A method for selecting a location of a demand-response bus operation area in a central city

Abstract

The application discloses a center city demand response type bus operation area site selection method, relates to the technical field of traffic planning, and comprises the following steps: acquiring traffic demand data and traffic supply data of each period in the center city; matching the traffic demand data and the traffic supply data of each period to geographical grids of the center city; determining a commuting tidal region and a traffic supply and demand imbalance region as a candidate site selection range of the demand response type bus operation area according to the geographical grids; and removing an unoperable region in the center city in the candidate site selection range as a target site selection range of the demand response type bus operation area. The application accurately identifies a high demand region, fully considers actual operation conditions of the center city, removes the unoperable region, quantifies peak period capacity waste and peak connection demand, provides space site selection support for a differentiated operation mode, and improves bus resource utilization.

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 central urban areas. Background Technology

[0002] With the deepening of urbanization, public transportation, as the core lifeline of urban operation, directly affects citizens' travel experience, urban transportation capacity, and the level of sustainable development through its service efficiency and quality. The central urban area, as the core of a city's highly concentrated population, commerce, and employment, has a massive travel demand with distinct spatial and temporal distribution characteristics. It is not only a key area for public transportation services but also a concentrated area where the contradictions of traditional public transportation models erupt.

[0003] However, the rigid service model of traditional public transportation systems, centered on fixed routes and schedules, presents a significant mismatch between its inherent rigidity and the unique travel demands of central urban areas. Travel demand in central urban areas exhibits a strong "tidal effect," with highly concentrated commuter traffic during morning and evening rush hours. While traditional bus networks are dense, they are prone to congestion and paralysis, resulting in prominent issues such as long queues at stops and overcrowded carriages, significantly diminishing the service experience. Conversely, during off-peak hours, as commuter traffic decreases, travel demand becomes more dispersed and personalized. Traditional fixed routes, lacking flexibility, generally face high empty-load rates and wasted capacity. Furthermore, the central urban area is characterized by a mix of commercial complexes, office clusters, and older residential communities, with some areas having "last-mile" connectivity blind spots. Traditional public transportation struggles to accurately cover diverse travel scenarios, reducing the attractiveness of public transportation and causing inefficient allocation of public resources. Summary of the Invention

[0004] In view of this, the embodiments of this application provide a method and related equipment for selecting the location of demand-responsive public transport operation areas in central urban areas, so as to scientifically select the operation areas of demand-responsive public transport and thereby improve traffic efficiency.

[0005] One aspect of this application provides a method for selecting a location for a demand-responsive public transport operation area in a central urban area, the method comprising the following steps:

[0006] Obtain traffic demand and supply data for the central urban area at different times;

[0007] Match the traffic demand data and traffic supply data for each time period to the geographic grid of the central urban area;

[0008] Based on the geographic grid, commuter tidal zones and areas of traffic supply-demand imbalance are identified as candidate site selection areas for demand-responsive public transport operation areas;

[0009] The non-operable areas in the central urban area are excluded from the candidate site selection range, which is then used as the target site selection range for demand-responsive public transport operation areas.

[0010] In some embodiments, obtaining traffic demand data and traffic supply data for different time periods in the central urban area includes the following steps:

[0011] The data on bus passenger flow, subway passenger flow, ride-hailing orders, and shared bicycle orders in the central urban area at various time periods are obtained as the traffic demand data.

[0012] The bus stops, bus routes, and bus departure frequencies in the central urban area at various times are obtained as the traffic supply data.

[0013] The time periods mentioned include the morning peak, evening peak, and off-peak periods.

[0014] In some embodiments, matching the traffic demand data and traffic supply data for each time period to the geographic grid of the central urban area includes the following steps:

[0015] The traffic demand data and traffic supply data for each time period are cleaned, their coordinates are unified, and distance weighted interpolation is used for smoothing, and then matched to the geographic grid with a set resolution.

[0016] In some embodiments, determining the commuter tidal zones and traffic supply-demand imbalance zones as candidate site selection areas for demand-responsive transit operations based on the geographic grid includes the following steps:

[0017] The commuting tidal zone is identified in the geographic grid by the tidal passenger flow intensity index and the proportion of peak hours.

[0018] The tidal passenger flow intensity index (TII) is:

[0019] ;

[0020] Among them, F bus，1 To reduce bus passenger volume during the morning rush hour, F bus ,2 represents the evening peak bus passenger volume, F bus,3 For off-peak bus passenger flow;

[0021] The percentage of peak hours (PR) is:

[0022] ;

[0023] The Commuting Directionality Index (CTI) is calculated as follows:

[0024] ;

[0025] Calculate the off-peak location quotient based on the tidal passenger flow intensity index, the peak period percentage, and the commuting directionality index;

[0026] The regions with traffic supply and demand imbalances are obtained by filtering the geographic grid based on the off-peak location quotient.

[0027] In some embodiments, calculating the off-peak location quotient based on the tidal passenger flow intensity index, the peak period percentage, and the commuting directionality index includes the following steps:

[0028] Calculate the standard values ​​for each traffic indicator; wherein, the traffic indicators include the tidal passenger flow intensity index, the peak hour percentage, and the commuting directionality index;

[0029] ;

[0030] in, This represents the standard value. Represents any of the aforementioned traffic indicators. This represents the maximum value of the traffic indicator across all analysis grids. This represents the minimum value of the traffic indicator across all analysis grids;

[0031] Calculate the demand index D t for:

[0032] ;

[0033] Where t corresponds to the off-peak period; This represents a standardized population activity index within time period t. Indicators representing standardized public transport demand; This represents a standardized indicator of shared bicycle travel demand. Indicators representing standardized motorized travel demand;

[0034] Calculate the supply index S t for:

[0035] ;

[0036] in, To standardize the number of sites, To standardize the number of lines, To standardize time-segmented service levels;

[0037] Calculate the off-peak location quotient LQ i for:

[0038] ;

[0039] in, For the grid i-peak supply index, For the grid i peak demand index, For the total peak-hour supply across the entire region, This represents the total off-peak demand across the entire region.

[0040] In some embodiments, the weights required to calculate the demand index and the supply index are obtained through the following steps:

[0041] The rule layer is defined as comprising the target layer, criterion layer, and indicator layer;

[0042] The importance of each supply and demand indicator under the same rule layer is compared pairwise by experts in the transportation field, and then the values ​​are assigned to each supply and demand indicator.

[0043] Construct a judgment matrix based on the assigned values ​​of each of the aforementioned supply and demand indicators. ;

[0044] ;

[0045] in, n represents the number of supply and demand indicators under the same rule layer. This indicates that experts determine supply and demand indicators. Relative supply and demand indicators Importance scale Indicators of supply and demand Relative supply and demand indicators The importance scale is derived by the remaining elements of the judgment matrix following the reciprocity rule;

[0046] Calculate the largest eigenvalue of the judgment matrix to perform a consistency check;

[0047] ;

[0048] in, The largest eigenvalue, The eigenvectors of the judgment matrix are... The judgment matrix With the feature vector The i-th element of the product.

[0049] In some embodiments, the method further includes a step of boundary optimization of the target location range, the step of boundary optimization of the target location range including the following steps:

[0050] The target site selection range is optimized based on regional OD coverage efficiency, operational area calculation, peak supply and demand matching rate, off-peak capacity utilization rate, and road density constraints.

[0051] The OD coverage efficiency of the region is: Among them, OD inside The number of complete OD logarithms, where both the start and end points are within the target location range. total The total number of OD pairs starting from the target location range;

[0052] The operating area is calculated as follows: Where k is the adjustment coefficient, and N high V represents the number of high-conflict grids within the target location range. avg The average road traffic speed within the target location area;

[0053] The peak supply-demand matching rate is: ;in, It is the morning rush hour demand index. It is the evening peak demand index. It is the morning rush hour supply index. It is the evening peak supply index;

[0054] The off-peak transport capacity utilization rate is: ;in, It is the off-peak demand index. It is the off-peak supply index;

[0055] The road density constraint is: ;in, It is the road density within the target site selection area.

[0056] Another aspect of this application embodiment provides a site selection device for demand-responsive public transport operation areas in central urban areas, the device comprising:

[0057] The data acquisition unit is used to acquire traffic demand and traffic supply data for various time periods in the central urban area.

[0058] A data matching unit is used to match the traffic demand data and the traffic supply data for each time period to the geographic grid of the central urban area;

[0059] The first site selection unit is used to determine, based on the geographic grid, commuter tidal areas and areas of traffic supply and demand imbalance as candidate site selection areas for demand-responsive public transport operation areas;

[0060] The second site selection unit is used to eliminate non-operable areas in the central urban area from the candidate site selection range as the target site selection range for demand-responsive public transport operation areas.

[0061] Another aspect of this application embodiment provides an electronic device, including a processor and a memory;

[0062] The memory is used to store programs;

[0063] The processor executes the program to implement any of the methods described above.

[0064] 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.

[0065] This application includes at least the following beneficial effects:

[0066] This application obtains traffic demand and supply data for the central urban area at various time periods; matches this data to a geographic grid within the central urban area; identifies commuter tidal zones and areas with traffic supply-demand imbalances based on the geographic grid as candidate locations for demand-responsive public transport operation areas; and eliminates non-operational areas within the candidate locations to determine the target locations for demand-responsive public transport operation areas. This application accurately identifies high-demand areas while fully considering the actual operating conditions of the central urban area, thereby eliminating non-operational areas, quantifying off-peak capacity waste and peak-peak connection demand, providing spatial location support for differentiated operation models, and improving the utilization rate of public transport resources. Attached Figure Description

[0067] 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.

[0068] Figure 1 A flowchart illustrating a method for selecting a location for a demand-responsive public transport operation area in a central urban area, as provided in this application embodiment;

[0069] Figure 2 An example flowchart of a site selection method for demand-responsive public transport operation areas in central urban areas, provided as an embodiment of this application;

[0070] Figure 3 This is a structural block diagram of a site selection device for a demand-responsive public transport operation area in a central urban area, provided as an embodiment of this application. Detailed Implementation

[0071] 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.

[0072] 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:

[0073] To address the rigid limitations of traditional public transportation, demand-responsive public transportation has emerged as an innovative supplementary public transportation model. It breaks free from the constraints of fixed routes and schedules, using an intelligent dispatch platform to integrate passenger travel demands in real time and dynamically plan routes and departure frequencies. The responsive bus system in the central urban area adopts a hybrid operation mode of "fixed routes and fixed stops during peak hours, and demand response during off-peak hours". During weekday morning and evening peak hours (such as 7:00-9:00 and 17:00-19:00), it relies on fixed routes and high-frequency departures to ensure the concentrated travel needs of commuters, covering key nodes of core commuter corridors and subway connections, and ensuring that the supply of transport capacity is accurately matched with the tidal passenger flow. During off-peak hours (9:00-17:00 and after 19:00), it switches to demand response mode, flexibly connecting dispersed travel needs through online reservations and dynamic scheduling, and planning routes and stops on demand. This avoids the waste of empty runs on fixed routes, and can meet niche and personalized travel needs such as business, medical treatment, and community connections. It takes into account the operational efficiency during peak hours and the service flexibility during off-peak hours. It is an innovative operation mode that adapts to the tidal passenger flow characteristics of the central urban area and optimizes the allocation of public transport resources.

[0074] The effectiveness of demand-responsive public transport relies heavily on scientific site selection planning. The complex traffic environment and diverse travel demands of central urban areas make this a key focus and challenge for site selection planning. Currently, the selection of service areas for demand-responsive public transport in central urban areas largely depends on planners' macro-level experience and judgments, lacking in-depth integration and analysis of multi-source traffic data. This makes it difficult to accurately identify core areas with significant tidal passenger flow and off-peak supply-demand imbalances, leading to problems such as "insufficient peak-hour capacity and weak off-peak demand" in some already operational areas, failing to fully leverage its core advantage of flexible scheduling. Therefore, how to construct a scientific site selection model based on multi-source data-driven methods and accurately delineate the service areas of demand-responsive public transport in central urban areas has become an urgent technical challenge to be solved.

[0075] Existing responsive transit site selection methods in central urban areas generally rely on planners' macro-level experience and judgment, lacking deep integration support from multi-source transportation data such as buses, subways, ride-hailing services, and shared bicycles. This leads to highly subjective site selection decisions that are out of touch with actual needs. Furthermore, traditional methods fail to design specific evaluation systems for the tidal passenger flow characteristics of central urban areas, characterized by "concentrated peak-hour commutes and dispersed off-peak demand." They only consider single-dimensional passenger flow or supply data, failing to accurately identify core areas of supply-demand imbalance during off-peak periods. This makes it difficult to support a differentiated operation model of "fixed routes during peak hours and demand-responsive during off-peak hours," and easily leads to wasted capacity during off-peak periods and insufficient connections during peak hours. In addition, multi-source transportation data lacks standardized processing procedures, resulting in inconsistent spatiotemporal formats. Moreover, the delineation of regional boundaries does not consider the distribution of origin-destination (OD) pairs and the actual road network pattern, easily leading to fragmented service areas, incomplete coverage, or detours across geographical barriers, reducing operational efficiency. Some site selections are even difficult to implement due to constraints such as road conditions and scheduling convenience.

[0076] First, a standardized multi-source traffic data fusion and processing system needs to be constructed to address issues such as spatiotemporal unification, data cleaning and filtering, and aggregation analysis of different types of data. Through a unified coordinate system, standard time period division, spatial smoothing, and resampling, dispersed demand and supply data can be integrated into a high-quality grid dataset, providing a reliable data foundation for site selection. Second, a multi-dimensional quantitative evaluation index system adapted to the tidal characteristics of the central urban area needs to be designed. This system should overcome the limitations of traditional single indicators and accurately characterize peak commuting demand and off-peak supply-demand imbalance through indicators such as tidal intensity index, peak-hour percentage, and off-peak location quotient, enabling scientific selection of target areas.

[0077] Secondly, a data-driven, refined site selection model should be established to overcome the limitations of traditional experience-based decision-making and achieve a three-dimensional linkage between demand, supply, and operation in site selection. This ensures that the site selection results match both travel demand and operational realities. Simultaneously, the method for delineating service area boundaries should be optimized, combining the complete coverage requirements of travel origin-destination pairs with constraints such as road network connectivity, geographical and administrative boundaries to avoid regional fragmentation and ensure continuous and efficient service coverage. Finally, a balance must be struck between demand orientation and feasibility. While accurately identifying high-demand areas, actual operational conditions such as road density and parking scheduling in the central urban area should be fully considered. Off-peak capacity waste and peak-peak connection needs should be quantified to provide spatial site selection support for differentiated operation models and improve the utilization rate of public transport resources.

[0078] Reference Figure 1 This application provides a method for selecting the location of demand-responsive public transport operation areas in central urban areas, specifically including the following steps S100~S130:

[0079] S100: Obtain traffic demand and supply data for the central urban area at various times;

[0080] S110: Match the traffic demand data and traffic supply data for each time period to the geographic grid of the central urban area;

[0081] S120: Based on the geographic grid, determine the commuter tidal areas and traffic supply-demand imbalance areas as candidate site selection areas for demand-responsive public transport operation areas;

[0082] S130: Eliminate non-operable areas in the central urban area from the candidate site selection range as the target site selection range for demand-responsive public transport operation areas.

[0083] Optionally, obtaining traffic demand and supply data for different time periods in the central urban area includes the following steps:

[0084] The data on bus passenger flow, subway passenger flow, ride-hailing orders, and shared bicycle orders in the central urban area at various time periods are obtained as the traffic demand data.

[0085] The bus stops, bus routes, and bus departure frequencies in the central urban area at various times are obtained as the traffic supply data.

[0086] The time periods mentioned include the morning peak, evening peak, and off-peak periods.

[0087] Optionally, matching the traffic demand data and traffic supply data for each time period to the geographic grid of the central urban area includes the following steps:

[0088] The traffic demand data and traffic supply data for each time period are cleaned, their coordinates are unified, and distance weighted interpolation is used for smoothing, and then matched to the geographic grid with a set resolution.

[0089] Optionally, determining the commuter tidal zones and traffic supply-demand imbalance zones as candidate site selection areas for demand-responsive public transport operation areas based on the geographic grid includes the following steps:

[0090] The commuting tidal zone is identified in the geographic grid by the tidal passenger flow intensity index and the proportion of peak hours.

[0091] The tidal passenger flow intensity index (TII) is:

[0092] ;

[0093] Among them, F bus，1 To reduce bus passenger volume during the morning rush hour, F bus ,2 represents the evening peak bus passenger volume, F bus,3 For off-peak bus passenger flow;

[0094] The percentage of peak hours (PR) is:

[0095] ;

[0096] The Commuting Directionality Index (CTI) is calculated as follows:

[0097] ;

[0098] Calculate the off-peak location quotient based on the tidal passenger flow intensity index, the peak period percentage, and the commuting directionality index;

[0099] The regions with traffic supply and demand imbalances are obtained by filtering the geographic grid based on the off-peak location quotient.

[0100] Optionally, calculating the off-peak location quotient based on the tidal passenger flow intensity index, the peak period percentage, and the commuting directionality index includes the following steps:

[0101] Calculate the standard values ​​for each traffic indicator; wherein, the traffic indicators include the tidal passenger flow intensity index, the peak hour percentage, and the commuting directionality index;

[0102] ;

[0103] in, This represents the standard value. Represents any of the aforementioned traffic indicators. This represents the maximum value of the traffic indicator across all analysis grids. This represents the minimum value of the traffic indicator across all analysis grids;

[0104] Calculate the demand index D t for:

[0105] ;

[0106] Where t corresponds to the off-peak period; This represents a standardized population activity index within time period t. Indicators representing standardized public transport demand; This represents a standardized indicator of shared bicycle travel demand. Indicators representing standardized motorized travel demand;

[0107] Calculate the supply index S t for:

[0108] ;

[0109] in, To standardize the number of sites, To standardize the number of lines, To standardize time-segmented service levels;

[0110] Calculate the off-peak location quotient LQ i for:

[0111] ;

[0112] in, For the grid i-peak supply index, For the grid i peak demand index, For the total peak-hour supply across the entire region, This represents the total off-peak demand across the entire region.

[0113] Optionally, the weights required to calculate the demand index and the supply index are obtained through the following steps:

[0114] The rule layer is defined as comprising the target layer, criterion layer, and indicator layer;

[0115] The importance of each supply and demand indicator under the same rule layer is compared pairwise by experts in the transportation field, and then the values ​​are assigned to each supply and demand indicator.

[0116] Construct a judgment matrix based on the assigned values ​​of each of the aforementioned supply and demand indicators. ;

[0117] ;

[0118] in, n represents the number of supply and demand indicators under the same rule layer. This indicates that experts determine supply and demand indicators. Relative supply and demand indicators Importance scale Indicators of supply and demand Relative supply and demand indicators The importance scale is derived by the remaining elements of the judgment matrix following the reciprocity rule;

[0119] Calculate the largest eigenvalue of the judgment matrix to perform a consistency check;

[0120] ;

[0121] in, The largest eigenvalue, The eigenvectors of the judgment matrix are... The judgment matrix With the feature vector The i-th element of the product.

[0122] Optionally, the method further includes a step of boundary optimization of the target location range, wherein the step of boundary optimization of the target location range includes the following steps:

[0123] The target site selection range is optimized based on regional OD coverage efficiency, operational area calculation, peak supply and demand matching rate, off-peak capacity utilization rate, and road density constraints.

[0124] The OD coverage efficiency of the region is: Among them, OD inside The number of complete OD logarithms, where both the start and end points are within the target location range. total The total number of OD pairs starting from the target location range;

[0125] The operating area is calculated as follows: Where k is the adjustment coefficient, and N high V represents the number of high-conflict grids within the target location range. avg The average road traffic speed within the target location area;

[0126] The peak supply-demand matching rate is: ;in, It is the morning rush hour demand index. It is the evening peak demand index. It is the morning rush hour supply index. It is the evening peak supply index;

[0127] The off-peak transport capacity utilization rate is: ;in, It is the off-peak demand index. It is the off-peak supply index;

[0128] The road density constraint is: ;in, It is the road density within the target site selection area.

[0129] The following section will provide a detailed introduction and explanation of the solutions in the embodiments of this application, using specific application examples.

[0130] I. Overall Approach.

[0131] Reference Figure 2 This application aims to establish a responsive public transport operation boundary delineation process adapted to the significant tidal passenger flow characteristics of central urban areas. Based on the core concepts of "problem-oriented, data-driven, and time-period-adaptive," it sequentially achieves a closed-loop process from multi-source data spatiotemporal fusion, significant tidal passenger flow, quantitative identification of supply-demand imbalances, to the generation of geographical and operationally feasible boundaries.

[0132] The first step involves spatiotemporal data fusion and standardization. This includes integrating demand data such as bus passenger flow, subway passenger flow, ride-hailing orders, and shared bicycle orders, as well as supply data such as bus stops, routes, and departure frequencies. Peak hours (7:00-9:00), evening peak hours (17:00-19:00), and off-peak hours (6:00-7:00, 9:00-17:00, 19:00-20:00) are uniformly defined. Through cleaning, coordinate unification, and IDW interpolation smoothing, the data is matched to a 500m×500m geographic grid, forming a three-dimensional data foundation of "time period-space-supply and demand".

[0133] The second step is to quantitatively identify the supply and demand imbalance during peak hours and construct a multi-dimensional indicator system: identify areas with significant commuting tides through the Tidal Passenger Flow Intensity Index (TII) and Peak Hour Percentage (PR), distinguish between residential areas and office / business areas using the Commuting Directional Index (CTI), and then screen the supply and demand imbalance grid of "dispersed demand and redundant supply" through the Off-Peak Location Quotient (LQ), and aggregate them to form the core candidate range of "peak tide + off-peak imbalance".

[0134] The third step is to optimize the geographical and operational boundaries. This involves combining the complete origin-destination (OD) pair distribution of ride-hailing / bike-sharing vehicles within the region, connecting geographical barriers such as main roads and rivers, as well as administrative boundaries, eliminating unoperable areas such as mountainous areas and gated communities, and adjusting the boundaries to ensure coverage of the core travel chain. At the same time, the area is controlled within 3-10 square kilometers to adapt to the "peak-hour fixed-point and fixed-route connection, off-peak on-demand response" operation model, forming a continuous and feasible final service unit.

[0135] II. Model Preparation.

[0136] (1) Determine the evaluation index system.

[0137] This application's embodiments are based on the core characteristics of central urban areas: "significant tidal passenger flow, concentrated peak demand, and redundant off-peak supply." Following the comprehensive principles of "representativeness, quantifiability, data availability, and time-of-day adaptability," a multi-dimensional evaluation index system adapted to differentiated operation models has been constructed. This system approaches the issue from two core dimensions: "time-of-day travel demand" and "time-of-day public transport supply," accurately capturing the supply-demand mismatch at different times and providing data support for the "peak-hour fixed-route, off-peak demand-responsive" operation strategy.

[0138] The demand indicators focus on the time-based differentiation and rigidity of travel in the central urban area. By quantifying the spatiotemporal characteristics of population agglomeration, public transport dependence, short-distance connections, and flexible travel from multiple dimensions, they accurately identify peak-hour commuting gaps and off-peak sporadic demand, including the following four core indicators:

[0139] 1. Grid population size: As a population base measure for highly concentrated spaces, it directly reflects the scale of travel demand generation in the central urban area. It is the core geodemographic basis for inferring the total weekday peak commuting volume and off-peak travel potential, and is adapted to the demand generation characteristics of the high-density population in the central urban area.

[0140] 2. Time-based passenger flow statistics: The responsive public transport demand and regular public transport supply are statistically analyzed separately for morning peak (7:00-9:00), evening peak (17:00-19:00), and off-peak (6:00-7:00, 9:00-17:00, 19:00-20:00). This directly reflects the actual carrying capacity of the public transport network at different times and is a key explicit indicator for observing rigid commuting demand and scattered off-peak travel, providing direct data support for identifying tidal demand fluctuations.

[0141] 3. Shared bike order volume within 5km: Focusing on short-distance connections and last-mile travel, its spatiotemporal distribution reflects the connection chain characteristics of "bus / subway + shared bike" in the central urban area, especially highlighting the "last mile" demand gap around subway stations and business districts during peak hours, as well as the distribution pattern of scattered leisure and business trips during off-peak hours.

[0142] 4.5km of ride-hailing orders: This represents personalized and flexible alternative travel demand. Its traffic intensity and spatial distribution can indirectly identify the shortcomings of regular public transportation in terms of timeliness and flexibility (such as insufficient service during peak congestion periods and remote sections during off-peak hours). It is an important supplementary indicator for judging blind spots and weak links in public transportation services.

[0143] The supply indicators focus on the core contradiction of "peak-hour capacity shortage and off-peak surplus waste" in public transportation in the central urban area. They quantify the supply level from three aspects: coverage density, route diversity, and time-of-day adaptability, with a particular emphasis on the matching degree between supply and demand at different times. These indicators include the following three core metrics:

[0144] 1. Number of bus stops within a grid: This represents the distribution density of bus stops within a spatial unit and is a fundamental element for measuring the physical accessibility of public transportation in the central urban area. Its spatial coupling with population and employment positions directly affects the efficiency of walking connections during peak commutes.

[0145] 2. Number of bus routes passing through the grid: This reflects the diversity of bus routes passing through the spatial unit, embodies the connectivity potential and route selection freedom of the route network, and focuses on meeting the route selection needs of multi-destination travel in the central urban area. At the same time, the differences in its time-of-day operation (such as increased frequency during peak hours and reduced frequency during off-peak hours) directly affect the efficiency of supply and demand matching.

[0146] 3. Service Level 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.

[0147] (2) Data collection and preprocessing.

[0148] The data is mainly divided into demand data and supply data. The raw passenger flow data and bus departure frequency are classified and statistically analyzed according to different time periods, and then analyzed according to a 500m grid: the morning peak period is 7:00-9:00; the evening peak period is 17:00-19:00; and the off-peak period is 6:00-7:00, 9:00-17:00, and 19:00-20:00.

[0149] Data preprocessing proceeds according to the process of "cleaning and standardization → business filtering → transformation and integration → quality verification": First, the time is standardized to datetime format and divided into morning / evening peak and off-peak periods. The spatial coordinate system is uniformly adopted as WGS84, and field errors are corrected simultaneously. Then, out-of-range and abnormal passenger flow orders are removed, duplicate data in key fields is deduplicated, and records with a missing rate of more than 5% are deleted. Next, travel distance and duration are calculated, and spatial smoothing is performed on public transport passenger flow. Population data is resampled into a 500m grid. Finally, it is verified that the data missing rate is <5%, the coordinate accuracy is >98%, and the logic is consistent with commuting patterns, ultimately forming a three-dimensional dataset of "time period-space-supply and demand".

[0150] (3) Distance weight (IDW) interpolation processing.

[0151] The raw bus passenger flow statistics obtained from grid-based bus passenger flow data show that only grids containing bus stops have non-zero values, while the rest have zero values, exhibiting a "discrete point distribution" characteristic that fails to reflect the service radiation of bus stops to the surrounding areas. Therefore, inverse distance weighting (IDW) interpolation is used to achieve spatial smoothing of passenger flow and simulate the service radiation effect of bus stops on surrounding grids. The principle of IDW is that the bus passenger flow of a grid is inversely proportional to the distance to the nearest bus stop; the closer the distance, the greater the weight. The calculation formula for this method is as follows:

[0152] ;

[0153] in, Interpolated passenger flow for the target grid. Let i be the passenger flow of the i-th station. = Let p be the Euclidean distance between the target grid and the i-th station, and p be the weighting coefficient, which is set to 2 here to conform to the conventional spatial decay law.

[0154] III. Model Construction.

[0155] The model in this application embodiment is based on "precise time-segmented adaptation and deep supply-demand coupling". It comprehensively integrates the quantitative formulas and algorithm logic related to the site selection of the central urban area in the materials, and constructs a complete model system of "indicator quantification - standardization processing - weight assignment - supply and demand index calculation - time-segmented screening - boundary optimization - verification closed loop" to ensure coverage of all core formulas and achieve scientific delineation of responsive public transport operation areas.

[0156] (a) “Time-segmented supply and demand coupling screening” model.

[0157] 1. Quantization and transformation of basic features.

[0158] The responsive public transport operation in the central urban area is located in areas with high passenger flow intensity, significant commuter tidal flow, insufficient flexibility of fixed routes, high peak-hour travel demand, and low and dispersed off-peak travel demand, low bus occupancy rates during off-peak hours, and passengers' desire for high-quality travel. We defined relevant parameters to describe the meaning of these phenomena: the Tidal Intensity Index (TII), which measures the intensity of the commuter tidal phenomenon within the grid; the Peak Hour Percentage (PR), which measures the concentration of demand during peak hours; and the Commuter Directionality Index (CTI), which measures commuter directionality. The specific steps are as follows:

[0159] (1) Tidal Passenger Flow Intensity Index (TII):

[0160] ;

[0161] Among them, F bus，1 To reduce bus passenger volume during the morning rush hour, F bus ,2 represents the evening peak bus passenger volume, F bus,3 This refers to off-peak bus passenger volume.

[0162] (2) Peak hour percentage (PR):

[0163] ;

[0164] (3) Commuting Directionality Index (CTI):

[0165] ;

[0166] Among them, grids with CTI>0.5 are residential areas, and grids with CTI<0.5 are office and business areas;

[0167] (5) Standardized range (MinMax) of each traffic indicator:

[0168] ;

[0169] in, This represents the standardized value. Represents the original data value. This indicates the maximum value of the index across all analysis grids. This indicates the minimum value of the index across all analysis grids.

[0170] The calculation of the supply and demand index needs to consider the time-sharing characteristics of the morning peak, evening peak, and off-peak hours in the central urban area. Based on standardized indicators, the analytic hierarchy process (AHP) is used to determine subjective weights. Specifically, on the demand side, the weight of time-sharing public transport passenger flow accounts for 23%, time-sharing shared bicycle passenger flow accounts for 36%, and time-sharing ride-hailing passenger flow accounts for 41%. On the supply side, the weight of the number of grid stations accounts for 28%, the number of routes passing through accounts for 32%, and the time-sharing station service level accounts for 40%. The result is obtained by weighting and summing the standardized indicators according to their corresponding weights. The demand index integrates the basic population size, rigid public transport travel demand, short-distance connection demand, and flexible travel demand to quantify the travel demand intensity of the grid during specific time periods. The supply index integrates station coverage density, route richness, and time-sharing service quality to characterize the public transport service supply capacity of the grid. Together, they provide core quantitative support for judging the state of supply and demand imbalance and conducting time-sharing site selection.

[0171] (1) Demand Index (D) t The formula is as follows:

[0172] ;

[0173] Where t corresponds to the flat peak, and the weights are determined by the subjective and objective weighting method, with a subjective weight of 40% and an objective weight of 60%.

[0174] (2) Supply Index (S) t The formula is as follows:

[0175] ;

[0176] in, To standardize the number of sites, To standardize the number of lines, To standardize time-segmented service levels.

[0177] (3) Location Quotient during Off-Peak Periods (LQ) i The formula is as follows:

[0178] ;

[0179] in, For the grid i-peak supply index, For grid i peak demand index, For the total peak-hour supply across the entire region, This represents the total off-peak demand across the entire region.

[0180] (ii) Regional boundary optimization and operation adaptation model.

[0181] 1. Optimize the boundary of the region:

[0182] (1) Regional OD coverage efficiency: , (OD) inside The complete logarithm of OD, where both the starting and ending points are within the region. total (The total number of OD pairs starting from the origin within the region).

[0183] (2) Calculation of operating area: k is an adjustment factor of 0.002-0.005, N high V represents the number of grid cells with high conflict within the region. avg The average road speed in the region is 1 km / h.

[0184] (3) Peak supply and demand matching rate: , ( It is the morning rush hour demand index. It is the evening peak demand index. It is the morning rush hour supply index. This is the evening peak supply index; a matching rate of less than 1.2 ensures that peak supply meets connection demand.

[0185] (4) Off-peak capacity utilization rate: , ( It is the off-peak demand index. It is the off-peak supply index; to avoid wasting off-peak transport capacity.

[0186] (5) Road density constraints: ( It refers to the regional road density, which is used to ensure road connectivity within the region.

[0187] 2. Determining the weights of the indicators.

[0188] To balance expert experience with objective data patterns, the "multi-indicator supply and demand evaluation index combined with location quotient" model also employs the analytic hierarchy process (AHP) to determine the final weights. The specific steps are as follows:

[0189] (1) Analytic Hierarchy Process (AHP): This method structures and quantifies the complex decision-making problem of "determining the importance of responsive public transport supply and demand indicators in the central urban area." By integrating expert experience and mathematical logic, it obtains scientific subjective weights. Its core is to transform vague qualitative judgments into precise quantitative weights through a process of hierarchical decomposition, pairwise comparison, and consistency testing, thus adapting to the importance assessment scenario of public transport supply and demand indicators in the central urban area. The rule layer includes the following:

[0190] Target layer: Subjective weighting of responsive public transport supply and demand indicators in the central urban area;

[0191] Criteria Level: Demand-side indicators, supply-side indicators;

[0192] Indicator layer: Population size Time-based bus passenger flow Shared bicycle passenger flow Ride-hailing passenger traffic (Demand side), number of grid sites Number of routes Time-based site service levels (Supply side).

[0193] (2) Construct the judgment matrix:

[0194] Experts in transportation planning and public transportation operations were invited to conduct pairwise importance comparisons of supply and demand indicators under the same rule level. A 1-9 scale (1 = equally important, 3 = slightly important, 5 = significantly important, 7 = strongly important, 9 = extremely important, even numbers represent intermediate levels) was used to assign values ​​and construct a judgment matrix. (n represents the number of supply and demand indicators under the same rule layer):

[0195] ;

[0196] Among them, matrix properties: ; Experts determine supply and demand. Relative supply and demand Importance scale Indicating supply and demand Relative supply and demand The importance scale is used for the remaining elements, which are derived according to the reciprocity rule.

[0197] (3) Consistency check (to ensure the reasonableness of weights):

[0198] Calculate the largest eigenvalue of the judgment matrix :

[0199] ;

[0200] in, The eigenvectors (initial weight vectors) of the judgment matrix. For matrix with vector The i-th element of the product; The core parameter for measuring the consistency of the judgment matrix.

[0201] 3. Calculation of grid location quotient during off-peak periods.

[0202] 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, spatial areas with prominent supply-demand imbalances are quantified and identified by calculating the deviation of the ratio of supply to demand indices for each grid unit during off-peak periods relative to the city's average level. The calculation formula is as follows:

[0203] ;

[0204] in, Let i be the location quotient of the i-th grid. Let S be the supply index of the i-th grid, D be the demand index of the i-th grid, S be the total supply of all grids, and D be the total demand of all grids. The larger the location quotient, the higher the supply of regular public transport in that grid exceeds the demand, and the more suitable it is to convert regular public transport to responsive public transport during off-peak hours.

[0205] IV. Adjustment of the operating area boundaries for responsive public transport.

[0206] The delineation of operational boundaries is guided by the core principles of "demand adaptation, feasibility, and operational efficiency," taking into account the differentiated operational characteristics of responsive public transport and the geographical and traffic realities of the central urban area, and clarifying two core principles:

[0207] 1. Principle of Demand Continuity:

[0208] Focusing on the core characteristics of "concentrated peak commuting and dispersed off-peak demand," adjacent high-contact grids (off-peak location quotient LQi ≥ 1.2, tidal intensity index TII ≥ 1.0) are prioritized to aggregate into continuous areas, ensuring the integrity of service coverage. If the distance between a single high-contact grid and its surrounding high-contact grids exceeds 500m, and the grid independently meets the horizontal requirements, it is divided into a separate small service unit to avoid a decrease in response efficiency due to an excessively large service radius.

[0209] 2. Geographical and administrative feasibility principle:

[0210] Using natural geographical barriers (main roads, rivers, railways, etc.) in the central urban area as boundary benchmarks, the impact of detours across these barriers on operational efficiency is reduced. For example, Guangzhou Avenue is used to divide the eastern and western areas of Tianhe District, and the Pearl River is used to divide the northern areas of Haizhu and Panyu Districts. At the same time, priority is given to aligning with street administrative boundaries to facilitate subsequent integration with community management, parking scheduling, station layout, and other resources, thereby reducing implementation and coordination costs.

[0211] An optional implementation method of this application is as follows:

[0212] This paper focuses on the demand-responsive transit location problem in the central urban area of ​​a certain city, and is based on the city's multi-source traffic data.

[0213] First, demand-side data (including time-segmented public transport passenger flow, shared bicycle order volume, ride-hailing order volume, and population within a 500-meter grid) and supply-side data (such as the number of stations within the grid, the number of routes passing through, and time-segmented station service levels) were specifically processed. Abnormal records such as missing fields and out-of-range travel were removed. The MinMax range standardization method was used to normalize each indicator to eliminate the impact of differences in units. Subsequently, the processed multi-source data was uniformly integrated into a 500m × 500m regular gridded geographic data package (GPKG format) for the city. At the same time, basic geographic information layers such as road networks, administrative divisions, high-resolution satellite imagery, and OD distribution of ride-hailing and shared bicycle orders were integrated as the analysis base map to support time-segmented supply and demand analysis.

[0214] Based on this, a "time-segmented supply and demand coupling screening model" was constructed. This model uses weighted and standardized demand and supply indicators, combined with the Tidal Intensity Index (TII), Peak Hour Percentage (PR), and Commuting Directionality Index (CTI), to quantitatively identify the tidal characteristics and demand intensity of each grid unit. Areas with significant tidal characteristics (TII ≥ 1.0, PR ≥ 0.45) were initially selected as candidate areas. Screening analysis was conducted using two core indicator layers at the grid level: the Tidal Intensity Index (TII) and the Peak Hour Percentage (PR). A higher TTI value indicates a more prominent commuting tidal passenger flow characteristic during morning and evening peak hours, and a more significant difference between peak and off-peak passenger flow. Further verification using the Peak Hour Percentage (PR) index confirms that the selected areas all exhibit typical characteristics of a significant decrease in off-peak passenger flow compared to morning and evening peak hours, and sparse passenger flow distribution during off-peak hours.

[0215] Next, the "off-peak grid location quotient model" was introduced to further analyze the matching degree between the off-peak bus supply capacity and the dispersed travel demand within each grid. Grids with significant off-peak supply-demand imbalances (LQ≥1.2) were identified from the initial candidate areas, thus selecting key areas for "peak-hour fixed-route connections and off-peak demand response." Location quotient verification showed that the selected areas all shared the common problem of low off-peak demand and oversupply, aligning with the core application scenarios of responsive public transport: "cost reduction in low-passenger-flow areas" and "service quality improvement in areas." Dynamic scheduling replaces fixed routes, matching scattered off-peak demand and reducing wasted capacity.

[0216] The selected areas in the central urban area serve as the initial areas for responsive public transport operation. To accurately define the core potential demand distribution of responsive public transport in these areas and optimize service boundaries, a detailed analysis is conducted using one of these areas as an example: First, based on the initially defined candidate areas for responsive public transport, the system filters ride-hailing and shared bicycle travel data from all trip orders where "either the origin or destination is located within the candidate area," completing the initial aggregation of target orders; Second, a new dedicated layer for trip origin-destination (OD) distribution is added, spatially mapping the filtered orders according to the precise coordinates of the origin and destination, intuitively presenting the flow characteristics and aggregation of trips within the area. The study focuses on the distribution patterns of hotspots and cross-boundaries. Crucially, it combines the core definition of responsive public transport—that "only when both the origin and destination (OD) points of a travel order within a region fall within the same service area can it be considered valid potential demand"—within the context of the OD distribution layer, further comparing the endogenous travel aggregation range reflected in the OD distribution layer. Simultaneously, it connects with the actual road network pattern of the Financial City (including key elements such as the direction of main and secondary roads, the distribution of intersection nodes, and road network connectivity) to scientifically optimize and adjust the initially delineated regional boundaries. Ultimately, this achieves a high degree of alignment between the service boundaries and the complete OD distribution, ensuring that the adjusted area can cover the actual endogenous travel demand to the greatest extent possible. This provides precise spatial basis for subsequent responsive public transport route planning and capacity allocation.

[0217] Through the above steps, this application embodiment completes a full site selection process, from time-segmented multi-source data preparation, time-segmented tidal passenger flow intensity calculation, off-peak supply and demand contradiction identification model quantitative analysis to site selection area and boundary determination, which can provide an operable practical reference for the optimized allocation of public transport resources and the precise layout of responsive public transport in central urban areas.

[0218] In summary, the embodiments of this application include the following key technical solutions:

[0219] 1. Considering multi-source heterogeneous traffic data, a new intelligent site selection method for demand-responsive public transport in central urban areas is proposed, which integrates multi-source heterogeneous traffic data.

[0220] 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.

[0221] 3. A boundary correction process based on high-resolution geographic information (satellite imagery, road network, administrative divisions) was designed to ensure the feasibility of the operating area.

[0222] This application's implementation, based on multi-source data fusion, constructs a refined site selection system adapted to the tidal passenger flow characteristics of central urban areas, breaking through the limitations of traditional experience-based decision-making. It integrates time-segmented bus, shared bicycle, ride-hailing, and population data, quantifying indicators through standardized processing and subjective / objective weighting methods. Combined with multi-dimensional formulas such as tidal intensity and location quotient, it accurately identifies core areas for "fixed-point, fixed-route connections during peak hours and demand-response during off-peak hours," resolving the contradiction between fixed routes and the spatial-temporal imbalance of supply and demand. Simultaneously, it optimizes boundaries through quantitative constraints such as OD coverage efficiency, road density, and dispatch accessibility, ensuring that areas possess both demand integrity and operational feasibility, adapting to the "fixed-point, fixed-route connections during peak hours and on-demand response during off-peak hours" model. This application's implementation not only improves the scientific rigor and accuracy of site selection but also enhances its feasibility through geographical constraints and operational indicator verification, effectively improving the utilization rate of public transport resources, reducing off-peak capacity waste, and improving peak-hour connection experience, providing a data-driven and replicable practical solution for public transport optimization in central urban areas.

[0223] Reference Figure 3 This application provides a site selection device for demand-responsive public transport operation areas in central urban areas, comprising:

[0224] The data acquisition unit is used to acquire traffic demand and traffic supply data for various time periods in the central urban area.

[0225] A data matching unit is used to match the traffic demand data and the traffic supply data for each time period to the geographic grid of the central urban area;

[0226] The first site selection unit is used to determine, based on the geographic grid, commuter tidal areas and areas of traffic supply and demand imbalance as candidate site selection areas for demand-responsive public transport operation areas;

[0227] The second site selection unit is used to eliminate non-operable areas in the central urban area from the candidate site selection range as the target site selection range for demand-responsive public transport operation areas.

[0228] 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.

[0229] 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.

[0230] 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.

[0231] 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.

[0232] 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-including 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.

[0233] 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.

[0234] 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.

[0235] 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.

[0236] 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.

[0237] 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 demand-responsive public transport operation areas in central urban areas, characterized in that, The method includes the following steps: Obtain traffic demand and supply data for the central urban area at different times; Match the traffic demand data and traffic supply data for each time period to the geographic grid of the central urban area; Based on the geographic grid, commuter tidal zones and areas of traffic supply-demand imbalance are identified as candidate site selection areas for demand-responsive public transport operation areas; The non-operable areas in the central urban area are excluded from the candidate site selection range, which is then used as the target site selection range for demand-responsive public transport operation areas.

2. The site selection method for a demand-responsive public transport operation area in a central urban area according to claim 1, characterized in that, The acquisition of traffic demand and supply data for the central urban area at various time periods includes the following steps: The data on bus passenger flow, subway passenger flow, ride-hailing orders, and shared bicycle orders in the central urban area at various time periods are obtained as the traffic demand data. The bus stops, bus routes, and bus departure frequencies in the central urban area at various times are obtained as the traffic supply data. The time periods mentioned include the morning peak, evening peak, and off-peak hours.

3. The method for selecting a location for a demand-responsive public transport operation area in a central urban area according to claim 1, characterized in that, Matching the traffic demand data and traffic supply data for each time period to the geographic grid of the central urban area includes the following steps: The traffic demand data and traffic supply data for each time period are cleaned, their coordinates are unified, and distance weighted interpolation is used for smoothing, and then matched to the geographic grid with a set resolution.

4. The method for selecting a location for a demand-responsive public transport operation area in a central urban area according to claim 1, characterized in that, The process of determining commuter tidal zones and traffic supply-demand imbalance zones as candidate site selection areas for demand-responsive public transport operation areas based on the geographic grid includes the following steps: The commuting tidal zone is identified in the geographic grid by the tidal passenger flow intensity index and the proportion of peak hours. The Tidal Passenger Flow Intensity Index (TII) is: ； Among them, F bus，1 To reduce bus passenger volume during the morning rush hour, F bus ,2 represents the evening peak bus passenger volume, F bus,3 For off-peak bus passenger flow; The percentage of peak hours (PR) is: ； The Commuting Directionality Index (CTI) is calculated as follows: ； Calculate the off-peak location quotient based on the tidal passenger flow intensity index, the peak period percentage, and the commuting directionality index; The regions with traffic supply and demand imbalances are obtained by filtering the geographic grid based on the off-peak location quotient.

5. The site selection method for a demand-responsive public transport operation area in a central urban area according to claim 4, characterized in that, The calculation of the off-peak location quotient based on the tidal passenger flow intensity index, the peak period percentage, and the commuting directionality index includes the following steps: Calculate the standard values ​​for each traffic indicator; wherein, the traffic indicators include the tidal passenger flow intensity index, the peak hour percentage, and the commuting directionality index; ； in, This represents the standard value. This represents any of the aforementioned traffic indicators. This represents the maximum value of the traffic indicator across all analysis grids. This represents the minimum value of the traffic indicator across all analysis grids; Calculate the demand index D t for: ； Where t corresponds to the off-peak period; This represents a standardized population activity index within time period t. Indicators representing standardized public transport demand; This represents a standardized indicator of shared bicycle travel demand. Indicators representing standardized motorized travel demand; Calculate the supply index S t for: ； in, To standardize the number of sites, To standardize the number of lines, To standardize time-segmented service levels; Calculate the off-peak location quotient LQ i for: ； in, For the grid i-peak supply index, For the grid i peak demand index, For the total peak-hour supply across the entire region, This represents the total off-peak demand across the entire region.

6. The site selection method for a demand-responsive public transport operation area in a central urban area according to claim 5, characterized in that, The weights required to calculate the demand index and the supply index are obtained through the following steps: The rule layer is defined as comprising the target layer, criterion layer, and indicator layer; The importance of each supply and demand indicator under the same rule layer is compared pairwise by experts in the transportation field, and then the values ​​are assigned to each supply and demand indicator. Construct a judgment matrix based on the assigned values ​​of each of the aforementioned supply and demand indicators. ; ； in, n represents the number of supply and demand indicators under the same rule layer. This indicates that experts determine supply and demand indicators. Relative supply and demand indicators Importance scale Indicators of supply and demand Relative supply and demand indicators The importance scale is derived by the remaining elements of the judgment matrix following the reciprocity rule; Calculate the largest eigenvalue of the judgment matrix to perform a consistency check; ； in, The largest eigenvalue, The eigenvectors of the judgment matrix are... The judgment matrix With the feature vector The i-th element of the product.

7. A site selection method for a demand-responsive public transport operation area in a central urban area according to any one of claims 1 to 6, characterized in that, The method further includes a step of boundary optimization of the target location range, which includes the following steps: The target site selection range is optimized based on regional OD coverage efficiency, operational area calculation, peak supply and demand matching rate, off-peak capacity utilization rate, and road density constraints. The OD coverage efficiency of the region is: Among them, OD inside The number of complete OD logarithms, where both the start and end points are within the target location range. total The total number of OD pairs starting from the target location range; The operating area is calculated as follows: Where k is the adjustment coefficient, and N high V represents the number of high-conflict grids within the target location range. avg The average road traffic speed within the target location area; The peak supply-demand matching rate is: ;in, It is the morning rush hour demand index. It is the evening peak demand index. It is the morning rush hour supply index. It is the evening peak supply index; The off-peak transport capacity utilization rate is: ;in, It is the off-peak demand index. It is the off-peak supply index; The road density constraint is: ;in, It is the road density within the target site selection area.

8. A site selection device for demand-responsive public transport operation areas in central urban areas, characterized in that, The device includes: The data acquisition unit is used to acquire traffic demand and traffic supply data for various time periods in the central urban area. A data matching unit is used to match the traffic demand data and the traffic supply data for each time period to the geographic grid of the central urban area; The first site selection unit is used to determine, based on the geographic grid, commuter tidal areas and areas of traffic supply and demand imbalance as candidate site selection areas for demand-responsive public transport operation areas; The second site selection unit is used to eliminate non-operable areas in the central urban area from the candidate site selection range as the target site selection range for demand-responsive public transport operation areas.

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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