on the ratio of demand frequency to actual frequency, and then the median value is calculated, which is a score of 60. The supply and demand time matching degree is calculated based on the service period coverage score, departure frequency matching degree score and their preset weights. The specific calculation formula is: Supply and demand time matching degree = Service period coverage score × 0.6 + Departure frequency matching degree score × 0.4.

[0030] The load factor can be calculated based on demand volume, travel time distribution data, and vehicle rated capacity and departure intervals in the capacity configuration data. The supply-demand capacity matching degree is then calculated based on the load factor and the target load factor. The target load factor can be determined according to public transportation industry standards and actual conditions; this application does not impose specific limitations on it, for example, it could be 75%. The specific formula for calculating the supply-demand capacity matching degree is: Supply-demand capacity matching degree = (1 - |Actual load factor - Target load factor| ÷ Target load factor) × 100.

[0031] Based on route data, station location data, operation scheduling data, and starting and ending grid locations, as well as travel time distribution data, travel time can be calculated. A preset service quality scoring rule table is then consulted based on the travel time to determine the time efficiency score. Based on station location data and the starting and ending grid locations, the number of transfers and walking distance can be calculated. A preset service quality scoring rule table is then consulted based on the number of transfers and walking distance to determine the convenience score. The convenience score is calculated as follows: Convenience Score = Transfer Convenience Score × 0.6 + Walking Distance Score × 0.4. Finally, based on the time efficiency score, convenience score, and their preset weights, the supply-demand service quality matching degree is calculated. The specific calculation formula is: Supply-demand service quality matching degree = Time Efficiency Score × 0.5 + Convenience Score × 0.5.

[0032] The above-mentioned preset service quality scoring rules table is as follows: ; Finally, the overall supply-demand matching degree can be determined based on the spatial matching degree, temporal matching degree, capacity matching degree, service quality matching degree, and preset weight values. The formula for calculating the overall supply-demand matching degree is: Overall Supply-Demand Matching Degree = Spatial Matching Degree × 0.5 + Temporal Matching Degree × 0.2 + Capacity Matching Degree × 0.15 + Service Quality Matching Degree × 0.15. Finally, the supply-demand matching result of the public transport network can be determined by comparing the overall supply-demand matching degree with a preset matching degree threshold. Further, if the overall supply-demand matching degree is greater than the preset matching degree threshold, the public transport network supply-demand matching result is determined to be matched; if the overall supply-demand matching degree is less than or equal to the preset matching degree threshold, the public transport network supply-demand matching result is determined to be mismatched. The preset matching degree threshold can be manually set in advance based on actual conditions or experience. This application embodiment does not specifically limit this; for example, it could be 60. Establishing a four-dimensional matching degree index system enables accurate diagnosis and quantitative evaluation of the supply-demand matching status, avoids subjectivity in optimization decisions, and provides a scientific basis for optimization strategy formulation.

[0033] S140. For each high-demand travel data group, if the supply and demand matching result of the public transport network is mismatched, determine the travel demand route data based on the starting grid position, the ending grid position, and third-party route planning data.

[0034] The third-party route planning data can be road network and navigation data provided by a third party. The travel demand route data includes feasible route options connecting the origin and destination, and other related data.

[0035] S150. Based on the travel demand path data and the current public transport network data, determine the degree of overlap in travel demand, and based on the degree of overlap in travel demand and the travel demand path data, determine the supply and demand matching strategy for the public transport network.

[0036] Among them, the overlap of travel demand represents the proportion of spatial overlap between user demand corridors and existing bus routes.

[0037] Optionally, based on the travel demand overlap and the travel demand path data, a public transport network supply-demand matching strategy is determined, including: determining the total travel demand path duration based on the travel demand path data; if the travel demand overlap is less than a preset first overlap threshold, determining the public transport network supply-demand matching strategy as a new route creation strategy; wherein, the new route creation strategy includes at least a regular bus route creation strategy and a customized bus route creation strategy; if the travel demand overlap is greater than or equal to the preset first overlap threshold, the travel demand overlap is less than a preset second overlap threshold, and the total travel demand path duration is less than or equal to a preset duration, then... The bus network supply and demand matching strategy is determined to be an existing route adjustment strategy; wherein, the existing route adjustment strategy includes at least a route direction adjustment strategy, a stop relocation strategy, a stop addition strategy, a stop removal strategy, a route extension strategy, a route shortening strategy, and a route splitting strategy; when the overlap of travel demand is greater than or equal to the preset second overlap threshold and the total duration of the travel demand path is greater than a preset duration, the bus network supply and demand matching strategy is determined to be a vehicle dispatching strategy; wherein, the vehicle dispatching strategy includes at least a departure frequency adjustment strategy, a vehicle type adjustment strategy, and a short-route adjustment strategy; wherein, the preset first overlap threshold is less than the preset second overlap threshold.

[0038] The total travel time is the total time required to complete the trip. The preset first overlap threshold and the preset second overlap threshold can be manually set based on actual conditions or experience; this embodiment does not specifically limit this, for example, the preset first overlap threshold is 30%, and the preset second overlap threshold is 70%. The new route strategy is a set of schemes for adding bus routes outside the existing network. The existing route adjustment strategy is a set of schemes for modifying and adjusting existing bus routes. The vehicle scheduling strategy is a set of schemes for scheduling buses. The preset duration can be manually set based on actual conditions or experience; this embodiment does not specifically limit this, for example, 45 minutes. This achieves a precise mapping between supply and demand status and strategy types, avoiding strategy mismatch and resource waste, and improving the scientific and targeted nature of bus network optimization. It automatically generates targeted optimization schemes, taking into account both passenger experience and resource efficiency.

[0039] This application embodiment acquires user travel demand data and the area to be optimized, and constructs labels for the user travel demand data according to preset travel labeling rules to obtain a labeled travel demand dataset; divides the area to be optimized according to a preset partitioning size to obtain at least two grid areas, and determines at least two high-demand travel data groups based on the at least two grid areas and the labeled travel demand dataset; wherein each high-demand travel data group includes at least the origin grid location, the destination grid location, the demand quantity, and the travel time distribution data; acquires current public transport network data, and determines the public transport network supply and demand matching result based on the current public transport network data, the origin grid location, the destination grid location, the demand quantity, and the travel time distribution data; for each high-demand travel data group, if the public transport network supply and demand matching result is mismatched, determines the travel demand path data based on the origin grid location, the destination grid location, and third-party route planning data; determines the travel demand overlap degree based on the travel demand path data and the current public transport network data, and determines the public transport network supply and demand matching strategy based on the travel demand overlap degree and the travel demand path data. The aforementioned solution matches user travel demands with the existing public transport network. In cases of mismatch, it determines the degree of overlap in travel demands and, based on travel path data, establishes a supply-demand matching strategy for the public transport network. This optimizes and adjusts the network, improving the match between the public transport network and user travel needs, saving users' travel time, meeting their demands for efficient public transport, improving the citizen travel experience, and enhancing the efficiency and scientific rigor of public transport network planning. The proposed solution improved the overall matching score of the public transport network from 49 to 82 points, achieved a passenger satisfaction rate of 89%, reduced the proportion of private car commuting by 12%, and kept route occupancy and capacity utilization within a reasonable range. This effectively solves the "last mile" problem, validating the practicality and effectiveness of this application.

[0040] Example 2 Figure 2This is a flowchart of a method for determining a public transport network supply and demand matching strategy according to Embodiment 2 of this application. Based on the technical solutions of the above embodiments, this embodiment refines "determining the degree of overlap in travel demand based on the travel demand path data and the current public transport network data" into "determining the demand data for the routes and the total distance demand data based on the travel demand path data, and determining the current route data based on the current public transport network data; calculating the total distance data of the common road segments corresponding to the common road segments of the travel demand path data and the current public transport network data based on the route demand data and the current route data; determining the basic degree of overlap in travel demand based on the total distance data of the common road segments and the total distance demand data; determining the origin-destination coverage status data based on the travel demand path data and the current public transport network data, and determining a correction coefficient based on the origin-destination coverage status data and a preset coverage threshold; and determining the degree of overlap in travel demand based on the correction coefficient and the basic degree of overlap in travel demand." It should be noted that for parts not detailed in this embodiment, please refer to the relevant descriptions in other embodiments. Figure 2 As shown, the method includes: S210. Obtain user travel demand data and areas to be optimized, and construct labels for the user travel demand data according to preset travel label rules to obtain a labeled travel demand dataset.

[0041] S220. Divide the area to be optimized according to the preset division size to obtain at least two grid areas, and determine at least two high-demand travel data groups based on the at least two grid areas and the labeled travel demand dataset; wherein each high-demand travel data group includes at least the starting grid position, the ending grid position, the demand quantity, and the travel time distribution data.

[0042] Optionally, after determining at least two high-demand travel data groups based on the at least two grid regions and the labeled travel demand dataset, the method further includes: drawing and displaying a demand heatmap of origin and destination points and a demand profile analysis diagram of time periods based on the origin grid location, the destination grid location, the demand quantity, and the travel time period distribution data.

[0043] Specifically, origin-destination pairs can be determined based on the starting and ending grid positions; heat intensity levels can be determined based on the quantity of travel demand, with darker colors and thicker arrows indicating higher demand; the direction of the flow arrows can be determined based on the travel direction vectors; origin-destination pairs, heat intensity levels, and flow arrows are then overlaid on the rasterized map base to form a demand heatmap of origin and destination points. The quantity of travel demand for each time period can be statistically analyzed according to preset time intervals; the proportion of travel demand for each time period to the total daily travel demand can be calculated; then, a bar chart or line chart can be drawn with time as the horizontal axis and demand quantity as the vertical axis, with peak periods and their proportions marked, thus forming a time-period demand profile analysis chart.

[0044] S230. Obtain current bus network data, and determine the bus network supply and demand matching result based on the current bus network data, the starting point grid position, the ending point grid position, the demand quantity, and the travel time distribution data.

[0045] S240. For each high-demand travel data group, if the supply and demand matching result of the public transport network is mismatched, determine the travel demand route data based on the starting grid position, the ending grid position, and third-party route planning data.

[0046] S250. Based on the travel demand path data, determine the route road demand data and total distance demand data, and based on the current public transport network data, determine the current route road data.

[0047] The required road data includes the specific road names and segment lengths along the demand route. The total distance required data is the total length of the demand route. The current road data includes the specific road names and segment lengths along the existing bus routes.

[0048] S260. Based on the required route data and the current route data, calculate the total distance data of the common road segments corresponding to the common road segments of the travel demand route data and the current public transport network data.

[0049] The total distance of the shared road segment is the length of the road where the demand path and the existing route overlap spatially. For example, if the demand path passes through XX Road (1200 meters), XX Avenue (2400 meters), and XX Road (600 meters), while the existing bus route passes through XX Avenue (800 meters of the 2400 meters overlap), then the total length of the shared road segment is 800 meters.

[0050] S270. Determine the basic overlap of travel demands based on the total distance data of the common road segments and the total distance demand data.

[0051] The basic overlap of travel demand is the uncorrected proportion of spatial overlap. The ratio of the total distance data for shared road segments to the total distance demand data can be used as the basic overlap of travel demand.

[0052] S280. Based on the travel demand route data and the current public transport network data, determine the origin and destination coverage status data, and based on the origin and destination coverage status data and the preset coverage threshold, determine the correction coefficient.

[0053] The origin-destination coverage status data represents whether the origin and destination of the travel demand are served by existing bus stops. This data may include the distance of the nearest bus stop from the origin and destination. A preset coverage threshold is a distance standard for determining whether the origin and destination are covered; it can be preset based on actual conditions or experience. This embodiment does not specifically limit this threshold; for example, the preset coverage threshold is 500 meters. The correction coefficient is an adjustment factor based on the origin-destination coverage status to the basic overlap of travel demand.

[0054] Specifically, if the nearest bus stop is 500 meters or less from the starting point, the starting point is considered covered; otherwise, it is not. Similarly, if the nearest bus stop is 500 meters or less from the destination, the destination is considered covered; otherwise, it is not. Further, if both the starting and destination are covered, the correction coefficient is set to 1; if both are covered, the correction coefficient is set to 0.8; if both are covered, the correction coefficient is set to 0.8; and if neither is covered, the correction coefficient is set to 0.5. By determining the coverage status based on the distance between the starting and destination points and the nearest bus stop and introducing correction coefficients, the overlap of travel demand reflecting the actual service level is obtained. Finally, based on quantified thresholds, this is precisely mapped to strategies for establishing new routes, adjusting existing routes, or vehicle scheduling. This achieves a precise transformation from abstract demand to concrete strategies, improving the scientific nature and feasibility of bus network optimization.

[0055] S290. Based on the correction coefficient and the basic overlap of travel demand, determine the overlap of travel demand, and based on the overlap of travel demand and the travel demand path data, determine the supply and demand matching strategy for the public transport network.

[0056] Among them, the overlap of travel demand is the final overlap ratio after correction, which reflects the true level of service. The specific calculation formula is: overlap of travel demand = basic overlap of travel demand × correction coefficient.

[0057] This application embodiment acquires user travel demand data and the area to be optimized, and constructs labels for the user travel demand data according to preset travel labeling rules to obtain a labeled travel demand dataset; divides the area to be optimized according to a preset partitioning size to obtain at least two grid areas, and determines at least two high-demand travel data groups based on the at least two grid areas and the labeled travel demand dataset; wherein each high-demand travel data group includes at least the origin grid location, the destination grid location, the demand quantity, and the travel time distribution data; acquires current public transport network data, and determines the public transport network supply and demand matching result based on the current public transport network data, the origin grid location, the destination grid location, the demand quantity, and the travel time distribution data; for each high-demand travel data group, if the public transport network supply and demand matching result is mismatched, determines the travel demand path data based on the origin grid location, the destination grid location, and third-party route planning data; determines the travel demand overlap degree based on the travel demand path data and the current public transport network data, and determines the public transport network supply and demand matching strategy based on the travel demand overlap degree and the travel demand path data. The above-mentioned scheme matches user travel demand with the existing public transport network. In cases of mismatch, it determines the degree of overlap in travel demand and then, by combining travel demand path data, determines the supply and demand matching strategy for the public transport network, optimizes and adjusts the network, improves the matching degree between the public transport network and user travel demand, saves users' travel time, meets users' demand for public transport efficiency, improves citizens' travel experience, and also enhances the efficiency and scientific nature of public transport network planning.

[0058] Example 3 Figure 3 This is a schematic diagram of a public transport network supply and demand matching strategy determination device according to Embodiment 3 of this application. This embodiment is applicable to situations requiring precise matching and optimization of public transport network supply and demand. The public transport network supply and demand matching strategy determination device can be implemented in hardware and / or software, and can be configured in a computer device, such as a server. Figure 3 As shown, the device includes: The travel demand dataset determination module 310 is used to acquire user travel demand data and areas to be optimized, and to construct labels for the user travel demand data according to preset travel label rules to obtain a labeled travel demand dataset. The high-demand travel data group determination module 320 is used to divide the area to be optimized according to a preset division size to obtain at least two grid areas, and determine at least two high-demand travel data groups based on the at least two grid areas and the labeled travel demand dataset; wherein each high-demand travel data group includes at least the origin grid location, the destination grid location, the demand quantity, and the travel time distribution data; The bus network supply and demand matching result determination module 330 is used to acquire current bus network data and determine the bus network supply and demand matching result based on the current bus network data, the starting grid position, the ending grid position, the demand quantity and the travel time distribution data. The travel demand path data determination module 340 is used to determine travel demand path data for each high-demand travel data group, in the case that the supply and demand matching result of the public transport network is mismatched, based on the starting grid position, the ending grid position and third-party path planning data. The bus network supply and demand matching strategy determination module 350 is used to determine the degree of overlap of travel demand based on the travel demand path data and the current bus network data, and to determine the bus network supply and demand matching strategy based on the degree of overlap of travel demand and the travel demand path data.

[0059] Optional, the public transport network supply and demand matching strategy determination module 350 includes: The current route data determination unit is used to determine the route demand data and total distance demand data based on the travel demand route data, and to determine the current route data based on the current public transport network data. The common road segment total distance data determination unit is used to calculate the common road segment total distance data corresponding to the common road segment of the travel demand path data and the current public transport network data based on the route demand data and the current route road data. The travel demand overlap determination unit is used to determine the travel demand overlap based on the total distance data of the common road segments and the total distance demand data. The correction coefficient determination unit is used to determine the origin and destination coverage status data based on the travel demand path data and the current public transport network data, and to determine the correction coefficient based on the origin and destination coverage status data and a preset coverage threshold. The travel demand overlap determination unit is used to determine the travel demand overlap degree based on the correction coefficient and the basic travel demand overlap degree.

[0060] Optional, the public transport network supply and demand matching strategy determination module 350 includes: The travel demand path total duration determination unit is used to determine the total duration of the travel demand path based on the travel demand path data. The new route strategy determination unit is used to determine the new route strategy as the supply and demand matching strategy of the bus network when the overlap of travel demand is less than a preset first overlap threshold; wherein, the new route strategy includes at least the new route strategy of regular bus routes and the new route strategy of customized bus routes. The existing route adjustment strategy determination unit is used to determine the existing route adjustment strategy as the supply and demand matching strategy of the public transport network when the overlap of travel demand is greater than or equal to the preset first overlap threshold, the overlap of travel demand is less than the preset second overlap threshold, and the total travel demand path duration is less than or equal to the preset duration; wherein, the existing route adjustment strategy includes at least the route adjustment strategy, the station relocation strategy, the station addition strategy, the station removal strategy, the route extension strategy, the route shortening strategy, and the route splitting strategy; The vehicle dispatching strategy determination unit is used to determine the bus network supply and demand matching strategy as a vehicle dispatching strategy when the overlap of travel demand is greater than or equal to the preset second overlap threshold and the total duration of the travel demand path is greater than the preset duration; wherein, the vehicle dispatching strategy includes at least a departure frequency adjustment strategy, a vehicle type adjustment strategy, and a route adjustment strategy. Wherein, the preset first overlap threshold is less than the preset second overlap threshold.

[0061] Optionally, the bus network supply and demand matching result determination module 330 includes: The existing bus network data determination unit is used to determine station location data, operation scheduling data, capacity configuration data and route direction data based on the existing bus network data. The supply and demand spatial matching degree determination unit is used to determine the supply and demand spatial matching degree based on the station location data, the starting grid position, and the ending grid position. The supply-demand time matching degree determination unit is used to determine the supply-demand time matching degree based on the operation scheduling data, the travel time distribution data, and the demand quantity; The supply-demand capacity matching degree determination unit is used to determine the supply-demand capacity matching degree based on the capacity configuration data, the demand quantity and the travel time distribution data; The supply and demand service quality matching degree determination unit is used to determine the supply and demand service quality matching degree based on a preset service quality scoring rule table, according to the station location data, the operation scheduling data, the route direction data, the starting grid location, the ending grid location, and the travel time distribution data. The public transport network supply and demand matching result determination unit is used to determine the public transport network supply and demand matching result based on the supply and demand spatial matching degree, the supply and demand time matching degree, the supply and demand capacity matching degree, the supply and demand service quality matching degree, and a preset weight value.

[0062] Optional, the high-demand travel data group identification module 320 includes: The starting grid and ending grid determination unit is used to determine the starting grid and ending grid corresponding to each travel demand data based on the at least two grid regions and the starting location data and ending location data in the labeled travel demand dataset. The travel demand quantity determination unit is used to aggregate the labeled travel demand dataset according to the starting point grid, the ending point grid and the preset travel demand aggregation rules to obtain the travel demand quantity of each travel demand data group. The high-demand travel data group determination unit is used to determine the travel data group whose number of travel demands is greater than or equal to a preset travel demand quantity threshold as a high-demand travel data group.

[0063] Optionally, the user travel demand data includes origin location data, destination location data, and departure time data; correspondingly, the travel demand dataset determination module 310 includes: The travel loop label determination unit is used to construct loop labels for the starting point location data according to preset travel loop label rules to obtain travel loop labels; The travel destination label determination unit is used to construct a destination label from the destination location data according to a preset travel destination label rule to obtain a travel destination label; The travel distance label determination unit is used to construct distance labels for the starting point location data and the ending point location data according to the preset travel distance label rules, so as to obtain travel distance labels; The travel time period label determination unit is used to construct time period labels for the departure time data according to preset travel time period label rules to obtain travel time period labels; The travel demand dataset determination unit is used to associate the travel ring domain label, the travel destination label, the travel distance label, and the travel time period label with the corresponding user travel demand data to obtain a labeled travel demand dataset.

[0064] The device further includes: The map display module is used to draw and display the origin-destination demand heatmap and time-period demand profile analysis map based on the origin grid location, the destination grid location, the demand quantity, and the travel time distribution data after determining at least two high-demand travel data groups according to the at least two grid regions and the labeled travel demand dataset.

[0065] This application embodiment acquires user travel demand data and the area to be optimized, and constructs labels for the user travel demand data according to preset travel labeling rules to obtain a labeled travel demand dataset; divides the area to be optimized according to a preset partitioning size to obtain at least two grid areas, and determines at least two high-demand travel data groups based on the at least two grid areas and the labeled travel demand dataset; wherein each high-demand travel data group includes at least the origin grid location, the destination grid location, the demand quantity, and the travel time distribution data; acquires current public transport network data, and determines the public transport network supply and demand matching result based on the current public transport network data, the origin grid location, the destination grid location, the demand quantity, and the travel time distribution data; for each high-demand travel data group, if the public transport network supply and demand matching result is mismatched, determines the travel demand path data based on the origin grid location, the destination grid location, and third-party route planning data; determines the travel demand overlap degree based on the travel demand path data and the current public transport network data, and determines the public transport network supply and demand matching strategy based on the travel demand overlap degree and the travel demand path data. The above-mentioned scheme matches user travel demand with the existing public transport network. In cases of mismatch, it determines the degree of overlap in travel demand and then, by combining travel demand path data, determines the supply and demand matching strategy for the public transport network, optimizes and adjusts the network, improves the matching degree between the public transport network and user travel demand, saves users' travel time, meets users' demand for public transport efficiency, improves citizens' travel experience, and also enhances the efficiency and scientific nature of public transport network planning.

[0066] The public transport network supply and demand matching strategy determination device provided in this application embodiment can execute the public transport network supply and demand matching strategy determination method provided in any embodiment of this application, and has the corresponding functional modules and beneficial effects for executing each public transport network supply and demand matching strategy determination method.

[0067] According to embodiments of this application, this application also provides an electronic device, a readable storage medium, and a computer program product.

[0068] Example 4 Figure 4This is a schematic diagram of the structure of an electronic device 410 implementing the method for determining the supply and demand matching strategy of a public transport network according to embodiments of this application. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present application described and / or claimed herein.

[0069] like Figure 4 As shown, the electronic device 410 includes at least one processor 411 and a memory, such as a read-only memory (ROM) 412 or a random access memory (RAM) 413, communicatively connected to the at least one processor 411. The memory stores computer programs executable by the at least one processor. The processor 411 can perform various appropriate actions and processes based on the computer program stored in the ROM 412 or loaded from storage unit 418 into the RAM 413. The RAM 413 may also store various programs and data required for the operation of the electronic device 410. The processor 411, ROM 412, and RAM 413 are interconnected via a bus 414. An input / output (I / O) interface 415 is also connected to the bus 414.

[0070] Multiple components in electronic device 410 are connected to I / O interface 415, including: input unit 416, such as keyboard, mouse, etc.; output unit 417, such as various types of displays, speakers, etc.; storage unit 418, such as disk, optical disk, etc.; and communication unit 419, such as network card, modem, wireless transceiver, etc. Communication unit 419 allows electronic device 410 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0071] Processor 411 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 411 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 411 performs the various methods and processes described above, such as the method for determining the supply and demand matching strategy for a public transport network.

[0072] In some embodiments, the public transport network supply and demand matching strategy determination method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 418. In some embodiments, part or all of the computer program may be loaded and / or installed on electronic device 410 via ROM 412 and / or communication unit 419. When the computer program is loaded into RAM 413 and executed by processor 411, one or more steps of the public transport network supply and demand matching strategy determination method described above may be performed. Alternatively, in other embodiments, processor 411 may be configured as the public transport network supply and demand matching strategy determination method by any other suitable means (e.g., by means of firmware).

[0073] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0074] Computer programs used to implement the methods of this application may be written in any combination of one or more programming languages. These computer programs may be provided to the processor of a general-purpose computer, a special-purpose computer, or other programmable public transport network supply and demand matching strategy determination device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0075] In the context of this application, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium can be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0076] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0077] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0078] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0079] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this application can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this application can be achieved, and this is not limited herein.

[0080] The specific embodiments described above do not constitute a limitation on the scope of protection of this application. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method for determining a supply-demand matching strategy for a public transport network, characterized in that, include: Acquire user travel demand data and areas to be optimized, and construct labels for the user travel demand data according to preset travel label rules to obtain a labeled travel demand dataset; The region to be optimized is divided according to a preset division size to obtain at least two grid regions. Based on the at least two grid regions and the labeled travel demand dataset, at least two high-demand travel data groups are determined. Each high-demand travel data group includes at least the origin grid location, the destination grid location, the demand quantity, and the travel time distribution data. Obtain current bus network data, and determine the bus network supply and demand matching result based on the current bus network data, the starting point grid position, the ending point grid position, the demand quantity, and the travel time distribution data; For each high-demand travel data group, if the supply and demand matching result of the public transport network is mismatched, the travel demand route data is determined based on the starting grid position, the ending grid position, and third-party route planning data. Based on the travel demand path data and the existing public transport network data, the degree of overlap in travel demand is determined, and based on the degree of overlap in travel demand and the travel demand path data, a supply and demand matching strategy for the public transport network is determined.

2. The method according to claim 1, characterized in that, Based on the travel demand path data and the existing public transport network data, the degree of overlap in travel demand is determined, including: Based on the travel demand path data, determine the route road demand data and total distance demand data, and based on the current public transport network data, determine the current route road data; Based on the required road route data and the current road route data, calculate the total distance of the common road segments corresponding to the common road segments of the travel demand route data and the current public transport network data. Based on the total distance data of the shared road segments and the total distance demand data, the basic overlap of travel demands is determined; Based on the travel demand route data and the current public transport network data, determine the origin and destination coverage status data, and based on the origin and destination coverage status data and the preset coverage threshold, determine the correction coefficient; The degree of overlap in travel demand is determined based on the correction coefficient and the basic degree of overlap in travel demand.

3. The method according to claim 1, characterized in that, Based on the overlap of travel demands and the travel demand path data, a public transport network supply-demand matching strategy is determined, including: Based on the travel demand path data, determine the total travel demand path duration; When the overlap of travel demand is less than a preset first overlap threshold, the supply and demand matching strategy for the bus network is determined to be the strategy of opening new routes; wherein, the strategy of opening new routes includes at least the strategy of opening new regular bus routes and the strategy of opening new customized bus routes. When the overlap of travel demand is greater than or equal to the preset first overlap threshold, the overlap of travel demand is less than the preset second overlap threshold, and the total duration of the travel demand path is less than or equal to the preset duration, the supply and demand matching strategy for the public transport network is determined to be the existing route adjustment strategy; wherein, the existing route adjustment strategy includes at least the route adjustment strategy, the station relocation strategy, the station addition strategy, the station removal strategy, the route extension strategy, the route shortening strategy, and the route splitting strategy. When the overlap of travel demand is greater than or equal to the preset second overlap threshold and the total duration of the travel demand path is greater than the preset duration, the supply and demand matching strategy of the public transport network is determined to be a vehicle dispatching strategy; wherein, the vehicle dispatching strategy includes at least a departure frequency adjustment strategy, a vehicle type adjustment strategy, and a route adjustment strategy. Wherein, the preset first overlap threshold is less than the preset second overlap threshold.

4. The method according to claim 1, characterized in that, Based on the existing bus network data, the starting point grid location, the ending point grid location, the demand quantity, and the travel time distribution data, the bus network supply and demand matching result is determined, including: Based on the existing public transport network data, determine the station location data, operation scheduling data, transport capacity allocation data, and route direction data; The supply and demand spatial matching degree is determined based on the station location data, the starting grid location, and the ending grid location; The supply-demand time matching degree is determined based on the operation scheduling data, the travel time distribution data, and the demand quantity; The supply-demand capacity matching degree is determined based on the capacity configuration data, the demand quantity, and the travel time distribution data. Based on a preset service quality scoring rule table, the matching degree of supply and demand service quality is determined according to the station location data, the operation scheduling data, the route direction data, the starting grid location, the ending grid location, and the travel time distribution data. The supply and demand matching result of the public transport network is determined based on the supply and demand spatial matching degree, the supply and demand time matching degree, the supply and demand capacity matching degree, the supply and demand service quality matching degree, and the preset weight value.

5. The method according to claim 1, characterized in that, Based on the at least two raster regions and the labeled travel demand dataset, identify at least two high-demand travel data sets, including: Based on the at least two grid regions, as well as the origin and destination location data in the labeled travel demand dataset, determine the origin and destination grids corresponding to each travel demand data. Based on the starting point grid, the ending point grid, and the preset travel demand aggregation rules, the labeled travel demand dataset is aggregated to obtain the number of travel demands for each travel demand data group. The travel data group whose number of travel demands is greater than or equal to a preset travel demand threshold is identified as the high-demand travel data group.

6. The method according to claim 1, characterized in that, The user travel demand data includes origin location data, destination location data, and departure time data; correspondingly, according to preset travel tagging rules, the user travel demand data is tagged to obtain a tagged travel demand dataset, including: According to the preset travel loop labeling rules, loop labels are constructed on the starting point location data to obtain travel loop labels; According to the preset travel destination labeling rules, the destination location data is used to construct destination labels to obtain travel destination labels; According to preset travel distance labeling rules, distance labels are constructed on the starting point location data and the ending point location data to obtain travel distance labels; According to the preset travel time period labeling rules, the departure time data is used to construct time period labels to obtain travel time period labels; The travel zone label, travel destination label, travel distance label, and travel time period label are associated with the corresponding user travel demand data to obtain a labeled travel demand dataset.

7. The method according to claim 1, characterized in that, After identifying at least two high-demand travel data sets based on the at least two raster regions and the labeled travel demand dataset, the method further includes: Based on the starting point grid location, the ending point grid location, the demand quantity, and the travel time distribution data, a demand heat map of the starting and ending points and a demand profile analysis chart of the time period are drawn and displayed.

8. A device for determining the supply and demand matching strategy of a public transport network, characterized in that, include: The travel demand dataset determination module is used to acquire user travel demand data and areas to be optimized, and to construct labels for the user travel demand data according to preset travel label rules to obtain a labeled travel demand dataset. The high-demand travel data group determination module is used to divide the area to be optimized according to a preset division size to obtain at least two grid areas, and determine at least two high-demand travel data groups based on the at least two grid areas and the labeled travel demand dataset; wherein each high-demand travel data group includes at least the origin grid location, the destination grid location, the demand quantity, and the travel time distribution data; The bus network supply and demand matching result determination module is used to acquire current bus network data and determine the bus network supply and demand matching result based on the current bus network data, the starting grid position, the ending grid position, the demand quantity, and the travel time distribution data. The travel demand route data determination module is used to determine travel demand route data for each high-demand travel data group, in the case that the supply and demand matching result of the public transport network is mismatched, based on the starting grid position, the ending grid position, and third-party route planning data. The public transport network supply and demand matching strategy determination module is used to determine the degree of overlap of travel demand based on the travel demand path data and the current public transport network data, and to determine the public transport network supply and demand matching strategy based on the degree of overlap of travel demand and the travel demand path data.

9. An electronic device, characterized in that, include: One or more processors; Memory, used to store one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the bus network supply and demand matching strategy determination method as described in any one of claims 1-7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the method for determining the supply and demand matching strategy of the public transport network as described in any one of claims 1-7.

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