A multi-modal transportation attraction accessibility measurement method for cold region city tourism transportation

Abstract

This invention belongs to the field of tourism transportation optimization technology, and particularly relates to a multi-modal transportation accessibility measurement method for tourist attractions in cold-region cities. The purpose of this invention is to solve the problem of low accuracy in existing multi-modal transportation accessibility measurement methods for tourist attractions in cold-region cities. This invention provides a multi-modal transportation accessibility measurement method for tourist attractions in cold-region cities. It uses measurable spatial-transportation attributes such as attraction area, entrances and exits, road connectivity, public transport coverage, parking facility capacity, and the accessible boundaries and activity spaces of open attractions to characterize the service capacity of attractions. This avoids reliance on a single rating assignment, making accessibility measurement more closely reflect the actual service carrying capacity and spatial support characteristics of attractions, thus solving the problem of low accuracy in existing multi-modal transportation accessibility measurement methods for tourist attractions in cold-region cities.

Description

Technical Field

[0001] This invention belongs to the field of tourism transportation optimization technology, and in particular relates to a multi-modal transportation accessibility measurement method for tourist attractions in cold-region cities. Background Technology

[0002] With the continuous advancement of the national strategy of integrating transportation and tourism, accessibility to tourist attractions has become an important quantitative indicator for measuring the level of urban cultural and tourism public service provision, the degree of matching between transportation networks and tourism resources, and the fairness of residents' travel opportunities. The two-step search-for-cruise (2SFCA) method can simultaneously integrate supply, demand, and travel resistance, and is therefore widely used to measure the accessibility of public service facilities and tourist attractions. However, existing multimodal transportation accessibility studies still have several shortcomings in their application to cold-region cities:

[0003] First, the spatial characterization of demand is not precise enough. Existing studies mostly use the geometric center of streets or the center of gravity of administrative units as the starting point for residents' travel, which makes it difficult to reflect the real differences in the distribution of residential space within the city. In cold regions, the population density difference between the core area and the outer streets is significant. Using a coarse-scale starting point is prone to scale distortion and starting point offset, which in turn leads to a serious disconnect between the accessibility measurement results and residents' actual travel scenarios.

[0004] Second, there is a discrepancy between the supply and demand competition logic of multi-modal transportation and actual travel behavior. Conventional multi-modal transportation 2SFCA (2-SFCA) systems often divide the total population into walking, public transport, and driving groups at a fixed ratio, and share the supply and demand ratio across all modes. This fails to reflect the actual decision-making logic of residents prioritizing walking for short distances and choosing motorized travel for longer distances. Consequently, the accessibility of walkable units around tourist attractions is easily diluted by motorized travel over longer distances, resulting in a distorted view and weakening the ability to identify differences in service radius and beneficiary groups among different transportation modes.

[0005] Third, there is a lack of systematic adaptation to cold-region snow and ice scenarios. Winter snow and ice in cold-region cities systematically alter the efficiency of walking, public transportation, and driving by reducing road surface adhesion coefficients, increasing safe following distances, compressing operating speeds, and increasing the friction costs of walking. Existing models mostly lack parameterized correction mechanisms for traffic modes specific to snow and ice disturbances, making it difficult to accurately depict the real spatial patterns of tourist accessibility in cold-region cities under all-season conditions, and also failing to provide effective support for winter traffic organization and tourism service improvement.

[0006] Fourth, the handling of scenic spot supply capacity suffers from homogenization and insufficient objectivity. Existing methods often use homogenized indicators such as administrative ratings and single-area size to approximate service capacity, making it difficult to reliably reflect the actual carrying capacity and service radiation capacity of scenic spots. On the other hand, some popular scenic spots that have not been rated according to national standards but have high market attention also play an important service role in actual urban tourism activities. Completely ignoring them will lead to incomplete supply identification, thereby affecting the authenticity and explanatory power of accessibility measurement results.

[0007] Fifth, there is a lack of a priori diagnostic step based on the "demand-supply-transportation" ternary system. Measuring accessibility results alone can identify high- and low-value areas, but it is difficult to further distinguish whether the causes stem from population demand concentration, insufficient scenic spot supply, or inadequate transportation support. For tourism transportation management in cold-region cities, having only result measurements without structural diagnosis is not conducive to subsequent optimization of zoning and differentiated strategy configuration.

[0008] In summary, existing technologies have not yet achieved a coupled technical solution that combines detailed demand characterization at the residential community scale, independent supply and demand competition among pedestrian-priority traffic modes, Gaussian time decay, and parameterized correction of traffic modes in cold regions with ice and snow. They cannot simultaneously solve the technical problems of insufficient measurement accuracy, result distortion, poor adaptability to cold regions, and insufficient objectivity in supply characterization. There is an urgent need for a technical solution for measuring the accessibility of attractions in cold-region cities with multiple traffic modes based on objective spatial data and executed entirely by computer equipment. Summary of the Invention

[0009] The purpose of this invention is to address the problem of low accuracy in existing multi-modal transportation accessibility measurement methods for tourist attractions in cold-region cities. It provides a multi-modal transportation accessibility measurement method for tourist transportation in cold-region cities, including:

[0010] Step 1: Obtain the basic database of the study area; perform standardization preprocessing on the basic database of the study area to obtain the standardized preprocessed database of the study area;

[0011] Step 2: Construct multi-scale demand starting points based on the standardized preprocessed database; including: community-scale demand starting points and street-scale demand starting points;

[0012] Step 3: Based on the objective space-transportation attribute evaluation system for scenic spot service capacity, the service capacity of tourist attractions in the study area is comprehensively quantified according to the standardized preprocessed database of the study area to obtain the standardized service capacity value of each scenic spot in the study area.

[0013] Step 4: Based on the standardized preprocessed database of the study area, calibrate the speed correction coefficient for the snow and ice scenario in the study area;

[0014] Based on the multi-scale demand starting point and the speed correction coefficient of the ice and snow scenario in the study area, a multi-modal door-to-door OD travel time matrix is ​​constructed.

[0015] Step 5: Based on the standardized preprocessed database and multi-modal door-to-door OD travel time matrix of the study area, calibrate the accessibility measurement model parameters of the study area;

[0016] Step 6: Based on the accessibility measurement model parameters of the study area and the multi-traffic mode door-to-door OD travel time matrix, calculate the time impedance weight from each demand unit to each attraction;

[0017] Step 7: Based on the standardized preprocessed database obtained in Step 1 and the accessibility measurement model parameters of the study area calibrated in Step 5, the population of each demand unit in the study area is divided into effective competitive populations according to transportation modes, so as to obtain the effective competitive population of each demand unit in the study area for each attraction in each transportation mode.

[0018] Wherein, the effective competing population of demand unit i for attraction j under traffic mode m is represented as: ;

[0019] Step 8: Based on the effective competing population of each demand unit in the study area for each attraction in each mode of transportation obtained in Step 7, the time impedance weight from each demand unit to each attraction in the study area obtained in Step 6, and the standardized service capacity value of each attraction in the study area obtained in Step 3, calculate the comprehensive accessibility of each demand unit in the study area.

[0020] Wherein, the comprehensive reachability of demand unit i is expressed as: ;

[0021] A multimodal transportation accessibility measurement device for tourist attractions in cold-region cities is disclosed. The device includes a processor and a memory. The memory stores at least one instruction, which is loaded and executed by the processor to implement the multimodal transportation accessibility measurement method for tourist attractions in cold-region cities.

[0022] The accessibility measurement results of this invention can be directly applied to specific technical scenarios such as urban transportation facility layout optimization, public transportation route adjustment, and allocation of emergency resources for ice and snow, and have significant technical practical value.

[0023] This invention also provides a technical solution for urban transportation planning in cold regions. By using multi-source spatial data fusion, multi-modal travel time modeling, and snow and ice scenario parameter correction, it solves technical problems such as inaccurate measurement of accessibility to attractions in cold regions and lack of snow and ice adaptability, providing technical support for urban transportation optimization decisions.

[0024] The present invention provides an accessibility measurement method for further application in urban traffic optimization decision support, including:

[0025] A three-dimensional diagnostic system was constructed to calculate the demand index, supply index, and transportation support index for each street unit.

[0026] By comparing the accessibility differences between the baseline scenario and the snow and ice scenario, the snow and ice resilience level of each region is assessed.

[0027] Based on the aforementioned ternary diagnostic results and snow and ice resilience assessment, the city is divided into a high-value pressure testing zone, a basic support cultivation zone, a transition and connection enhancement zone, and a comprehensive vulnerability intervention zone.

[0028] Different optimization strategy suggestions are provided for different partitions.

[0029] For example, by comparing the overall accessibility level, accessibility retention rate, and coverage retention rate under the baseline scenario and the snow and ice scenario, the snow and ice resilience type of different street units can be identified.

[0030] Further combining benchmark accessibility, attraction supply shortage index, and transportation support index, the research unit is divided into a high-pressure adjustment zone, a basic support cultivation zone, a transitional connection improvement zone, and a comprehensive vulnerability intervention zone.

[0031] It also matches strategies such as adding attractions, optimizing bus schedules or connections, improving pedestrian walkways, configuring parking and transfer facilities, and ensuring smooth traffic during winter snow removal.

[0032] Meanwhile, sensitivity and robustness tests are conducted on dimensions such as time threshold, motorized travel weight, snow and ice speed correction coefficient, decay function form, combination and aggregation method of objective service capacity indicators of scenic spots, and output single-mode accessibility, comprehensive accessibility, snow and ice scenario disturbance results and resilience classification results at the residential community scale and street scale.

[0033] Comprehensive accessibility at the street scale was measured under both baseline and snow / ice-related scenarios, and accessibility retention rate and coverage retention rate indices were constructed. Combining the baseline comprehensive accessibility level and the resilience index, street units were classified into resilience types to identify key intervention targets for winter traffic security and service improvement. Empirical results show that the overall comprehensive accessibility of Harbin's main urban area exhibits a core-periphery gradient pattern, with the decline in peripheral areas generally exceeding that of the core area under snow / ice-related scenarios. Furthermore, the 114 streets can be divided into 24 high-level robust streets, 8 high-level stressed streets, 6 high-level vulnerable streets, 44 low-level stable streets, and 32 low-level vulnerable streets to identify key intervention targets under snow / ice-related weather conditions.

[0034] Eleven perturbation schemes were designed, including time threshold, motorized travel weight, speed correction coefficient for snow and ice scenarios, distance decay function form, combination of scenic spot capability indicators, and aggregation method. In the implementation example, except for the scenario with tightened walking threshold, the overlap rate of the top 20% high-value streets in the other schemes all exceeded 85%, and the quartile level consistency rate remained above 78%, indicating that the model output has good robustness.

[0035] The final output includes single-modal accessibility results for pedestrian, public transport, and driving at the residential community and street scales, comprehensive accessibility results for multimodal transportation, snow and ice scenario disturbance results, and resilience classification results. Based on baseline comprehensive accessibility, snow and ice reduction, scenic spot supply shortage index, and transportation support level, four types of optimization zones are generated: high-value pressure adjustment zone, basic support cultivation zone, transitional connection improvement zone, and comprehensive vulnerability intervention zone. These zones correspond to transportation-scenic spot coordinated improvement strategies such as scenic spot replenishment, public transport connection enhancement, pedestrian system repair, and winter snow removal and traffic maintenance.

[0036] The beneficial effects of this invention are as follows:

[0037] (1) This invention sinks the demand unit down to the scale of residential communities, completes the spatialization of population through the area weight conservation method, and replaces the traditional street geometric center with the population weight center, which reduces the starting point offset and scale distortion caused by the aggregation of administrative units, restores the true distribution of residents' travel starting points, and is especially suitable for the regional characteristics of large spatial differences in population density in cold cities.

[0038] (2) This invention establishes an independent supply and demand calculation framework for different transportation modes and a pedestrian-priority population segmentation rule, which avoids the defects of traditional models such as repeated calculation of population across transportation modes and unreasonable dilution of pedestrian accessibility. This makes the model more in line with the real decision-making logic of residents prioritizing walking for short distances and choosing motorization for long distances when traveling for cultural and tourism purposes, and the measurement results are more explanatory in reality.

[0039] (3) This invention designs a parameterized correction scheme for ice and snow scenarios based on the seasonal characteristics of cold-region cities. By using a differentiated speed attenuation factor, it quantifies the systematic disturbance of ice and snow weather on different traffic modes. It can realize the accessibility comparison measurement of the baseline and ice and snow scenarios, identify the accessibility vulnerability area and the difference in ice and snow resilience, improve the problem of insufficient adaptation of existing technology to ice and snow scenarios in cold regions, and the measurement results can provide a scientific reference for the optimization of tourism transportation in all seasons in cold-region cities.

[0040] (4) This invention uses measurable spatial-transportation attributes such as scenic spot area, entrances and exits, road connectivity, bus stop coverage, parking facility capacity, and open scenic spot access boundaries and activity space to characterize the service capacity of scenic spots, avoiding reliance on a single rating and making accessibility measurement more in line with the actual service carrying conditions and spatial support characteristics of scenic spots.

[0041] (5) The present invention introduces a demand-supply-transportation three-element diagnostic link before accessibility measurement, which can further decompose the low result into different types of reasons such as supply shortage, weak transportation or high demand, thereby improving the pertinence of subsequent optimization zoning and governance strategy configuration.

[0042] (6) The present invention designs a multi-dimensional robustness test scheme covering core parameters and model structure, verifies output stability through multiple complementary indicators, reduces subjective bias in parameter setting, and supports localized calibration based on the characteristics of different cold-region cities, and has strong engineering applicability and transferability. Attached Figure Description

[0043] Figure 1 This is a schematic diagram of the overall process of the method of the present invention;

[0044] Figure 2 A schematic diagram of refined preprocessing of the transportation network;

[0045] Figure 3 A schematic diagram of the refined preprocessing process for transportation networks;

[0046] Figure 4 A schematic diagram illustrating the spatial distribution and service capacity of tourist attractions;

[0047] Figure 5 A schematic diagram illustrating the mapping between the spatial distribution of tourist attractions and their service capacity;

[0048] Figure 6 This is a schematic diagram of the spatial distribution of comprehensive accessibility at the street scale under the baseline scenario;

[0049] Figure 7 A schematic diagram comparing the accessibility disturbances of single-mode transportation in icy and snowy scenarios;

[0050] Figure 8 A schematic diagram of the spatial distribution of the rate of change of comprehensive accessibility at the street scale under icy and snowy conditions;

[0051] Figure 9 A schematic diagram illustrating the street accessibility resilience classification under icy and snowy conditions;

[0052] Figure 10 This is a schematic diagram of the accessibility optimization zones in the main urban area. Detailed Implementation

[0053] Specific implementation method one: Combining Figure 1-10 The present invention will be further described in detail using a specific implementation case in the main urban area of ​​Harbin. This embodiment is used to explain the technical solution of the present invention, and not to limit the scope of protection of the present invention. All steps in this embodiment are automatically executed on a computer device equipped with QGIS version 3.34 and Python version 3.9, without any manual intervention in the rule execution process.

[0054] The present invention provides a multi-modal transportation accessibility measurement method for tourist attractions in cold-region cities, comprising:

[0055] Step 1: Obtain the basic database of the study area; perform standardization preprocessing on the basic database of the study area to obtain the standardized preprocessed database of the study area;

[0056] Step 2: Construct multi-scale demand starting points based on the standardized preprocessed database; including: community-scale demand starting points and street-scale demand starting points;

[0057] Step 3: Based on the objective space-transportation attribute evaluation system for scenic spot service capacity, the service capacity of tourist attractions in the study area is comprehensively quantified according to the standardized preprocessed database of the study area to obtain the standardized service capacity value of each scenic spot in the study area.

[0058] Step 4: Based on the standardized preprocessed database of the study area, calibrate the speed correction coefficient for the snow and ice scenario in the study area;

[0059] Based on the multi-scale demand starting point and the speed correction coefficient of the ice and snow scenario in the study area, a multi-modal door-to-door OD travel time matrix is ​​constructed.

[0060] Step 5: Based on the standardized preprocessed database and multi-modal door-to-door OD travel time matrix of the study area, calibrate the accessibility measurement model parameters of the study area;

[0061] Step 6: Based on the accessibility measurement model parameters of the study area and the multi-traffic mode door-to-door OD travel time matrix, calculate the time impedance weight from each demand unit to each attraction;

[0062] Step 7: Based on the standardized preprocessed database obtained in Step 1 and the accessibility measurement model parameters of the study area calibrated in Step 5, the population of each demand unit in the study area is divided into effective competitive populations according to transportation modes, so as to obtain the effective competitive population of each demand unit in the study area for each attraction in each transportation mode.

[0063] Wherein, the effective competing population of demand unit i for attraction j under traffic mode m is represented as: ;

[0064] Step 8: Based on the effective competing population of each demand unit in the study area for each attraction in each mode of transportation obtained in Step 7, the time impedance weight from each demand unit to each attraction in the study area obtained in Step 6, and the standardized service capacity value of each attraction in the study area obtained in Step 3, calculate the comprehensive accessibility of each demand unit in the study area.

[0065] Wherein, the comprehensive reachability of demand unit i is expressed as: ;

[0066] The accessibility measurement results of this invention can be directly applied to specific technical scenarios such as urban transportation facility layout optimization, public transportation route adjustment, and allocation of emergency resources for ice and snow, and have significant technical practical value.

[0067] This invention also provides a technical solution for urban transportation planning in cold regions. By using multi-source spatial data fusion, multi-modal travel time modeling, and snow and ice scenario parameter correction, it solves technical problems such as inaccurate measurement of accessibility to attractions in cold regions and lack of snow and ice adaptability, providing technical support for urban transportation optimization decisions.

[0068] In one embodiment of a technical solution for urban transportation planning in cold regions provided by the present invention, the accessibility measurement method is further used for urban transportation optimization decision support, including:

[0069] A three-dimensional diagnostic system was constructed to calculate the demand index, supply index, and transportation support index for each street unit.

[0070] By comparing the accessibility differences between the baseline scenario and the snow and ice scenario, the snow and ice resilience level of each region is assessed.

[0071] Based on the aforementioned ternary diagnostic results and snow and ice resilience assessment, the city is divided into a high-value pressure testing zone, a basic support cultivation zone, a transition and connection enhancement zone, and a comprehensive vulnerability intervention zone.

[0072] Different optimization strategy suggestions are provided for different partitions.

[0073] For example, by comparing the overall accessibility level, accessibility retention rate, and coverage retention rate under the baseline scenario and the snow and ice scenario, the snow and ice resilience type of different street units can be identified.

[0074] Further combining benchmark accessibility, attraction supply shortage index, and transportation support index, the research unit is divided into a high-pressure adjustment zone, a basic support cultivation zone, a transitional connection improvement zone, and a comprehensive vulnerability intervention zone.

[0075] It also matches strategies such as adding attractions, optimizing bus schedules or connections, improving pedestrian walkways, configuring parking and transfer facilities, and ensuring smooth traffic during winter snow removal.

[0076] Meanwhile, sensitivity and robustness tests are conducted on dimensions such as time threshold, motorized travel weight, snow and ice speed correction coefficient, decay function form, combination and aggregation method of objective service capacity indicators of scenic spots, and output single-mode accessibility, comprehensive accessibility, snow and ice scenario disturbance results and resilience classification results at the residential community scale and street scale.

[0077] Comprehensive accessibility at the street scale was measured under both baseline and snow / ice-related scenarios, and accessibility retention rate and coverage retention rate indices were constructed. Combining the baseline comprehensive accessibility level and the resilience index, street units were classified into resilience types to identify key intervention targets for winter traffic security and service improvement. Empirical results show that the overall comprehensive accessibility of Harbin's main urban area exhibits a core-periphery gradient pattern, with the decline in peripheral areas generally exceeding that of the core area under snow / ice-related scenarios. Furthermore, the 114 streets can be divided into 24 high-level robust streets, 8 high-level stressed streets, 6 high-level vulnerable streets, 44 low-level stable streets, and 32 low-level vulnerable streets to identify key intervention targets under snow / ice-related weather conditions.

[0078] Eleven perturbation schemes were designed, including time threshold, motorized travel weight, speed correction coefficient for snow and ice scenarios, distance decay function form, combination of scenic spot capability indicators, and aggregation method. In the implementation example, except for the scenario with tightened walking threshold, the overlap rate of the top 20% high-value streets in the other schemes all exceeded 85%, and the quartile level consistency rate remained above 78%, indicating that the model output has good robustness.

[0079] The final output includes single-mode accessibility results for pedestrian, public transportation, and driving at the residential community and street scales, comprehensive accessibility results for multi-modal transportation, snow and ice scenario disturbance results, and resilience classification results. Based on the baseline comprehensive accessibility, snow and ice reduction, scenic spot supply shortage index, and transportation support level, four types of optimization zones are generated: high-value pressure adjustment zone, basic support cultivation zone, transitional connection improvement zone, and comprehensive vulnerability intervention zone. These zones correspond to transportation-scenic spot coordinated improvement strategies such as scenic spot replenishment, public transportation connection enhancement, pedestrian system repair, and winter snow removal and smooth traffic.

[0080] Specific Implementation Method Two: The difference between this implementation method and Specific Implementation Method One is that:

[0081] The basic database of the study area in step one includes: administrative division vector data of the study area, road network data of the study area, public transportation routes and stations data of the study area, rail transit data of the study area, street-level population data of the study area, spatial and attribute data of residential communities of the study area, boundary or location data of scenic spots of the study area, entrance and exit data of scenic spots of the study area, supporting parking facilities data of the study area, and meteorological observation data of the study area.

[0082] The standardized preprocessed database of the study area in step one includes: standardized preprocessed administrative division vector data of the study area, standardized preprocessed road network data of the study area, standardized preprocessed public transportation route and station data of the study area, standardized preprocessed rail transit data of the study area, standardized preprocessed street-level population data of the study area, standardized preprocessed residential community spatial and attribute data of the study area, standardized preprocessed scenic spot boundary or location data of the study area, standardized preprocessed scenic spot entrance and exit data of the study area, standardized preprocessed supporting parking facility data of the study area, and standardized preprocessed meteorological observation data of the study area.

[0083] The standardized preprocessing includes: coordinate system-1 and projection transformation processing, feature cleaning processing, topology correction processing, and spatial association processing;

[0084] The extracted bus stop data is cleaned to remove completely duplicate points (coordinates and names are the same), spatially duplicate points (coordinates are approximately overlapping and the distance is less than 5 meters), and transfer stations (name similarity is not less than 80% and the distance is less than 10 meters).

[0085] Topology correction is performed on road network data to reduce dangling nodes and disconnected subgraphs, ensuring the overall structure and connectivity of the road network and improving the accuracy of time impedance calculation.

[0086] The road network data of the study area undergoes hierarchical merging, deduplication of dual-lane roads into single-lane roads, repair of suspended nodes, and correction of disconnected subgraphs (corresponding to topology correction and spatial association processing in standardization) to obtain standardized preprocessed road network data of the study area.

[0087] The public transportation routes and station data of the study area were processed by deduplication of stations, vectorization correction of routes, and fusion of road-bus-rail multi-transportation mode networks (corresponding to coordinate system I and projection transformation processing, element cleaning processing) to obtain standardized preprocessed public transportation routes and station data of the study area.

[0088] This embodiment takes the main urban area of ​​Harbin as the research object, and the research scope is divided into six administrative districts: Daoli, Daowai, Nangang, Xiangfang, Songbei and Pingfang. It takes 114 streets as macro analysis units and more than 2,000 residential community demand units as micro analysis objects.

[0089] This invention collects OSM road network data, Gaode Map's bus and rail transit station and line data, the seventh national population census's street-level population data, scenic spot boundary or POI data, scenic spot entrance and exit and parking facility data, and concurrent meteorological observation data. Coordinate system and projection transformations are performed in the QGIS environment, and Python is used to clean and correct the road network and public transportation network topology. The 18 road categories of the original OSM road network are categorized into six levels: expressways, arterial roads, secondary arterial roads, local roads, unclassified roads, and pedestrian / non-motorized vehicle lanes. Complete duplicate point removal, spatial duplicate point merging, and transfer station integration are performed on bus stops to form a basic database that can be used for multi-modal network analysis. Other steps and parameters are the same as in Specific Implementation Method 1.

[0090] Specific Implementation Method Three: The difference between this implementation method and Specific Implementation Methods One and Two is that:

[0091] In step two, a multi-scale demand starting point is constructed based on the standardized preprocessed database; the specific process is as follows:

[0092] Based on the standardized preprocessed database, construct the starting points for community-scale and street-scale requirements;

[0093] Specifically, the starting point for the community-scale requirements of the a-th street and the starting point for the street-scale requirements of the a-th street are constructed based on the standardized preprocessed database. The specific process is as follows:

[0094] Step 21: Use the total population of the a-th street as the total constraint.

[0095] The total population of the a-th street in this invention is obtained from the standardized preprocessed street-level population data of the study area.

[0096] Step 22: Take all residential communities in street a as the starting point for the community-scale requirements of street a.

[0097] The present invention uses the spatial and attribute data of all residential communities in the a-th street as standardized preprocessed data of the study area;

[0098] Steps 2 and 3: Based on the proportion of residential land area of ​​all residential communities in street a within its jurisdiction, allocate the total population of the street to obtain the allocated population for each residential community in street a; the allocation of the total population of the street can be expressed by the formula:

[0099]

[0100] In the formula, Let a be the total population of the a-th street. For the a-th street, the first The residential land area of ​​each residential community Assign street a to street a Population allocation for each residential community This represents the total number of residential communities within the a-th street.

[0101] Step 24: Calculate the population weighting center of street a based on the spatial coordinates of all residential communities in street a and the allocated population of each residential community in street a, and use it as the starting point for the street scale requirement of street a; this is used to replace the traditional street geometric center as the anchor point for street scale analysis.

[0102] This embodiment takes the main urban area of ​​Harbin as the research object, and the research scope is divided into six administrative districts: Daoli, Daowai, Nangang, Xiangfang, Songbei, and Pingfang. It uses 114 streets as the macro-analysis unit and over 2,000 residential community demand units as the micro-analysis object. Using the total population of the 114 streets in Harbin's main urban area as a constraint, the population is allocated to each residential community according to the proportion of residential land area. Based on the allocated community population and spatial coordinates, the population weighted center of each street is calculated to replace the traditional street geometric center, thereby alleviating the starting point offset problem caused by the large difference in population density between the core area and the outer streets.

[0103] The other steps and parameters are the same as in one of the specific implementation methods one or two.

[0104] Specific Implementation Method Four: This implementation method differs from Specific Implementation Methods One to Three in that, in that,

[0105] In step three, the scenic spot service capacity evaluation system based on objective space-transportation attributes comprehensively quantifies the service capacity of tourist attractions in the study area based on the standardized preprocessed database of the study area, obtaining the standardized service capacity value of each scenic spot in the study area; the specific process is as follows:

[0106] Step 31: Extract the physical support indicators for each scenic spot from the standardized preprocessed database of the study area;

[0107] The physical support indicators for the j-th scenic spot include: the visitable area of ​​the j-th scenic spot, the number of main entrances and exits of the j-th scenic spot, the connectivity of the surrounding roads of the j-th scenic spot, the number of bus stops within the buffer zone of the j-th scenic spot, and the parking facility capacity of the j-th scenic spot.

[0108] The attractions of this invention include: a first type of attraction and a second type of attraction;

[0109] The first type of attraction is a surface-shaped attraction with clearly defined boundaries, while the second type is an attraction without clearly defined boundaries (an open attraction or one that is not included in the administrative hierarchy but has a stable space for recreational activities).

[0110] The physical support indicators for Type I attractions include: the visitable area of ​​Type I attractions, the number of main entrances and exits of Type I attractions, the connectivity of surrounding roads to Type I attractions, the number of bus stops within the buffer zone of Type I attractions, and the parking facility capacity of Type I attractions.

[0111] The physical support indicators for the second type of tourist attractions include: the equivalent visitable area of ​​the second type of tourist attraction, the number of effective entrances and exits of the second type of tourist attraction, the connectivity of surrounding roads to the second type of tourist attraction, the number of bus stops within the buffer zone of the second type of tourist attraction, and the parking facility capacity of the second type of tourist attraction.

[0112] The physical support indicators are obtained based on the standardized preprocessed data of scenic spot boundaries or locations in the study area, the standardized preprocessed data of scenic spot entrances and exits in the study area, the standardized preprocessed data of supporting parking facilities in the study area, and the standardized preprocessed data of public transportation routes and stations in the study area. The calculation method is known in the field.

[0113] The physical support indicators for the second type of tourist attractions are based on the boundaries of open public spaces, continuous pedestrian-accessible areas, distribution of landscape facilities, and the characteristics of entrances and exits to identify their core service spaces. Under the same indicator framework, equivalent tourable area, number of effective entrances and exits, connectivity of surrounding roads, public transport accessibility, and parking capacity are extracted. The calculation method is known in the field.

[0114] Step 32: Calculate the standardized service capacity value for each attraction based on its physical support indicators; whereby, the attraction... The standardized service capability value is expressed as ; expressed as a formula:

[0115]

[0116] In the formula, This represents the visitable area of ​​the j-th scenic spot. This represents the number of main entrances and exits of the j-th scenic spot. Indicates the connectivity of the surrounding roads of the j-th scenic spot. This represents the number of bus stops within the buffer zone of the j-th scenic spot. This represents the parking capacity of the j-th attraction; Indicates the weight of the visitable area of ​​the attraction. Indicates the weight of the number of main entrances and exits of the scenic spot. Indicates the weight of the connectivity of the surrounding roads of the scenic spot. Indicates the weight of the number of bus stops within the tourist attraction's buffer zone. Indicates the weight of the parking facility capacity of the attraction;

[0117] An objective weighting method based on sample statistical dispersion is used to assign weights to each indicator, forming a standardized service capacity value for each scenic spot. After dimensionless processing of the physical support indicators of each scenic spot, the standardized service capacity value is calculated.

[0118] The other steps and parameters are the same as those in one of the specific implementation methods one to three.

[0119] Specific Implementation Method Five: The difference between this implementation method and Specific Implementation Methods One to Four is that:

[0120] The multi-modal door-to-door OD travel time matrix in step four includes:

[0121] Door-to-door OD time matrix for pedestrian traffic mode under baseline scenario, door-to-door OD time matrix for public transportation mode under baseline scenario, and OD travel time matrix for driving traffic mode under baseline scenario;

[0122] Door-to-door OD time matrix for pedestrian traffic patterns, door-to-door OD time matrix for public transportation patterns, and OD travel time matrix for driving traffic patterns in snow and ice scenarios;

[0123] In step four, the speed correction coefficient for the ice and snow scenario in the study area is determined based on the standardized preprocessed database of the study area.

[0124] Based on the multi-scale demand starting point and the speed correction coefficient for snow and ice scenarios in the study area, a multi-modal door-to-door origin-destination (OD) travel time matrix is ​​constructed; the specific process is as follows:

[0125] Step 41: Based on the standardized preprocessed database of the study area, calibrate the speed correction coefficient for the snow and ice scenario in the study area, expressed by the formula:

[0126]

[0127] In the formula, Indicates traffic patterns under the baseline scenario Average running speed, This indicates the average running speed under icy and snowy conditions. The speed correction factor indicates the speed correction factor for snow and ice conditions. Walk represents pedestrian mode of transportation, pt represents public transportation mode of transportation, and car represents driving mode of transportation.

[0128] Based on winter meteorological observations, road surface adhesion conditions, and traffic mode operating speed statistics, differentiated speed correction coefficients for walking, public transportation, and driving are calculated. These correction coefficients are determined by the ratio of the average operating speed under icy and snowy conditions to the average operating speed under the baseline scenario. For driving, the correction coefficients are weighted according to different road grades; for public transportation, station stop and operation delay corrections are added to the road speed correction; and for walking, the correction coefficients are determined by the ratio of the safe walking speed on icy and snowy surfaces to the normal walking speed. In this embodiment, based on winter road operation observation data in Harbin, winter public transportation operation organization plans, and research data on walking speed on icy and snowy surfaces, the calculated speed correction coefficients for driving, public transportation, and walking are 0.70, 0.65, and 0.75, respectively.

[0129] Step 42: Determine the supply endpoint based on the standardized preprocessed database of the study area;

[0130] This invention uses the main entrance or representative point of a scenic spot as the supply endpoint;

[0131] Step 42: Based on the multi-scale demand start point, supply end point, and snow and ice scenario speed correction coefficient, construct the following in the GIS network analysis module: door-to-door OD time matrix for pedestrian traffic mode under the baseline scenario, door-to-door OD time matrix for public transportation mode under the baseline scenario, OD travel time matrix for driving traffic mode under the baseline scenario, door-to-door OD time matrix for pedestrian traffic mode under the snow and ice scenario, door-to-door OD time matrix for public transportation mode under the snow and ice scenario, and OD travel time matrix for driving traffic mode under the snow and ice scenario.

[0132] (1) Pedestrian traffic mode: The shortest walking time is calculated based on the walkable network and the preset benchmark walking speed;

[0133] (2) Driving traffic mode: The shortest driving time is calculated based on the benchmark driving speed corresponding to different road grades;

[0134] (3) Public transportation mode: Construct a composite network consisting of pedestrian connections, bus network and rail network, and calculate the shortest door-to-door travel time consisting of three parts: connecting walking, bus / rail travel and leaving the station walking.

[0135] Taking the construction of OD time matrices for three traffic modes under a baseline scenario based on the QGIS network analysis module as an example.

[0136] Walking mode: Set a baseline walking speed of 4.5km / h and calculate the shortest walking time from each residential area to each attraction.

[0137] Driving mode: Set the driving speed according to the road level: 60km / h for expressways, 50km / h for main roads, 40km / h for secondary roads, 30km / h for tertiary roads, and 20km / h for unclassified roads, and calculate the shortest driving time.

[0138] Public transportation mode: The operating speed of rail transit is set at 55km / h, and the operating speed of buses is set according to road level as follows: 40km / h for expressways, 30km / h for main roads, 22km / h for secondary roads, 15km / h for tertiary roads, and 10km / h for unclassified roads. The walking time, stop time and transfer time are also included in the calculation of the shortest door-to-door time.

[0139] The other steps and parameters are the same as those in one of the specific implementation methods one to four.

[0140] Specific Implementation Method Six: The difference between this implementation method and Specific Implementation Methods One through Five is that:

[0141] The reachability measurement model parameters in step five include:

[0142] Study area walking time threshold Thresholds for public transportation travel time in the study area Threshold for driving travel time in the study area ;

[0143] Public transport travel structure weight in the study area The weight of driving trip structure in the study area ;

[0144] The standardized preprocessed database and multi-modal door-to-door OD travel time matrix based on the study area are used to calibrate the accessibility measurement model parameters for the study area; the specific process is as follows:

[0145] Step 51: Calculate the distribution of the shortest travel time from demand units to attractions under the three types of transportation modes based on the multi-modal door-to-door OD travel time matrix.

[0146] Step 52: Calculate the cumulative change of the proportion of the population that can be covered over time based on the distribution of the shortest travel time from the demand unit to the attraction under the three types of transportation modes, and obtain the cumulative coverage curve under the three types of transportation modes;

[0147] Step 53: When the cumulative coverage curve enters the interval where the marginal gain converges significantly, take the corresponding inflection point time as the statistical threshold, which is used as the travel time threshold for the study area; the inflection point time is different for each traffic mode.

[0148] In this embodiment, the time thresholds for walking, public transportation, and driving, after calibration, are 20 minutes, 30 minutes, and 30 minutes, respectively. This data is then checked for consistency with the average door-to-door operating speed and the physically feasible range corresponding to the reasonable service radius.

[0149] Step 54: Based on statistics on motor vehicle ownership, resident population, and resident travel, determine the baseline weights for public transportation and driving as two types of motorized travel. In one embodiment, the number of motor vehicles in Harbin in 2020 was 0.687, and the number of public transportation vehicles was 0.313; therefore, their weights are 0.687 and 0.313, respectively. Other steps and parameters are the same as in one of the specific implementation methods 1 to 5.

[0150] Specific Implementation Method Seven: The difference between this implementation method and Specific Implementation Methods One through Six is ​​that:

[0151] In step six, the time impedance weight from each demand unit to each attraction is calculated based on the accessibility measurement model parameters of the study area and the multi-traffic mode door-to-door OD travel time matrix; the specific process is as follows:

[0152] Calculate the time impedance weight from demand unit i to attraction j under traffic mode m. ; expressed as a formula:

[0153]

[0154] In the formula, For demand unit To the scenic spot Travel time under traffic mode m This represents the travel time threshold under traffic mode m.

[0155] The other steps and parameters are the same as those in any of the specific implementation methods one to six.

[0156] Specific Implementation Method Eight: The difference between this implementation method and Specific Implementation Methods One through Seven is that:

[0157] In step seven, based on the standardized preprocessed database obtained in step one and the accessibility measurement model parameters of the study area calibrated in step five, the population of each demand unit in the study area is divided into effective competing populations according to transportation modes, thus obtaining the effective competing population of each demand unit for each attraction in each transportation mode. The specific process is as follows:

[0158] Calculate the effective competing population of demand unit i for attraction j under traffic mode m. ; expressed as a formula:

[0159]

[0160] This represents the allocated population of demand unit i. This represents the effective competing population of demand unit i for attraction j under the walking transportation mode. This represents the effective competing population of demand unit i for attraction j under public transportation mode. This represents the effective competing population of demand unit i for attraction j under driving traffic mode. This represents the travel time from demand unit i to attraction j under the pedestrian transportation mode.

[0161] If the walking time is not greater than the time threshold of the walking traffic mode, the pairing relationship is considered to have direct walking accessibility, and the entire population of the residential community is included in the supply and demand competition of the walking traffic mode, and is no longer included in the public transportation and driving traffic modes.

[0162] If the walking time exceeds the time threshold for the walking traffic mode, the population of the residential community will be divided into public transportation population and driving population according to the defined motorized travel structure, and will be included in the supply and demand competition of the corresponding traffic modes respectively.

[0163] The other steps and parameters are the same as those in any of the specific implementation methods one to seven.

[0164] Specific Implementation Method Nine: The difference between this implementation method and Specific Implementation Methods One through Eight is that:

[0165] In step eight, based on the effective competing population of each demand unit in the study area for each attraction under each transportation mode obtained in step seven, the time impedance weight from each demand unit to each attraction in the study area obtained in step six, and the standardized service capacity value of each attraction in the study area obtained in step three, the comprehensive accessibility of each demand unit in the study area is calculated; the specific process is as follows:

[0166] Calculate the overall accessibility of demand unit i ; expressed as a formula:

[0167] ;

[0168] ;

[0169] ;

[0170] In the formula, Indicates tourist attractions The supply-demand ratio under traffic mode m; Indicated as tourist attractions Standardized service capability value, Indicates traffic mode Next demand unit To the scenic spot Travel time, Representing the demand unit In transportation modes Accessibility below; This represents the time impedance weight from demand unit i to attraction j under traffic mode m; other steps and parameters are the same as in one of the specific implementation methods one to eight.

[0171] Specific Implementation Method 10: This implementation method is a multi-modal transportation accessibility measurement device for tourist attractions in cold-region cities. The device includes a processor and a memory. It should be understood that it includes any device with a processor and a memory described in this invention. The device may also include other units and modules that perform display, interaction, processing, control and other functions through signals or instructions.

[0172] The memory stores at least one instruction, which is loaded and executed by the processor to implement the multimodal transportation accessibility measurement method for tourism in cold-region cities.

[0173] Those skilled in the art will understand that at least one stored instruction constitutes a computer program product corresponding to a method or system. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. The solutions in the embodiments of this application can be implemented using various computer languages, such as the object-oriented programming language Java and the interpreted scripting language JavaScript.

[0174] The above description is merely of preferred embodiments of the present invention. It should be understood that the present invention is not limited to the specific embodiments described above. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent substitutions, and improvements made to the above embodiments without departing from the scope of the present invention, based on the technical essence of the present invention, and within the spirit and principles of the present invention, shall still fall within the protection scope of the present invention.

Claims

1. A method for measuring the accessibility of tourist attractions through multimodal transportation in cold-region cities, characterized in that... include: Step 1: Obtain the basic database of the study area; perform standardization preprocessing on the basic database of the study area to obtain the standardized preprocessed database of the study area; Step 2: Construct multi-scale demand starting points based on the standardized preprocessed database; including: community-scale demand starting points and street-scale demand starting points; Step 3: Based on the standardized preprocessed database of the study area, the service capacity of tourist attractions in the study area is comprehensively quantified to obtain the standardized service capacity value of each attraction in the study area; Step 4: Based on the standardized preprocessed database of the study area, calibrate the speed correction coefficient for the snow and ice scenario in the study area; Based on the multi-scale demand starting point and the speed correction coefficient of the ice and snow scenario in the study area, a multi-modal door-to-door OD travel time matrix is ​​constructed. Step 5: Based on the standardized preprocessed database and multi-modal door-to-door OD travel time matrix of the study area, calibrate the accessibility measurement model parameters of the study area; Step 6: Based on the accessibility measurement model parameters of the study area and the multi-traffic mode door-to-door OD travel time matrix, calculate the time impedance weight from each demand unit to each attraction; Step 7: Based on the standardized preprocessed database obtained in Step 1 and the accessibility measurement model parameters of the study area calibrated in Step 5, the population of each demand unit in the study area is divided into effective competitive populations according to transportation modes, so as to obtain the effective competitive population of each demand unit in the study area for each attraction in each transportation mode. Wherein, the effective competing population of demand unit i for attraction j under traffic mode m is represented as: ; Step 8: Based on the effective competing population of each demand unit in the study area for each attraction in each mode of transportation obtained in Step 7, the time impedance weight from each demand unit to each attraction in the study area obtained in Step 6, and the standardized service capacity value of each attraction in the study area obtained in Step 3, calculate the comprehensive accessibility of each demand unit in the study area. Wherein, the comprehensive reachability of demand unit i is expressed as: .

2. The method for measuring the accessibility of tourist attractions through multimodal transportation in cold-region cities according to claim 1, characterized in that, The basic database of the study area in step one includes: administrative division vector data of the study area, road network data of the study area, public transportation routes and stations data of the study area, rail transit data of the study area, street-level population data of the study area, spatial and attribute data of residential communities of the study area, boundary or location data of scenic spots of the study area, entrance and exit data of scenic spots of the study area, supporting parking facilities data of the study area, and meteorological observation data of the study area. The standardized preprocessed database of the study area in step one includes: standardized preprocessed administrative division vector data of the study area, standardized preprocessed road network data of the study area, standardized preprocessed public transportation route and station data of the study area, standardized preprocessed rail transit data of the study area, standardized preprocessed street-level population data of the study area, standardized preprocessed residential community spatial and attribute data of the study area, standardized preprocessed scenic spot boundary or location data of the study area, standardized preprocessed scenic spot entrance and exit data of the study area, standardized preprocessed supporting parking facility data of the study area, and standardized preprocessed meteorological observation data of the study area.

3. The method for measuring the accessibility of tourist attractions through multimodal transportation in cold-region cities according to claim 2, characterized in that, In step two, a multi-scale demand starting point is constructed based on the standardized preprocessed database. The specific process is as follows: Based on the standardized preprocessed database, construct the starting points for community-scale and street-scale requirements; Specifically, the starting point for the community-scale requirements of the a-th street and the starting point for the street-scale requirements of the a-th street are constructed based on the standardized preprocessed database. The specific process is as follows: Step 21: Use the total population of the a-th street as the total constraint. Step 22: Take all residential communities in street a as the starting point for the community-scale requirements of street a. Steps 2 and 3: Based on the proportion of residential land area of ​​all residential communities in street a within its jurisdiction, the total population of street a is allocated to obtain the allocated population of each residential community in street a. The distribution of the total population of the a-th street can be expressed by the following formula: In the formula, Let a be the total population of the a-th street. For the a-th street, the first The residential land area of ​​each residential community Assign street a to street a Population allocation for each residential community This represents the total number of residential communities within the a-th street. Step 24: Calculate the population weighting center of street a based on the spatial coordinates of all residential communities in street a and the allocated population of each residential community in street a, which serves as the starting point for the street-scale demand of street a.

4. The method for measuring the accessibility of tourist attractions through multimodal transportation in cold-region cities according to claim 3, characterized in that, In step three, the service capacity of tourist attractions in the study area is comprehensively quantified based on the standardized preprocessed database of the study area to obtain the standardized service capacity value of each attraction in the study area. The specific process is as follows: Step 31: Extract the physical support indicators for each scenic spot from the standardized preprocessed database of the study area; The physical support indicators for the j-th scenic spot include: the visitable area of ​​the j-th scenic spot, the number of main entrances and exits of the j-th scenic spot, the connectivity of the surrounding roads of the j-th scenic spot, the number of bus stops within the buffer zone of the j-th scenic spot, and the parking facility capacity of the j-th scenic spot. Step 32: Calculate the standardized service capacity value for each attraction based on its physical support indicators; whereby, the attraction... The standardized service capability value is expressed as The formula is as follows: In the formula, This represents the visitable area of ​​the j-th scenic spot. This represents the number of main entrances and exits of the j-th scenic spot. Indicates the connectivity of the surrounding roads of the j-th scenic spot. This represents the number of bus stops within the buffer zone of the j-th scenic spot. This represents the parking capacity of the j-th attraction; Indicates the weight of the visitable area of ​​the attraction. Indicates the weight of the number of main entrances and exits of the scenic spot. Indicates the weight of the connectivity of the surrounding roads of the scenic spot. Indicates the weight of the number of bus stops within the tourist attraction's buffer zone. This indicates the weight of the parking facility capacity at the attraction.

5. The method for measuring the accessibility of tourist attractions through multimodal transportation in cold-region cities according to claim 4, characterized in that, The multi-modal door-to-door OD travel time matrix in step four includes: Door-to-door OD time matrix for pedestrian traffic mode under baseline scenario, door-to-door OD time matrix for public transportation mode under baseline scenario, and OD travel time matrix for driving traffic mode under baseline scenario; Door-to-door OD time matrix for pedestrian traffic patterns, door-to-door OD time matrix for public transportation patterns, and OD travel time matrix for driving traffic patterns in snow and ice scenarios; In step four, the speed correction coefficient for the ice and snow scenario in the study area is determined based on the standardized preprocessed database of the study area. Based on the multi-scale demand starting point and the speed correction coefficient for snow and ice scenarios in the study area, a multi-modal door-to-door origin-destination (OD) travel time matrix is ​​constructed; the specific process is as follows: Step 41: Based on the standardized preprocessed database of the study area, calibrate the speed correction coefficient for the snow and ice scenario in the study area, expressed by the formula: In the formula, Indicates traffic patterns under the baseline scenario Average running speed, This indicates the average running speed under icy and snowy conditions. The speed correction factor indicates the speed correction factor for snow and ice conditions. Walk represents pedestrian mode of transportation, pt represents public transportation mode of transportation, and car represents driving mode of transportation. Step 42: Determine the supply endpoint based on the standardized preprocessed database of the study area; Step 42: Based on the multi-scale demand starting point, supply ending point, and speed correction coefficient for snow and ice scenarios, construct the following in the GIS network analysis module: door-to-door OD time matrix for pedestrian traffic patterns under the baseline scenario, door-to-door OD time matrix for public transportation patterns under the baseline scenario, OD travel time matrix for driving traffic patterns under the baseline scenario, door-to-door OD time matrix for pedestrian traffic patterns under the snow and ice scenarios, door-to-door OD time matrix for public transportation patterns under the snow and ice scenarios, and OD travel time matrix for driving traffic patterns under the snow and ice scenarios.

6. The method for measuring the accessibility of tourist attractions through multimodal transportation in cold-region cities according to claim 5, characterized in that, The reachability measurement model parameters in step five include: Study area walking travel time threshold Thresholds for public transportation travel time in the study area Threshold for driving travel time in the study area ; Public transport travel structure weight in the study area The weight of driving trip structure in the study area .

7. A method for measuring the accessibility of tourist attractions through multimodal transportation in cold-region cities according to claim 6, characterized in that, In step six, the time impedance weight from each demand unit to each attraction is calculated based on the accessibility measurement model parameters of the study area and the multi-traffic mode door-to-door OD travel time matrix. The specific process is as follows: Calculate the time impedance weight from demand unit i to attraction j under traffic mode m. The formula is as follows: In the formula, For demand unit To the scenic spot Travel time under traffic mode m Let m be the travel time threshold under traffic mode m.

8. A method for measuring the accessibility of tourist attractions through multimodal transportation in cold-region cities according to claim 7, characterized in that, In step seven, based on the standardized preprocessed database obtained in step one and the accessibility measurement model parameters of the study area calibrated in step five, the population of each demand unit in the study area is divided into effective competing populations according to transportation modes, thus obtaining the effective competing population of each demand unit for each attraction in each transportation mode. The specific process is as follows: Calculate the effective competing population of demand unit i for attraction j under traffic mode m. The formula is as follows: This represents the allocated population of demand unit i. This represents the effective competing population of demand unit i for attraction j under the pedestrian transportation mode. This represents the effective competing population of demand unit i for attraction j under public transportation mode. This represents the effective competing population of demand unit i for attraction j under driving traffic mode. This represents the travel time from demand unit i to attraction j under the pedestrian transportation mode.

9. A method for measuring the accessibility of tourist attractions through multimodal transportation in cold-region cities according to claim 8, characterized in that, In step eight, the comprehensive accessibility of each demand unit in the study area is calculated based on the effective competing population of each demand unit to each attraction in each transportation mode obtained in step seven, the time impedance weight from each demand unit to each attraction in the study area obtained in step six, and the standardized service capacity value of each attraction in the study area obtained in step three. The specific process is as follows: Calculate the overall accessibility of demand unit i The formula is as follows: ； ； ； In the formula, Indicates tourist attractions The supply-demand ratio under traffic mode m; Indicated as tourist attractions Standardized service capability value, Indicates traffic mode Next demand unit To the scenic spot Travel time, Representing the demand unit In transportation modes Accessibility below; This represents the time impedance weight from demand unit i to attraction j under traffic mode m.

10. A multi-modal transportation accessibility measurement device for tourist attractions in cold-region cities, characterized in that, The device includes a processor and a memory, the memory storing at least one instruction, which is loaded and executed by the processor to implement a multimodal transportation accessibility measurement method for tourism transportation in cold-region cities as described in any one of claims 1 to 9.

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