An evaluation method and an evaluation device for urban rail transit and bus two-network integration

Abstract

The application provides an evaluation method and an evaluation device for urban rail transit and bus network integration, which realizes full-dimensional, objective and hierarchical fine quantification evaluation of the integration level of urban rail transit and bus network by constructing a full-dimensional evaluation index system corresponding to the station level and the line network region level, adopting an objective weighting method based on measured micro data, establishing a macro-micro data linkage and weight transmission mechanism, setting multi-scale differentiated evaluation rules and standardized integration levels, accurately determining the overall integration level and overall advantage and disadvantage situation of the global region, accurately positioning the low-efficiency short-board station and weak evaluation dimension, and adapting to various spatial differentiation evaluation scenarios, so as to provide accurate, objective and comprehensive quantification decision basis for urban rail transit and bus network optimization, station reconstruction, operation coordination and emergency system construction.

Description

Technical Field

[0001] This application relates to the field of urban public transportation planning and evaluation technology, and in particular to an evaluation method and device for the integration of urban rail transit and public transport networks. Background Technology

[0002] As the two backbone networks of the public transportation system, urban rail transit and conventional buses directly determine the overall service capacity and travel efficiency of urban public transportation through their coordinated operation. In actual operation, the problem of "separation of the two networks" between rail transit and conventional buses has long existed, mainly manifested in: lack of overall coordination in network layout, poor connection between rail transit stations and bus lines, excessively long transfer distances, mismatched operating hours, and resource waste caused by duplication of some bus lines with rail transit lines.

[0003] Currently, evaluation methods for the integration of rail transit and public transport networks have the following shortcomings: Existing evaluation index systems often focus on a single dimension, such as network coverage or transfer convenience; the determination of index weights in existing evaluation methods often adopts subjective weighting methods (e.g., expert scoring, analytic hierarchy process, AHP), which are highly subjective and different evaluators may give significantly different weighting results, affecting the objectivity and repeatability of the evaluation results; existing evaluation methods fail to effectively utilize micro-evaluation data at the station level to support macro-evaluation at the network / regional level; considering the characteristics of mega-city rail transit operating as a network in urban areas, there is a lack of consideration for optimizing public transport services precisely around rail transit; and refined evaluation is lacking, with poor coverage of existing evaluation index systems in areas such as emergency response capacity assessment under large-scale networks and identification and optimization of inefficient lines. Summary of the Invention

[0004] In view of this, the purpose of this application is to provide an evaluation method and device for the integration of urban rail transit networks. By constructing a full-dimensional evaluation index system corresponding to the station level and the network area level, adopting an objective weighting method based on measured micro data, establishing a macro-micro data linkage and weight transmission mechanism, and setting multi-scale differentiated evaluation rules and standardized integration levels, this application solves the problems of existing rail transit network integration evaluation dimensions being single and incomplete, weight assignment being highly subjective, macro-micro evaluations being disconnected, lacking the ability to pinpoint shortcomings in detail, lacking emergency support evaluation dimensions, and having poor adaptability. This application achieves a full-dimensional, objective, and hierarchical refined quantitative evaluation of the integration level of urban rail transit networks. It can accurately determine the overall integration level and overall advantages and disadvantages of the entire region, and accurately locate inefficient and weak stations and evaluation dimensions. It can adapt to various spatially differentiated evaluation scenarios, providing accurate, objective, and comprehensive quantitative decision-making basis for urban rail transit network optimization, station renovation, operation coordination, and emergency system construction.

[0005] This application provides an evaluation method for the integration of urban rail transit and public transport networks, the evaluation method including: Obtain the original city data corresponding to the city area to be evaluated at the station level and the network area level, and based on the original city data, determine the station indicator data corresponding to multiple preset station level indicators and the network area indicator data corresponding to multiple preset network area level indicators; wherein, the station level indicators correspond to the network area level indicators. Based on the site indicator data, the information content corresponding to each site-level indicator is determined by using a preset objective weighting method, and the information content is normalized to obtain the objective weight parameters corresponding to each network area level indicator or site level indicator. Based on the network area index data and the objective weight parameters, the comprehensive two-network integration index corresponding to the city area to be evaluated is determined, and based on the station index data and the objective weight parameters, the station integration score corresponding to each target station in the city area to be evaluated is determined. Based on the comprehensive two-network integration index, the evaluation result of the two-network integration level corresponding to the city area to be evaluated is determined.

[0006] Furthermore, the station-level indicator data corresponding to each station includes at least the following for each target station in the urban area to be evaluated: the rate of job improvement for the population connected to public transportation at the rail station, information on short-distance public transportation connections at the rail station, information on inefficient public transportation routes at the rail station, information on compliance with public transportation transfer distance standards at the rail station, information on compliance with public transportation operating time standards at the rail station, and information on emergency operation coverage of public transportation at the rail station. The station indicator data is determined through the following steps: For each target station, the following information is determined from the original city data at the station level: the number of residents covered by the combined preset radius of the rail station and connecting bus line stations, the number of residents covered by the preset radius of the rail station, the number of jobs covered by the combined preset radius of the rail station and connecting bus line stations, the number of jobs covered by the preset radius of the rail station, information on bus lines with short-distance connecting lines, information on inefficient bus lines that continuously overlap with the rail at a preset first distance, information on bus stations within a preset second distance from entrances and exits, information on the preset time for the first bus of connecting buses that is earlier than the first time of the rail, information on the preset time for the last bus of connecting buses that is later than the last time of the rail, and information on emergency operation coverage of surrounding bus lines. Based on the number of residents covered by the combined preset radius of the rail station and the connecting bus line station, the number of residents covered by the preset radius of the rail station, the number of jobs covered by the combined preset radius of the rail station and the connecting bus line station, and the number of jobs covered by the preset radius of the rail station, the job improvement rate of the rail station connecting bus population corresponding to the station is determined. Based on the bus route information with short-distance shuttle lines, determine the short-distance bus shuttle information corresponding to the rail station at that station. Based on the inefficient bus route information with a preset first distance that continuously overlaps with the track, determine the inefficient bus route information of the track station corresponding to the station; Based on the bus stop information within the preset second distance of the entrance / exit, determine the rail station bus transfer distance compliance information corresponding to the station; Based on the information that the first bus time of the connecting bus is earlier than the first bus time of the rail transit by a preset amount of time and the information that the last bus time of the connecting bus is later than the last bus time of the rail transit by a preset amount of time, the matching and compliance information of the bus operating time of the rail transit station corresponding to the station is determined. Based on the emergency operation coverage information provided by the surrounding bus routes, determine the emergency operation coverage information of the rail station corresponding to this station.

[0007] Furthermore, the network area indicator data corresponding to the network area level indicators respectively include at least the population job coverage rate of rapid public transportation, the proportion of short-distance bus connections between rail stations, the overlap ratio of rail and inefficient bus routes, the compliance rate of rail-bus transfer distance, the compliance rate of rail-bus operating time matching, and the proportion of emergency operation of rail network buses; the network area indicator data are determined through the following steps: In the original urban data corresponding to the network area level, determine the following for the urban area to be evaluated: the number of residents covered within the preset radius of the rail and bus trunk line stations, the total number of residents, the number of jobs covered within the preset radius of the rail and bus trunk line stations, the total number of jobs, the number of rail stations with short-distance connecting bus lines, the total number of rail stations, the number of inefficient bus lines that continuously overlap with rail lines at a preset first distance, the total number of bus lines, the number of rail stations where the rail-bus transfer distance is less than a preset second distance, the number of rail stations where the first bus departure time of the connecting bus is a preset amount of time earlier than the first bus departure time of the rail line, the number of rail stations where the last bus departure time of the connecting bus is a preset amount of time later than the last bus departure time of the rail line, and the number of rail stations where the bus can provide emergency support for rail incidents. Based on the number of residents within the preset radius of the rail and bus trunk line stations and the total number of residents, the rapid transit population coverage rate of the urban area to be evaluated is determined. Based on the number of jobs within the preset radius of the rail and bus trunk line stations and the total number of jobs, the rapid transit job coverage rate of the urban area to be evaluated is determined. Based on the rapid transit population coverage rate, the rapid transit job coverage rate, and the preset population weight coefficient, the rapid transit population job coverage rate of the urban area to be evaluated is determined. The ratio between the number of rail stations for short-distance shuttle bus routes and the total number of rail stations is determined as the short-distance shuttle bus ratio of the rail stations in the urban area to be evaluated. The ratio between the number of inefficient bus routes that continuously overlap with the rail line at a preset first distance and the total number of bus routes is determined as the overlap ratio of the rail line and the inefficient bus routes corresponding to the urban area to be evaluated. The ratio between the number of rail stations whose rail-to-public transfer distance is less than a preset second distance and the total number of rail stations is determined as the rail-to-public transfer distance compliance rate for the urban area to be evaluated. The ratio of the number of rail stations whose first bus departure time is a preset amount of time earlier than the first rail departure time to the total number of rail stations is determined as the first bus departure time matching compliance rate for the city area to be evaluated. The ratio of the number of rail stations whose last bus departure time is a preset amount of time later than the last rail departure time to the total number of rail stations is determined as the last bus departure time matching compliance rate for the city area to be evaluated. The average of the first bus departure time matching compliance rate and the last bus departure time matching compliance rate is determined as the rail transit operating time matching compliance rate for the city area to be evaluated. The ratio between the number of rail stations that can be used for emergency support in rail emergencies by public transport and the total number of rail stations is determined as the proportion of emergency operation of public transport in the rail network corresponding to the urban area to be evaluated.

[0008] Furthermore, based on the site indicator data, the step of determining the information content corresponding to each site-level indicator using a preset objective weighting method, and normalizing the information content to obtain the objective weight parameters corresponding to each network area-level indicator or site-level indicator includes: Based on the site indicator data, an initial data matrix corresponding to the site level is constructed, and the initial data matrix is ​​dimensionless according to whether the site level indicator is a positive or negative indicator to obtain the target data matrix. Based on each data element in the target data matrix, the standard deviation corresponding to each site-level indicator is determined, and the standard deviation is determined as the contrast intensity parameter corresponding to each site-level indicator. Based on each data element in the target data matrix, determine the Pearson correlation coefficient between every two data elements in the target data matrix, and based on the Pearson correlation coefficient, determine the conflict parameter corresponding to each site-level indicator. The product of the contrast intensity parameter and the conflict parameter is determined as the information content corresponding to each of the site-level indicators; The proportion of the information content corresponding to each of the site-level indicators to the total amount of information content is determined as the objective weight parameter corresponding to each of the network area-level indicators or the site-level indicators.

[0009] Furthermore, determining the comprehensive two-network integration index corresponding to the city area to be evaluated based on the network area indicator data and the objective weight parameters includes: Based on whether the network area level indicator is a positive or negative indicator, the network area indicator data corresponding to each network area level indicator is normalized to obtain the target network area indicator parameters corresponding to each network area level indicator. For each of the network area level indicators, the target network area indicator parameter corresponding to the network area level indicator is multiplied by the objective weight parameter corresponding to the network area level indicator to obtain the first two-network integration index corresponding to the network area level indicator. The sum of the first two-network integration indices corresponding to each of the network area level indicators is determined as the comprehensive two-network integration index corresponding to the city area to be evaluated.

[0010] Furthermore, determining the site fusion score for each target site in the urban area to be evaluated based on the site indicator data and the objective weight parameters includes: Based on whether the site-level indicator is a positive or negative indicator, the site indicator data corresponding to each site-level indicator is normalized to obtain the target site indicator parameters corresponding to each site-level indicator. For each of the site-level indicators, the target site indicator parameter corresponding to the site-level indicator is multiplied by the objective weight parameter corresponding to the site-level indicator to obtain the second two-network convergence index corresponding to the site-level indicator. The sum of the second two-network fusion indices corresponding to each of the site-level indicators is determined as the site fusion score for each target site in the urban area to be evaluated.

[0011] Furthermore, determining the evaluation result of the two-network convergence level corresponding to the city area to be evaluated based on the comprehensive two-network convergence index includes: Determine the preset two-network convergence index range to which the comprehensive two-network convergence index belongs; When the comprehensive two-network integration index belongs to the first preset two-network integration index range, the two-network integration level evaluation result of the city area to be evaluated is determined to be deep integration; When the comprehensive two-network convergence index belongs to the second preset two-network convergence index range, the two-network convergence level evaluation result is determined to be good convergence; When the comprehensive two-network convergence index belongs to the third preset two-network convergence index range, the two-network convergence level evaluation result is determined to be general convergence; When the comprehensive two-network convergence index belongs to the fourth preset two-network convergence index range, the two-network convergence level evaluation result is determined to be preliminary convergence; When the comprehensive two-network convergence index belongs to the fifth preset two-network convergence index range, the two-network convergence level evaluation result is determined to be insufficient convergence.

[0012] Furthermore, after determining the evaluation result of the two-network convergence level corresponding to the urban area to be evaluated, the evaluation method further includes: Based on the evaluation results of the two-network integration level, the comprehensive two-network integration index, the site indicator data, and the network area indicator data, the corresponding visualized evaluation results at the network area level and the site level are output; wherein, the visualized evaluation results include at least a spatial distribution map of integration level, an indicator radar chart, a site compliance heat map, and a time change trend chart.

[0013] This application embodiment also provides an evaluation device for the integration of urban rail transit and public transport networks, the evaluation device comprising: The indicator calculation module is used to obtain the original urban data corresponding to the city area to be evaluated at the station level and the network area level, and based on the original urban data, to determine the station indicator data corresponding to a number of preset station level indicators and the network area indicator data corresponding to a number of preset network area level indicators; wherein, the station level indicators correspond to the network area level indicators. The weight determination module is used to determine the information content corresponding to each of the site-level indicators based on the site indicator data using a preset objective weighting method, and to normalize the information content to obtain the objective weight parameters corresponding to each of the network area level indicators or the site level indicators. The fusion calculation module is used to determine the comprehensive two-network fusion index corresponding to the city area to be evaluated based on the network area index data and the objective weight parameters, and to determine the site fusion score corresponding to each target site in the city area to be evaluated based on the site index data and the objective weight parameters. The comprehensive evaluation module is used to determine the evaluation result of the two-network integration level of the city area to be evaluated based on the comprehensive two-network integration index.

[0014] Furthermore, the evaluation device also includes a visualization output module, which is used for: Based on the evaluation results of the two-network integration level, the comprehensive two-network integration index, the site indicator data, and the network area indicator data, the corresponding visualized evaluation results at the network area level and the site level are output; wherein, the visualized evaluation results include at least a spatial distribution map of integration level, an indicator radar chart, a site compliance heat map, and a time change trend chart.

[0015] This application also provides an electronic device, including: a processor, a memory, and a bus. The memory stores machine-readable instructions executable by the processor. When the electronic device is running, the processor communicates with the memory via the bus. When the machine-readable instructions are executed by the processor, the steps of the evaluation method for the integration of urban rail transit networks as described above are performed.

[0016] This application also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, performs the steps of the evaluation method for the integration of urban rail transit and public transport networks as described above.

[0017] The present application provides an evaluation method and device for the integration of urban rail transit networks. The evaluation method includes: acquiring original urban data corresponding to the urban area to be evaluated at the station level and the network area level; and based on the original urban data, determining station indicator data corresponding to multiple preset station-level indicators and network area indicator data corresponding to multiple preset network area indicators; wherein the station-level indicators correspond to the network area indicators; based on the station indicator data, determining the information content corresponding to each station-level indicator using a preset objective weighting method, and normalizing the information content to obtain objective weight parameters corresponding to each network area indicator or station-level indicator; based on the network area indicator data and the objective weight parameters, determining a comprehensive integration index for the urban area to be evaluated, and determining a station integration score corresponding to each target station in the urban area to be evaluated based on the station indicator data and the objective weight parameters; and determining the integration level evaluation result for the urban area to be evaluated based on the comprehensive integration index.

[0018] Compared with existing evaluation methods for the integration of rail transit and public transport networks, this method constructs a comprehensive evaluation index system corresponding to both station and network area levels. It employs an objective weighting method based on measured micro-data, establishes a macro-micro data linkage and weight transmission mechanism, and sets multi-scale differentiated evaluation rules and standardized integration levels. This addresses issues such as the single and incomplete coverage of existing rail transit and public transport network integration evaluation dimensions, strong subjectivity in weight assignment, disconnect between macro and micro evaluations, lack of refined shortcoming identification capabilities, absence of emergency support evaluation dimensions, and poor adaptability. It achieves a comprehensive, objective, and hierarchical quantitative evaluation of the integration level of rail transit and public transport networks in large cities. This method can accurately determine the overall integration level and overall strengths and weaknesses of the entire region, accurately locate inefficient and weak stations and evaluation dimensions, and is adaptable to various spatially differentiated evaluation scenarios. It provides accurate, objective, and comprehensive quantitative decision-making basis for the optimization of rail transit networks, station renovation, operational coordination, and emergency system construction in large cities.

[0019] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0020] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 One of the flowcharts for an evaluation method of urban rail transit network integration provided in this application embodiment; Figure 2 A second flowchart illustrating an evaluation method for the integration of urban rail transit and public transport networks provided in this application embodiment; Figure 3 One of the structural schematic diagrams of an evaluation device for the integration of urban rail transit and public transport networks provided in this application embodiment; Figure 4 A second schematic diagram of the structure of an evaluation device for the integration of urban rail transit and public transport networks provided in this application embodiment; Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0022] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. The components of the embodiments of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely represents selected embodiments of this application. Based on the embodiments of this application, every other embodiment obtained by those skilled in the art without inventive effort falls within the scope of protection of this application.

[0023] Research has found that transportation demand in megacities (e.g., cities with an urban resident population of over 10 million) continues to grow. Urban rail transit and conventional buses, as the two backbone networks of the public transportation system, directly determine the overall service capacity and travel efficiency of urban public transportation through their coordinated operation. Meanwhile, conventional bus systems have extensive coverage and a large number of routes in cities across the country, making them an indispensable component of the urban public transportation system.

[0024] However, in actual operation, the problem of "separation of the two networks" between rail transit and conventional buses has long existed. This is mainly manifested in the following ways: lack of overall coordination in the network layout, poor connection between rail transit stations and bus lines, excessively long transfer distances, mismatched operating hours, and waste of resources due to duplication of some bus lines and rail transit lines.

[0025] Here, the integration of rail transit and public transport networks refers to the formation of a public transport network system that is functionally complementary, efficiently connected, and provides integrated services through means such as network optimization, station connection, operational coordination, and emergency response.

[0026] The core concept of the integration of the two networks is to use rail transit as the backbone and regular buses as the feeder and supplement. By optimizing the network layout, shortening transfer distances, matching operating hours, reducing inefficient and repetitive routes, and improving emergency connection mechanisms, the overall attractiveness and service level of public transportation can be improved.

[0027] In megacities, the necessity and urgency of integrating urban rail transit and conventional public transport are even more pronounced due to high population density, large travel demand, and complex transportation structures. Relevant policy documents from the Ministry of Transport and various provinces and cities have clearly stated the need to promote the integrated development of urban rail transit and conventional public transport to improve the quality of public transport services.

[0028] Currently, research on the evaluation of the integration of rail transit and public transport networks, both domestically and internationally, is still in its early stages, and existing evaluation methods have the following shortcomings: First, the existing evaluation index system focuses on a single dimension, such as only paying attention to the network coverage or the convenience of transfers, and lacks a comprehensive evaluation index system that covers multiple dimensions such as network layout, station connection, operation coordination and emergency support.

[0029] Second, the determination of indicator weights in existing evaluation methods often adopts subjective weighting methods (such as expert scoring, analytic hierarchy process, AHP, etc.), which are highly subjective. Different evaluators may give different weight results, affecting the objectivity and repeatability of the evaluation results.

[0030] Third, existing evaluation methods fail to effectively utilize micro-evaluation data at the station level to support macro-evaluation at the network / region level, and there is a lack of organic data correlation and weight transmission mechanism between station evaluation and network evaluation.

[0031] Fourth, given the characteristics of mega-city rail networks operating in urban areas, there is a lack of consideration for optimizing public transport services precisely around rail transit.

[0032] Fifth, there is a lack of refined evaluation. The existing evaluation indicator system does not cover aspects such as the assessment of emergency support capabilities under large-scale networks and the identification and optimization of inefficient lines.

[0033] Based on this, this application provides an evaluation method for the integration of urban rail transit networks. By constructing a full-dimensional evaluation index system corresponding to the station level and the network area level, adopting an objective weighting method based on measured micro data, establishing a macro-micro data linkage and weight transmission mechanism, and setting multi-scale differentiated evaluation rules and standardized integration levels, this method solves the problems of existing rail transit network integration evaluation dimensions being single and incomplete, weight assignment being highly subjective, macro-micro evaluations being disconnected, lacking the ability to pinpoint shortcomings in detail, lacking emergency support evaluation dimensions, and having poor adaptability. This method achieves a full-dimensional, objective, and hierarchical refined quantitative evaluation of the integration level of urban rail transit networks. It can accurately determine the overall integration level and overall advantages and disadvantages of the entire region, accurately locate inefficient and weak stations and evaluation dimensions, and is adaptable to various spatially differentiated evaluation scenarios. It provides accurate, objective, and comprehensive quantitative decision-making basis for urban rail transit network optimization, station renovation, operation coordination, and emergency system construction.

[0034] Please see Figure 1 , Figure 1 This is one of the flowcharts for an evaluation method of urban rail transit network integration provided in an embodiment of this application.

[0035] like Figure 1 As shown in the embodiments of this application, the evaluation method for the integration of urban rail transit and public transport networks can achieve macro-regional evaluation and micro-station evaluation of the integration level of urban rail transit and public transport networks through the construction of a two-layer indicator system, objective weighting of CRITIC, multi-dimensional index calculation and level classification. It is applicable to the evaluation of the integration degree of urban rail transit and conventional public transport networks, and provides a scientific quantitative basis for the planning, construction and operation optimization of public transport systems in megacities. The evaluation method includes: S101. Obtain the original city data corresponding to the city area to be evaluated at the station level and the network area level, and based on the original city data, determine the station indicator data corresponding to a number of preset station level indicators and the network area indicator data corresponding to a number of preset network area level indicators.

[0036] The site-level indicators correspond to the network area-level indicators.

[0037] In this embodiment of the application, the evaluation index system for the integration of rail transit and public transport networks in megacities adopts a two-layer architecture of "network area level and station level". The network area level index is used to macroscopically evaluate the overall integration level of rail transit and conventional public transport at a certain spatial scale (e.g., the whole city, urban area, key functional area or suburbs), while the station level index is used to microscopically evaluate the integration status of individual rail stations.

[0038] Here, the relationship between station-level indicators and network area-level indicators is not a simple aggregation relationship, but rather a weight transmission mechanism is established through the CRITIC method: the station indicator data corresponding to the station-level indicators provides an objective basis for determining the weights of the network area indicator data corresponding to the network area-level indicators.

[0039] In this embodiment of the application, the urban area to be evaluated refers to the target spatial range for the evaluation of the integration of rail transit and public transport networks carried out by the method described in this embodiment of the application. It supports four different spatial scales: citywide, urban area, key functional area, and suburban area, which can be flexibly selected according to evaluation needs.

[0040] Here, the original urban data refers to the basic measured data of the city required for the evaluation, including but not limited to population data, job data, rail station data, bus route data, transfer space data, operating time data, emergency transport capacity data, etc., which are the basic data sources for calculating all indicators in the method described in the embodiments of this application.

[0041] In this embodiment of the application, six evaluation indicators are set at the network area level to measure the level of integration between the two networks from six dimensions: spatial coverage, connection service, route optimization, transfer convenience, time coordination and emergency support.

[0042] Among them, the network area indicators corresponding to the network area indicators include, but are not limited to, the population and job coverage rate of rapid public transportation, the proportion of short-distance bus connections between rail stations, the overlap ratio of rail and inefficient bus routes, the compliance rate of rail-bus transfer distance, the compliance rate of rail-bus operating time matching, and the proportion of emergency operation of rail network buses.

[0043] In this embodiment of the application, six station-level indicators corresponding to the network area level indicators are set at the station level to evaluate the integration status of individual rail stations at a micro level.

[0044] Among them, the station indicator data corresponding to the station-level indicators include, but are not limited to, the job improvement rate of the rail station bus connection population for each target station in the urban area to be evaluated, the short-line bus connection information of the rail station, the inefficient bus route information of the rail station, the compliance information of the rail station bus transfer distance, the compliance information of the rail station bus operating time, and the emergency operation coverage information of the rail station.

[0045] In this step, the city area to be evaluated (e.g., the whole city, urban area, key functional area, suburbs) is selected, and the original city data corresponding to the city area to be evaluated is collected. Based on the collected original city data, the indicator data is extracted from two dimensions: micro and macro. First, at the station level, all target rail stations in the area are traversed, and the station-level indicator data corresponding to each station is calculated one by one. Second, at the network area level, the original data of the whole area is aggregated, and the network area-level indicator data corresponding to the entire area is calculated.

[0046] The station-level indicators correspond to the network area-level indicators in terms of dimensions, respectively covering evaluation dimensions such as spatial coverage, short-distance connections, inefficient line rectification, transfer convenience, operating time coordination, and emergency support.

[0047] This approach constructs a dual-layered evaluation data foundation, linking and matching refined data from individual sites with macro-level data from the entire region. This provides a comprehensive, accurate, and unified data source for subsequent objective weighting and index calculation, addressing the issues of single data dimensions and disconnect between macro and micro data in existing technologies. It creates a dual-layered dataset of "site micro-data and regional macro-data," fully covering all core evaluation dimensions of the two-network integration. The data is consistent in scope and complementary in dimensions, laying a solid data foundation for high-precision quantitative evaluation.

[0048] In one possible implementation of this application, the step of determining the site indicator data in step S101 may include: S10111. For each target station, determine the following from the original city data at the station level: the number of residents covered by the combined preset radius of the rail station and connecting bus line stations, the number of residents covered by the preset radius of the rail station, the number of jobs covered by the combined preset radius of the rail station and connecting bus line stations, the number of jobs covered by the preset radius of the rail station, information on bus lines with short-distance connecting lines, information on inefficient bus lines that continuously overlap with the rail at a preset first distance, information on bus stations within a preset second distance from entrances and exits, information on the preset time for connecting buses that is earlier than the first time of the rail, information on the preset time for connecting buses that is later than the last time of the rail, and information on emergency operation coverage of surrounding bus lines.

[0049] For example, for each target rail station within the evaluation area, the following core raw data are extracted one by one: the number of residents and jobs covered by a preset 500-meter radius of the rail station alone; the number of residents and jobs covered by a preset 500-meter radius combined with the rail station and connecting bus station; whether the station has a standard short-distance connecting bus line; whether there are inefficient bus lines around the station that continuously overlap with the rail for a preset first distance of 5 kilometers; whether there are bus stops within a preset second distance of 50 meters from the station entrance / exit; the time difference between the first departure time of the connecting bus and the first departure time of the rail, and the time difference between the last departure time of the connecting bus and the last departure time of the rail; and whether the bus lines around the station have the emergency operation coverage capability for rail emergencies.

[0050] All data here is taken from urban GIS spatial data, public transport and rail operation ledgers, mobile phone information population and job data, and traffic emergency filing data.

[0051] S10112. Based on the number of residents covered by the combined preset radius of the rail station and the connecting bus line station, the number of residents covered by the preset radius of the rail station, the number of jobs covered by the combined preset radius of the rail station and the connecting bus line station, and the number of jobs covered by the preset radius of the rail station, determine the job improvement rate of the rail station connecting bus population corresponding to the station.

[0052] Here, the job improvement rate of the population connected to rail stations is used to measure the degree to which the bus connection routes improve the service coverage of rail stations. It reflects the actual expansion effect of bus connections on the service range of rail stations. The higher the improvement rate, the more significant the feeding and supplementing effect of the connecting bus routes on rail stations.

[0053] In this embodiment of the application, the job creation rate of the rail transit connection population corresponding to each station is calculated using the following formula: S1= [(P_rb - P_r) / P_r + (E_rb - E_r) / E_r)] / 2.

[0054] Wherein, S1 represents the job increase rate of the population connected to the rail station by public transport, P_rb represents the number of residents covered by the combined preset radius of the rail station and the connecting public transport line station, P_r represents the number of residents covered by the preset radius of the rail station, E_rb represents the number of jobs covered by the combined preset radius of the rail station and the connecting public transport line station, and E_r represents the number of jobs covered by the preset radius of the rail station.

[0055] S10113. Based on the information of the bus routes with short-distance connecting lines, determine the short-distance bus connecting information of the rail station corresponding to the station.

[0056] Here, the short-line bus connection information for rail stations is a positive binary indicator. The short-line bus connection information for rail stations records whether a station has short-line connection services in a binary manner, which simply and intuitively reflects the connection service configuration of the station.

[0057] For example, when a rail station has a short-distance shuttle bus line, the rail station bus short-distance shuttle information (S2) is 1; otherwise, S2 is 0.

[0058] S10114. Based on the inefficient bus route information of the preset first distance that continuously overlaps with the track, determine the inefficient bus route information of the track station corresponding to the station.

[0059] Here, the information on inefficient bus routes at rail stations is a negative binary indicator. This information indicates whether there is a problem of duplicate inefficient routes at the station level. A value of 1 indicates that there is a competition for routes that is not conducive to the integration of the two networks.

[0060] For example, when there are inefficient bus routes that continuously overlap with the rail for more than 5 kilometers around the rail station, the inefficient bus route information (S3) of the rail station is 1; otherwise, S3 is 0.

[0061] S10115. Based on the bus stop information within the second preset distance of the entrance / exit, determine the rail station bus transfer distance compliance information corresponding to the station.

[0062] Here, the information on the distance between rail stations and public transportation is a positive binary indicator. This information is used to determine whether the convenience of transfers at a station meets the standard for efficient transfers.

[0063] For example, when the walking distance from the entrance / exit of a rail station to the nearest bus stop is less than 50 meters, the rail station-bus transfer distance compliance information (S4) is 1; otherwise, S4 is 0.

[0064] S10116. Based on the information that the first bus time of the connecting bus is earlier than the first bus time of the rail transit by a preset time and the information that the last bus time of the connecting bus is later than the last bus time of the rail transit by a preset time, determine the matching compliance information of the rail transit station bus operation time corresponding to the station.

[0065] In this embodiment of the application, the matching compliance information of bus operating hours at rail stations is determined by the following formula: S5 = (S5) f + S5 l ) / 2.

[0066] S5 represents the matching compliance information of bus operating hours at rail stations (positive indicator). The value range of S5 is {0, 0.5, 1}, corresponding to three states: no match between the first and last buses, match at only one end, and match at both ends, respectively. f For example, when the first bus departure time is more than 10 minutes earlier than the first rail departure time, S5 f Select 1, otherwise S5 f Set to 0; S5 l For example, when the last bus time of the connecting bus is more than 10 minutes later than the last rail time, S5 is a binary variable. l Select 1, otherwise S5 l Take 0.

[0067] S10117. Based on the emergency operation coverage information provided by the surrounding bus routes, determine the emergency operation coverage information of the rail station corresponding to the station.

[0068] Here, the information on emergency bus operation coverage at rail stations is a positive binary indicator, which is used to determine whether a station has the capability for emergency bus connections.

[0069] For example, when a rail station can be covered by emergency operations from surrounding bus routes, the rail station bus emergency operation coverage information (S6) is 1; otherwise, S6 is 0.

[0070] In one possible implementation of this application, the step of determining the network area index data in step S101 may include: S10121. In the original urban data corresponding to the network area level, determine the following information for the urban area to be evaluated: the number of residents covered within the preset radius of the rail and bus trunk line stations, the total number of residents, the number of jobs covered within the preset radius of the rail and bus trunk line stations, the total number of jobs, the number of rail stations with short-distance connecting bus lines, the total number of rail stations, the number of inefficient bus lines that continuously overlap with rail lines at a preset first distance, the total number of bus lines, the number of rail stations where the rail-bus transfer distance is less than a preset second distance, the number of rail stations where the first bus departure time of the connecting bus is a preset amount of time earlier than the first bus departure time of the rail line, the number of rail stations where the last bus departure time of the connecting bus is a preset amount of time later than the last bus departure time of the rail line, and the number of rail stations where the bus can provide emergency support for rail incidents.

[0071] For example, the core data of the entire area to be evaluated are as follows: the residential population within 500 meters of rail and bus trunk lines, and the total residential population of the area; the job positions within 500 meters of rail and bus trunk lines, and the total number of job positions in the area; the number of rail stations with short-line connecting lines, and the total number of rail stations in the area; the number of inefficient bus routes that overlap with rail lines for more than 5 kilometers, and the total number of bus routes in the area; the number of qualified stations with a transfer distance of less than 50 meters; the number of qualified stations with matching first and last bus times; and the number of rail stations with emergency bus support capabilities.

[0072] S10122. Based on the number of residents within the preset radius of the rail and bus trunk line stations and the total number of residents, determine the rapid transit population coverage rate of the urban area to be evaluated. Based on the number of jobs within the preset radius of the rail and bus trunk line stations and the total number of jobs, determine the rapid transit job coverage rate of the urban area to be evaluated. Based on the rapid transit population coverage rate, the rapid transit job coverage rate, and the preset population weight coefficient, determine the rapid transit population job coverage rate of the urban area to be evaluated.

[0073] Here, the rapid transit population job coverage rate is used to measure the spatial coverage of rail transit and bus trunk line stations to the residential population and jobs within the urban area to be evaluated. The higher the coverage rate, the better the accessibility of the rapid transit system, and the more residents and employees can reach rail transit stations or bus trunk line stations within walking distance.

[0074] Among them, the population and job coverage rate of rapid public transportation comprehensively reflects the rationality of the network layout and the sufficiency of spatial services. It is a basic indicator of the integration of the two networks in the spatial dimension. Population coverage and job coverage measure the public transportation service capacity from the residential end and the employment end, respectively. The sum of the two comprehensively reflects the overall level of urban activity space covered by the rapid public transportation network.

[0075] In this embodiment of the application, the population coverage rate of rapid public transport is calculated using the following formula: I1 p = P_c / P_total.

[0076] Where P_c is the number of residents covered within the preset radius of the rail and bus trunk line stations, P_total is the total number of residents, and I1 p To increase the population coverage of rapid public transportation.

[0077] In this embodiment of the application, the coverage rate of rapid transit jobs is calculated using the following formula: I1 e = E_c / E_total.

[0078] Where E_c represents the number of jobs covered within the preset radius of rail and bus trunk line stations, E_total represents the total number of jobs, and I1 e To increase the coverage of public transportation jobs.

[0079] In this embodiment of the application, the population job coverage rate of rapid public transport is calculated using the following formula: I1 = α × I1 p + (1-α) × I1 e .

[0080] Where I1 represents the population job coverage rate of rapid public transportation; α is a preset population weighting coefficient, with a value range of [0.3, 0.7], and in this embodiment, α can be 0.5; I1 p To rapidly increase public transportation coverage, I1 e To increase the coverage of public transportation jobs.

[0081] S10123. The ratio between the number of rail stations for short-distance shuttle bus routes and the total number of rail stations is determined as the short-distance bus connection ratio of the rail stations in the urban area to be evaluated.

[0082] Here, the proportion of short-distance bus connections at rail stations is used to measure the proportion of rail stations within the evaluation area that have short-distance bus connections.

[0083] For example, short-line feeder lines refer to bus lines that start near the entrances or exits of rail stations or pass through stations, with a length of no more than 5 kilometers in urban areas and no more than 10 kilometers in suburban areas. Their function is to connect the surrounding residential areas, commercial areas, and other passenger flow sources with the rail station more efficiently. The higher the proportion of short-line feeder lines, the higher the level of feeder service between the rail station and the surrounding public transportation, and the better the "last mile" problem for passengers to reach the rail station from their residence or workplace is solved. In large cities, due to the limited coverage of rail stations, the setting of short-line feeder lines plays a key role in expanding the service range of rail stations and enhancing the attractiveness of rail passenger flow.

[0084] In this embodiment of the application, the proportion of short-line bus connections to rail stations is calculated using the following formula: I2 = N_feeder / N_rail.

[0085] Where I2 is the proportion of short-distance bus connections to rail stations, N_feeder is the number of rail stations with short-distance bus connections, and N_rail is the total number of rail stations.

[0086] S10124. The ratio between the number of inefficient bus routes that continuously overlap with the rail line at a preset first distance and the total number of bus routes is determined as the overlap ratio of the rail line and the inefficient bus routes corresponding to the urban area to be evaluated.

[0087] Here, the overlap ratio between rail transit and inefficient bus routes is used to measure the proportion of inefficient bus routes that highly overlap with the routes of rail transit lines to the total number of bus routes.

[0088] For example, when a bus route and a rail transit line overlap continuously for more than 5 kilometers, and the number of passengers per 100 kilometers of the bus route is less than 50% of the average, the route is identified as an inefficient and redundant route. The existence of inefficient and redundant routes not only wastes public transportation capacity resources, but may also lead to competition for passenger flow between rail and bus rather than a complementary relationship, which is not conducive to the integration of the two networks.

[0089] The lower the overlap between rail and inefficient bus routes, the higher the degree of network optimization, and the more bus resources tend to play a feeding and supplementing role rather than competing with rail.

[0090] In this embodiment, the overlap ratio between the rail line and the inefficient bus route is calculated using the following formula: I3 = N_overlap / N_bus.

[0091] Wherein, N_overlap is the number of inefficient bus routes that continuously overlap with the rail line at a preset first distance, N_bus is the total number of bus routes, and I3 is the overlap ratio between the rail line and the inefficient bus routes.

[0092] S10125. The ratio between the number of rail stations with a rail-to-public transport transfer distance less than a preset second distance and the total number of rail stations is determined as the rail-to-public transport transfer distance compliance rate for the urban area to be evaluated.

[0093] Here, the rail-bus transfer distance compliance rate is used to measure the proportion of rail stations and bus stations in the evaluation area that meet the convenience standards. The higher the rail-bus transfer distance compliance rate, the better the integration of the station spatial layout.

[0094] For example, the transfer distance refers to the walking distance from the entrance / exit of a rail station to the nearest bus stop, with 50 meters as the threshold for compliance. Transfer distance is one of the key factors affecting passenger travel experience. Excessive transfer distance will significantly reduce passengers' willingness to choose to transfer from public transport to rail, and increase transfer time costs and physical exertion. The 50-meter threshold is determined based on existing research and industry practice. Within this distance, passengers can complete the walking transfer within 1 minute, which falls into the category of efficient transfer.

[0095] In this embodiment of the application, the compliance rate of rail transit transfer distance is calculated using the following formula: I4 = N_transfer / N_rail.

[0096] Wherein, N_transfer is the number of rail stations where the distance between rail and public transport transfer is less than the preset second distance, N_rail is the total number of rail stations, and I4 is the rail-public transport transfer distance compliance rate.

[0097] S10126. The ratio between the number of rail stations whose first bus departure time is a preset amount of time earlier than the first rail departure time and the total number of rail stations is determined as the first bus departure time matching compliance rate for the city area to be evaluated. The ratio between the number of rail stations whose last bus departure time is a preset amount of time later than the last rail departure time and the total number of rail stations is determined as the last bus departure time matching compliance rate for the city area to be evaluated. The average of the first bus departure time matching compliance rate and the last bus departure time matching compliance rate is determined as the rail transit operating time matching compliance rate for the city area to be evaluated.

[0098] Here, the rail transit operating time matching compliance rate is used to measure the degree of matching between the operating time of connecting buses and the rail transit within the evaluation area. The rail transit operating time matching compliance rate directly affects the service coverage of public transportation for people traveling during morning and evening peak hours and at night, and is one of the core indicators of operation integration.

[0099] For example, time matching is an important manifestation of the coordinated operation of the two networks, which includes two aspects: First, the first departure time of the connecting bus should be more than 10 minutes earlier than the first departure time of the rail transit, so as to ensure that passengers in the morning can take the first bus to the rail transit station to catch the first train; Second, the last departure time of the connecting bus should be more than 10 minutes later than the last departure time of the rail transit, so as to ensure that passengers taking the last train in the evening can transfer to the bus to reach their destination.

[0100] In this embodiment of the application, the first flight time matching compliance rate is calculated using the following formula: I5 f = N_first / N_rail.

[0101] Among them, I5 f To match the first departure time with the target rate, N_first is the preset time for the first departure of the connecting bus to be earlier than the first departure time of the rail transit, and N_rail is the total number of rail transit stations.

[0102] In this embodiment of the application, the last-shift time matching compliance rate is calculated using the following formula: I5 l= N_last / N_rail.

[0103] Among them, I5 l To match the target rate for the last bus time, N_last is the number of rail stations where the last bus time of the connecting bus is delayed by a preset time compared to the last rail time, and N_rail is the total number of rail stations.

[0104] In this embodiment of the application, the rail transit operating time matching compliance rate is calculated using the following formula: I5 = (I5) f + I5 l ) / 2.

[0105] Among them, I5 represents the compliance rate of rail transit operating hours. f To match the first flight time with the target rate, I5 l Match the target rate to the last train time.

[0106] S10127. The ratio between the number of rail stations that can be used for emergency support in rail emergencies by public transport and the total number of rail stations is determined as the proportion of emergency operation of the rail network by public transport in the urban area to be evaluated.

[0107] Here, the proportion of emergency bus operation in the rail network is used to measure the proportion of rail transit lines in the evaluation area that can be supported by regular buses in the event of an emergency (such as equipment failure, signal abnormality, extreme weather, etc.) when the rail transit lines are shut down. The higher the proportion of emergency bus operation in the rail network, the stronger the emergency support capability of the bus system for rail transit and the better the collaborative resilience between the two networks.

[0108] In large cities, the passenger flow of rail transit is high. Once a line is shut down, a large number of stranded passengers need to be evacuated by ground public transportation. The emergency support capacity of public transportation is an important manifestation of the depth of integration between the two networks and a key component of urban transportation resilience. For example, the specific judgment criteria are: if there is a bus line with emergency transport capacity reserves within 300 meters around the rail station, and the bus line is included in the rail emergency connection park recognized by the operating company, it is considered that emergency support can be provided by public transportation.

[0109] In this embodiment of the application, the proportion of emergency operation of public transport on the rail network is calculated using the following formula: I6 = N_emergency / N_rail.

[0110] Among them, I6 represents the proportion of emergency operation of public transport on the rail network, N_emergency represents the number of rail stations that can be supported by public transport in the event of a rail emergency, and N_rail represents the total number of rail stations.

[0111] S102. Based on the site indicator data, the information content corresponding to each site-level indicator is determined using a preset objective weighting method, and the information content is normalized to obtain the objective weight parameters corresponding to each network area level indicator or site level indicator.

[0112] Here, the presupposed objective weighting method may include a CRITIC method (Criteria Importance Through Intercriteria Correlation). As an objective weighting method, the CRITIC method can simultaneously consider the variability (contrast strength) of indicators and the correlation (conflict) between indicators. It determines the weight of indicators in a data-driven manner, effectively avoiding the bias problem of subjective weighting methods.

[0113] In this embodiment, the CRITIC method is applied to site-level indicator data to determine the weight of network area indicators, establishing a data correlation mechanism between site micro-evaluation and network macro-evaluation, and realizing closed-loop feedback of the evaluation system.

[0114] Here, information content is the core parameter of the preset objective weighting method, which is obtained by multiplying the intensity of indicator comparison and the degree of conflict. It is used to characterize the total amount of independent and effective information carried by a single evaluation indicator in the overall evaluation system. The greater the information content, the higher the evaluation value of the indicator. The objective weight parameter is the weight coefficient obtained after normalizing the information content of each indicator. The sum of all objective weight parameters is 1. It is calculated and generated entirely from measured data without subjective bias and is used for the weighted calculation of the fusion index.

[0115] In this step, a standardized initial data matrix is ​​constructed based on the site-level indicator data of all target sites as the basic data source. The positive and negative indicators are distinguished to complete the dimensionless processing and eliminate the difference in dimensions. The comparative strength (standard deviation) and conflict (indicator correlation) of each site-level indicator are calculated by using the preset objective weighting method (CRITIC method). The two are multiplied to obtain the information content of each indicator. Finally, the information content of all indicators is normalized to output a unified objective weight parameter. This objective weight parameter is adapted to the evaluation of indicators at both the site level and the network area level.

[0116] In this way, the traditional subjective weighting method is abandoned, and objective weights are calculated based on actual measurement data from real stations, eliminating human subjective bias. At the same time, a weight transmission mechanism is established from micro-station data to macro-regional evaluation. The weight results are completely data-driven, objective and traceable, and can be dynamically adapted according to the traffic characteristics of different urban areas. Different weights can be generated at different spatial scales to accurately match the shortcomings of the two-network integration in different regions.

[0117] In one possible implementation of this application, step S102 may include: S1021. Based on the site indicator data, construct an initial data matrix corresponding to the site level, and perform dimensionless processing on the initial data matrix according to whether the site level indicator is a positive or negative indicator to obtain the target data matrix.

[0118] For example, assuming there are z rail stations and m station-level indicators within the urban area to be evaluated, the initial data matrix X is (x ij ) zxm , where x ij Let m be the original value of the i-th station on the j-th indicator (i = 1, 2, ..., z; j = 1, 2, ..., m), where m is set to 6 in this embodiment.

[0119] For example, for an evaluation area in a megacity, if there are 150 rail stations, the initial data matrix is ​​a "150×6" matrix.

[0120] Here, since the dimensions and orders of magnitude of the indicator data for each site are different (e.g., S1 is a percentage, and S2 to S6 are binary variables), standardization is required to eliminate the influence of dimensions.

[0121] In this embodiment, the inefficient bus route information at rail stations is redundant and has a negative impact on the fusion evaluation. That is, the inefficient bus route information at rail stations is a negative indicator and needs to be reversed during standardization. The job improvement rate of the population connected to rail stations by buses, the short-distance connection information of rail stations by buses, the information on the compliance of rail station bus transfer distance, the information on the compliance of rail station bus operating time, and the information on the coverage of rail station bus emergency operation are all positive indicators.

[0122] In this step, the site indicator data for positive indicators are standardized for range using the following formula: x ij ' = (x ij -x jmin ) / (x jmax - x jmin The site indicator data for negative indicators are standardized using the following formula: x ij ' = (x jmax -x ij ) / (x jmax - x jmin ).

[0123] Where, x ij 'This refers to the dimensionless (standardized) site indicator data, x' ij x is a data element in the initial data matrix. jmin and x jmaxThese are the maximum and minimum values ​​of the columns in the initial data matrix, respectively.

[0124] S1022. Based on each data element in the target data matrix, determine the standard deviation corresponding to each site-level indicator, and determine the standard deviation as the contrast intensity parameter corresponding to each site-level indicator.

[0125] In this embodiment of the application, the standard deviation of each site-level indicator is calculated using the following formula.

[0126] S j = [Σ i=1 n (x ij ' - j ')² / (n-1)] 1 / 2 .

[0127] Among them, S j This represents the standard deviation (comparison strength parameter) corresponding to the j-th site-level indicator. j ' represents the mean of the site-level metrics data, n represents the number of site-level metrics, and x represents the mean value. ij 'This refers to the dimensionless (standardized) site indicator data (each data element in the target data matrix).

[0128] Here, the standard deviation reflects the degree of variation of the indicator across different sites. If the value of an indicator tends to be consistent across all sites, its standard deviation is small and the contrast strength is low, indicating that the indicator contributes little to distinguishing the integration level of sites. Conversely, if an indicator differs significantly across different sites, its standard deviation is large and the contrast strength is high, indicating that the indicator contains more distinguishing information.

[0129] S1023. Based on each data element in the target data matrix, determine the Pearson correlation coefficient between every two data elements in the target data matrix, and based on the Pearson correlation coefficient, determine the conflict parameter corresponding to each site-level indicator.

[0130] Here, the conflict parameter reflects the degree of independence between an indicator and other indicators. If an indicator is highly positively correlated with other indicators (the Pearson correlation coefficient is close to 1), it provides less independent information and has low conflict, because the information of the indicator can be approximated by other indicators. If an indicator has low or even negative correlation with other indicators, it provides unique evaluation information, has high conflict, and should be given a higher weight.

[0131] In this embodiment of the application, the Pearson correlation coefficient is calculated using the following formula: r jk= Σ i=1 n (x ij ' - j ')(x ik ' - k ') / [Σ i=1 n (x ij ' - j ')² ] 1 / 2 × [Σ i=1 n (x ik ' - k ')²] 1 / 2 .

[0132] Where, r jk Let x be the Pearson correlation coefficient between the j-th and k-th indicators. ij For each data element in the target data matrix, j 'and k ' represents the mean of the site metrics data, and n represents the number of site-level metrics.

[0133] In this embodiment, the conflict parameter corresponding to each site-level indicator is calculated using the following formula: R j = Σ k=1 m (1 - r jk ).

[0134] Among them, R j Let r be the conflict parameter corresponding to the j-th site-level metric. jk Let be the Pearson correlation coefficient between the j-th and k-th indicators, and m be the number of site-level indicators.

[0135] S1024. The product between the contrast intensity parameter and the conflict parameter is determined as the information content corresponding to each of the site-level indicators.

[0136] In this embodiment of the application, the amount of information corresponding to each site-level indicator is calculated using the following formula: C j = S j × R j .

[0137] Among them, C j R represents the amount of information corresponding to the j-th site-level indicator.j S is the conflict parameter corresponding to the j-th site-level metric. j This represents the contrast intensity parameter corresponding to the j-th site-level indicator.

[0138] Here, the amount of information comprehensively reflects both the variability and independence of the indicator: it takes into account both the indicator's ability to distinguish between sites (the larger the standard deviation, the greater the amount of information) and the degree of independence between the indicator and other indicators (the greater the conflict, the greater the amount of information). An indicator with a large amount of information means that it carries more objective information in the evaluation system and should be given a higher weight.

[0139] S1025. The proportion of the information content corresponding to each of the site-level indicators to the total amount of information content is determined as the objective weight parameter corresponding to each of the network area level indicators or the site-level indicators.

[0140] In this embodiment, the objective weight parameter corresponding to each network area-level indicator or station-level indicator is calculated using the following formula: W j = C j / Σ k=1 m C k , j = 1, 2, ..., 6, Σ j=1 6 W j = 1.

[0141] Among them, W j C represents the objective weight parameter corresponding to the j-th network area-level indicator or station-level indicator. j Let Σ be the amount of information corresponding to the j-th site-level indicator. k=1 m C k This represents the total amount of information.

[0142] Here, the objective weight parameters are calculated entirely based on actual data at the site level, reflecting the variability and independence of each indicator among sites in the current evaluation area, avoiding the bias of subjective weighting, and the weights can be dynamically adjusted when site data is updated.

[0143] S103. Based on the network area index data and the objective weight parameters, determine the comprehensive two-network integration index corresponding to the city area to be evaluated, and based on the station index data and the objective weight parameters, determine the station integration score corresponding to each target station in the city area to be evaluated.

[0144] Here, the comprehensive two-network integration index is a macro-quantitative value calculated based on regional indicators and objective weights of the network, with a value range of [0,1]. The larger the value, the higher the overall level of two-network integration in the area to be evaluated. The station integration score is a micro-quantitative value calculated based on single-station indicator data and unified objective weights, with a value range of [0,1]. The larger the value, the better the two-network integration effect of a single rail station.

[0145] In this step, on the one hand, the regional index data of the network is normalized and corrected for positive and negative directions, and the comprehensive two-network integration index of the region to be evaluated is calculated by weighted summation model in combination with objective weight parameters to represent the overall integration level of the region; on the other hand, the site-level index data of each target site is normalized and the site integration score is calculated for each site in combination with unified objective weight parameters to represent the degree of integration of a single site.

[0146] This approach achieves dual coverage of macro-level regional overall evaluation and micro-level precise evaluation of individual sites, enabling the assessment of the overall integration level of the entire region while also pinpointing specific inefficient or weak points. It generates a two-tiered quantitative result of a comprehensive macro-index and individual point micro-scores, addressing the limitation of existing technologies that can only provide overall evaluation but cannot precisely locate target sites for improvement, thus supporting tiered and refined optimization and transformation.

[0147] In one possible implementation of this application, in specific implementation, step S103, which involves determining the comprehensive two-network integration index corresponding to the urban area to be evaluated based on the network area index data and the objective weight parameters, may include: S1031. Based on whether the network area level indicator is a positive or negative indicator, normalize the network area indicator data corresponding to each network area level indicator to obtain the target network area indicator parameters corresponding to each network area level indicator.

[0148] In this step, the positive indicators (rapid public transport population job coverage rate, short-line bus connection ratio at rail stations, rail-bus transfer distance compliance rate, rail-bus operating time matching compliance rate, and rail network bus emergency operation ratio) are normalized using the following formula: I k ' = (I k - I kmin ) / (I kmax - I kmin For negative indicators (the proportion of overlap between rail and inefficient bus routes), the following formula is used for normalization: I3' = (I 3max - I3) / (I 3max -I 3min ).

[0149] Among them, I k'I' represents the target network area index parameter. k For network area indicator data, I kmin and I kmax These are the reasonable lower and upper limits for the k-th indicator, respectively. kmin The lowest value of the evaluation base period or industry standard can be used, I kmax The target value or the industry-leading value can be taken; I3' is the normalized overlap ratio between the target rail line and the inefficient bus route, and I3 is the overlap ratio between the rail line and the inefficient bus route. 3min and I 3max These are reasonable lower and upper limits for the overlap ratio between rail and inefficient bus routes.

[0150] S1032. For each of the network area level indicators, multiply the target network area indicator parameter corresponding to the network area level indicator with the objective weight parameter corresponding to the network area level indicator to obtain the first two-network integration index corresponding to the network area level indicator.

[0151] Here, the first two-network integration index refers to the contribution of each network regional level indicator to the comprehensive two-network integration index.

[0152] S1033. The sum of the first two-network integration indices corresponding to each of the network area level indicators is determined as the comprehensive two-network integration index corresponding to the city area to be evaluated.

[0153] In this embodiment of the application, the comprehensive two-network convergence index is determined by the following formula: TI = Σ k=1 6 (W k × I k ').

[0154] Wherein, TI is the comprehensive two-network convergence index, and the value of TI ranges from [0, 1]. The larger the value, the higher the degree of convergence between the two networks; I k 'and W k These are the target network area indicator parameters and objective weight parameters corresponding to each network area-level indicator; "W" k × I k The first index is the integration index of the two networks.

[0155] In one possible implementation of this application, in specific implementation, step S103, which involves determining the site fusion score corresponding to each target site in the urban area to be evaluated based on the site indicator data and the objective weight parameters, may include: S1034. Based on whether the site-level indicator is a positive or negative indicator, normalize the site indicator data corresponding to each site-level indicator to obtain the target site indicator parameters corresponding to each site-level indicator.

[0156] The description of S1034 can refer to the description of S1031 and achieve the same technical effect, except that the network area level indicators are replaced with site level indicators, which will not be elaborated further.

[0157] S1035. For each of the site-level indicators, multiply the target site indicator parameter corresponding to the site-level indicator by the objective weight parameter corresponding to the site-level indicator to obtain the second two-network convergence index corresponding to the site-level indicator.

[0158] S1036. The sum of the second two-network fusion indices corresponding to each of the site-level indicators is determined as the site fusion score value corresponding to each target site in the urban area to be evaluated.

[0159] In this embodiment of the application, the site fusion score is determined by the following formula: TS_i = Σ j=1 6 (S ij ' × W j ).

[0160] Among them, S ij 'W represents the target site metric parameter for the i-th target site at the j-th site level.' j For each site-level metric, there is an objective weighting parameter, where TS_i is the site fusion score for the i-th target site; "S ij '× W j "This is the second index for the integration of the two networks."

[0161] S104. Based on the comprehensive two-network integration index, determine the two-network integration level evaluation result corresponding to the urban area to be evaluated.

[0162] Here, the evaluation results of the two-network convergence level are based on the standardized level results divided into the comprehensive two-network convergence index range. The evaluation results of the two-network convergence level include one of the following: deep convergence, good convergence, general convergence, preliminary convergence, and insufficient convergence.

[0163] In this step, five standardized two-network convergence index ranges are preset. Based on the range to which the comprehensive two-network convergence index belongs, the corresponding standardized convergence level is matched to complete the overall level evaluation of the area to be evaluated and output the final evaluation result.

[0164] In this way, abstract quantitative indices are transformed into intuitive and comparable standardized levels, facilitating cross-regional and cross-time-period comparisons of integration levels and tracking of effectiveness. The standardized, visualized, and quantifiable evaluation results provide clear grading criteria and optimization directions for transportation planning decisions, route optimization, and station renovation.

[0165] In one possible implementation of this application, step S104 may include: S1041. Determine the preset two-network convergence index range to which the comprehensive two-network convergence index belongs.

[0166] Here, the preset two-network convergence index ranges include the first preset two-network convergence index range, the second preset two-network convergence index range, the third preset two-network convergence index range, the fourth preset two-network convergence index range, and the fifth preset two-network convergence index range.

[0167] In this embodiment of the application, the first preset two-network convergence index range can be set to [0.85, 1.0], the second preset two-network convergence index range can be set to [0.70, 0.85], the third preset two-network convergence index range can be set to [0.50, 0.70], the fourth preset two-network convergence index range can be set to [0.30, 0.50], and the fifth preset two-network convergence index range can be set to [0, 0.30].

[0168] S1042. When the comprehensive two-network integration index belongs to the first preset two-network integration index range, the two-network integration level evaluation result corresponding to the city area to be evaluated is determined to be deep integration.

[0169] Here, deep integration indicates that the rail transit and public transport networks in the urban area to be evaluated have achieved a high degree of synergy, with excellent performance across all dimensions, making it a benchmark area.

[0170] S1043. When the comprehensive two-network convergence index belongs to the second preset two-network convergence index range, the two-network convergence level evaluation result is determined to be good convergence.

[0171] Here, good integration indicates that the level of integration between the two networks in the urban area to be evaluated is relatively good, with most indicators meeting the standards, while a few indicators have room for improvement.

[0172] S1044. When the comprehensive two-network convergence index belongs to the third preset two-network convergence index range, the two-network convergence level evaluation result is determined to be general convergence.

[0173] Here, general integration indicates that the level of integration between the two networks in the urban area to be evaluated is moderate, and there are obvious shortcomings in some dimensions and indicators, which need to be optimized in a targeted manner.

[0174] S1045. When the comprehensive two-network convergence index belongs to the fourth preset two-network convergence index range, the two-network convergence level evaluation result is determined to be preliminary convergence.

[0175] Here, the preliminary integration indicates that the integration of the two networks in the urban area to be evaluated is in its initial stage, with multiple indicators failing to meet the standards, and the integration work needs to be systematically promoted.

[0176] S1046. When the comprehensive two-network convergence index belongs to the fifth preset two-network convergence index range, the two-network convergence level evaluation result is determined to be insufficient convergence.

[0177] Here, insufficient integration indicates that the two networks in the urban area to be evaluated are basically operating separately, with an extremely low level of integration, requiring a complete replanning.

[0178] Optional, please refer to Figure 2 , Figure 2 This is a second flowchart illustrating an evaluation method for the integration of urban rail transit and public transport networks, provided as an embodiment of this application. Figure 2 As shown in the embodiments of this application, the evaluation method for the integration of urban rail transit and public transport networks also includes: S105. Based on the evaluation results of the two-network convergence level, the comprehensive two-network convergence index, the site indicator data, and the network area indicator data, output the corresponding visual evaluation results at the network area level and the site level.

[0179] The visual evaluation results include at least a spatial distribution map of fusion level, an indicator radar chart, a heat map of site compliance, and a time trend chart.

[0180] This step integrates the overall integration level, comprehensive index, site micro-indicator data, and regional macro-indicator data to generate a visual evaluation result.

[0181] In this embodiment, the spatial distribution map of fusion level displays the spatial differences of fusion level across the entire region, with traffic zones as the unit; the indicator radar chart intuitively displays the compliance status of the six dimensions of indicators and identifies the weak dimensions; the station compliance heat map displays the fusion score of each station with heat gradient and locates the weak stations; and the time change trend chart compares the evaluation data of multiple periods and tracks the dynamic changes of fusion level.

[0182] All visualization results can be exported as reports and data tables to support the implementation of decisions.

[0183] For example, the evaluation results of urban areas are visualized, with radar charts showing low scores in transfer distance and emergency support dimensions; heat maps clearly marking low-scoring and weak stations on the edge of the urban area; and spatial distribution maps showing the overall characteristic that the integration level of the central urban area is better than that of the suburbs.

[0184] As an example of the method described in this application, taking a megacity urban area as an example, the urban area has 120 rail stations and 350 regular bus routes. The evaluation data collection period is one calendar month.

[0185] Step 1: Data Collection. Data on station attributes, connecting bus routes, transfer walking distances, and first and last bus times for 120 rail stations were obtained from the urban public transportation operation database; the number of residents and jobs within a 500-meter radius of each station was obtained from mobile signaling data and census data; and information on distances and spatial relationships between stations was obtained from GIS data.

[0186] Step 2: Calculation of station-level indicators. Calculate the values ​​for the following indicators for each of the 120 stations: the rate of job improvement for the population connected to rail transit at rail stations, information on short-distance rail transit connections at rail stations, information on inefficient rail transit routes at rail stations, information on compliance of rail transit transfer distances at rail stations, information on compliance of rail transit operating time matching at rail stations, and information on emergency operation coverage of rail transit stations. Construct an initial data matrix X of 120×6. Calculations show that the average job creation rate (S1) for the public transport connection population at rail stations is 35.2%, with a standard deviation of 18.6%, indicating significant differences in job creation effectiveness among different stations. The mean (S2) for short-line public transport connection information at rail stations is 0.58 (58% of stations have short-line connection lines). The mean (S3) for inefficient public transport route information at rail stations is 0.15 (15% of stations have inefficient and redundant routes nearby). The mean (S4) for meeting public transport transfer distance standards at rail stations is 0.42 (42% of stations meet transfer distance standards). The mean (S5) for meeting public transport operating time matching standards at rail stations is 0.65. The mean (S6) for public transport emergency operation coverage information at rail stations is 0.38.

[0187] Step 3: CRITIC Weight Calculation. The initial data matrix is ​​dimensionless, and then the standard deviation, conflict factor, and information content of each indicator are calculated. Assume the calculated information content of each indicator is: C1 = 0.186, C2 = 0.142, C3 = 0.098, C4 = 0.165, C5 = 0.128, C6 = 0.151, with a total information content of 0.870. After normalization, the weights are: W1 = 0.214, W2 = 0.163, W3 = 0.113, W4 = 0.190, W5 = 0.147, W6 = 0.174. It can be seen that in this urban area, the population job coverage rate (W1=0.214) and the transfer distance compliance rate (W4=0.190) have the highest weights, indicating that these two indicators have the strongest variability and independence among stations, and are key factors affecting the evaluation of the integration level of this urban area.

[0188] Step 4: Calculation and Normalization of Network / Regional Level Indicators. Calculate the values ​​of six network / regional level indicators, assuming the following calculated values: Rapid Public Transport Population Job Coverage Rate I1 = 0.72, Rail-to-Bus Short-Line Connection Ratio I2 = 0.58, Rail-to-Inefficient Bus Line Overlap Ratio I3 = 0.15, Rail-to-Bus Transfer Distance Compliance Rate I4 = 0.42, Rail-to-Bus Operating Time Matching Compliance Rate I5 = 0.65, Rail-to-Bus Emergency Operation Ratio I6 = 0.38. Normalize the indicator values ​​(assuming a reasonable lower limit of 0 and an upper limit of 1 for each indicator). The normalized value of a positive indicator is itself, and the normalized value of the negative indicator I3 is I3' = (1 - 0.15) / (1 - 0) = 0.85. After normalization: I1' = 0.72, I2' = 0.58, I3' = 0.85, I4' = 0.42, I5' = 0.65, I6' = 0.38.

[0189] Step 5: Calculate the integration index of the two networks. TI = 0.214×0.72 + 0.163×0.58 + 0.113×0.85 + 0.190×0.42 + 0.147×0.65 + 0.174×0.38 = 0.154 + 0.095 + 0.096 + 0.080 + 0.096 + 0.066 = 0.587. According to the grading standard, TI = 0.587 belongs to the "general integration" level (TI ∈ [0.50, 0.70)), indicating that the integration level of the urban rail transit network is at a medium-to-high level, but some dimensions of the indicators have obvious shortcomings.

[0190] In terms of the contribution of each indicator, I1' (population job coverage rate) and I3' (the inverse value of the inefficient route duplication indicator, i.e. the degree of network optimization) contribute significantly to the comprehensive index, while I4' (transfer distance compliance rate) and I6' (emergency operation ratio) are the main shortcomings. It is recommended to prioritize improving transfer convenience and emergency support capabilities.

[0191] Step 6: Station-level evaluation. Calculate the station integration score for each station and identify those with lower scores as priority targets for renovation. For example, a rail station located on the edge of the city has the following scores: S1 = 12%, S2 = 0, S3 = 1, S4 = 0, S5 = 0.5, S6 = 0, with a station integration score of only 0.108, far below the average level. This station should be the focus of renovation in terms of connecting line setup, transfer distance optimization, and emergency preparedness.

[0192] In summary, the methods described in this application, compared to the prior art, have the following beneficial effects: The evaluation system is comprehensive and multi-dimensional: It constructs a two-layer evaluation system covering six dimensions: spatial coverage, short-distance connection, inefficient line rectification, convenient transfer, operational coordination, and emergency support. It fills the evaluation gaps in existing technologies for emergency support and inefficient line identification. The indicators are precisely defined, the calculation formulas are standardized, and the system is highly operable and repeatable.

[0193] The weighting results are objective and accurate: The innovative approach uses micro-level measured data from individual stations to drive objective CRITIC weighting, while also considering the variability and independence of indicators, completely eliminating subjective bias. At the same time, it realizes the weight transmission from micro-level station data to macro-level regional evaluation, establishing a macro-micro data closed loop.

[0194] The evaluation results are standardized and comparable: a quantitative comprehensive index ranging from 0 to 1 and a five-level standardized grading system are constructed to support horizontal comparisons between different regions and vertical tracking over different time periods, providing a unified standard for performance evaluation of transportation planning and the formulation of optimization plans.

[0195] Adaptable to differentiated evaluation across multiple scenarios: Supports independent evaluation at four spatial scales: citywide, urban areas, key functional areas, and suburbs. Differentiated weights can be dynamically generated for each scale, accurately adapting to the traffic characteristics and integration shortcomings of different regions, making optimization strategies more targeted.

[0196] It is highly practical and applicable: it enables refined evaluation by determining the level at the macro level and the target at the micro level, accurately locating inefficient lines, weak stations, and vulnerable dimensions, while providing visualized output results to directly support practical engineering applications such as network optimization, station renovation, operation time adjustment, and emergency system improvement.

[0197] The evaluation method for the integration of urban rail transit networks provided in this application constructs a comprehensive evaluation index system corresponding to the station level and the network area level. It adopts an objective weighting method based on measured micro data, establishes a macro-micro data linkage and weight transmission mechanism, and sets multi-scale differentiated evaluation rules and standardized integration levels. This solves the problems of existing rail transit network integration evaluation dimensions being single and incomplete, weight assignment being highly subjective, macro-micro evaluations being disconnected, lacking the ability to pinpoint shortcomings in detail, lacking emergency support evaluation dimensions, and having poor adaptability. It achieves a comprehensive, objective, and hierarchical refined quantitative evaluation of the integration level of urban rail transit networks. It can accurately determine the overall integration level and overall advantages and disadvantages of the entire region, accurately locate inefficient and weak stations and evaluation dimensions, and is adaptable to various spatially differentiated evaluation scenarios. It provides accurate, objective, and comprehensive quantitative decision-making basis for urban rail transit network optimization, station renovation, operation coordination, and emergency system construction.

[0198] Please see Figure 3 , Figure 4 , Figure 3This is one of the structural schematic diagrams of an evaluation device for the integration of urban rail transit and public transport networks provided in an embodiment of this application. Figure 4 This is a second schematic diagram of the structure of an evaluation device for the integration of urban rail transit and public transport networks, provided as an embodiment of this application. Figure 3 As shown, the evaluation device 300 includes: The indicator calculation module 310 is used to obtain the original urban data corresponding to the city area to be evaluated at the station level and the network area level, and based on the original urban data, to determine the station indicator data corresponding to a plurality of preset station level indicators and the network area indicator data corresponding to a plurality of preset network area level indicators; wherein the station level indicators correspond to the network area level indicators. The weight determination module 320 is used to determine the information content corresponding to each of the site-level indicators based on the site indicator data using a preset objective weighting method, and to normalize the information content to obtain the objective weight parameters corresponding to each of the network area level indicators or the site level indicators. The fusion calculation module 330 is used to determine the comprehensive two-network fusion index corresponding to the urban area to be evaluated based on the network area index data and the objective weight parameters, and to determine the site fusion score corresponding to each target site in the urban area to be evaluated based on the site index data and the objective weight parameters. The comprehensive evaluation module 340 is used to determine the evaluation result of the two-network integration level of the city area to be evaluated based on the comprehensive two-network integration index.

[0199] Furthermore, the station-level indicator data corresponding to each station includes at least the following for each target station in the urban area to be evaluated: the rate of job improvement for the population connected to public transportation at the rail station, short-distance public transportation information at the rail station, inefficient public transportation routes at the rail station, compliance information for public transportation transfer distances at the rail station, compliance information for public transportation operating time matching at the rail station, and emergency operation coverage information for public transportation at the rail station; when the indicator calculation module 310 is used to determine the station indicator data, the indicator calculation module 310 is used for: For each target station, the following information is determined from the original city data at the station level: the number of residents covered by the combined preset radius of the rail station and connecting bus line stations, the number of residents covered by the preset radius of the rail station, the number of jobs covered by the combined preset radius of the rail station and connecting bus line stations, the number of jobs covered by the preset radius of the rail station, information on bus lines with short-distance connecting lines, information on inefficient bus lines that continuously overlap with the rail at a preset first distance, information on bus stations within a preset second distance from entrances and exits, information on the preset time for the first bus of connecting buses that is earlier than the first time of the rail, information on the preset time for the last bus of connecting buses that is later than the last time of the rail, and information on emergency operation coverage of surrounding bus lines. Based on the number of residents covered by the combined preset radius of the rail station and the connecting bus line station, the number of residents covered by the preset radius of the rail station, the number of jobs covered by the combined preset radius of the rail station and the connecting bus line station, and the number of jobs covered by the preset radius of the rail station, the job improvement rate of the rail station connecting bus population corresponding to the station is determined. Based on the bus route information with short-distance shuttle lines, determine the short-distance bus shuttle information corresponding to the rail station at that station. Based on the inefficient bus route information with a preset first distance that continuously overlaps with the track, determine the inefficient bus route information of the track station corresponding to the station; Based on the bus stop information within the preset second distance of the entrance / exit, determine the rail station bus transfer distance compliance information corresponding to the station; Based on the information that the first bus time of the connecting bus is earlier than the first bus time of the rail transit by a preset amount of time and the information that the last bus time of the connecting bus is later than the last bus time of the rail transit by a preset amount of time, the matching and compliance information of the bus operating time of the rail transit station corresponding to the station is determined. Based on the emergency operation coverage information provided by the surrounding bus routes, determine the emergency operation coverage information of the rail station corresponding to this station.

[0200] Furthermore, the network area indicator data corresponding to the network area level indicators respectively include at least the rapid public transport population job coverage rate, the proportion of short-distance bus connections between rail stations, the overlap ratio of rail and inefficient bus routes, the rail-bus transfer distance compliance rate, the rail-bus operating time matching compliance rate, and the proportion of emergency operation of rail network buses; when the indicator calculation module 310 is used to determine the network area indicator data, the indicator calculation module 310 is used for: In the original urban data corresponding to the network area level, determine the following for the urban area to be evaluated: the number of residents covered within the preset radius of the rail and bus trunk line stations, the total number of residents, the number of jobs covered within the preset radius of the rail and bus trunk line stations, the total number of jobs, the number of rail stations with short-distance connecting bus lines, the total number of rail stations, the number of inefficient bus lines that continuously overlap with rail lines at a preset first distance, the total number of bus lines, the number of rail stations where the rail-bus transfer distance is less than a preset second distance, the number of rail stations where the first bus departure time of the connecting bus is a preset amount of time earlier than the first bus departure time of the rail line, the number of rail stations where the last bus departure time of the connecting bus is a preset amount of time later than the last bus departure time of the rail line, and the number of rail stations where the bus can provide emergency support for rail incidents. Based on the number of residents within the preset radius of the rail and bus trunk line stations and the total number of residents, the rapid transit population coverage rate of the urban area to be evaluated is determined. Based on the number of jobs within the preset radius of the rail and bus trunk line stations and the total number of jobs, the rapid transit job coverage rate of the urban area to be evaluated is determined. Based on the rapid transit population coverage rate, the rapid transit job coverage rate, and the preset population weight coefficient, the rapid transit population job coverage rate of the urban area to be evaluated is determined. The ratio between the number of rail stations for short-distance shuttle bus routes and the total number of rail stations is determined as the short-distance shuttle bus ratio of the rail stations in the urban area to be evaluated. The ratio between the number of inefficient bus routes that continuously overlap with the rail line at a preset first distance and the total number of bus routes is determined as the overlap ratio of the rail line and the inefficient bus routes corresponding to the urban area to be evaluated. The ratio between the number of rail stations whose rail-to-public transfer distance is less than a preset second distance and the total number of rail stations is determined as the rail-to-public transfer distance compliance rate for the urban area to be evaluated. The ratio of the number of rail stations whose first bus departure time is a preset amount of time earlier than the first rail departure time to the total number of rail stations is determined as the first bus departure time matching compliance rate for the city area to be evaluated. The ratio of the number of rail stations whose last bus departure time is a preset amount of time later than the last rail departure time to the total number of rail stations is determined as the last bus departure time matching compliance rate for the city area to be evaluated. The average of the first bus departure time matching compliance rate and the last bus departure time matching compliance rate is determined as the rail transit operating time matching compliance rate for the city area to be evaluated. The ratio between the number of rail stations that can be used for emergency support in rail emergencies by public transport and the total number of rail stations is determined as the proportion of emergency operation of public transport in the rail network corresponding to the urban area to be evaluated.

[0201] Furthermore, when the weight determination module 320 is used to determine the information content corresponding to each of the site-level indicators based on the site indicator data using a preset objective weighting method, and to normalize the information content to obtain the objective weight parameters corresponding to each of the network area-level indicators or the site-level indicators, the weight determination module 320 is used to: Based on the site indicator data, an initial data matrix corresponding to the site level is constructed, and the initial data matrix is ​​dimensionless according to whether the site level indicator is a positive or negative indicator to obtain the target data matrix. Based on each data element in the target data matrix, the standard deviation corresponding to each site-level indicator is determined, and the standard deviation is determined as the contrast intensity parameter corresponding to each site-level indicator. Based on each data element in the target data matrix, determine the Pearson correlation coefficient between every two data elements in the target data matrix, and based on the Pearson correlation coefficient, determine the conflict parameter corresponding to each site-level indicator. The product of the contrast intensity parameter and the conflict parameter is determined as the information content corresponding to each of the site-level indicators; The proportion of the information content corresponding to each of the site-level indicators to the total amount of information content is determined as the objective weight parameter corresponding to each of the network area-level indicators or the site-level indicators.

[0202] Furthermore, when the fusion calculation module 330 determines the comprehensive two-network integration index corresponding to the city area to be evaluated based on the network area index data and the objective weight parameters, the fusion calculation module 330 is used to: Based on whether the network area level indicator is a positive or negative indicator, the network area indicator data corresponding to each network area level indicator is normalized to obtain the target network area indicator parameters corresponding to each network area level indicator. For each of the network area level indicators, the target network area indicator parameter corresponding to the network area level indicator is multiplied by the objective weight parameter corresponding to the network area level indicator to obtain the first two-network integration index corresponding to the network area level indicator. The sum of the first two-network integration indices corresponding to each of the network area level indicators is determined as the comprehensive two-network integration index corresponding to the city area to be evaluated.

[0203] Furthermore, when the fusion calculation module 330 determines the site fusion score corresponding to each target site in the urban area to be evaluated based on the site indicator data and the objective weight parameters, the fusion calculation module 330 is used to: Based on whether the site-level indicator is a positive or negative indicator, the site indicator data corresponding to each site-level indicator is normalized to obtain the target site indicator parameters corresponding to each site-level indicator. For each of the site-level indicators, the target site indicator parameter corresponding to the site-level indicator is multiplied by the objective weight parameter corresponding to the site-level indicator to obtain the second two-network convergence index corresponding to the site-level indicator. The sum of the second two-network fusion indices corresponding to each of the site-level indicators is determined as the site fusion score for each target site in the urban area to be evaluated.

[0204] Furthermore, when the comprehensive evaluation module 340 determines the evaluation result of the two-network integration level corresponding to the city area to be evaluated based on the comprehensive two-network integration index, the comprehensive evaluation module 340 is used to: Determine the preset two-network convergence index range to which the comprehensive two-network convergence index belongs; When the comprehensive two-network integration index belongs to the first preset two-network integration index range, the two-network integration level evaluation result of the city area to be evaluated is determined to be deep integration; When the comprehensive two-network convergence index belongs to the second preset two-network convergence index range, the two-network convergence level evaluation result is determined to be good convergence; When the comprehensive two-network convergence index belongs to the third preset two-network convergence index range, the two-network convergence level evaluation result is determined to be general convergence; When the comprehensive two-network convergence index belongs to the fourth preset two-network convergence index range, the two-network convergence level evaluation result is determined to be preliminary convergence; When the comprehensive two-network convergence index belongs to the fifth preset two-network convergence index range, the two-network convergence level evaluation result is determined to be insufficient convergence.

[0205] Furthermore, such as Figure 4 As shown, the evaluation device 300 further includes a visualization output module 350, which is used for: Based on the evaluation results of the two-network integration level, the comprehensive two-network integration index, the site indicator data, and the network area indicator data, the corresponding visualized evaluation results at the network area level and the site level are output; wherein, the visualized evaluation results include at least a spatial distribution map of integration level, an indicator radar chart, a site compliance heat map, and a time change trend chart.

[0206] The evaluation device for the integration of urban rail transit networks provided in this application constructs a comprehensive evaluation index system corresponding to the station level and the network area level. It adopts an objective weighting method based on measured micro data, establishes a macro-micro data linkage and weight transmission mechanism, and sets multi-scale differentiated evaluation rules and standardized integration levels. This solves the problems of existing rail transit network integration evaluation dimensions being single and incomplete, weight assignment being highly subjective, macro-micro evaluations being disconnected, lacking the ability to pinpoint shortcomings in detail, lacking emergency support evaluation dimensions, and having poor adaptability. It achieves a comprehensive, objective, and hierarchical refined quantitative evaluation of the integration level of urban rail transit networks. It can accurately determine the overall integration level and overall advantages and disadvantages of the entire region, and accurately locate inefficient and weak stations and evaluation dimensions. It can adapt to various spatially differentiated evaluation scenarios, providing accurate, objective, and comprehensive quantitative decision-making basis for urban rail transit network optimization, station renovation, operation coordination, and emergency system construction.

[0207] Please see Figure 5 , Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Figure 5 As shown, the electronic device 500 includes a processor 510, a memory 520, and a bus 530.

[0208] The memory 520 stores machine-readable instructions executable by the processor 510. When the electronic device 500 is running, the processor 510 and the memory 520 communicate via the bus 530. When the machine-readable instructions are executed by the processor 510, they can perform the operations described above. Figure 1 as well as Figure 2 The steps of the evaluation method for the integration of urban rail transit and public transport networks in the method embodiment shown are described in detail in the method embodiment, and will not be repeated here.

[0209] This application also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, can perform the above-described actions. Figure 1 as well as Figure 2 The steps of the evaluation method for the integration of urban rail transit and public transport networks in the method embodiment shown are described in detail in the method embodiment, and will not be repeated here.

[0210] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0211] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. Furthermore, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Additionally, the shown or discussed mutual couplings, direct couplings, or communication connections may be through some communication interfaces; indirect couplings or communication connections between devices or units may be electrical, mechanical, or other forms.

[0212] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0213] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0214] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a processor-executable, non-volatile, computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0215] Finally, it should be noted that the above-described embodiments are merely specific implementations of this application, used to illustrate the technical solutions of this application, and not to limit them. The scope of protection of this application is not limited thereto. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments, or make equivalent substitutions for some of the technical features, within the scope of the technology disclosed in this application. Such modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be covered within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. An evaluation method for the integration of urban rail transit and public transport networks, characterized in that, The evaluation methods include: The process involves acquiring raw urban data for the city region to be evaluated at both the station level and the network area level. Based on this raw urban data, several preset station-level indicators and several preset network area indicators are determined. The station-level indicators correspond to the network area indicators. The network area indicators are used to macroscopically evaluate the overall integration level of rail transit and conventional public transport at a spatial scale, while the station-level indicators are used to microscopically evaluate the integration status of individual rail stations. The station-level indicator data provides an objective basis for determining the weights of the network area indicator data corresponding to the network area indicators. Based on the site indicator data, the information content corresponding to each site-level indicator is determined by using a preset objective weighting method, and the information content is normalized to obtain the objective weight parameters corresponding to each network area level indicator or site level indicator. Based on the network area index data and the objective weight parameters, the comprehensive two-network integration index corresponding to the city area to be evaluated is determined, and based on the station index data and the objective weight parameters, the station integration score corresponding to each target station in the city area to be evaluated is determined. Based on the comprehensive two-network integration index, the evaluation result of the two-network integration level corresponding to the city area to be evaluated is determined.

2. The method according to claim 1, characterized in that, The station-level indicators respectively include at least the following station-level indicators: the job improvement rate of the population connected to the rail transit at each target station in the urban area to be evaluated, the short-distance rail transit information, the inefficient rail transit route information, the rail transit transfer distance compliance information, the rail transit operating time matching compliance information, and the rail transit emergency operation coverage information. The site metric data is determined through the following steps: For each target station, the following information is determined from the original city data at the station level: the number of residents covered by the combined preset radius of the rail station and connecting bus line stations, the number of residents covered by the preset radius of the rail station, the number of jobs covered by the combined preset radius of the rail station and connecting bus line stations, the number of jobs covered by the preset radius of the rail station, information on bus lines with short-distance connecting lines, information on inefficient bus lines that continuously overlap with the rail at a preset first distance, information on bus stations within a preset second distance from entrances and exits, information on the preset time for the first bus of connecting buses that is earlier than the first time of the rail, information on the preset time for the last bus of connecting buses that is later than the last time of the rail, and information on emergency operation coverage of surrounding bus lines. Based on the number of residents covered by the combined preset radius of the rail station and the connecting bus line station, the number of residents covered by the preset radius of the rail station, the number of jobs covered by the combined preset radius of the rail station and the connecting bus line station, and the number of jobs covered by the preset radius of the rail station, the job improvement rate of the rail station connecting bus population corresponding to the station is determined. Based on the bus route information with short-distance shuttle lines, determine the short-distance bus shuttle information corresponding to the rail station at that station. Based on the inefficient bus route information with a preset first distance that continuously overlaps with the track, determine the inefficient bus route information of the track station corresponding to the station; Based on the bus stop information within the preset second distance of the entrance / exit, determine the rail station bus transfer distance compliance information corresponding to the station; Based on the information that the first bus time of the connecting bus is earlier than the first bus time of the rail transit by a preset amount of time and the information that the last bus time of the connecting bus is later than the last bus time of the rail transit by a preset amount of time, the matching and compliance information of the bus operating time of the rail transit station corresponding to the station is determined. Based on the emergency operation coverage information provided by the surrounding bus routes, determine the emergency operation coverage information of the rail station corresponding to this station.

3. The method according to claim 1, characterized in that, The network area indicators mentioned above include at least the population job coverage rate of rapid public transportation, the proportion of short-distance bus connections between rail stations, the overlap between rail and inefficient bus routes, the compliance rate of rail-bus transfer distance, the compliance rate of rail-bus operating time matching, and the proportion of emergency operation of rail network buses. The following steps are used to determine the network area indicator data: In the original urban data corresponding to the network area level, determine the following for the urban area to be evaluated: the number of residents covered within the preset radius of the rail and bus trunk line stations, the total number of residents, the number of jobs covered within the preset radius of the rail and bus trunk line stations, the total number of jobs, the number of rail stations with short-distance connecting bus lines, the total number of rail stations, the number of inefficient bus lines that continuously overlap with rail lines at a preset first distance, the total number of bus lines, the number of rail stations where the rail-bus transfer distance is less than a preset second distance, the number of rail stations where the first bus departure time of the connecting bus is a preset amount of time earlier than the first bus departure time of the rail line, the number of rail stations where the last bus departure time of the connecting bus is a preset amount of time later than the last bus departure time of the rail line, and the number of rail stations where the bus can provide emergency support for rail incidents. Based on the number of residents within the preset radius of the rail and bus trunk line stations and the total number of residents, the rapid transit population coverage rate of the urban area to be evaluated is determined. Based on the number of jobs within the preset radius of the rail and bus trunk line stations and the total number of jobs, the rapid transit job coverage rate of the urban area to be evaluated is determined. Based on the rapid transit population coverage rate, the rapid transit job coverage rate, and the preset population weight coefficient, the rapid transit population job coverage rate of the urban area to be evaluated is determined. The ratio between the number of rail stations for short-distance shuttle bus routes and the total number of rail stations is determined as the short-distance shuttle bus ratio of the rail stations in the urban area to be evaluated. The ratio between the number of inefficient bus routes that continuously overlap with the rail line at a preset first distance and the total number of bus routes is determined as the overlap ratio of the rail line and the inefficient bus routes corresponding to the urban area to be evaluated. The ratio between the number of rail stations whose rail-to-public transfer distance is less than a preset second distance and the total number of rail stations is determined as the rail-to-public transfer distance compliance rate for the urban area to be evaluated. The ratio of the number of rail stations whose first bus departure time is a preset amount of time earlier than the first rail departure time to the total number of rail stations is determined as the first bus departure time matching compliance rate for the city area to be evaluated. The ratio of the number of rail stations whose last bus departure time is a preset amount of time later than the last rail departure time to the total number of rail stations is determined as the last bus departure time matching compliance rate for the city area to be evaluated. The average of the first bus departure time matching compliance rate and the last bus departure time matching compliance rate is determined as the rail transit operating time matching compliance rate for the city area to be evaluated. The ratio between the number of rail stations that can be used for emergency support in rail emergencies by public transport and the total number of rail stations is determined as the proportion of emergency operation of public transport in the rail network corresponding to the urban area to be evaluated.

4. The method according to claim 1, characterized in that, Based on the site indicator data, the information content corresponding to each site-level indicator is determined using a preset objective weighting method, and the information content is normalized to obtain the objective weight parameters corresponding to each network area-level indicator or site-level indicator, including: Based on the site indicator data, an initial data matrix corresponding to the site level is constructed, and the initial data matrix is ​​dimensionless according to whether the site level indicator is a positive or negative indicator to obtain the target data matrix. Based on each data element in the target data matrix, the standard deviation corresponding to each site-level indicator is determined, and the standard deviation is determined as the contrast intensity parameter corresponding to each site-level indicator. Based on each data element in the target data matrix, determine the Pearson correlation coefficient between every two data elements in the target data matrix, and based on the Pearson correlation coefficient, determine the conflict parameter corresponding to each site-level indicator. The product of the contrast intensity parameter and the conflict parameter is determined as the information content corresponding to each of the site-level indicators; The proportion of the information content corresponding to each of the site-level indicators to the total amount of information content is determined as the objective weight parameter corresponding to each of the network area-level indicators or the site-level indicators.

5. The method according to claim 1, characterized in that, The determination of the comprehensive two-network integration index corresponding to the urban area to be evaluated based on the network area indicator data and the objective weight parameters includes: Based on whether the network area level indicator is a positive or negative indicator, the network area indicator data corresponding to each network area level indicator is normalized to obtain the target network area indicator parameters corresponding to each network area level indicator. For each of the network area level indicators, the target network area indicator parameter corresponding to the network area level indicator is multiplied by the objective weight parameter corresponding to the network area level indicator to obtain the first two-network integration index corresponding to the network area level indicator. The sum of the first two-network integration indices corresponding to each of the network area level indicators is determined as the comprehensive two-network integration index corresponding to the city area to be evaluated.

6. The method according to claim 1, characterized in that, The process of determining the site fusion score for each target site in the urban area to be evaluated, based on the site indicator data and the objective weight parameters, includes: Based on whether the site-level indicator is a positive or negative indicator, the site indicator data corresponding to each site-level indicator is normalized to obtain the target site indicator parameters corresponding to each site-level indicator. For each of the site-level indicators, the target site indicator parameter corresponding to the site-level indicator is multiplied by the objective weight parameter corresponding to the site-level indicator to obtain the second two-network convergence index corresponding to the site-level indicator. The sum of the second two-network fusion indices corresponding to each of the site-level indicators is determined as the site fusion score for each target site in the urban area to be evaluated.

7. The method according to claim 1, characterized in that, The step of determining the evaluation result of the two-network integration level of the city area to be evaluated based on the comprehensive two-network integration index includes: Determine the preset two-network convergence index range to which the comprehensive two-network convergence index belongs; When the comprehensive two-network integration index belongs to the first preset two-network integration index range, the two-network integration level evaluation result of the city area to be evaluated is determined to be deep integration; When the comprehensive two-network convergence index belongs to the second preset two-network convergence index range, the two-network convergence level evaluation result is determined to be good convergence; When the comprehensive two-network convergence index belongs to the third preset two-network convergence index range, the two-network convergence level evaluation result is determined to be general convergence; When the comprehensive two-network convergence index belongs to the fourth preset two-network convergence index range, the two-network convergence level evaluation result is determined to be preliminary convergence; When the comprehensive two-network convergence index belongs to the fifth preset two-network convergence index range, the two-network convergence level evaluation result is determined to be insufficient convergence.

8. The method according to claim 1, characterized in that, After determining the evaluation result of the two-network convergence level corresponding to the urban area to be evaluated, the evaluation method further includes: Based on the evaluation results of the two-network integration level, the comprehensive two-network integration index, the site indicator data, and the network area indicator data, the corresponding visualized evaluation results at the network area level and the site level are output; wherein, the visualized evaluation results include at least a spatial distribution map of integration level, an indicator radar chart, a site compliance heat map, and a time change trend chart.

9. An evaluation device for the integration of urban rail transit and public transport networks, characterized in that, The evaluation device includes: The indicator calculation module is used to acquire the original urban data corresponding to the urban area to be evaluated at the station level and the network area level. Based on the original urban data, it determines the station indicator data corresponding to multiple preset station-level indicators and the network area indicator data corresponding to multiple preset network area-level indicators. The station-level indicators correspond to the network area-level indicators. The network area-level indicators are used to macroscopically evaluate the overall integration level of rail transit and conventional public transport at a spatial scale, while the station-level indicators are used to microscopically evaluate the integration status of individual rail stations. The station indicator data corresponding to the station-level indicators provides an objective basis for determining the weights of the network area indicator data corresponding to the network area-level indicators. The weight determination module is used to determine the information content corresponding to each of the site-level indicators based on the site indicator data using a preset objective weighting method, and to normalize the information content to obtain the objective weight parameters corresponding to each of the network area level indicators or the site level indicators. The fusion calculation module is used to determine the comprehensive two-network fusion index corresponding to the city area to be evaluated based on the network area index data and the objective weight parameters, and to determine the site fusion score corresponding to each target site in the city area to be evaluated based on the site index data and the objective weight parameters. The comprehensive evaluation module is used to determine the evaluation result of the two-network integration level of the city area to be evaluated based on the comprehensive two-network integration index.

10. An electronic device, characterized in that, include: The device includes a processor, a memory, and a bus. The memory stores machine-readable instructions executable by the processor. When the electronic device is running, the processor communicates with the memory via the bus. The machine-readable instructions are executed by the processor to perform the steps of the evaluation method for the integration of urban rail transit networks as described in any one of claims 1 to 8.

11. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, performs the steps of the evaluation method for the integration of urban rail transit and public transport networks as described in any one of claims 1 to 8.

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