A site selection method for demand responsive bus operation area in a newly built urban area

Abstract

The application discloses a site selection method for demand response type bus operation area in a newly-built urban area, and relates to the technical field of traffic planning.The method comprises the following steps: matching traffic supply data of the newly-built urban area to corresponding geographical grids of the newly-built urban area; constructing multiple traffic demand indexes and multiple traffic supply indexes; determining demand levels of each grid in the geographical grids according to the multiple traffic demand indexes; determining supply-demand contradiction levels of each grid in the geographical grids according to the multiple traffic demand indexes and the multiple traffic supply indexes; and determining the operation area of the demand response type bus in the newly-built urban area according to the demand levels and the supply-demand contradiction levels of each grid in the geographical grids.The application realizes traffic data mining of the newly-built urban area based on multiple traffic demand indexes and traffic supply indexes, can mine the area with highly dispersed travel demand and weak traditional bus service in the newly-built urban area as the operation area, and provides real data decision support for scientific planning and efficient operation of the demand response type bus.

Description

Technical Field

[0001] This application relates to the field of transportation planning technology, and in particular to a site selection method for demand-responsive public transport operation areas in newly built urban areas. Background Technology

[0002] As urbanization deepens, public transportation, as a crucial component of urban operations, directly impacts citizens' travel experience and sustainable urban development through its service efficiency and quality. However, the rigid service model of traditional public transportation systems, centered on fixed routes and schedules, makes it difficult to adapt to increasingly diverse and personalized travel demands, resulting in significant service blind spots in specific times and areas. This not only reduces the service level and attractiveness of public transportation but also leads to inefficient allocation of public resources.

[0003] To compensate for the shortcomings of traditional public transportation, demand-responsive public transportation has emerged as an innovative supplementary public transportation model. Demand-responsive public transportation breaks free from the constraints of fixed routes, integrating passenger travel demands in real time through an intelligent dispatch platform and dynamically planning routes, transforming the situation from "people waiting for buses" to "buses finding people." This on-demand service model can precisely fill service blind spots that traditional public transportation struggles to reach, significantly improving the coverage and attractiveness of public transportation, and demonstrating enormous application potential in solving the "last mile" problem and meeting personalized travel needs. However, the demand-responsive public transportation service model is not suitable for all urban areas; its effectiveness highly depends on scientific site selection and planning. In many urban areas, newly built urban areas, due to their low residential density and significant separation of work and residence, result in highly dispersed travel demand in time and space, making them ideal application scenarios for the demand-responsive public transportation model. In such areas, if traditional public transportation blindly adds routes, it easily falls into the operational dilemma of low passenger flow and high costs; if no routes are established, a service vacuum will be created. The flexibility of demand-responsive public transportation can precisely solve this dilemma.

[0004] Although demand-responsive transit is technologically advanced, planners often rely on macro-level experience when selecting sites, making this decision-making approach inaccurate. Summary of the Invention

[0005] In view of this, embodiments of this application provide a method and related equipment for selecting the location of demand-responsive public transport operation areas in newly built urban areas, so as to scientifically plan the operation areas of demand-responsive public transport in newly built urban areas.

[0006] One aspect of this application provides a method for selecting the location of a demand-responsive public transport operation area in a newly built urban area, the method comprising the following steps:

[0007] Match the traffic supply data of the newly built urban area to the geographic grid corresponding to the newly built urban area;

[0008] Construct multiple traffic demand indicators and multiple traffic supply indicators;

[0009] The demand level of each grid in the geographic grid is determined based on the various traffic demand indicators.

[0010] The supply-demand imbalance level of each grid in the geographic grid is determined based on each of the aforementioned traffic demand indicators and each of the aforementioned traffic supply indicators;

[0011] The operating area of ​​the demand-responsive public transport system in the newly built urban area is determined based on the demand level and the supply-demand imbalance level of each grid in the geographic grid.

[0012] In some embodiments, determining the demand level of each grid in the geographic grid based on each of the traffic demand indicators includes the following steps:

[0013] Calculate the first comprehensive weight corresponding to each of the aforementioned traffic demand indicators;

[0014] Calculate the potential demand index of each grid in the geographic grid based on each of the aforementioned traffic demand indicators and the corresponding first comprehensive weight;

[0015] The geographic grid is divided into corresponding demand levels based on the potential demand index.

[0016] In some embodiments, the traffic demand indicators include public transport passenger flow, shared bicycle passenger flow within a set range, and ride-hailing passenger flow within a set range;

[0017] The step of calculating the potential demand index of each grid in the geographic grid based on each of the traffic demand indicators and the corresponding first comprehensive weight includes the following steps:

[0018] The potential demand index is calculated using the following formula:

[0019] ;

[0020] in, For grid The potential demand index mentioned above, For grid Standardized ride-hailing passenger flow For grid Standardized shared bicycle passenger flow, For grid Standardized bus passenger flow , , These are the corresponding first comprehensive weights.

[0021] In some embodiments, determining the supply-demand imbalance level of each grid in the geographic grid based on each of the traffic demand indicators and each of the traffic supply indicators includes the following steps:

[0022] Calculate the second comprehensive weights corresponding to the demand evaluation index and the supply evaluation index;

[0023] The demand evaluation index of each grid in the geographic grid is calculated based on each of the aforementioned traffic demand indicators and the corresponding second comprehensive weight;

[0024] The supply evaluation index of each grid in the geographic grid is calculated based on each of the aforementioned traffic supply indicators and the corresponding second comprehensive weight;

[0025] The location quotient of the grid corresponding to the demand evaluation index and the supply evaluation index of each grid;

[0026] Based on the location quotient, each grid in the geographic grid is assigned to the corresponding supply and demand imbalance level.

[0027] In some embodiments, the traffic demand indicators include population size, public transport passenger volume, shared bicycle passenger volume within a set range, and ride-hailing passenger volume within a set range; the traffic supply indicators include the number of bus stops, the number of passing bus routes, and the station service level score.

[0028] The step of calculating the demand evaluation index for each grid in the geographic grid based on each of the traffic demand indicators and the corresponding second comprehensive weight includes the following steps:

[0029] The demand evaluation index is calculated using the following formula:

[0030] ;

[0031] in, For grid The aforementioned demand evaluation index For grid Standardized population size For grid Standardized bus passenger flow For grid Standardized shared bicycle passenger flow, For grid Standardized ride-hailing passenger flow These are the corresponding second comprehensive weights, and ;

[0032] The step of calculating the supply evaluation index for each grid in the geographic grid based on each of the aforementioned traffic supply indicators and the corresponding second comprehensive weight includes the following steps:

[0033] The supply evaluation index is calculated according to the following formula:

[0034] ;

[0035] in, For grid The aforementioned supply evaluation index. For grid The number of standardized bus stops For grid The number of standardized bus routes For grid Standardized site service level rating, These are the corresponding second comprehensive weights, and ;

[0036] The location quotient of the grid corresponding to the demand evaluation index and the supply evaluation index of each grid includes the following steps:

[0037] Calculate the location quotient according to the following formula:

[0038] ;

[0039] in, For grid The location quotient mentioned above, For grid The aforementioned demand evaluation index For grid The aforementioned supply evaluation index The sum of the demand evaluation indices for all grids. This is the sum of the supply evaluation indices for all grids.

[0040] In some embodiments, the first comprehensive weight and the second comprehensive weight are calculated using a subjective-objective combined weighting method, which includes the following steps:

[0041] The system obtains scores from multiple experts for the indicators to be processed; wherein, when calculating the first comprehensive weight, the indicators to be processed include each of the traffic demand indicators; when calculating the second comprehensive weight, the indicators to be processed include each of the traffic demand indicators and multiple traffic supply indicators.

[0042] For each of the aforementioned indicators to be processed ,calculate The average score from 10 experts, and the formulas for each indicator include:

[0043] ;

[0044] in, Indicates the first Experts on the indicators to be processed The rating;

[0045] The average value of each of the indicators to be processed is normalized to obtain the corresponding subjective weight, and the formula includes:

[0046] ;

[0047] definition One sample, The remaining indicators are to be processed, and a sample matrix is ​​constructed as follows: :

[0048] ;

[0049] in, Indicates the first The sample at the th The values ​​of the indicators to be processed;

[0050] Normalize the data for each indicator to be processed to obtain the proportion of each sample under the corresponding indicator, as shown in the following formula:

[0051] ;

[0052] in, For the i-th grid sample in the index to be processed The proportion of the surface;

[0053] Calculate the first The information entropy of the indicator to be processed is given by the following formula:

[0054] ;

[0055] in, For information entropy, if Then it is stipulated ;

[0056] The objective weight is calculated based on information entropy, and the formula includes:

[0057] ;

[0058] The subjective weight and the objective weight are weighted and summed with their corresponding weight coefficients to obtain the first comprehensive weight or the second comprehensive weight.

[0059] In some embodiments, determining the operating area of ​​the demand-responsive public transport in the newly built urban area based on the demand level and the supply-demand imbalance level of each grid in the geographic grid includes the following steps:

[0060] The area formed by the grids where both the demand level and the supply-demand imbalance level meet the corresponding thresholds is determined as the candidate site selection area for the demand-responsive public transport in the newly built urban area.

[0061] By eliminating non-operable areas from the candidate site selection areas and correcting the boundaries of the candidate site selection areas based on linear geographical features that have a separating effect, the target site selection areas for the new urban area demand-responsive public transport are obtained.

[0062] Another aspect of this application embodiment provides a site selection device for demand-responsive public transport operation areas in newly built urban areas, the device comprising:

[0063] A data matching unit is used to match the traffic supply data of the newly built urban area to the geographic grid corresponding to the newly built urban area;

[0064] The indicator construction unit is used to construct multiple traffic demand indicators and multiple traffic supply indicators.

[0065] Demand grading unit, used to determine the demand level of each grid in the geographic grid based on each of the traffic demand indicators;

[0066] The supply-demand imbalance classification unit is used to determine the supply-demand imbalance level of each grid in the geographic grid based on each of the traffic demand indicators and each of the traffic supply indicators.

[0067] The regional site selection unit is used to determine the operating area of ​​the demand-responsive public transport in the newly built urban area based on the demand level and the supply-demand imbalance level of each grid in the geographic grid.

[0068] Another aspect of this application embodiment provides an electronic device, including a processor and a memory;

[0069] The memory is used to store programs;

[0070] The processor executes the program to implement any of the methods described above.

[0071] Another aspect of this application provides a computer-readable storage medium storing a program that is executed by a processor to implement the method described in any of the above embodiments.

[0072] This application includes at least the following beneficial effects:

[0073] This application matches traffic supply data of newly built urban areas to corresponding geographic grids; constructs multiple traffic demand indicators and multiple traffic supply indicators; determines the demand level of each grid within the geographic grid based on each traffic demand indicator; determines the supply-demand imbalance level of each grid within the geographic grid based on each traffic demand indicator and each traffic supply indicator; and determines the operating area of ​​demand-responsive public transport in the newly built urban area based on the demand level and supply-demand imbalance level of each grid within the geographic grid. This application achieves traffic data mining in newly built urban areas based on multiple traffic demand indicators and multiple traffic supply indicators, and can identify and mine potential areas in newly built urban areas with highly dispersed travel demand and weak traditional public transport services as operating areas, providing real-data decision support for the scientific planning and efficient operation of demand-responsive public transport. Attached Figure Description

[0074] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0075] Figure 1 A flowchart illustrating a method for selecting a location for a demand-responsive public transport operation area in a newly built urban area, as provided in this application embodiment;

[0076] Figure 2 An example flowchart of a site selection method for demand-responsive public transport operation areas in newly built urban areas, provided as an embodiment of this application;

[0077] Figure 3 This is a structural block diagram of a site selection device for a demand-responsive public transport operation area in a newly built urban area, provided as an embodiment of this application. Detailed Implementation

[0078] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0079] Before providing a detailed description of the embodiments of this application, some related technologies involved in the embodiments of this application will be described first, as follows:

[0080] Currently, in the planning practice of demand-responsive public transport in China, the selection of service areas and the determination of regional size still face significant bottlenecks. Mainstream methods rely on planners' macro-level experience and subjective judgment, resulting in a lack of scientific rigor and replicability in the decision-making process.

[0081] Therefore, the industry urgently needs an intelligent site selection methodology that can deeply integrate multi-source heterogeneous traffic data to achieve precise insights and scientific decision-making. However, a mature and universally applicable data-driven solution has not yet been developed, and this technological gap limits the full realization of the effectiveness of demand-responsive public transport services.

[0082] To fill this gap, this application proposes an intelligent site selection scheme for demand-responsive public transport operation areas in newly built urban areas, based on multi-source traffic data fusion analysis. This scheme aims to accurately identify and exploit potential areas in newly built urban areas with highly dispersed travel demand and weak traditional public transport services through data mining and algorithmic modeling, providing core decision support for the scientific planning and efficient operation of demand-responsive public transport.

[0083] Reference Figure 1 This application provides a method for selecting the location of a demand-responsive public transport operation area in a newly built urban area, specifically including the following steps S100~S140:

[0084] S100: Match the traffic supply data of the newly built urban area to the geographic grid corresponding to the newly built urban area;

[0085] S110: Construct multiple traffic demand indicators and multiple traffic supply indicators;

[0086] S120: Determine the demand level of each grid in the geographic grid based on the various traffic demand indicators;

[0087] S130: Determine the supply-demand imbalance level of each grid in the geographic grid based on each of the aforementioned traffic demand indicators and each of the aforementioned traffic supply indicators;

[0088] S140: Determine the operating area of ​​the demand-responsive public transport in the newly built urban area based on the demand level and the supply-demand imbalance level of each grid in the geographic grid.

[0089] Optionally, determining the demand level of each grid in the geographic grid based on each of the traffic demand indicators includes the following steps:

[0090] Calculate the first comprehensive weight corresponding to each of the aforementioned traffic demand indicators;

[0091] Calculate the potential demand index of each grid in the geographic grid based on each of the aforementioned traffic demand indicators and the corresponding first comprehensive weight;

[0092] The geographic grid is divided into corresponding demand levels based on the potential demand index.

[0093] Optionally, the traffic demand indicators include public transport passenger flow, shared bicycle passenger flow within a set range, and ride-hailing passenger flow within a set range;

[0094] The step of calculating the potential demand index of each grid in the geographic grid based on each of the traffic demand indicators and the corresponding first comprehensive weight includes the following steps:

[0095] The potential demand index is calculated using the following formula:

[0096] ;

[0097] in, For grid The potential demand index mentioned above. For grid Standardized ride-hailing passenger flow For grid Standardized shared bicycle passenger flow For grid Standardized bus passenger flow , , These are the corresponding first comprehensive weights.

[0098] Optionally, determining the supply-demand imbalance level of each grid in the geographic grid based on each of the traffic demand indicators and each of the traffic supply indicators includes the following steps:

[0099] Calculate the second comprehensive weights corresponding to the demand evaluation index and the supply evaluation index;

[0100] The demand evaluation index of each grid in the geographic grid is calculated based on each of the aforementioned traffic demand indicators and the corresponding second comprehensive weight;

[0101] The supply evaluation index of each grid in the geographic grid is calculated based on each of the aforementioned traffic supply indicators and the corresponding second comprehensive weight;

[0102] The location quotient of the grid corresponding to the demand evaluation index and the supply evaluation index of each grid;

[0103] Based on the location quotient, each grid in the geographic grid is assigned to the corresponding supply and demand imbalance level.

[0104] Optionally, the traffic demand indicators include population size, public transport passenger flow, shared bicycle passenger flow within a set range, and ride-hailing passenger flow within a set range; the traffic supply indicators include the number of bus stops, the number of passing bus routes, and the station service level score.

[0105] The step of calculating the demand evaluation index for each grid in the geographic grid based on each of the traffic demand indicators and the corresponding second comprehensive weight includes the following steps:

[0106] The demand evaluation index is calculated using the following formula:

[0107] ;

[0108] in, For grid The aforementioned demand evaluation index For grid Standardized population size For grid Standardized bus passenger flow For grid Standardized shared bicycle passenger flow For grid Standardized ride-hailing passenger flow These are the corresponding second comprehensive weights, and ;

[0109] The step of calculating the supply evaluation index for each grid in the geographic grid based on each of the aforementioned traffic supply indicators and the corresponding second comprehensive weight includes the following steps:

[0110] The supply evaluation index is calculated according to the following formula:

[0111] ;

[0112] in, For grid The aforementioned supply evaluation index For grid The number of standardized bus stops For grid The number of standardized bus routes For grid Standardized site service level rating, These are the corresponding second comprehensive weights, and ;

[0113] The location quotient of the grid corresponding to the demand evaluation index and the supply evaluation index of each grid includes the following steps:

[0114] Calculate the location quotient according to the following formula:

[0115] ;

[0116] in, For grid The location quotient mentioned above, For grid The aforementioned demand evaluation index For grid The aforementioned supply evaluation index The sum of the demand evaluation indices for all grids. This is the sum of the supply evaluation indices for all grids.

[0117] Optionally, the first comprehensive weight and the second comprehensive weight are calculated using a subjective-objective combined weighting method, which includes the following steps:

[0118] The system obtains scores from multiple experts for the indicators to be processed; wherein, when calculating the first comprehensive weight, the indicators to be processed include each of the traffic demand indicators; when calculating the second comprehensive weight, the indicators to be processed include each of the traffic demand indicators and multiple traffic supply indicators.

[0119] For each of the aforementioned indicators to be processed ,calculate The average score from 10 experts, and the formulas for each indicator include:

[0120] ;

[0121] in, Indicates the first Experts on the indicators to be processed The rating;

[0122] The average value of each of the indicators to be processed is normalized to obtain the corresponding subjective weight, and the formula includes:

[0123] ;

[0124] definition One sample, The remaining indicators are to be processed, and a sample matrix is ​​constructed as follows: :

[0125] ;

[0126] in, Indicates the first The sample at the th The values ​​of the indicators to be processed;

[0127] Normalize the data for each indicator to be processed to obtain the proportion of each sample under the corresponding indicator, as shown in the following formula:

[0128] ;

[0129] in, For the i-th grid sample in the index to be processed The proportion of the surface;

[0130] Calculate the first The information entropy of the indicator to be processed is given by the following formula:

[0131] ;

[0132] in, For information entropy, if Then it is stipulated ;

[0133] The objective weight is calculated based on information entropy, and the formula includes:

[0134] ;

[0135] The subjective weight and the objective weight are weighted and summed with their corresponding weight coefficients to obtain the first comprehensive weight or the second comprehensive weight.

[0136] Optionally, determining the operating area of ​​the demand-responsive public transport in the newly built urban area based on the demand level and the supply-demand imbalance level of each grid in the geographic grid includes the following steps:

[0137] The area formed by the grids where both the demand level and the supply-demand imbalance level meet the corresponding thresholds is determined as the candidate site selection area for the demand-responsive public transport in the newly built urban area.

[0138] Non-operable areas in the candidate site selection areas are removed, and the boundaries of the candidate site selection areas are modified according to linear geographical elements that have a separating effect, so as to obtain the target site selection area for the demand-responsive public transport in the newly built urban area.

[0139] The following section will provide a detailed introduction and explanation of the solutions in the embodiments of this application, using specific application examples.

[0140] I. Overall Approach.

[0141] Reference Figure 2 This embodiment establishes a systematic and operable responsive public transport operation boundary delineation process. Based on the core concepts of "problem-oriented and data-driven," it sequentially achieves the process from basic data processing and quantitative identification of potential demand and supply-demand imbalances to the final generation of geographically feasible boundaries.

[0142] The first step involves data processing and multi-source fusion, integrating travel data from public transportation, shared bicycles, and ride-hailing services, as well as supply data such as stations and routes. This data is then cleaned, standardized, and matched to a geographic grid to form a data foundation suitable for computation. The second step involves model building and quantitative analysis to establish a potential demand index to identify high-demand grids. A location quotient model is then used to screen areas with significant supply-demand imbalances, i.e., key areas with "strong demand and weak supply." The third step combines geographic optimization and boundary delineation, utilizing road network, topography, and land use data to spatially refine candidate areas, eliminating natural and functional barriers, and forming operationally feasible continuous service units based on existing boundaries such as main roads and rivers.

[0143] II. Model Preparation.

[0144] (1) Determine the evaluation index system.

[0145] This embodiment, based on a complex systems perspective and following the comprehensive principles of "representativeness, quantifiability, and data availability," constructs a multi-dimensional evaluation index system. This system approaches the issue from two dimensions: "travel demand" and "public transport supply."

[0146] The traffic demand index aims to measure the intensity of residents' travel demand within a region. It measures population activity characteristics and current traffic use in urban space from multiple dimensions and includes the following four indicators:

[0147] 1. Grid population size: As a measure of population agglomeration scale of basic spatial units, this indicator constitutes the geodemographic basis for travel demand generation and is the original basis for inferring the total potential travel volume.

[0148] 2. Public transport passenger flow: This directly reflects the actual carrying capacity of the established public transport network under the current operating model and is a key explicit indicator for observing rigid public transport demand and the attractiveness of the system.

[0149] 3. Shared bicycle passenger flow within 5km: As an important connection between slow traffic and motorized travel, the spatial distribution and intensity of its passenger flow reflect the demand for short-distance last-mile travel and the reality of the "last mile" problem of public transportation.

[0150] 4. Ride-hailing passenger flow within 5km: This represents the highly flexible and personalized travel market demand. Ride-hailing can be seen to some extent as an alternative to conventional public transportation services, and its flow can indirectly identify blind spots or weaknesses in public transportation services.

[0151] The transportation supply index aims to measure the intensity of public transport travel supply within a region. It quantifies the allocation level of existing public transport infrastructure and transportation resources through multiple dimensions, specifically covering the following three core indicators:

[0152] 1. Number of bus stops in a grid: This represents the density of bus stops within a spatial unit and is a fundamental element for measuring the breadth of service coverage and physical accessibility.

[0153] 2. Number of bus routes passing through the grid: This reflects the diversity of bus routes passing through a certain spatial unit, embodies the richness and connectivity potential of the route network, and relates to the freedom of travel route selection.

[0154] 3. Service Level Rating of Stations within the Grid: A composite quality indicator calculated by aggregating the products of "departure frequency of all routes stopping at the station" and "maximum passenger capacity per vehicle". This indicator comprehensively considers service frequency and capacity allocation, aiming to quantify the real-time service supply capacity that a station can provide from both capacity and frequency perspectives.

[0155] (2) Data collection and preprocessing.

[0156] The data mainly consists of two parts: first, core data, which is a GPKG geospatial data file divided into 500m×500m grids within the urban area, which integrates multiple results of previous spatial data processing, such as public transport passenger flow data and population distribution data after resampling; second, auxiliary data, including basic geographic information such as Guangzhou city road layers and administrative division layers.

[0157] During the data cleaning phase, outliers were removed from grids that clearly did not conform to reality or contained recording errors. The specific criteria were: if the weekly passenger volume of ride-hailing or shared bicycles in a grid significantly exceeded the reasonable demand scale of the area, or if the public transport passenger volume was negative, the corresponding grid was considered outlier and excluded to ensure the reliability of subsequent analysis. To accurately select newly built urban areas suitable for implementing responsive public transport, the urban area was divided into multiple spatial grids with a unit of 500 meters. By statistically analyzing the total passenger volume of shared bicycles, ride-hailing vehicles, and public transport within each grid, a regional travel demand assessment standard was established. Grids with a total passenger volume less than 50 were removed, thus accurately excluding areas with sparse passenger volume and lacking the basic conditions for public transport operation, such as mountainous areas and rivers, providing a scientific basis for subsequent responsive public transport route planning and station layout.

[0158] (3) Standardization of indicators.

[0159] Because the units and dimensions of various demand and supply indicators differ significantly—for example, population is counted in "people" and the number of stations in "units"—a range standardization method is used to map the original values ​​of each indicator to the [0,1] interval to eliminate the influence of dimensions and ensure comparability between different indicators. The calculation formula for this method is as follows:

[0160] ;

[0161] in, The standardized value after processing, and , Represents the original values ​​of the above indicators. Represents the maximum value among the original values. This represents the minimum value among the original values. After this processing, all indicators are converted into dimensionless standardized values, which facilitates subsequent comprehensive evaluation and modeling analysis.

[0162] III. Model Construction.

[0163] This embodiment will construct a "responsive public transport potential demand index" model and a "multi-indicator supply and demand evaluation index combined with location quotient" model to quantify the demand intensity of each grid and the relative supply and demand level of a single grid, and identify grids with high demand for responsive public transport potential demand and grids with supply and demand imbalance.

[0164] (a) "Responsive Public Transport Potential Demand Index" model.

[0165] 1. Calculate weights using the subjective and objective weighting method.

[0166] The "Responsive Public Transport Potential Demand Index" model primarily considers standardized ride-hailing passenger flow. Standardized shared bicycle passenger flow Standardized bus passenger flow Three categories of indicators. A combination of "subjective weighting (Delphi method) + objective weighting (entropy weighting method)" is adopted to balance expert experience and objective data laws in calculating the weights of the three categories of indicators.

[0167] The specific steps of the subjective and objective empowerment method are as follows:

[0168] Subjective Weighting (Delphi Method): Subjective weighting is a method of assigning weights based on expert knowledge and experience, with the Delphi method being a typical example. This method gradually builds consensus among experts through multiple rounds of anonymous consultation and feedback, ultimately determining the relative importance of each indicator. Therefore, this patent chooses to use the Delphi method to determine subjective weights. The specific steps of the Delphi method are as follows:

[0169] (1) Selecting experts: Inviting Several experts in related fields participated in the evaluation.

[0170] (2) Design a scoring sheet: Each expert is asked to score each indicator according to its importance, with a scoring range of 1-5 points, where 5 points represents "most important".

[0171] (3) Collection of scores: Let the score be... Experts on the indicators The score Then when The time interval indicates that the j-th expert considers indicator i to be extremely important.

[0172] (4) For each indicator ,calculate The average score from the experts, and the formulas for each indicator are as follows:

[0173] ;

[0174] (5) Normalization: The average score of each indicator is normalized to obtain its subjective weight, as shown in the following formula:

[0175] ;

[0176] Subjective weights calculated using the Delphi method are as follows: ride-hailing passenger flow weight is 0.40, shared bicycle passenger flow weight is 0.35, and public transportation passenger flow weight is 0.25.

[0177] Objective weighting (entropy weighting method): The objective weighting method is a method for determining the weight of indicators based on the actual data distribution characteristics, among which the entropy weighting method is one of the commonly used methods. This method measures the importance of each indicator based on the amount of effective information (i.e., entropy value) provided by the data. The greater the dispersion of the indicator data, the smaller the entropy value, the more information it provides, and the greater its corresponding weight. Therefore, this patent chooses to use the entropy weighting method to determine the objective weights. The specific steps of the entropy weighting method are as follows:

[0178] (1) Sample matrix construction. Assume we have... One sample, Items, sample matrix is :

[0179] ;

[0180] in, Indicates the first The sample at the th The numerical value of the indicator.

[0181] (2) Calculation of indicator weights. Normalize the data for each indicator to obtain the weight of each sample under that indicator, using the following formula:

[0182] ;

[0183] in, For the i-th grid sample in the index The proportion of the weight.

[0184] (3) Information entropy calculation. Calculate the first... The information entropy of the item indicator. The formula is as follows:

[0185] ;

[0186] in, For information entropy, if Then it is stipulated .

[0187] (4) Objective weight calculation. Objective weights are obtained based on information entropy. The formulas for each indicator are as follows:

[0188] ;

[0189] The objective weights calculated using the entropy weight method are as follows: ride-hailing passenger flow weight is 0.42, shared bicycle passenger flow weight is 0.36, and public transportation passenger flow weight is 0.22.

[0190] Determining the First Overall Weight: To balance expert experience with objective data, a combined subjective and objective weighting approach can be adopted. Setting a weighting method of 40% for subjective factors and 60% for objective factors, the first overall weight is:

[0191] ;

[0192] in, The subjective weights obtained by the Delphi method, The objective weights are obtained using the entropy weight method.

[0193] The final weights were calculated using a weighted method of "subjective weighting of 40% and objective weighting of 60%": ride-hailing passenger flow weighting was 0.412; shared bicycle passenger flow weighting was 0.356; and public transportation passenger flow weighting was 0.232.

[0194] 2. Formula for calculating the potential demand index.

[0195] The potential demand index uses positive indicators from the demand side to reflect the intensity of actual travel demand, while using indicators from the supply side as a reverse adjustment indicator. The two are combined to measure the potential demand for responsive public transport services in the region.

[0196] Grid The potential demand index is set by the formula:

[0197] ;

[0198] in, Representing the grid Standardized ride-hailing passenger flow Representing the grid Standardized shared bicycle passenger flow, Representing the grid Standardized public transport passenger flow. Ride-hailing and shared bicycle passenger flow are positive indicators; the higher the value, the stronger the regional travel demand. Public transport passenger flow is a negative indicator; the lower the value, the more insufficient the existing public transport supply is, and the greater the need for responsive public transport supplementation.

[0199] 3. Classification of potential demand levels.

[0200] Based on the numerical distribution of the potential demand index, all grids within the study area are divided into the following three demand levels:

[0201] High-demand grid ( This type of grid-based travel demand is significantly higher than the public transport supply level, with ride-hailing and bike-sharing activities being particularly active, and existing public transport service coverage being severely insufficient. This is an area where responsive public transport should be prioritized.

[0202] Medium demand grid ( ): The demand for travel in this type of grid is roughly balanced with the supply of public transportation, and it can be used as a candidate area for responsive public transportation expansion or service optimization.

[0203] Low-demand grid ( The existing public transport services are sufficient to meet local travel needs, and there is no need to add new responsive bus routes.

[0204] (ii) "Multi-indicator supply and demand evaluation index combined with location quotient" model.

[0205] 1. Calculation of supply and demand evaluation index.

[0206] To quantitatively analyze the supply and demand matching of public transportation in various regions, a "demand evaluation index" and a "supply evaluation index" were constructed respectively.

[0207] The grid demand evaluation index represents the overall intensity of public transport demand within a grid unit. It is obtained by weighted summation of four standardized demand indicators, and the calculation formula is as follows:

[0208] ;

[0209] in, For grid Demand evaluation index; These are the standardized population size, public transport passenger flow, shared bicycle passenger flow, and ride-hailing passenger flow, respectively. The weights of the corresponding indicators, and .

[0210] The grid supply evaluation index represents the supply level of existing public transportation services within a grid unit. It is obtained by weighted summation of three standardized supply-related indicators, and the calculation formula is as follows:

[0211] ;

[0212] in, For grid Supply evaluation index; The scores are based on the standardized number of bus stops, the number of bus routes passing through the stops, and the service level of the stops. The weights of the corresponding indicators, and .

[0213] 2. Determining the weights of the indicators.

[0214] To balance expert experience with objective data principles, the "Multi-Indicator Supply and Demand Evaluation Index Combining Location Quotient" model also employs a combined subjective and objective weighting method to determine the final weights. The specific steps are as follows:

[0215] Subjective empowerment (Delphi method): Invitation Each expert independently scores the importance of each indicator on a scale of 1 to 5. The average score of each indicator is calculated and normalized to obtain the subjective weight. This method can systematically incorporate domain knowledge and experience-based judgment.

[0216] Objective weighting (entropy weighting method): This method calculates information entropy based on the dispersion of actual data for each indicator. The smaller the entropy value, the greater the amount of information provided by the indicator, and therefore the higher its weight. Objective weights are obtained by normalizing the difference coefficient. This method is entirely data-driven and avoids subjective bias.

[0217] The second comprehensive weight is determined by combining subjective and objective weights. Generally, the subjective weight accounts for 40% and the objective weight accounts for 60%. The combined weights will be substituted into the calculation formulas of the demand index and supply index mentioned above.

[0218] The calculation formula for this part is the same as the indicator weight determination part of the "Responsive Public Transport Potential Demand Index" model in the embodiments of this application.

[0219] 3. Location quotient calculation.

[0220] Location quotient can be used to measure the relative concentration or imbalance of a factor's distribution within a local area and the overall region. In this study, the spatial areas with prominent supply-demand imbalances are quantified and identified by calculating the deviation of the ratio of demand to supply indices in each grid unit from the city's average level. The calculation formula is as follows:

[0221] ;

[0222] in, For grid Location advantage, For grid Demand evaluation index, For grid Supply evaluation index, The city's overall demand evaluation index is the sum of the demand indices of all grids. This is the city-wide total supply evaluation index, which is the sum of the supply indices of all grids. If the grid... Supply Index (No public transport supply) and a high demand index, to avoid the denominator being zero, let This assignment represents an extremely high supply-demand imbalance, indicating that demand is completely lacking in supply support, and the contradiction is most prominent.

[0223] Based on the location quotient calculation results, the studied grid area is divided into the following three levels to support differentiated decision-making:

[0224] High-conflict grid area ( Demand is significantly higher than supply, and public transport services are severely inadequate. Response-oriented public transport should be prioritized.

[0225] Conflict grid area ( ( ): Supply and demand are roughly balanced, with slight shortages or redundancies in some areas, which can be considered as candidate areas for opening responsive public transport.

[0226] Low-conflict grid area: ( The supply is relatively sufficient, and the existing public transportation basically meets the travel needs, so there is no need to open up responsive public transportation.

[0227] IV. Adjustment of the operating area boundaries for responsive public transport.

[0228] Based on the initial identification using rule-based grids, candidate areas need further boundary optimization by combining the real geographical environment and urban spatial structure to form service units with operational feasibility. The specific process is as follows:

[0229] 1. Initial selection based on the fusion of multiple indicators.

[0230] The results of the potential demand index and location quotient calculations are combined to select regions that simultaneously meet the following two conditions: Strong demand base: The potential demand index is higher than a set threshold of 0.6, indicating a strong willingness to use motorized transportation within the region; Prominent supply-demand imbalance: The regional location quotient is greater than 1.2, reflecting a significant insufficiency of existing conventional public transportation supply relative to demand. Regions meeting these conditions will be the focus of subsequent refined delineation.

[0231] 2. Geographical constraints and urban form modification.

[0232] Using high-resolution satellite imagery and related geographic data, restrictive adjustments were made to the initially selected area.

[0233] First, unsuitable areas are excluded, such as waterways, mountains, large green spaces, railway stations, airports, and other natural barriers or land with special functions that make it impossible to deploy public transport services. Second, the target land type is identified. This patent is applicable to the site selection of demand-responsive public transport in newly built urban areas, therefore only land in newly built urban areas is selected. Finally, relying on natural and artificial boundaries, and taking linear elements with obvious separating functions such as main roads, expressways, rivers, and railways as references, the boundaries of the operating units are modified to ensure their relative integrity, good internal connectivity, and resistance to being interrupted by physical barriers, thereby improving the operability and service efficiency of route deployment.

[0234] An optional implementation method of this application is as follows:

[0235] This paper focuses on the demand-responsive public transport site selection problem in a newly built urban area of ​​a certain city, and is based on multi-source traffic data of the city.

[0236] Based on the city's 500-meter grid bus supply index and bus route network distribution, it can be seen that the supply index is highest in the areas covered by the routes, and gradually decreases along both sides of the routes. The bus supply index (calculated by combining core dimensions such as the distance between the station and the grid center, and the number of routes passing through) exhibits a spatial distribution characteristic of "high-value clustering in the core, gradient decrease in the periphery, and precise corridor adaptation." The grid supply index of each central urban area is at the highest level in the entire region, forming a peak area because the density of bus stops within 500 meters exceeds 3 and the number of routes passing through is generally more than 15. As the core area extends outward, the grid index of the suburban areas gradually decreases, forming strip-shaped high-value corridors mostly relying on main roads. Overall, the distribution of the supply index is deeply coupled with population density, employment concentration, and rail transit network.

[0237] To ensure that the selected service areas meet the requirements for responsive bus site selection and to avoid wasting service areas, after comprehensive judgment, grids with a combined ride-hailing, bike-sharing, and public transport passenger flow of more than 50 were selected for analysis (a total of 7227 grids). Areas such as rivers and mountains were excluded, and satellite maps were added as the base map to more intuitively present the distribution of supply and demand. The constructed responsive bus potential demand index model quantifies the demand intensity of the grids. The higher the potential demand, the more suitable it is to open responsive bus services.

[0238] Location quotient (MR) is a crucial metric for determining whether an area is suitable for implementing responsive public transport. A higher MR indicates that the demand level significantly exceeds the supply level, suggesting greater potential for responsive public transport implementation. The MR is influenced by the population of each grid, public transport passenger volume, shared bicycle passenger volume, ride-hailing passenger volume, and the grid's supply index. Calculating the MR for each grid area yields a corresponding heatmap of the city's grid location quotient. In this heatmap, the intensity of the color indicates the MR; darker colors represent higher MRs. The darkest grid corresponds to the patent screening criteria. "≥1.2", precisely matching responsive bus adaptation scenarios where "demand exceeds supply".

[0239] The target location for responsive bus services in this embodiment is a newly developed urban area suitable for responsive bus service. Given that both the demand and supply indices in the city center are high, the supply side is saturated, and road conditions during peak hours are poor, with demand characteristics that are not well-suited to responsive bus services, it is not suitable to implement responsive bus services. The core reason is that the area has a bus stop coverage rate exceeding 95%, dense routes, and peak departure frequency, with no coverage blind spots or capacity gaps. This negates the core value of responsive buses in "filling gaps." Furthermore, severe peak-hour congestion, narrow road space, and complex traffic control in the core area directly stifle the advantages of responsive buses in "flexible response and rapid turnover." The area's passenger flow is mainly concentrated fixed commuting, with stable demand even during off-peak hours, which does not match the "dispersed, niche, and low-frequency" demand characteristics that responsive buses are designed to meet. In addition, high operating costs and low efficiency would divert regular bus passenger flow, resulting in resource waste, contradicting the planning direction of "optimizing existing bus efficiency and alleviating congestion" in the core area. Therefore, the application scenarios for responsive buses should focus on under-supplied areas such as outlying new districts and subway termini, rather than the city center.

[0240] Therefore, the application scenarios of responsive public transport in newly built urban areas need to focus on areas outside the central urban area. The key is to extract areas with high potential demand indices and contiguous grid spaces; then, overlay a location quotient layer to analyze the supply and demand matching in high-demand areas, filtering out grids with high potential demand and large location quotients (i.e., supply falling short of demand); subsequently, using satellite maps, the area is checked for large urban villages, excluding areas unsuitable for operation such as mountains and rivers, accurately defining the scope of the newly built urban area; finally, the regional boundaries are further optimized and adjusted based on the topography, road conditions, and land use distribution characteristics presented by the satellite map, ensuring that the site selection combines demand adaptability and operational feasibility.

[0241] After initial site selection and screening, the potential demand for responsive public transport is defined. Passenger flow can only be considered potential demand for responsive public transport when the origin and destination points (OD points) of a travel order fall entirely within the same planned area. To further improve the accuracy of demand identification, a new travel OD distribution layer is added, clearly showing the spatial flow and aggregation characteristics of OD points within the initial site selection area for responsive public transport. The density reflects the concentration of travel demand within the travel area. Simultaneously, the original area boundaries are optimized and adjusted based on the actual road network layout (including road connectivity, traffic corridor orientation, and intersection node distribution) to ensure that the adjusted area boundaries fully cover the concentrated distribution range of OD passenger flow within the same area. Taking a region with adjusted boundaries based on OD distribution and the actual road network as an example, the original area boundaries are optimized and adjusted to efficiently adapt to the road network system, avoiding misjudgment of demand or service coverage deviations caused by unreasonable boundary delineation, ultimately yielding the boundary of the responsive public transport site selection area for that region.

[0242] For the 14 candidate areas for responsive public transport in newly built urban areas that were initially selected, the boundary adjustment process of "OD distribution verification - geographical constraint correction - road network adaptation optimization" was followed. Order flow analysis, topographic and geomorphological investigation, and road connectivity verification were carried out in sequence. Areas unsuitable for operation, such as mountains, rivers, and large urban villages, were eliminated, and the surrounding high-demand contiguous grids were included. Finally, the precise service range of responsive public transport in each newly built urban area was determined, and the final distribution of the operating area boundary of responsive public transport in newly built urban areas was obtained.

[0243] Through the above steps, this embodiment completes the entire site selection process from multi-source data preparation and model quantitative analysis to geospatial implementation, providing an operable practical reference for the optimization of public transportation systems in similar cities.

[0244] In summary, the embodiments of this application include the following key technical solutions:

[0245] 1. Considering multi-source heterogeneous traffic data, a new intelligent site selection method for demand-responsive public transport in newly built urban areas is proposed, which integrates multi-source heterogeneous traffic data.

[0246] 2. A dual-model quantitative analysis method is proposed, which integrates the "responsive public transport potential demand index" model and the "multi-index supply and demand evaluation index combined with location quotient" model, to identify potential opening areas.

[0247] 3. A boundary correction process based on high-resolution geographic information (satellite imagery, road network) and travel origin-destination (OD) distribution was designed to ensure the feasibility of the operating area.

[0248] This application proposes a method for delineating the operational boundaries of demand-responsive public transport in newly built urban areas based on multi-source data fusion and quantitative model analysis. This method addresses the problems of current demand-responsive public transport planning, which often relies on experience-based delineation, lacks objective data support, and struggles to accurately identify areas of supply-demand imbalance. It innovatively constructs a discriminative framework that combines a "potential demand index" and a "location quotient" model. In site selection analysis, it systematically integrates multi-dimensional dynamic travel data from ride-hailing and shared bicycles, achieving high-precision quantitative identification of potential travel demand and existing public transport supply gaps in newly built urban areas. Furthermore, it spatially corrects the theoretical results using high-resolution geographic information, ensuring that the final operational boundaries simultaneously meet three conditions: clear demand, significant supply-demand imbalance, and geographical feasibility. This overcomes the limitations of grid-based analysis, which is detached from actual road networks and terrain constraints.

[0249] Practice has proven that the embodiments of this application can automatically and reliably identify potential areas with weak existing public transportation services but strong demand for motorized travel from massive amounts of multi-source data, and the screening accuracy is significantly better than traditional methods that rely solely on experience.

[0250] Reference Figure 3 This application provides a site selection device for demand-responsive public transport operation areas in newly built urban areas, comprising:

[0251] A data matching unit is used to match the traffic supply data of the newly built urban area to the geographic grid corresponding to the newly built urban area;

[0252] The indicator construction unit is used to construct multiple traffic demand indicators and multiple traffic supply indicators.

[0253] Demand grading unit, used to determine the demand level of each grid in the geographic grid based on each of the traffic demand indicators;

[0254] The supply-demand imbalance classification unit is used to determine the supply-demand imbalance level of each grid in the geographic grid based on each of the traffic demand indicators and each of the traffic supply indicators.

[0255] The regional site selection unit is used to determine the operating area of ​​the demand-responsive public transport in the newly built urban area based on the demand level and the supply-demand imbalance level of each grid in the geographic grid.

[0256] It is understood that the content of the above method embodiments is applicable to the present device embodiments. The specific functions implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0257] In some alternative embodiments, the functions / operations mentioned in the block diagrams may not occur in the order shown in the operation diagrams. For example, depending on the functions / operations involved, two consecutively shown blocks may actually be executed substantially simultaneously, or the blocks may sometimes be executed in reverse order. Furthermore, the embodiments presented and described in the flowcharts of this application are provided by way of example to provide a more comprehensive understanding of the technology. The disclosed methods are not limited to the operations and logic flows presented herein. Alternative embodiments are contemplated in which the order of various operations is changed and sub-operations described as part of a larger operation are executed independently.

[0258] Furthermore, although this application is described in the context of functional modules, it should be understood that, unless otherwise stated, one or more of the described functions and / or features may be integrated into a single physical device and / or software module, or one or more functions and / or features may be implemented in a separate physical device or software module. It is also understood that a detailed discussion of the actual implementation of each module is unnecessary for understanding this application. Rather, given the properties, functions, and internal relationships of the various functional modules in the apparatus disclosed herein, the actual implementation of the module will be understood within the scope of conventional technology for an engineer. Therefore, those skilled in the art can implement the application set forth in the claims using ordinary techniques without excessive experimentation. It is also understood that the specific concepts disclosed are merely illustrative and not intended to limit the scope of this application, which is determined by the full scope of the appended claims and their equivalents.

[0259] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a 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.

[0260] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0261] More specific examples of computer-readable media (a non-exhaustive list) include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0262] It should be understood that various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0263] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0264] Although embodiments of this application have been shown and described, those skilled in the art will understand that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of this application, the scope of which is defined by the claims and their equivalents.

[0265] The above is a detailed description of the preferred embodiments of this application, but this application is not limited to the embodiments described. Those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of this application, and these equivalent modifications or substitutions are all included within the scope defined by the claims of this application.

Claims

1. A method for selecting the location of a demand-responsive public transport operation area in a newly built urban area, characterized in that, The method includes the following steps: Match the traffic supply data of the newly built urban area to the geographic grid corresponding to the newly built urban area; Construct multiple traffic demand indicators and multiple traffic supply indicators; The demand level of each grid in the geographic grid is determined based on the various traffic demand indicators. The supply-demand imbalance level of each grid in the geographic grid is determined based on each of the aforementioned traffic demand indicators and each of the aforementioned traffic supply indicators; The operating area of ​​the demand-responsive public transport system in the newly built urban area is determined based on the demand level and the supply-demand imbalance level of each grid in the geographic grid.

2. The method for selecting a site for a demand-responsive public transport operation area in a newly built urban area according to claim 1, characterized in that, Determining the demand level of each grid in the geographic grid based on the various traffic demand indicators includes the following steps: Calculate the first comprehensive weight corresponding to each of the aforementioned traffic demand indicators; Calculate the potential demand index of each grid in the geographic grid based on each of the aforementioned traffic demand indicators and the corresponding first comprehensive weight; The geographic grid is divided into corresponding demand levels based on the potential demand index.

3. The method for selecting a site for a demand-responsive public transport operation area in a newly built urban area according to claim 2, characterized in that, The traffic demand indicators include public transport passenger flow, shared bicycle passenger flow within a specified range, and ride-hailing passenger flow within a specified range. The step of calculating the potential demand index of each grid in the geographic grid based on each of the traffic demand indicators and the corresponding first comprehensive weight includes the following steps: The potential demand index is calculated using the following formula: ； in, For grid The potential demand index mentioned above. For grid Standardized ride-hailing passenger flow For grid Standardized shared bicycle passenger flow For grid Standardized bus passenger flow , , These are the corresponding first comprehensive weights.

4. The method for selecting a site for a demand-responsive public transport operation area in a newly built urban area according to claim 1, characterized in that, Determining the supply-demand imbalance level of each grid in the geographic grid based on each of the traffic demand indicators and each of the traffic supply indicators includes the following steps: Calculate the second comprehensive weights corresponding to the demand evaluation index and the supply evaluation index; The demand evaluation index of each grid in the geographic grid is calculated based on each of the aforementioned traffic demand indicators and the corresponding second comprehensive weight; The supply evaluation index of each grid in the geographic grid is calculated based on each of the aforementioned traffic supply indicators and the corresponding second comprehensive weight; The location quotient of the grid corresponding to the demand evaluation index and the supply evaluation index of each grid; Based on the location quotient, each grid in the geographic grid is assigned to the corresponding supply and demand imbalance level.

5. The method for selecting a site for a demand-responsive public transport operation area in a newly built urban area according to claim 4, characterized in that, The traffic demand indicators include population size, public transport passenger volume, shared bicycle passenger volume within a set range, and ride-hailing passenger volume within a set range. The traffic supply indicators include the number of bus stops, the number of bus routes passing through, and the service level rating of the stops. The step of calculating the demand evaluation index for each grid in the geographic grid based on each of the traffic demand indicators and the corresponding second comprehensive weight includes the following steps: The demand evaluation index is calculated using the following formula: ； in, For grid The aforementioned demand evaluation index, For grid Standardized population size For grid Standardized bus passenger flow For grid Standardized shared bicycle passenger flow For grid Standardized ride-hailing passenger flow These are the corresponding second comprehensive weights, and ; The step of calculating the supply evaluation index for each grid in the geographic grid based on each of the aforementioned traffic supply indicators and the corresponding second comprehensive weight includes the following steps: The supply evaluation index is calculated according to the following formula: ； in, For grid The aforementioned supply evaluation index. For grid The number of standardized bus stops For grid The number of standardized bus routes For grid Standardized site service level rating, These are the corresponding second comprehensive weights, and ; The location quotient of the grid corresponding to the demand evaluation index and the supply evaluation index of each grid includes the following steps: Calculate the location quotient according to the following formula: ； in, For grid The location quotient mentioned above, For grid The aforementioned demand evaluation index, For grid The aforementioned supply evaluation index. The sum of the demand evaluation indices for all grids. This is the sum of the supply evaluation indices for all grids.

6. A method for selecting a site for a demand-responsive public transport operation area in a newly built urban area according to claim 2 or 4, characterized in that, The first comprehensive weight and the second comprehensive weight are calculated using a subjective-objective combined weighting method, which includes the following steps: The system obtains scores from multiple experts for the indicators to be processed; wherein, when calculating the first comprehensive weight, the indicators to be processed include each of the traffic demand indicators; when calculating the second comprehensive weight, the indicators to be processed include each of the traffic demand indicators and multiple traffic supply indicators. For each of the aforementioned indicators to be processed ,calculate The average score from 10 experts, and the formulas for each indicator include: ； in, Indicates the first Experts on the indicators to be processed The rating; The average value of each of the indicators to be processed is normalized to obtain the corresponding subjective weight, and the formula includes: ； definition One sample, The remaining indicators are to be processed, and a sample matrix is ​​constructed as follows: : ； in, Indicates the first The sample at the th The values ​​of the indicators to be processed; Normalize the data for each indicator to be processed to obtain the proportion of each sample under the corresponding indicator, as shown in the following formula: ； in, For the i-th grid sample in the index to be processed The proportion of the surface; Calculate the first The information entropy of the indicator to be processed is given by the following formula: ； in, For information entropy, if Then it is stipulated ; The objective weight is calculated based on information entropy, and the formula includes: ； The subjective weight and the objective weight are weighted and summed with their corresponding weight coefficients to obtain the first comprehensive weight or the second comprehensive weight.

7. A method for selecting a site for a demand-responsive public transport operation area in a newly built urban area according to any one of claims 1 to 6, characterized in that, Determining the operating area of ​​the demand-responsive public transport in the newly built urban area based on the demand level and the supply-demand imbalance level of each grid in the geographic grid includes the following steps: The area formed by the grids where both the demand level and the supply-demand imbalance level meet the corresponding thresholds is determined as the candidate site selection area for the demand-responsive public transport in the newly built urban area. By eliminating non-operable areas from the candidate site selection areas and correcting the boundaries of the candidate site selection areas based on linear geographical features that have a separating effect, the target site selection areas for the new urban area demand-responsive public transport are obtained.

8. A site selection device for demand-responsive public transport operation areas in newly built urban areas, characterized in that, The device includes: A data matching unit is used to match the traffic supply data of the newly built urban area to the geographic grid corresponding to the newly built urban area; The indicator construction unit is used to construct multiple traffic demand indicators and multiple traffic supply indicators. Demand grading unit, used to determine the demand level of each grid in the geographic grid based on each of the traffic demand indicators; The supply-demand imbalance classification unit is used to determine the supply-demand imbalance level of each grid in the geographic grid based on each of the traffic demand indicators and each of the traffic supply indicators. The regional site selection unit is used to determine the operating area of ​​the demand-responsive public transport in the newly built urban area based on the demand level and the supply-demand imbalance level of each grid in the geographic grid.

9. An electronic device, characterized in that, The electronic device includes a processor and a memory; The memory is used to store programs; The processor executes the program to implement the method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The storage medium stores a program that is executed by a processor to implement the method as described in any one of claims 1 to 7.

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