A traffic tourism service efficiency evaluation method, device, equipment, medium and product

Abstract

This application discloses a method, apparatus, equipment, medium, and product for evaluating the effectiveness of transportation and tourism services, relating to the field of intelligent transportation technology. The method includes: acquiring data on first transportation stations, first tourist attractions, and first road network in a target area under a first scenario, and data on second transportation stations, second tourist attractions, and second road network in a second scenario; determining the number of first transportation stations, the proportion of first transfer stations, and a first accessibility index based on the data under the first scenario, and determining the number of second transportation stations, the proportion of second transfer stations, and a second accessibility index based on the data under the second scenario; and determining the effectiveness evaluation result based on the number of first transportation stations, the proportion of first transfer stations, the number of second transportation stations, the proportion of second transfer stations, the first accessibility index, and the second accessibility index. This method can achieve accurate evaluation of the effectiveness of transportation and tourism services.

Description

Technical Field

[0001] This application relates to the field of intelligent transportation technology, and in particular to a method, apparatus, equipment, medium and product for evaluating the effectiveness of transportation and tourism services. Background Technology

[0002] The booming development of the tourism industry has made high-quality tourism resources such as tourist attractions an important engine for driving urban economic growth, and tourists' demand for efficient, convenient and equitable transportation for travel is becoming increasingly urgent.

[0003] Currently, transportation-related assessment technologies have been applied in multiple fields, such as accessibility assessment, scenario assessment based on transportation planning tools, and efficiency assessment. However, they all have significant shortcomings, such as being out of touch with actual travel scenarios and having a single assessment dimension, which ultimately leads to the inability to accurately assess the effectiveness of transportation and tourism services.

[0004] Therefore, there is an urgent need for a method that can accurately assess the effectiveness of transportation and tourism services. Summary of the Invention

[0005] This application provides a method, apparatus, equipment, medium, and product for evaluating the effectiveness of transportation and tourism services, which can achieve accurate evaluation of the effectiveness of transportation and tourism services.

[0006] To achieve the above objectives, this application adopts the following technical solution: Firstly, this application provides a method for evaluating the effectiveness of transportation and tourism services, including: Acquire the first transportation station data, the first scenic spot data, and the first road network data of the target area under the first scenario, and the second transportation station data, the second scenic spot data, and the second road network data under the second scenario; Based on the data of the first transportation station, the data of the first scenic spot, and the data of the first road network, the number of the first transportation station, the proportion of the first transfer station, and the first accessibility index corresponding to each tourist attraction are determined. Based on the data of the second transportation station, the data of the second scenic spot, and the data of the second road network, the number of the second transportation station, the proportion of the second transfer station, and the second accessibility index corresponding to each tourist attraction are determined. Based on the first accessibility index corresponding to each tourist attraction, a first spatial equity index for transportation services among the tourist attractions is determined, and based on the second accessibility index corresponding to each tourist attraction, a second spatial equity index for transportation services among the tourist attractions is determined. The first scale efficiency is determined based on the number of first transportation stations, the proportion of first transfer stations, the first accessibility index, and the first spatial equity index; and the second scale efficiency is determined based on the number of second transportation stations, the proportion of second transfer stations, the second accessibility index, and the second spatial equity index. Based on the first accessibility index, the second accessibility index, the first spatial equity index, the second spatial equity index, the first scale efficiency, and the second scale efficiency, the effectiveness evaluation results of the target area under the first scenario and the second scenario are determined.

[0007] In some possible implementations, based on data from the first transportation hub, the first tourist attraction, and the first road network, the number of first transportation hubs, the proportion of first transfer stations, and the first accessibility indicators corresponding to each tourist attraction are determined, including: The total number of stations in the first transportation station data is taken as the number of first transportation stations; The number of stations in the first transportation station data that intersect with at least two transportation lines is taken as the number of first transfer stations. The ratio of the number of first transfer stations to the number of first transportation stations is taken as the proportion of first transfer stations; Based on the data from the first transportation station, the first scenic spot, and the first road network, the first accessibility index corresponding to each tourist attraction is calculated using the Gaussian two-step floating watershed area method.

[0008] In some possible implementations, a first spatial equity index for transportation services among tourist attractions is determined based on the first accessibility index corresponding to each tourist attraction, including: Based on the first accessibility index corresponding to each tourist attraction, a first Lorenz curve is constructed. Based on the first Lorenz curve, the first Gini coefficient is calculated and used as the first spatial fairness index.

[0009] In some possible implementations, the first scale efficiency is determined based on the first number of transportation stations, the first proportion of transfer stations, the first accessibility index, and the first spatial equity index, including: The first efficiency and the second efficiency are determined based on the number of first transportation stations, the proportion of first transfer stations, the first accessibility index, and the first spatial fairness index. The ratio of the first efficiency to the second efficiency is defined as the first scale efficiency.

[0010] In some possible implementations, the first efficiency and the second efficiency are determined based on the number of first transportation stations, the proportion of first transfer stations, a first accessibility index, and a first spatial equity index, including: With the number of first transportation stations and the proportion of first transfer stations as input variables, and the first accessibility index and the first spatial fairness index as output variables, efficiency is calculated to obtain the first efficiency and the second efficiency.

[0011] In some possible implementations, based on a first accessibility index, a second accessibility index, a first spatial fairness index, a second spatial fairness index, a first scale efficiency, and a second scale efficiency, the effectiveness evaluation results of the target area under the first scenario and the second scenario are determined, including: The difference between the second accessibility index and the first accessibility index is taken as the change in accessibility. The difference between the fairness index of the second space and the fairness index of the first space is used as the fairness change. The difference between the second-scale efficiency and the first-scale efficiency is taken as the change in efficiency; Based on changes in accessibility, fairness, and efficiency, the effectiveness assessment results of the target area under the first and second scenarios are determined.

[0012] Secondly, this application provides a device for evaluating the effectiveness of transportation and tourism services, comprising: The acquisition module is used to acquire the first traffic station data, the first scenic spot data, and the first road network data of the target area under the first scenario, and the second traffic station data, the second scenic spot data, and the second road network data under the second scenario. The accessibility determination module is used to determine the number of first transportation stations, the proportion of first transfer stations, and the first accessibility index corresponding to each tourist attraction based on first transportation station data, first attraction data, and first road network data; and to determine the number of second transportation stations, the proportion of second transfer stations, and the second accessibility index corresponding to each tourist attraction based on second transportation station data, second attraction data, and second road network data. The fairness determination module is used to determine the first spatial fairness index of transportation services among tourist attractions based on the first accessibility index corresponding to each tourist attraction, and to determine the second spatial fairness index of transportation services among tourist attractions based on the second accessibility index corresponding to each tourist attraction. The efficiency determination module is used to determine a first scale efficiency based on the number of first transportation stations, the proportion of first transfer stations, a first accessibility index, and a first spatial fairness index, and to determine a second scale efficiency based on the number of second transportation stations, the proportion of second transfer stations, a second accessibility index, and a second spatial fairness index. The evaluation module is used to determine the effectiveness evaluation results of the target area under the first scenario and the second scenario based on the first accessibility index, the second accessibility index, the first spatial fairness index, the second spatial fairness index, the first scale efficiency and the second scale efficiency.

[0013] Thirdly, this application provides a computing device, including a memory and a processor; The memory stores one or more computer programs, the one or more computer programs including instructions; when the instructions are executed by the processor, the computing device performs the method as described in any one of the first aspects.

[0014] Fourthly, this application provides a computer-readable storage medium for storing a computer program for performing the method as described in any one of the first aspects.

[0015] Fifthly, this application provides a computer program product comprising one or more computer instructions, wherein when the computer instructions are executed by a computer, the computer performs the method as described in any one of the first aspects.

[0016] As can be seen from the above technical solution, this application has at least the following beneficial effects: In this application, by acquiring data on transportation stations, scenic spots, and road networks under two scenarios (Scenario 1 and Scenario 2) in the target area, this application covers three important data categories: transportation stations, scenic spots, and urban road networks. It covers the entire chain of assessment of transportation supply, tourism demand, and travel routes, avoiding the one-sidedness of assessment due to data gaps. Furthermore, by distinguishing between Scenario 1 and Scenario 2, it breaks through the limitations of traditional assessments that only evaluate the current situation, providing data support for the subsequent quantitative improvement of planning schemes. For example, the differences in station data under the two scenarios can be directly linked to the adjustment of the network scale in the planning.

[0017] Based on data calculations of the number of transportation stations, the proportion of transfer stations, and accessibility indicators, the basic data is transformed into evaluation elements. By adopting a network-based Gaussian two-step floating watershed area method, the coarseness of traditional Euclidean distance or buffer analysis is eliminated. By using preset thresholds and Gaussian distance decay functions, combined with the actual road network topology, network distances are calculated, making accessibility indicators more closely reflect actual tourist travel behavior. For example, it can accurately identify the service differences between attractions near stations but blocked by the road network and attractions far stations but with unobstructed road networks, ensuring the accuracy of accessibility calculations. By directly reflecting network scale, network connectivity, and transfer convenience, the indicator system achieves multi-dimensional characterization, realizing characterization from three dimensions: scale, structure, and service. This avoids misjudgments of network service capabilities caused by single indicators and provides quantitative input for subsequent fairness and efficiency assessments.

[0018] Calculating the Gini coefficient based on accessibility indicators to determine spatial equity indicators fills the gap in traditional rail transit assessments that prioritize efficiency over equity. Its beneficial effects are reflected in the quantification of equity and the balanced orientation of planning. By ranking the accessibility indicators of various attractions and constructing Lorenz curves, the abstract concept of spatial equity is transformed into comparable numerical values ​​using the Gini coefficient. For example, a Gini coefficient of 0.82 in the baseline scenario indicates poor equity, while a decrease to 0.80 in the planning scenario reflects improvement. This makes the equity differences under different scenarios intuitively measurable, avoiding the limitations of traditional assessments where equity is only qualitatively described, and achieving a quantitative transformation of equity. Furthermore, by accurately identifying clusters of attractions with weak transportation services—for example, a 5A-level scenic spot in a certain area may have extremely low accessibility due to its distance from transportation stations, thus raising the Gini coefficient—if a new station is added to cover this area in the planning scenario, the change in equity indicators can directly verify whether the plan takes into account the disadvantaged attractions. This ensures that the planning scheme not only improves the overall service level but also narrows the service gap between attractions, meeting the needs of tourism-oriented rail transit for universal accessibility, and achieving a balance verification of the plan.

[0019] Scale efficiency is determined by inputs (number of stations, proportion of transfer stations) and outputs (accessibility, equity). The DEA model (CCR+BCC) enables the correlation assessment of inputs and outputs, breaking through the traditional misconception of emphasizing input while neglecting matching. By linking the resource inputs (scale, structure) of the transportation network with the outputs (convenience, equity) of tourism services, the ratio of comprehensive technical efficiency (CCR) and pure technical efficiency (BCC) (scale efficiency) is used to accurately identify the matching degree between network scale and tourism demand. For example, a scale efficiency of 0.38 in the baseline scenario indicates a serious mismatch between scale and demand, while an improvement to 0.45 in the planning scenario reflects scale optimization, achieving a systematic approach to efficiency assessment. It also avoids the waste of resources from blind expansion. For example, if the number of stations increases significantly in the planning scenario, but the scale efficiency decreases, it indicates that the new resources have not been effectively converted into tourism service outputs (e.g., new stations are far from attractions), which can guide planning adjustments (e.g., prioritizing coverage of densely populated tourist areas), ensuring that the quantity and efficiency of rail transit construction are synchronized, and improving the rationality of investment.

[0020] Determining the final effectiveness assessment result by comprehensively considering four categories of indicators (accessibility, equity, and scale efficiency) and the changes in the two scenarios is a crucial step in transforming dispersed indicators into decision-making criteria, achieving both comprehensive assessment and direct decision-making. By analyzing changes in accessibility (whether it is more convenient), equity (whether it is more balanced), and efficiency (whether it is more efficient), the effectiveness differences between the two scenarios are comprehensively judged from three important dimensions: convenience, equity, and efficiency. For example, if the planning scenario achieves an accessibility increase of 9.1%, an equity decrease of 2.4% (a reduction in the Gini coefficient), and a scale efficiency decrease of 0.05, the advantages (improved convenience and equity) and disadvantages (slightly reduced scale matching) of the planning can be fully identified. The comprehensive integration of assessment dimensions for internship sites breaks through the limitations of traditional assessments that focus only on a single dimension (such as accessibility); and achieves quantitative guidance for decision support. The output assessment results are not isolated values, but comprehensive conclusions including improvement effects, degree of improvement, and investment efficiency. For example, "Network expansion improves overall accessibility by 9.1% and fairness by 2.4%, but it is necessary to optimize the matching of new lines with passenger flow to improve scale efficiency." It can directly answer key questions from planning departments such as "whether to promote the plan" and "how to optimize the plan," avoiding the subjectivity of experience-based decision-making and providing quantitative support for the scientific formulation of tourism-oriented rail transit planning; ultimately achieving an accurate assessment of the effectiveness of transportation and tourism services.

[0021] It should be understood that the descriptions of technical features, technical solutions, beneficial effects, or similar language in this application do not imply that all features and advantages can be achieved in any single embodiment. Rather, it is understood that the description of a feature or beneficial effect means that a specific technical feature, technical solution, or beneficial effect is included in at least one embodiment. Therefore, the descriptions of technical features, technical solutions, or beneficial effects in this specification do not necessarily refer to the same embodiment. Furthermore, the technical features, technical solutions, and beneficial effects described in this embodiment can be combined in any suitable manner. Those skilled in the art will understand that embodiments can be implemented without one or more specific technical features, technical solutions, or beneficial effects of a particular embodiment. In other embodiments, additional technical features and beneficial effects may be identified in specific embodiments that do not embody all embodiments. Attached Figure Description

[0022] Figure 1 An application environment diagram of a transportation and tourism service efficiency evaluation method provided in this application embodiment; Figure 2 A flowchart illustrating a method for evaluating the effectiveness of transportation and tourism services provided in this application embodiment; Figure 3 A structural diagram of a transportation and tourism service efficiency evaluation device provided in this application embodiment; Figure 4This is a schematic diagram of a computing device provided in an embodiment of this application. Detailed Implementation

[0023] The terms "first," "second," and "third," etc., used in this application specification and accompanying drawings are used to distinguish different objects, not to limit a specific order.

[0024] In the embodiments of this application, the terms "exemplary" or "for example" are used to indicate that something is an example, illustration, or description. Any embodiment or design that is described as "exemplary" or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design. Specifically, the use of the terms "exemplary" or "for example" is intended to present the relevant concepts in a specific manner.

[0025] To ensure clarity and conciseness in the description of the following embodiments, a brief introduction to the related technologies is given first: Currently, existing technologies largely focus on assessing urban commuting accessibility or general public services such as healthcare and education, with limited systematic research on tourist attractions as specific functional nodes. In the limited tourism-related assessments available, methodological approaches often rely on simple Euclidean distance or buffer zone analysis, failing to adequately consider actual road network topology, travel impedance, and the competitive substitution effects between attractions, leading to a disconnect between assessment results and actual tourist travel scenarios. In terms of assessment dimensions, they often focus only on accessibility levels themselves, neglecting spatial equity and input-output efficiency, making it difficult to comprehensively reflect the tourism service value of rail transit. Furthermore, existing technologies are mostly limited to assessing the current situation in a single city, lacking standardized multi-scenario simulation frameworks, and unable to predict and optimize the tourism service effects of future rail transit planning schemes. This is mainly because traditional planning is commuting demand-oriented, failing to fully adapt to the specific characteristics of tourist travel, and lacking integrated technical tools for data fusion, multi-dimensional assessment, and scenario comparison, resulting in a lack of accurate quantitative support for planning decisions.

[0026] In view of this, embodiments of this application provide a method for evaluating the effectiveness of transportation and tourism services. To make the technical solution of this application clearer and easier to understand, the application scenarios of the technical solution of this application are described below with reference to the accompanying drawings. Figure 1 As shown, this figure is an application environment diagram provided by an embodiment of this application.

[0027] In this application environment, users initiate a transportation and tourism service efficiency assessment request to the server via their terminals, simultaneously uploading important parameters, including the target area (e.g., Beijing), the data source configuration for the first scenario (e.g., the baseline scenario), and the second scenario (e.g., the planning extension scenario) (e.g., rail transit station data interface addresses, 5A-level scenic spot directory files, road network data storage paths). Users can also directly upload pre-processed data from the first and second transportation stations, scenic spots, and road networks. Upon receiving the request and relevant data, the server immediately returns a "data received successfully" response to the terminal and initiates the assessment process. Following preset steps, the server sequentially completes multi-source data fusion, tourism accessibility calculation, spatial equity assessment, scale efficiency measurement, and comprehensive analysis of the efficiency results. After the assessment is complete, the server encapsulates the efficiency assessment results into structured data (e.g., JSON format) or a visual report (e.g., PDF) and pushes it to the terminal. The results include accessibility indicators, spatial equity indicators, scale efficiency values ​​for both scenarios, changes in each indicator, and targeted planning decision recommendations. After receiving the evaluation results, the terminal displays them. Users (such as transportation planners) can initiate secondary operation requests to the server through the terminal as needed, such as adjusting the number of new stations in the second scenario or modifying the distance threshold for accessibility calculation. After receiving the adjusted parameters, the server re-executes the complete evaluation process and returns the updated results until the user confirms the final evaluation conclusion.

[0028] To make the technical solution of this application clearer and easier to understand, the following describes a method for evaluating the effectiveness of transportation and tourism services provided in the embodiments of this application, in conjunction with the above application scenarios. Figure 2 As shown, this figure is a flowchart illustrating a method for evaluating the effectiveness of transportation and tourism services provided in an embodiment of this application. The method for evaluating the effectiveness of transportation and tourism services includes: S201. Obtain the first transportation station data, the first scenic spot data, and the first road network data of the target area under the first scenario, and the second transportation station data, the second scenic spot data, and the second road network data under the second scenario.

[0029] The target area refers to the specific geographical area for conducting the assessment of the effectiveness of transportation and tourism services. It is usually a city or city cluster (such as Beijing or the important tourism city cluster in the Yangtze River Delta), and is the boundary range for data collection and assessment analysis.

[0030] The first scenario can be the baseline scenario, which is constructed based on the existing operational transportation network in the target area.

[0031] The second scenario can be a planning extension scenario, which is constructed based on the existing operating network of the target area and newly added (including under construction) transportation stations and lines, and is used to compare and evaluate the effect with the baseline scenario.

[0032] The first transportation station data is related to transportation stations under the first scenario, including the name, geographical coordinates, route, and operational status of the stations in operation.

[0033] The second transportation station data is transportation station information under the second scenario. It is based on the first transportation station data and adds corresponding data for stations under construction or newly added.

[0034] The first scenic spot data consists of relevant information about tourist attractions of the target level within the target area under the first scenario, including scenic spot name, geographical coordinates, level attributes, etc.

[0035] The second set of attraction data consists of relevant information about tourist attractions of the target level within the target area under the second scenario, including attraction names, geographical coordinates, and level attributes; optionally, the attraction data under the first and second scenarios are usually kept consistent (when no attractions are added / removed).

[0036] The first road network data refers to the urban road network information of the target area under the first scenario, including road topology, road type, traffic attributes, etc., which is used to calculate the real network distance between transportation stations and tourist attractions.

[0037] The second road network data refers to the urban road network information of the target area under the second scenario, including road topology, road type, traffic attributes, etc.; optionally, the road network data under the first and second scenarios are generally the same, and are only updated synchronously when the second scenario includes road construction / reconstruction.

[0038] For example, to obtain the multi-source basic data required for the assessment and to distinguish between the first scenario (such as the baseline scenario) and the second scenario (such as the planning expansion scenario) for comparative analysis, specifically, the target area for assessment (such as a tourist city) is first identified, and then three sets of important data are collected under the two scenarios (the first scenario and the second scenario). Optionally, for the first scenario, data on currently operating transportation stations can be obtained through public channels such as map APIs (Application Programming Interfaces), data on tourist attractions of the target level can be obtained from the official directory of the local cultural and tourism bureau, and urban road network data can be obtained from public platforms. For the second scenario, based on the data from the first scenario, data on transportation stations that have been approved for construction or newly added within the target area is supplemented. If the data on tourist attractions and road networks has not been adjusted, the data from the first scenario is used; if there are new attractions or road renovation plans, they are updated synchronously. Through this step, a complete dataset under the two scenarios can be formed, providing unified and standardized basic data support for subsequent calculation of assessment indicators such as accessibility, fairness, and efficiency.

[0039] It should be noted that the data for the first transportation station, the first scenic spot, the first road network, the second transportation station, the second scenic spot, and the second road network are all converted to a unified geographic coordinate system (such as WGS-84 or CGCS2000).

[0040] S202. Based on the data of the first transportation station, the data of the first scenic spot, and the data of the first road network, determine the number of the first transportation station, the proportion of the first transfer station, and the first accessibility index corresponding to each tourist attraction; and based on the data of the second transportation station, the data of the second scenic spot, and the data of the second road network, determine the number of the second transportation station, the proportion of the second transfer station, and the second accessibility index corresponding to each tourist attraction.

[0041] One possible approach is to use the total number of stations in the first transportation station data as the first transportation station quantity; the number of stations in the first transportation station data that intersect with at least two transportation lines as the first transfer station quantity; the ratio of the first transfer station quantity to the first transportation station quantity as the first transfer station ratio; and, based on the first transportation station data, the first scenic spot data, and the first road network data, to calculate the first accessibility index corresponding to each tourist attraction using the Gaussian two-step floating watershed area method.

[0042] Among them, the first transportation station data is the transportation station information under the first scenario, including the name, geographical coordinates, route, and operating status of the station in operation.

[0043] The second transportation station data is station information under the second scenario, which is based on the first transportation station data and includes the corresponding data for stations under construction or newly added.

[0044] The first scenic spot data focuses on the target area and the target level tourist attractions (such as 5A-level scenic spots) in the first scenario, recording information such as scenic spot name, geographical coordinates, and level attributes.

[0045] The second set of attraction data focuses on tourist attractions of the target level (such as 5A-level scenic spots) in the target area under the second scenario, recording information such as attraction name, geographical coordinates, and level attributes. Generally, without any additions or removals of attractions, the first and second attraction data are identical.

[0046] The first road network data includes information such as the road topology, road type, and traffic attributes of the target area under the first scenario, which is used to calculate the real network distance between transportation stations and tourist attractions.

[0047] The second road network data includes information such as the road topology, road type, and traffic attributes of the target area under the second scenario, used to calculate the actual network distance between transportation hubs and tourist attractions. Generally, when planning includes road modifications, the road network data is updated synchronously, meaning the road network data is usually identical in both scenarios.

[0048] The number of the first transportation station is the total number of transportation stations under the second scenario, directly reflecting the scale of the transportation network under the corresponding scenario.

[0049] The number of second transportation stops represents the total number of transportation stops under the second scenario, directly reflecting the scale of the transportation network under the corresponding scenario.

[0050] The first transfer station ratio is the ratio of the number of transfer stations to the total number of corresponding transportation stations under the first scenario, representing the connectivity and transfer convenience of the transportation network.

[0051] The second transfer station ratio is the ratio of the number of transfer stations to the total number of corresponding transportation stations under the second scenario, representing the connectivity and transfer convenience of the transportation network.

[0052] A transfer station is a station where two or more transportation lines intersect, enabling passenger transfers between different lines.

[0053] The first accessibility index is the quantitative value of the transportation accessibility of each tourist attraction under the first scenario, reflecting the ease with which tourists can access the attractions through the transportation network.

[0054] The second accessibility index is the quantitative value of the transportation accessibility of each tourist attraction under the second scenario, reflecting the ease with which tourists can access the attractions through the transportation network.

[0055] The Gaussian two-step floating watershed area method is a refined accessibility calculation method. Based on the actual road network distance and combined with the Gaussian distance decay function, it comprehensively considers the spatial interaction between service supply and demand to accurately calculate the accessibility of attractions.

[0056] For example, based on basic data under two scenarios, key indicators characterizing the scale, connectivity, and accessibility of tourist attractions are calculated to lay the foundation for subsequent fairness and efficiency assessments. Specifically, the transportation station data is processed first. For the first scenario, the total number of stations in the first transportation station data is directly counted as the number of first transportation stations. At the same time, stations where two or more lines intersect are selected and their number is counted as the number of first transfer stations. The ratio of the number of first transfer stations to the number of first transportation stations is used to obtain the proportion of first transfer stations. The calculation logic for the number of second transportation stations and the proportion of second transfer stations in the second scenario is the same as in the first scenario, only the data source is changed to second transportation station data. Subsequently, accessibility indices are calculated. For the first scenario, the first transportation station is considered the service supply point (assuming equal supply capacity), and the target-level tourist attractions in the first scenic spot data are considered the demand points (assuming uniform demand weights). Based on the first road network data, the shortest network distance between the station and the tourist attraction is calculated. Using the Gaussian two-step floating watershed area method, an 800-meter preset distance threshold and a Gaussian distance decay function are introduced to perform attenuation weighting processing on the supply capacity at different distances. Finally, the attenuated service supply capacity available to each tourist attraction is summarized to obtain the first accessibility index for each tourist attraction. The calculation logic for the second accessibility index in the second scenario is the same, relying only on the second transportation station data, the second scenic spot data, and the second road network data for calculation. Through this step, the structural indicators of the transportation network and the quantitative results of scenic spot accessibility under both scenarios can be comprehensively obtained, providing data support for subsequent comparative analysis.

[0057] For example, based on the first transportation station data, the first scenic spot data, and the first road network data, the first accessibility index corresponding to each tourist attraction is calculated using the Gaussian two-step floating watershed area method. The specific process can be as follows: For each transportation station in the target area... As a service supply point, its supply capacity Set to equal (for example, ). Each tourist attraction Considered as a service demand point, its demand size The weights are determined based on the scenic area's rating. For example, a 5A-level scenic area has the highest weight, a 4A-level scenic area has the lowest weight, and so on. Different weight parameters are assigned to tourist attractions of different ratings. For instance, if only 5A-level scenic areas are analyzed, their weights are uniformly set.

[0058] Secondly, set a search threshold. This refers to the effective service radius of a transportation hub. For example, The distance is set at 800 meters. Based on road network data, the distance to each traffic stop is calculated. With each tourist attraction Shortest path distance between .

[0059] For each transportation station Calculate the ratio of its service capacity to the weighted sum of demand from all demand points within the threshold range:

[0060] in, The service capacity ratio of the j-th transportation station reflects the service supply intensity corresponding to the unit weighted demand of that transportation station. Let i be the supply capacity of the j-th transportation station; i is the index of the tourist attraction, representing a single tourist attraction; j is the index of the transportation station, representing a single transportation station. This represents the shortest path distance between the j-th transportation station and the i-th tourist attraction (calculated based on the urban road network, not a straight-line distance). A preset distance threshold (effective service radius) is set, and attractions outside this distance are not included in the service area of ​​this transportation station; For the demand level of the i-th tourist attraction, a weight is assigned based on the attraction's level attribute or the intensity of service demand. Let be the Gaussian distance decay function, used to characterize the continuous decay of service capacity with distance. Its expression is:

[0061] For each tourist attraction By summing the attenuation ratios of all supply points that can provide services to it, the accessibility value of the tourist attraction can be obtained, which can be expressed as:

[0062] in, Let be the accessibility index (quantitative value) of the i-th tourist attraction, reflecting the ease of transportation to and from the tourist attraction.

[0063] Finally, to obtain the overall accessibility index of the target area, the accessibility values ​​of all tourist attractions are weighted and summed according to their level weights, which can be expressed as:

[0064] in, The overall accessibility index of the target area reflects the overall convenience of transportation services to tourist attractions; The weight of the i-th tourist attraction's level is set uniformly when there are only 5A-level scenic spots, and is set differently according to the level when there are multiple levels of scenic spots. This represents the total number of tourist attractions within the target area that participated in the evaluation.

[0065] It should be noted that the process for determining the first and second accessibility indicators is the same; both involve determining the accessibility indicator for the i-th tourist attraction. The calculation process.

[0066] S203. Based on the first accessibility index corresponding to each tourist attraction, determine the first spatial fairness index of transportation services among each tourist attraction, and based on the second accessibility index corresponding to each tourist attraction, determine the second spatial fairness index of transportation services among each tourist attraction.

[0067] One feasible approach is to construct a first Lorenz curve based on the first accessibility index corresponding to each tourist attraction; calculate a first Gini coefficient based on the first Lorenz curve, and use the first Gini coefficient as a first spatial fairness index.

[0068] It can be deduced that a second Lorenz curve is constructed based on the second accessibility index corresponding to each tourist attraction; based on the second Lorenz curve, the second Gini coefficient is calculated, and the second Gini coefficient is used as the second spatial fairness index.

[0069] Among them, the first accessibility index is a quantitative value of the convenience of transportation to each tourist attraction under the first scenario. It is calculated by the Gaussian two-step floating watershed area method and reflects the convenience of the current road network for the attractions.

[0070] The second accessibility index is a quantitative value of the convenience of transportation to various tourist attractions under the second scenario. The calculation logic is the same as that of the first accessibility index, but it is derived solely from the road network data after the second scenario and is used to compare the changes in convenience after the second scenario.

[0071] The first spatial equity index is a quantitative indicator of the degree of spatial balance of transportation services among tourist attractions under the first scenario. It is mainly characterized by the first Gini coefficient and measures the balance of current transportation services in resource allocation among different attractions.

[0072] The second spatial equity index is a quantitative indicator of the degree of spatial balance of transportation services among tourist attractions under the second scenario. It also uses the Gini coefficient as the main indicator to reflect the changing trend of the equity of transportation service distribution after the second scenario.

[0073] The first Lorenz curve is a statistical curve that describes the balance of data distribution. Here, it is constructed based on the accessibility index of attractions sorted from smallest to largest. The horizontal axis represents the cumulative percentage of attractions, and the vertical axis represents the cumulative percentage of the first accessibility index. The degree of deviation of the curve from the absolute fairness line (diagonal line) directly reflects the fairness of distribution.

[0074] The second Lorenz curve is a statistical curve that describes the balance of data distribution. Here, it is constructed based on the accessibility index of attractions sorted from smallest to largest. The horizontal axis represents the cumulative percentage of attractions, and the vertical axis represents the cumulative percentage of the second accessibility index. The degree of deviation of the curve from the absolute fairness line (diagonal line) directly reflects the fairness of distribution.

[0075] The first Gini coefficient is a fairness quantification indicator calculated based on the first Lorenz curve, with a value ranging from 0 to 1. The closer the value is to 0, the more evenly the transportation services are distributed among the attractions; the closer the value is to 1, the more uneven the distribution, and the more obvious the advantage some attractions have in obtaining services.

[0076] The second Gini coefficient is a fairness quantification indicator calculated based on the second Lorenz curve, with a value ranging from 0 to 1. The closer the value is to 0, the more evenly the transportation services are distributed among the attractions; the closer the value is to 1, the more uneven the distribution, and the more obvious the advantage some attractions have in obtaining services.

[0077] For example, a standardized statistical analysis process is used to quantify the spatial equity of transportation services under both the first and second scenarios, providing crucial indicator support for subsequent comparative analysis of the two scenarios. Specifically, to determine the first spatial equity indicator, the first accessibility indicators of all tourist attractions under the first scenario are first collected. These first accessibility indicators are then arranged in ascending order of value, constructing a correspondence between the cumulative percentage of attractions and the cumulative percentage of accessibility indicators, forming the first Lorenz curve. Subsequently, by calculating the area ratio between this first Lorenz curve and the absolute equity line (diagonal), the first Gini coefficient is obtained. This first Gini coefficient is directly used as the first spatial equity indicator, characterizing the degree of balance in the current road network's service to various attractions. The determination logic for the second spatial equity indicator is completely consistent with that of the first spatial equity indicator, only the data source is replaced with the second accessibility indicators of each tourist attraction under the second scenario. Using the same process of indicator sorting, constructing the first Lorenz curve, and calculating the first Gini coefficient, the second spatial equity indicator is obtained, thus clearly reflecting the improvement or change in the equity of the distribution of rail services among different attractions after planning. This step allows for the quantification of fairness in both scenarios, providing data support for determining whether a planning scheme can optimize the balance of services among attractions.

[0078] For example, obtaining the accessibility values ​​of all tourist attractions. Then, the Gini coefficient can be used to quantify the fairness of its spatial distribution. Specifically, this can be achieved by calculating the accessibility values ​​of all tourist attractions within the target area. Arrange the data in ascending order to construct the Lorenz curve, and calculate the area ratio between the Lorenz curve and the absolute fairness line (diagonal). Gini coefficient. The calculation formula is:

[0079] in, The Gini coefficient is a quantitative indicator of spatial fairness. Let i be the accessibility index for the i-th tourist attraction. Let j be the accessibility index of the j-th tourist attraction. This represents the average accessibility value for all tourist attractions.

[0080] It should be noted that the Gini coefficient ranges from 0 to 1. A value closer to 0 indicates a more equitable distribution of transportation services across different tourist attractions; a value closer to 1 indicates a more unequal distribution and higher resource concentration. The calculation process for the first and second Gini coefficients is similar to that of the Gini coefficient itself. The calculation process is the same.

[0081] S204. Determine the first scale efficiency based on the number of first transportation stations, the proportion of first transfer stations, the first accessibility index, and the first spatial equity index; and determine the second scale efficiency based on the number of second transportation stations, the proportion of second transfer stations, the second accessibility index, and the second spatial equity index.

[0082] One feasible approach is to determine a first efficiency and a second efficiency based on the number of first transportation stations, the proportion of first transfer stations, a first accessibility index, and a first spatial fairness index; and to determine the ratio of the first efficiency and the second efficiency as the first scale efficiency.

[0083] For example, with the number of first transportation stations and the proportion of first transfer stations as input variables, and the first accessibility index and the first spatial fairness index as output variables, efficiency calculations are performed to obtain the first efficiency and the second efficiency.

[0084] Among them, the number of primary transportation stops is the total number of transportation stops under the first scenario, which is an important indicator representing the current scale of road network construction.

[0085] The first transfer station ratio is the ratio of the number of transfer stations to the number of first transportation stations under the first scenario, reflecting the connectivity and convenience of the current road network.

[0086] The first accessibility index is a quantitative value of the transportation accessibility of each tourist attraction under the first scenario. It is calculated by the Gaussian two-step floating watershed area method and reflects the convenience of the current road network for the attractions.

[0087] The first spatial equity index is a quantitative indicator of the balanced distribution of transportation services among various attractions under the first scenario, mainly the Gini coefficient.

[0088] The first efficiency is the overall technical efficiency calculated using the Charnes Cooper Rhodes (CCR) model in Data Envelopment Analysis (DEA) based on first-scenario data. This model, based on the assumption of constant returns to scale, assesses the overall relative efficiency of the current road network in converting infrastructure inputs into tourism service outputs at a given scale of construction.

[0089] The second efficiency is a pure technical efficiency calculated using the Banker-Charnes-Cooper (BCC) model in DEA, based on the data from the first scenario. This model is based on the assumption of variable returns to scale, eliminates the influence of scale factors, and only evaluates the relative efficiency of technical aspects such as road network layout and management level.

[0090] First scale efficiency is a quantitative indicator of the degree of matching between the scale of the road network and the demand for tourism services under the first scenario. It is determined by the ratio of first efficiency (comprehensive technical efficiency) to second efficiency (pure technical efficiency) (the value ranges from 0 to 1, and the closer the value is to 1, the higher the degree of matching between scale and demand).

[0091] The number of secondary transportation stations refers to the total number of transportation stations within the target area under the second scenario. Its data source is the secondary transportation station data corresponding to the second scenario, and its function is to directly represent the construction scale of the planned rail transit network, serving as one of the important indicators for measuring resource input.

[0092] The second transfer station ratio refers to the ratio of the number of transfer stations to the number of second transportation stations within the target area under the second scenario. Transfer stations refer to stations that, after planning, will have the function of connecting two or more rail transit lines. This ratio reflects the connectivity and transfer convenience of the planned rail transit network and is an important indicator of the rationality of the road network structure.

[0093] The second accessibility index refers to the quantitative value of the convenience of transportation to each tourist attraction under the second scenario. Its calculation logic is completely consistent with that of the first accessibility index, both derived using the Gaussian two-step floating watershed area method. The only difference is that the data is based on the planned second transportation station data, second attraction data, and second road network data. Its purpose is to reflect the level of service convenience of the planned road network to each attraction.

[0094] The second spatial equity index is a quantitative indicator of the degree of spatial balance in the distribution of transportation services among various tourist attractions under the second scenario, primarily represented by the Gini coefficient. Its calculation logic is consistent with the first spatial equity index: a Lorenz curve is constructed based on the second accessibility index, and the Gini coefficient is then calculated from the curve. Its function is to measure the balance of resource allocation for post-planned services among different tourist attractions.

[0095] The second scale efficiency is a quantitative indicator of the degree of matching between the road network scale and the demand for tourism services in the second scenario, and its calculation logic is the same as that of the first scale efficiency.

[0096] For example, the Data Envelopment Analysis (DEA) model is used to quantify the matching degree between the scale of the transportation network and the demand for tourism services under the first and second scenarios, providing an important indicator for evaluating the scale rationality of the planning scheme. Specifically, for determining the first scale efficiency: firstly, the input and output variables under the first scenario are clarified. The input variables are the number of first transportation stations (network scale) and the proportion of first transfer stations (network connectivity), and the output variables are the first accessibility index (service convenience) and the first spatial equity index (service balance). Then, these variables are substituted into the DEA model, and efficiency is calculated using the CCR model and the BCC model respectively: the CCR model, based on the assumption of constant returns to scale, outputs the first efficiency (overall technical efficiency), reflecting the overall input-output efficiency of the current network; the BCC model, based on the assumption of variable returns to scale, outputs the second efficiency (pure technical efficiency), reflecting the technical efficiency after eliminating the influence of scale. Finally, the ratio of the first efficiency to the second efficiency is taken to obtain the first scale efficiency, which directly reflects whether the current road network scale matches the demand for tourism services.

[0097] The determination of the second scale efficiency follows the same logic as the first scale efficiency, except that the data sources are replaced with the number of second transportation stations and the proportion of second transfer stations (input variables) under the second scenario, as well as the second accessibility index and the second spatial fairness index (output variables). By using the same CCR model to calculate the comprehensive technical efficiency and the BCC model to calculate the pure technical efficiency, two sets of efficiency values ​​under the second scenario are obtained. The ratio of these values ​​is then taken as the second scale efficiency, which is used to reflect the matching changes between the scale of the transportation network and the demand for tourism services.

[0098] This step allows for the accurate quantification of the scale adaptability of the road network under the two scenarios, providing data support for determining whether the second scenario solution is too large or too small, and laying the foundation for investment efficiency evaluation in subsequent scenario comparisons.

[0099] For example, the process of determining the first scale efficiency under the first scenario can be specifically as follows: with the number of first transportation stations and the proportion of first transfer stations as input variables, and the first accessibility index and the first spatial fairness index as output variables, the CCR model based on the assumption of constant returns to scale is used to obtain the first expansion coefficient through linear programming, and the reciprocal of the first expansion coefficient is used as the comprehensive technical efficiency, i.e., the first efficiency; then, based on the BCC model based on the assumption of variable returns to scale, under the condition of introducing convexity constraints, the second expansion coefficient is obtained, and the reciprocal of the second expansion coefficient is used as the pure technical efficiency, i.e., the second efficiency; furthermore, the ratio of the first efficiency to the second efficiency is used as the first scale efficiency.

[0100] It should be noted that the determination process for the second scale efficiency is the same as that for the first scale efficiency, except that it is calculated based on the number of second transportation stations, the proportion of second transfer stations, the second accessibility index, and the second spatial equity index under the second scenario.

[0101] S205. Based on the first accessibility index, the second accessibility index, the first spatial fairness index, the second spatial fairness index, the first scale efficiency, and the second scale efficiency, determine the effectiveness evaluation results of the target area under the first scenario and the second scenario.

[0102] One feasible approach is to use the difference between the second accessibility index and the first accessibility index as the accessibility change; the difference between the second spatial equity index and the first spatial equity index as the equity change; and the difference between the second scale efficiency and the first scale efficiency as the efficiency change. Based on the accessibility change, equity change, and efficiency change, the effectiveness assessment results of the target area under the first and second scenarios are determined.

[0103] Among them, the first accessibility index is a quantitative value of the convenience of transportation to each tourist attraction under the first scenario. It is calculated by the Gaussian two-step floating watershed area method and reflects the convenience of the current road network for the attractions.

[0104] The second accessibility index is a quantitative value of the convenience of transportation to various tourist attractions under the second scenario. The calculation logic is the same as that of the first accessibility index, and it is derived solely from the road network data of the second scenario. It is used to compare the changes in convenience under the second scenario.

[0105] The first spatial equity index is a quantitative indicator of the degree of spatial balance of transportation services among tourist attractions under the first scenario. It is mainly the Gini coefficient, which reflects the balance of current service resource allocation among different attractions.

[0106] The second spatial equity index is a quantitative indicator of the degree of spatial balance of transportation services among tourist attractions under the second scenario. It also mainly uses the Gini coefficient to reflect the changing trend of service distribution equity in the second scenario.

[0107] First scale efficiency is a quantitative indicator of the degree of matching between the scale of the transportation network and the demand for tourism services under the first scenario. It can be determined by the ratio of the comprehensive technical efficiency (i.e., first efficiency) calculated by the CCR model in the DEA model to the pure technical efficiency (i.e., second efficiency) calculated by the BCC model (with a value of 0-1, the closer the value is to 1, the higher the degree of matching between scale and demand).

[0108] The second scale efficiency is a quantitative indicator of the degree of matching between the scale of the transportation network and the demand for tourism services under the second scenario. The calculation logic is the same as that of the first scale efficiency.

[0109] The change in accessibility is the extent to which accessibility improves or decreases in the second scenario compared to the first scenario. It is determined by the difference between the second accessibility index and the first accessibility index. A positive value indicates improved accessibility, while a negative value indicates decreased accessibility.

[0110] The change in fairness represents the degree of improvement or deterioration in service fairness in the second scenario compared to the first scenario. It is determined by the difference between the fairness index of the second space (Gini coefficient) and the fairness index of the first space. A negative value indicates improved fairness (a decrease in the Gini coefficient), while a positive value indicates deterioration in fairness.

[0111] The change in efficiency represents the degree to which the scale efficiency of the second scenario is optimized or weakened compared to the first scenario. It is determined by the difference between the scale efficiency of the second scenario and the scale efficiency of the first scenario. A positive value indicates an improvement in the matching degree between scale and demand, while a negative value indicates a decrease in the matching degree.

[0112] The effectiveness assessment results are a comprehensive conclusion on the effectiveness of transportation and tourism services, formed by combining the absolute levels and trends of accessibility, equity, and scale efficiency under both scenarios.

[0113] For example, by quantifying the differences in key indicators under two scenarios, the current effectiveness of the first scenario and the optimization effect of the second scenario can be comprehensively evaluated, providing a direct basis for transportation planning decisions. Specifically, three types of important changes can be calculated first: First, the change in accessibility, obtained by subtracting the first accessibility indicator from the second accessibility indicator. If the result is positive, it indicates that the overall transportation convenience of each tourist attraction has improved in the second scenario, and the larger the value, the more significant the improvement. Second, the change in equity, obtained by subtracting the first spatial equity indicator from the second spatial equity indicator (Gini coefficient). If the result is negative, it indicates that the distribution of transportation services among various attractions is more balanced in the second scenario, and equity has been improved. The larger the absolute value of the negative value, the more significant the improvement. Third, the change in efficiency, obtained by subtracting the first scale efficiency from the second scale efficiency. If the result is positive, it indicates that the matching degree between network scale and tourism service demand has improved in the second scenario, and resource utilization efficiency is better.

[0114] Subsequently, based on the aforementioned changes and the absolute levels of the indicators in the two scenarios, the comprehensive effectiveness evaluation results can be determined: First, under the first scenario, the performance level of the current road network in the three dimensions of accessibility, equity, and scale efficiency can be determined (e.g., moderate accessibility, poor equity, and low scale efficiency); then, the improvement of the second scenario compared to the first scenario can be analyzed, for example, a 10% improvement in accessibility, a 5% improvement in equity, and a 3% improvement in scale efficiency, clarifying the benefits of the second scenario plan; finally, combining the positive and negative values ​​and magnitude of the indicator changes, a comprehensive conclusion can be output, including the effect of improving tourism accessibility, the degree of improvement in spatial equity, and the evaluation of investment efficiency, to determine whether the second scenario plan is scientifically sound and whether it can achieve comprehensive optimization of transportation and tourism service efficiency. Through this step, scattered indicator data can be transformed into intuitive decision-making basis, clearly presenting the actual value of the second scenario plan, and providing support for whether to promote the implementation of the second scenario and optimize its details.

[0115] Based on the above, the transportation and tourism service efficiency evaluation method acquires data on transportation stations, scenic spots, and road networks under two scenarios (Scenario 1 and Scenario 2) in the target area. This covers three important data categories: transportation stations, scenic spots, and urban road networks, encompassing the entire evaluation chain of transportation supply, tourism demand, and travel routes, thus avoiding the one-sidedness of evaluation due to data gaps. Furthermore, by distinguishing between Scenario 1 and Scenario 2, it breaks through the limitations of traditional methods that only evaluate the current situation, providing data support for the subsequent quantitative improvement of planning schemes. For example, the differences in station data under the two scenarios can be directly linked to adjustments in the network scale of the plan.

[0116] Based on data calculations of the number of transportation stations, the proportion of transfer stations, and accessibility indicators, the basic data is transformed into evaluation elements. By adopting a network-based Gaussian two-step floating watershed area method, the coarseness of traditional Euclidean distance or buffer analysis is eliminated. By using preset thresholds and Gaussian distance decay functions, combined with the actual road network topology, network distances are calculated, making accessibility indicators more closely reflect actual tourist travel behavior. For example, it can accurately identify the service differences between attractions near stations but blocked by the road network and attractions far stations but with unobstructed road networks, ensuring the accuracy of accessibility calculations. By directly reflecting network scale, network connectivity, and transfer convenience, the indicator system achieves multi-dimensional characterization, realizing characterization from three dimensions: scale, structure, and service. This avoids misjudgments of network service capabilities caused by single indicators and provides quantitative input for subsequent fairness and efficiency assessments.

[0117] Calculating the Gini coefficient based on accessibility indicators to determine spatial equity indicators fills the gap in traditional rail transit assessments that prioritize efficiency over equity. Its beneficial effects are reflected in the quantification of equity and the balanced orientation of planning. By ranking the accessibility indicators of various attractions and constructing Lorenz curves, the abstract concept of spatial equity is transformed into comparable numerical values ​​using the Gini coefficient. For example, a Gini coefficient of 0.82 in the baseline scenario indicates poor equity, while a decrease to 0.80 in the planning scenario reflects improvement. This makes the equity differences under different scenarios intuitively measurable, avoiding the limitations of traditional assessments where equity is only qualitatively described, and achieving a quantitative transformation of equity. Furthermore, by accurately identifying clusters of attractions with weak transportation services—for example, a 5A-level scenic spot in a certain area may have extremely low accessibility due to its distance from transportation stations, thus raising the Gini coefficient—if a new station is added to cover this area in the planning scenario, the change in equity indicators can directly verify whether the plan takes into account the disadvantaged attractions. This ensures that the planning scheme not only improves the overall service level but also narrows the service gap between attractions, meeting the needs of tourism-oriented rail transit for universal accessibility, and achieving a balance verification of the plan.

[0118] Scale efficiency is determined by inputs (number of stations, proportion of transfer stations) and outputs (accessibility, equity). The DEA model (CCR+BCC) enables the correlation assessment of inputs and outputs, breaking through the traditional misconception of emphasizing input while neglecting matching. By linking the resource inputs (scale, structure) of the transportation network with the outputs (convenience, equity) of tourism services, the ratio of comprehensive technical efficiency (CCR) and pure technical efficiency (BCC) (scale efficiency) is used to accurately identify the matching degree between network scale and tourism demand. For example, a scale efficiency of 0.38 in the baseline scenario indicates a serious mismatch between scale and demand, while an improvement to 0.45 in the planning scenario reflects scale optimization, achieving a systematic approach to efficiency assessment. It also avoids the waste of resources from blind expansion. For example, if the number of stations increases significantly in the planning scenario, but the scale efficiency decreases, it indicates that the new resources have not been effectively converted into tourism service outputs (e.g., new stations are far from attractions), which can guide planning adjustments (e.g., prioritizing coverage of densely populated tourist areas), ensuring that the quantity and efficiency of rail transit construction are synchronized, and improving the rationality of investment.

[0119] Determining the final effectiveness assessment result by comprehensively considering four categories of indicators (accessibility, equity, and scale efficiency) and the changes in the two scenarios is a crucial step in transforming dispersed indicators into decision-making criteria, achieving both comprehensive assessment and direct decision-making. By analyzing changes in accessibility (whether it is more convenient), equity (whether it is more balanced), and efficiency (whether it is more efficient), the effectiveness differences between the two scenarios are comprehensively judged from three important dimensions: convenience, equity, and efficiency. For example, if the planning scenario achieves an accessibility increase of 9.1%, an equity decrease of 2.4% (a reduction in the Gini coefficient), and a scale efficiency decrease of 0.05, the advantages (improved convenience and equity) and disadvantages (slightly reduced scale matching) of the planning can be fully identified. The comprehensive integration of assessment dimensions for internship sites breaks through the limitations of traditional assessments that focus only on a single dimension (such as accessibility); and achieves quantitative guidance for decision support. The output assessment results are not isolated values, but comprehensive conclusions including improvement effects, degree of improvement, and investment efficiency. For example, "Network expansion improves overall accessibility by 9.1% and fairness by 2.4%, but it is necessary to optimize the matching of new lines with passenger flow to improve scale efficiency." It can directly answer key questions from planning departments such as "whether to promote the plan" and "how to optimize the plan," avoiding the subjectivity of experience-based decision-making and providing quantitative support for the scientific formulation of tourism-oriented rail transit planning; ultimately achieving an accurate assessment of the effectiveness of transportation and tourism services.

[0120] The above text combined Figures 1 to 2 The method for evaluating the efficiency of transportation and tourism services provided in this application embodiment has been described in detail. The apparatus and equipment provided in this application embodiment will be described below with reference to the accompanying drawings.

[0121] This application also provides a device for evaluating the effectiveness of transportation and tourism services, such as... Figure 3 As shown in the figure, this is a schematic diagram of a transportation and tourism service efficiency evaluation device provided in an embodiment of this application. The device includes: The acquisition module 301 is used to acquire the first traffic station data, the first scenic spot data, and the first road network data of the target area under the first scenario, and the second traffic station data, the second scenic spot data, and the second road network data under the second scenario. The reachability determination module 302 is used to determine the number of first transportation stations, the proportion of first transfer stations, and the first accessibility index corresponding to each tourist attraction based on the first transportation station data, the first scenic spot data, and the first road network data; and to determine the number of second transportation stations, the proportion of second transfer stations, and the second accessibility index corresponding to each tourist attraction based on the second transportation station data, the second scenic spot data, and the second road network data. The fairness determination module 303 is used to determine the first spatial fairness index of transportation services among tourist attractions based on the first accessibility index corresponding to each tourist attraction, and to determine the second spatial fairness index of transportation services among tourist attractions based on the second accessibility index corresponding to each tourist attraction. The efficiency determination module 304 is used to determine a first scale efficiency based on the number of first transportation stations, the proportion of first transfer stations, a first accessibility index, and a first spatial fairness index, and to determine a second scale efficiency based on the number of second transportation stations, the proportion of second transfer stations, a second accessibility index, and a second spatial fairness index. The evaluation module 305 is used to determine the effectiveness evaluation results of the target area under the first scenario and the second scenario based on the first accessibility index, the second accessibility index, the first spatial fairness index, the second spatial fairness index, the first scale efficiency and the second scale efficiency.

[0122] In some possible implementations, the reachability determination module 302 specifically includes: The total number of stations in the first transportation station data is taken as the number of first transportation stations; the number of stations in the first transportation station data that intersect with at least two transportation lines is taken as the number of first transfer stations; the ratio of the number of first transfer stations to the number of first transportation stations is taken as the proportion of first transfer stations; based on the first transportation station data, the first scenic spot data, and the first road network data, the first accessibility index corresponding to each tourist attraction is calculated using the Gaussian two-step floating watershed area method.

[0123] In some possible implementations, the fair determination module 303 specifically includes: Based on the first accessibility index corresponding to each tourist attraction, a first Lorenz curve is constructed; based on the first Lorenz curve, a first Gini coefficient is calculated, and the first Gini coefficient is used as the first spatial fairness index.

[0124] In some possible implementations, the efficiency determination module 304 specifically includes: Based on the number of first transportation stations, the proportion of first transfer stations, the first accessibility index, and the first spatial fairness index, the first efficiency and the second efficiency are determined; the ratio of the first efficiency and the second efficiency is determined as the first scale efficiency.

[0125] In some possible implementations, the efficiency determination module 304 specifically includes: With the number of first transportation stations and the proportion of first transfer stations as input variables, and the first accessibility index and the first spatial fairness index as output variables, efficiency is calculated to obtain the first efficiency and the second efficiency.

[0126] In some possible implementations, the evaluation module 305 specifically includes: The difference between the second accessibility index and the first accessibility index is taken as the accessibility change; the difference between the second spatial equity index and the first spatial equity index is taken as the equity change; the difference between the second scale efficiency and the first scale efficiency is taken as the efficiency change; based on the accessibility change, equity change, and efficiency change, the effectiveness assessment results of the target area under the first scenario and the second scenario are determined.

[0127] The transportation and tourism service efficiency evaluation device according to the embodiments of this application can correspondingly execute the method described in the embodiments of this application, and the other operations and / or functions of each module / unit of the transportation and tourism service efficiency evaluation device are respectively for realizing Figure 2 For the sake of brevity, the corresponding processes of each method in the illustrated embodiments will not be described in detail here.

[0128] This application also provides a computing device. For example... Figure 4 As shown in the figure, this is a schematic diagram of a computing device provided in an embodiment of this application. The computing device 400 includes a bus 401, a processor 402, a communication interface 403, and a memory 404. The processor 402, the memory 404, and the communication interface 403 communicate with each other via the bus 401.

[0129] Bus 401 can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of representation, Figure 4 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0130] Processor 402 can be any one or more of the following processors: central processing unit (CPU), graphics processing unit (GPU), microprocessor (MP), or digital signal processor (DSP).

[0131] Communication interface 403 is used for communication with external devices.

[0132] Memory 404 may include volatile memory, such as random access memory (RAM). Memory 404 may also include non-volatile memory, such as read-only memory (ROM), flash memory, hard disk drive (HDD), or solid state drive (SSD).

[0133] The memory 404 stores executable code, and the processor 402 executes the executable code to perform the aforementioned transportation and tourism service efficiency evaluation method.

[0134] Specifically, in achieving Figure 3 In the case of the illustrated embodiment, and Figure 3 When the modules or units of the transportation and tourism service efficiency evaluation device described in the embodiment are implemented by software, the following steps are performed: Figure 3 The software or program code required for the functions of each module / unit can be partially or wholly stored in memory 404. Processor 402 executes the program code corresponding to each unit stored in memory 404 to perform the aforementioned transportation and tourism service efficiency evaluation method.

[0135] This application also provides a computer-readable storage medium. The computer-readable storage medium can be any available medium that a computing device can store, or a data storage device such as a data center containing one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid-state drive). The computer-readable storage medium includes instructions that instruct the computing device to execute the aforementioned transportation and tourism service efficiency evaluation method.

[0136] This application also provides a computer program product comprising one or more computer instructions. When the computer instructions are loaded and executed on a computing device, all or part of the processes or functions described in this application are generated.

[0137] The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions may be transmitted from one website, computer, or data center to another website, computer, or data center via wired (e.g., coaxial cable, fiber optic) or wireless (e.g., infrared, wireless, microwave, etc.) means.

[0138] When the computer program product is executed by a computer, the computer performs any of the aforementioned methods for evaluating the effectiveness of transportation and tourism services. The computer program product can be a software installation package; when any of the aforementioned methods for evaluating the effectiveness of transportation and tourism services is required, the computer program product can be downloaded and executed on the computer.

[0139] The descriptions of the processes or structures corresponding to the above figures each have their own emphasis. For parts of a process or structure that are not described in detail, please refer to the relevant descriptions of other processes or structures.

[0140] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions within the technical scope disclosed in this application should be covered within the scope of protection of this application.

Claims

1. A method for evaluating the effectiveness of transportation and tourism services, characterized in that, The method includes: Acquire the first transportation station data, the first scenic spot data, and the first road network data of the target area under the first scenario, and the second transportation station data, the second scenic spot data, and the second road network data under the second scenario; Based on the first transportation station data, the first scenic spot data, and the first road network data, the number of first transportation stations, the proportion of first transfer stations, and the first accessibility index corresponding to each tourist attraction are determined. Based on the second transportation station data, the second scenic spot data, and the second road network data, the number of second transportation stations, the proportion of second transfer stations, and the second accessibility index corresponding to each tourist attraction are determined. Based on the first accessibility index corresponding to each tourist attraction, a first spatial equity index for transportation services among the tourist attractions is determined, and based on the second accessibility index corresponding to each tourist attraction, a second spatial equity index for transportation services among the tourist attractions is determined. A first scale efficiency is determined based on the number of the first transportation stations, the proportion of the first transfer stations, the first accessibility index, and the first spatial equity index; and a second scale efficiency is determined based on the number of the second transportation stations, the proportion of the second transfer stations, the second accessibility index, and the second spatial equity index. Based on the first accessibility index, the second accessibility index, the first spatial fairness index, the second spatial fairness index, the first scale efficiency, and the second scale efficiency, the effectiveness evaluation results of the target area under the first scenario and the second scenario are determined.

2. The method according to claim 1, characterized in that, The determination of the number of first transportation stations, the proportion of first transfer stations, and the first accessibility index corresponding to each tourist attraction based on the first transportation station data, the first scenic spot data, and the first road network data includes: The total number of stations in the first traffic station data is taken as the number of first traffic stations; The number of stations in the first transportation station data that intersect with at least two transportation lines is taken as the number of first transfer stations. The ratio of the number of the first transfer stations to the number of the first transportation stations is taken as the first transfer station ratio; Based on the first transportation station data, the first scenic spot data, and the first road network data, the first accessibility index corresponding to each tourist attraction is calculated using the Gaussian two-step floating watershed area method.

3. The method according to claim 1, characterized in that, The determination of the first spatial equity index of transportation services among various tourist attractions based on the first accessibility index corresponding to each tourist attraction includes: Based on the first accessibility index corresponding to each tourist attraction, a first Lorenz curve is constructed. Based on the first Lorenz curve, the first Gini coefficient is calculated and used as the first spatial fairness index.

4. The method according to claim 1, characterized in that, The determination of the first scale efficiency based on the first number of transportation stations, the first transfer station ratio, the first accessibility index, and the first spatial fairness index includes: Based on the number of the first transportation stations, the proportion of the first transfer stations, the first accessibility index, and the first spatial fairness index, the first efficiency and the second efficiency are determined. The ratio of the first efficiency to the second efficiency is defined as the first scale efficiency.

5. The method according to claim 4, characterized in that, The determination of the first efficiency and the second efficiency based on the first number of transportation stations, the first proportion of transfer stations, the first accessibility index, and the first spatial fairness index includes: With the number of the first transportation stations and the proportion of the first transfer stations as input variables, and the first accessibility index and the first spatial fairness index as output variables, efficiency calculations are performed to obtain the first efficiency and the second efficiency.

6. The method according to claim 1, characterized in that, The determination of the effectiveness evaluation results of the target area under the first scenario and the second scenario based on the first accessibility index, the second accessibility index, the first spatial fairness index, the second spatial fairness index, the first scale efficiency, and the second scale efficiency includes: The difference between the second accessibility index and the first accessibility index is taken as the change in accessibility. The difference between the second spatial fairness index and the first spatial fairness index is used as the fairness change measure; The difference between the second-scale efficiency and the first-scale efficiency is taken as the change in efficiency; Based on the changes in accessibility, fairness, and efficiency, the effectiveness assessment results of the target area under the first and second scenarios are determined.

7. A device for evaluating the effectiveness of transportation and tourism services, characterized in that, The device includes: The acquisition module is used to acquire the first traffic station data, the first scenic spot data, and the first road network data of the target area under the first scenario, and the second traffic station data, the second scenic spot data, and the second road network data under the second scenario. The accessibility determination module is used to determine the number of first transportation stations, the proportion of first transfer stations, and the first accessibility index corresponding to each tourist attraction based on the first transportation station data, the first scenic spot data, and the first road network data; and to determine the number of second transportation stations, the proportion of second transfer stations, and the second accessibility index corresponding to each tourist attraction based on the second transportation station data, the second scenic spot data, and the second road network data. The fairness determination module is used to determine the first spatial fairness index of transportation services among tourist attractions based on the first accessibility index corresponding to each tourist attraction, and to determine the second spatial fairness index of transportation services among tourist attractions based on the second accessibility index corresponding to each tourist attraction. The efficiency determination module is used to determine a first scale efficiency based on the number of the first transportation stations, the proportion of the first transfer stations, the first accessibility index, and the first spatial fairness index, and to determine a second scale efficiency based on the number of the second transportation stations, the proportion of the second transfer stations, the second accessibility index, and the second spatial fairness index. The evaluation module is used to determine the effectiveness evaluation results of the target area under the first scenario and the second scenario based on the first accessibility index, the second accessibility index, the first spatial fairness index, the second spatial fairness index, the first scale efficiency and the second scale efficiency.

8. A computing device, characterized in that, Including memory and processor; The memory stores one or more computer programs, the one or more computer programs including instructions; when the instructions are executed by the processor, the computing device performs the method as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium is used to store a computer program for performing the method as described in any one of claims 1 to 6.

10. A computer program product, characterized in that, The computer program product includes one or more computer instructions that, when executed by a computer, perform the method as described in any one of claims 1 to 6.

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