Urban traffic bearing capacity assessment method and system

Abstract

The invention provides an urban traffic bearing capacity assessment method and system, and the method comprises the steps: carrying out the block division of a target city region, obtaining a plurality of traffic subregions, and constructing a multi-mode traffic system of the target city region; determining the total space-time resource supply quantity of the multi-mode traffic system based on the basic parameters of the traffic subsystems; according to the travel matching degree among the traffic modes, allocating the traffic travel volume of the traffic subareas, and determining the total traffic demand of the multi-mode traffic system; and determining a multi-mode traffic bearing capacity index of the multi-mode traffic system based on the total space-time resource supply quantity and the total traffic demand quantity, and evaluating the traffic bearing state of the target city region according to the multi-mode traffic bearing capacity index. The problem that a traditional method only pays attention to a road network and ignores multi-mode collaboration is solved; the resource supply evaluation is expanded from a single dimension to a full system dimension, and the comprehensiveness and precision of supply potential measurement and calculation are improved.

Description

Technical Field

[0001] This invention relates to the field of transportation, and in particular to a method and system for assessing urban traffic carrying capacity. Background Technology

[0002] Traffic carrying capacity refers to the maximum traffic demand that a transportation facility or area can accommodate under specific transportation systems, service levels, and environmental constraints. Traffic carrying capacity is a crucial indicator of whether an urban transportation system can meet the travel needs of residents. If traffic demand far exceeds supply, it will cause problems such as road congestion and an overburdened public transportation system, seriously affecting the travel efficiency and quality of life of urban residents.

[0003] Existing traffic carrying capacity studies mainly focus on urban road networks, without considering the diverse travel and demand transitions between different modes of transportation, making it difficult to effectively characterize the real multimodal road network carrying capacity. Summary of the Invention

[0004] In view of this, the purpose of the present invention is to provide a method and system for assessing urban traffic carrying capacity, which solves the problem that the assessment of traffic carrying capacity in the prior art is difficult to represent the true multi-modal road network carrying capacity.

[0005] Firstly, this application provides a method for assessing urban traffic carrying capacity, including:

[0006] The target city area is divided into several traffic zones, and a multimodal transportation system for the target city area is constructed; the multimodal transportation system has multiple traffic subsystems based on different traffic modes;

[0007] Based on the basic parameters of each transportation subsystem, the corresponding spatiotemporal resource supply is determined, and the spatiotemporal resource supply of each transportation subsystem is superimposed to obtain the total spatiotemporal resource supply of the multimodal transportation system.

[0008] The traffic volume of traffic zones is allocated based on the travel matching degree between different traffic modes, the traffic demand of each traffic subsystem is determined by sub-mode, and the traffic demand of each traffic subsystem by sub-mode is superimposed to obtain the total traffic demand of the multi-modal transportation system.

[0009] Based on the total spatiotemporal resource supply and the total traffic demand, a multimodal transportation carrying capacity index for the multimodal transportation system is determined, and the traffic carrying capacity status of the target urban area is assessed based on the multimodal transportation carrying capacity index.

[0010] Secondly, this application provides an urban traffic carrying capacity assessment system, including a processor and a memory; wherein the memory stores a computer program, which is loaded by the processor and executed as described in any one of the first aspects of the urban traffic carrying capacity assessment method.

[0011] In the urban traffic carrying capacity assessment method and system of this embodiment, by dividing the target urban area into multiple traffic zones and constructing a multi-modal transportation system including roads, rail, and public transportation, a systematic model of the urban transportation system is achieved, solving the problem that traditional methods only focus on the road network and ignore multi-modal coordination. It expands resource supply assessment from a single dimension to a system-wide dimension, improving the comprehensiveness and accuracy of supply potential measurement. By constructing a multi-modal traffic carrying capacity index through the relationship between total supply and total demand, it comprehensively reflects the supply and demand balance of the urban transportation system. It can not only identify congestion bottlenecks, but also support cross-modal capacity allocation and facility connection optimization, thereby realizing the leap from static single-modal assessment to dynamic multi-dimensional assessment, and comprehensively improving the scientificity and practicality of transportation planning decisions. Attached Figure Description

[0012] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0013] Figure 1 This is a flowchart illustrating an urban traffic carrying capacity assessment method provided in one embodiment of this application.

[0014] Figure 2 This is a schematic diagram of the structure of an electronic device provided in one embodiment of this application. Detailed Implementation

[0015] The specific embodiments of the present invention will now be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are merely some, not all, of the embodiments of the present invention. Based on the description of the present invention, all other embodiments obtained by those skilled in the art without inventive effort are within the scope of protection of the present invention.

[0016] In the description of this invention, unless otherwise explicitly specified and limited, the terms "set," "install," "connect," etc., should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium. Those skilled in the art can understand the specific meaning of the above terms according to the specific circumstances.

[0017] The terms “upper,” “lower,” “left,” “right,” “front,” “back,” “top,” “bottom,” “inner,” and “outer,” etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, or the orientation or positional relationship commonly used when the product of the invention is in use. They are only for the convenience of description and simplification, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the present invention.

[0018] The terms “first,” “second,” “third,” etc., are used merely to distinguish elements with similar properties, not to indicate or imply relative importance or a specific order.

[0019] The terms “include,” “comprising,” or any other variation thereof are intended to cover non-exclusive inclusion, which includes not only the elements listed but also other elements not expressly listed.

[0020] like Figure 1 As shown in the figure, this embodiment provides a method for assessing urban traffic carrying capacity, including:

[0021] Step S10: Divide the target city area into several traffic zones and construct a multimodal transportation system for the target city area; the multimodal transportation system has multiple traffic subsystems based on different traffic modes;

[0022] Step S20: Determine the corresponding spatiotemporal resource supply based on the basic parameters of each transportation subsystem, and superimpose the spatiotemporal resource supply of each transportation subsystem to obtain the total spatiotemporal resource supply of the multimodal transportation system.

[0023] Step S30: Allocate traffic trip volume in traffic zones according to the travel matching degree between each traffic mode, determine the sub-mode traffic demand of each traffic subsystem, and superimpose the sub-mode traffic demand of each traffic subsystem to obtain the total traffic demand of the multi-modal traffic system.

[0024] Step S40: Determine the multimodal transportation carrying capacity index of the multimodal transportation system based on the total spatiotemporal resource supply and the total traffic demand, and assess the traffic carrying capacity status of the target urban area based on the multimodal transportation carrying capacity index.

[0025] In the urban traffic carrying capacity assessment method of this embodiment, by dividing the target urban area into multiple traffic zones and constructing a multi-modal transportation system including roads, rail, and public transportation, a systematic model of the urban transportation system is achieved, solving the problem that traditional methods only focus on the road network and ignore multi-modal coordination. This expands resource supply assessment from a single dimension to a system-wide dimension, improving the comprehensiveness and accuracy of supply potential measurement. By constructing a multi-modal traffic carrying capacity index through the relationship between total supply and total demand, it comprehensively reflects the supply and demand balance of the urban transportation system. This not only identifies congestion bottlenecks but also supports cross-modal capacity allocation and facility connection optimization, thereby achieving a leap from static single-modal assessment to dynamic multi-dimensional assessment, and comprehensively improving the scientificity and practicality of transportation planning decisions.

[0026] Step S10: Divide the target city area into blocks to obtain several traffic zones, and construct a multi-modal transportation system for the target city area; the multi-modal transportation system has multiple traffic subsystems based on different traffic modes.

[0027] The target urban area refers to the geographical area of ​​the city whose traffic carrying capacity is to be assessed. It can be used as the spatial basis for traffic carrying capacity assessment and to define the geographical boundaries of the analysis. For example, in this embodiment, the target urban area can be Haizhu District of Guangzhou City, or Jiangnan West Road and its surrounding area in Haizhu District of Guangzhou City.

[0028] Furthermore, traffic zoning involves dividing a target urban area into sub-regions based on spatial proximity and travel patterns. These sub-regions can serve as basic spatial units for generating and allocating traffic demand, supporting refined travel behavior modeling. In a specific embodiment, traffic zoning can be achieved through clustering or gridding based on data such as population density, land use type, and travel origin-destination distribution.

[0029] "Dividing the target city area into several traffic zones" refers to spatially segmenting the target city area to form multiple sub-regions. Furthermore, this can be achieved by using a regular grid method to divide the target city area into equally sized rectangular units, or by using clustering algorithms to divide functionally similar areas based on the travel origin-destination (OD) matrix. This allows for more refined spatial processing and improves the spatial resolution of subsequent demand allocation and supply measurement.

[0030] In one embodiment, dividing the target urban area into several traffic zones specifically includes: spatially overlaying the urban land use map, the road network vector map of the road subsystem, the rail subsystem line and station distribution map, and the public transport subsystem route to generate a comprehensive overlay analysis map; classifying the partition types of the comprehensive overlay analysis map according to the consistency of land use; and partitioning the blocks of the corresponding partition types in the comprehensive overlay analysis map according to preset spatial boundary rules and scale control threshold rules to obtain several traffic zones.

[0031] The comprehensive overlay analysis map is a composite geographic information layer that integrates urban land use properties with the spatial distribution of multi-modal transportation infrastructure. It can be used to provide a spatial analysis base reflecting the synergistic relationship between land use and transportation facilities, supporting function-oriented traffic zoning. In this embodiment, the comprehensive overlay analysis map is obtained by geometrically aligning and merging urban land use maps, road network vector maps, rail line and station distribution maps, and bus route and station distribution maps using GIS spatial overlay technology.

[0032] When overlaying comprehensive analysis maps, spatial overlay operations can be performed on multi-source geographic layers under a unified coordinate system to form a new layer that integrates land use and transportation network information. Precise spatial matching can be achieved by using layer intersection operations in GIS software, or by merging multi-layer data after rasterization and pixel-by-pixel assignment. This allows for the construction of spatial relationships that include land use and multi-modal transportation facilities, providing a data foundation for function-oriented zoning.

[0033] Zoning types are spatial categories with similar functional characteristics formed based on the consistency of land use. They can be used as the functional classification basis for traffic zoning, enabling the zoning results to reflect the actual travel generation and attraction characteristics. In a specific embodiment, zoning types can be obtained by clustering and identifying the land use layer in the comprehensive overlay analysis map to extract functionally homogeneous areas. Furthermore, zoning types can include, but are not limited to, one or more of the following: residential-dominated, employment-center, and mixed-use.

[0034] The preset spatial boundary rules and scale control threshold rules are a set of dual-element rules used to constrain the traffic zone generation process. The spatial boundary rules ensure the geographical continuity and boundary rationality of the zones, while the scale control threshold rules ensure that the travel volume within the zones meets the statistical validity requirements. They can be used to simultaneously satisfy the spatial logical rationality and data analysis feasibility in the zone generation process, avoiding fragmentation or excessively large zones.

[0035] When partitioning, spatial clustering or segmentation is performed within the identified partition types based on boundary continuity and minimum size requirements. This can be achieved by applying a region growing algorithm to expand connected regions that meet threshold conditions from seed points, or by merging fragmented units according to rules after grid aggregation. This generates traffic partitions that are both geographically sound and statistically valid, improving the spatial adaptability of subsequent demand allocation and supply assessment.

[0036] Taking the preparation stage of the traffic carrying capacity assessment of an urban renewal area as an example, for a certain old city renovation project, before assessing its future traffic carrying capacity, spatial data such as the land use planning map, existing road network, subway station distribution, and bus route layout of the area are first integrated. Layers are overlaid through a GIS platform to generate a comprehensive overlay analysis map. On this map, three types of zoning are identified: residential-dominated, commercial service-oriented, and mixed-use. Subsequently, based on the preset spatial boundary rules (such as not crossing overpasses or railway lines) and scale control threshold rules (each zone must cover at least 5,000 permanent residents), various functional blocks are divided in a coherent and statistically significant manner, ultimately forming several traffic zones. This provides an accurate spatial organization framework for the subsequent construction of a multimodal transportation system and quantitative analysis of supply and demand.

[0037] After zoning, the rationality of each traffic zone can be verified to avoid deviations in demand calculations due to insufficient transportation facility coverage within the zone. For example, by calculating the road network density and public transportation station coverage rate for each zone, the road network density is required to be no less than 4 km / km², and the public transportation station coverage rate no less than 80%. If there are zones with a road network density lower than 4 km / km² or a public transportation station coverage rate lower than 80%, the spatial boundaries of these zones need to be readjusted. This can be done by merging densely populated areas within adjacent zones or splitting sparsely populated areas, ensuring that the adjusted zones meet the requirements for road network density and public transportation station coverage rate. Road network density refers to the ratio of the total mileage of the road subsystem within a zone to the zone's land area; public transportation station coverage rate is the ratio of the sum of the service coverage areas of rail stations and conventional bus stations within a zone to the zone's land area.

[0038] A multimodal transportation system is a complex transportation network that integrates multiple modes of transportation. It can be used to provide a unified analysis platform covering diverse travel modes, supporting supply and demand assessments at the system-wide level. For example, a multimodal transportation system includes a road subsystem, a rail subsystem, and a public transport subsystem.

[0039] A transportation subsystem is an independent operating network corresponding to a specific transportation mode in a multimodal transportation system. It is used to characterize the supply capacity and operational characteristics of a single transportation mode and serves as the basic unit for resource supply and demand allocation.

[0040] When constructing a multimodal transportation system, network topology data of various transportation modes can be integrated through a GIS platform, and multi-layer network models can be built using traffic simulation software, thereby enabling the construction of a multimodal transportation system that covers multiple modes of travel.

[0041] For example, a road subsystem can be constructed by collecting basic road parameters of all roads within the target city area. These parameters include the starting and ending coordinates, segment length, number of lanes, lane width, design speed, pavement type (asphalt or concrete), and intersection type (signaled or unsignaled). A digital model of the road subsystem is then built using traffic simulation software (such as VISSIM) based on these parameters, recreating lane divisions, traffic light timings, speed limits, and yield rules for each segment. Simultaneously, traffic flow detection nodes are set up within the road subsystem. Each node is positioned at the midpoint of a segment or at the exit of an intersection. The detection parameters for each node include the number of vehicles passing per unit time, average vehicle speed, and the percentage of different vehicle types.

[0042] For example, a bus subsystem can be constructed by collecting basic bus parameters for all regular bus routes within the target city area. These basic parameters include the starting and ending points of each bus route, route mileage, names and locations of stops along the way, bus vehicle parameters (rated passenger capacity, number of carriages, average speed, fuel type), and operating parameters (departure interval, stop time at each stop, and speed differences between peak and off-peak hours). A digital model of the bus subsystem is then built based on these basic parameters in public transportation simulation software (such as TransModeler), recreating the route direction, stop locations and platform areas, and vehicle stopping rules. Simultaneously, passenger flow detection nodes are set up within the bus subsystem. Each detection node is located in the passenger boarding and alighting area of ​​a bus stop, and the detection parameters of each node include data such as the number of passengers boarding, alighting, and waiting within a unit of time.

[0043] By dividing the target urban area into multiple traffic zones and constructing a multimodal transportation system including roads, rail, and buses, a systematic model of the urban transportation system is achieved, solving the problem that traditional methods only focus on the road network and ignore multimodal coordination.

[0044] Step S20: Determine the corresponding spatiotemporal resource supply based on the basic parameters of each transportation subsystem, and superimpose the spatiotemporal resource supply of each transportation subsystem to obtain the total spatiotemporal resource supply of the multimodal transportation system.

[0045] The basic parameters are quantifiable key indicators collected from the actual operation and facility characteristics of each subsystem, including the basic parameters of each subsystem and the basic parameters of traffic demand; among which the basic parameters of traffic demand include the trip generation, trip attraction, initial values ​​of modal share of each traffic zone and the time value of residents' trips during the assessment period.

[0046] Spatiotemporal resource supply refers to the effective transportation service capacity that can be provided by the system within a specific time period. It can be used to reflect the actual available resource level of a single transportation mode in the temporal and spatial dimensions.

[0047] In one embodiment, determining the corresponding spatiotemporal resource supply based on the basic parameters of each transportation subsystem specifically includes:

[0048] Based on the equivalent passenger capacity per lane, service level coefficient, assessment period duration, total mileage, single-lane departure interval, average delay time, and spatiotemporal resource utilization efficiency coefficient of the road subsystem, the first spatiotemporal resource supply of the road subsystem is determined according to a preset first spatiotemporal resource formula; and / or

[0049] Based on the vehicle capacity, maximum load factor, evaluation period duration, total mileage, departure interval, and average dwell time of the track subsystem, the spatiotemporal resource supply of the track subsystem is determined according to a preset second spatiotemporal resource formula; and / or

[0050] The third spatiotemporal resource supply of the public transport subsystem is determined according to the preset third spatiotemporal resource formula based on the vehicle's rated passenger capacity, load factor, evaluation period duration, total mileage, departure interval, average stop time, and utilization efficiency coefficient.

[0051] Specifically, the expression for the first spatiotemporal resource formula is:

[0052]

[0053] In the formula, For the first time and space resource supply, The equivalent passenger capacity for a single lane can be calculated based on the proportion of vehicle types, with 1.5 people per vehicle for small cars, 3 people per vehicle for medium-sized cars, and 10 people per vehicle for large cars. The service level factor, with a value ranging from 0.65 to 0.95, is determined by the ratio of the average vehicle speed to the design speed. To assess the duration of the assessment period, its function is to provide a unified time benchmark for calculating the spatiotemporal resource supply and traffic demand of the road subsystem, and it is set according to durations such as 30 minutes, 1 hour or 2 hours. The total mileage of the road subsystem is equal to the sum of the products of the length of all road segments and the number of lanes corresponding to them. The departure interval for a single lane is determined by the number of vehicles passing through the traffic flow detection node per unit time. The average delay time is determined by the difference between the designed travel time and the actual travel time of the road segment; The spatiotemporal resource utilization efficiency coefficient has a value range of 0.75-0.9 and is determined by the ratio of the actual number of vehicles passing through to the designed number of vehicles passing through the lane.

[0054] The expression for the second spatiotemporal resource formula is:

[0055]

[0056] In the formula, For the supply of resources in the second spacetime; Vehicle capacity indicates the maximum number of passengers a rail vehicle can carry. The maximum load factor of the system is 0.7-1.0, and is determined by "the number of people entering the station during peak hours / (vehicle capacity × departure frequency)". To assess the duration of the assessment period, its function is to provide a unified time reference for calculating the supply of spatiotemporal resources and traffic demand of the rail subsystem, and it is set in durations such as 30 minutes, 1 hour or 2 hours. The total mileage is equal to the sum of the mileages of all rail lines. This refers to the system's departure interval; The average dwell time is calculated by averaging the dwell times of each station.

[0057] The expression for the third spacetime resource formula is:

[0058]

[0059] In the formula, For the supply of resources in the third spacetime; The rated passenger capacity of public transport vehicles; The load factor is 0.6-0.9, and is determined by "average number of passengers entering the station / (vehicle capacity × departure frequency)". To assess the duration of the assessment period, its function is to provide a unified time reference for calculating the supply of spatiotemporal resources and traffic demand of the rail subsystem, and it is set in durations such as 30 minutes, 1 hour or 2 hours. The total mileage is equal to the sum of the products of the mileage of all bus routes and the number of vehicles deployed on the corresponding routes. For departure intervals; The average stop time is calculated by averaging the stop times at each station. To utilize the efficiency coefficient, the value ranges from 0.7 to 0.85, and is determined by "actual operating time of the route / planned operating time".

[0060] The total spatiotemporal resource supply of superimposed multimodal transportation systems At that time, the resource supply in the first, second, and third time spaces can be directly added together in an additive manner, that is:

[0061]

[0062] Understandably, a weighted summation method can also be used to calculate the total spatiotemporal resource supply. .

[0063] Step S30: Allocate traffic volume in traffic zones according to the travel matching degree between each traffic mode, determine the traffic demand of each traffic subsystem by mode, and superimpose the traffic demand of each traffic subsystem by mode to obtain the total traffic demand of the multimodal transportation system.

[0064] Trip matching is an indicator that measures the degree of compatibility between different modes of transportation in terms of accessibility, convenience, and user preferences. It can be used to influence the distribution of transportation demand among different modes and reflect real travel choices.

[0065] Traffic volume refers to the total number of trips or the total demand for travel within a traffic zone, and can be used to represent the overall level of travel activity within the area. For example, traffic volume can include one or more of the following: commuter travel, daily travel, and business travel.

[0066] In one embodiment, traffic volume is allocated to traffic zones based on the travel matching degree between different traffic modes to determine the sub-mode traffic demand of each traffic subsystem, specifically including:

[0067] Step S301: Based on the trip generation and trip attraction of each traffic zone, calculate the traffic trip volume between each traffic zone using a preset trip volume determination formula;

[0068] Step S302: Based on the initial values ​​of the modal share of each traffic mode and the inter-modal demand conversion coefficient, the traffic trip volume is allocated to obtain the sub-modal traffic trip volume of each traffic subsystem.

[0069] Step S303: Calculate the traffic demand of each subsystem based on the traffic volume of each subsystem and the travel distance and travel demand intensity coefficient of the corresponding subsystem.

[0070] Step S304: Based on the trip generation and trip attraction of each traffic zone, calculate the traffic trip volume between each traffic zone using a preset trip volume determination formula.

[0071] In step S301, the trip generation volume of each traffic zone refers to the total number of trips originating within the traffic zone, which can be used to reflect the region's ability to generate trips. In a specific embodiment, the trip generation volume of each traffic zone can be estimated using a regression model or trip rate method based on socioeconomic data such as the zone's population, employment, and land use.

[0072] Trip generation refers to the total number of trip origins and destinations within a specific transportation zone during a given assessment period, encompassing the travel behavior of all participants within that zone. For example, a formula can be used... Determine the amount of travel generated ,in This refers to the number of permanent residents within the district. The number of jobs within the zone. The travel generation coefficient for permanent residents ranges from 0.8 to 1.2. The travel generation coefficient for employment positions ranges from 1.0 to 1.5.

[0073] Trip attraction refers to the total number of trips from other transportation zones to a specific transportation zone within a given assessment period, attracted by the zone's own functions. For example, a formula can be used... Determine travel attraction ,in This refers to the commercial building area within the zone. For the number of public service facilities, The commercial building's travel attraction coefficient ranges from 0.002 to 0.005. The travel attraction coefficient for public service facilities ranges from 0.5 to 1.0. In this embodiment, the travel attraction of each traffic zone may include, but is not limited to, one or more of the following: employment attraction, consumption attraction, and education attraction.

[0074] In one embodiment, the expression for the preset travel volume determination formula is:

[0075]

[0076] Among them, the For traffic volume; the aforementioned The average daily trip generation for the traffic zone; The average daily travel attraction of the traffic zone; To partition To partition The impedance function corresponding to the travel cost is expressed as follows: ,in The impedance coefficient has a value range of 0.01-0.05. To partition To partition The travel cost is expressed as: The higher the travel cost, the higher the impedance coefficient. The larger the value, the better. This is the time cost coefficient, with a value ranging from 1.0 to 2.0. To partition To partition Average travel time This is the economic cost coefficient, with a value ranging from 0.5 to 1.0. For partitioning To partition The average economic cost of travel.

[0077] In step S302, the initial modal share value is a baseline value representing the proportion of trips undertaken by each transportation mode before considering dynamic transitions between modes. For example, the initial modal share value may include one or more of the following: initial modal share value for rail transit, initial modal share value for conventional public transport, and initial modal share value for private cars.

[0078] Intermodal demand conversion coefficient is a sensitive parameter that describes the degree of change in demand for other modes when one mode of transportation changes. It can be used to quantify the substitution or complementarity between different modes of transportation and support dynamic demand reallocation.

[0079] Modal traffic trip volume is the number of trips allocated to various transportation subsystems after considering mode sharing and conversion relationships. It can be used to represent the trip frequency undertaken by each mode of transportation. Furthermore, modal traffic trip volume can include one or more of the following: subway trip volume, bus trip volume, and bicycle trip volume. In addition, modal traffic trip volume can serve as input for modal traffic demand, and after incorporating spatiotemporal consumption factors, it can be transformed into final demand volume.

[0080] In one embodiment, traffic trips are allocated based on the initial modal share of each traffic mode and the inter-modal demand conversion coefficient, resulting in the modal traffic trip volume for each traffic subsystem, specifically including:

[0081] Step S3021: Determine the initial modal share of each traffic mode, and determine the initial sub-modal traffic volume of each subsystem based on the initial modal share and traffic volume.

[0082] The initial modal share reflects the proportion of travel demand attracted by each transportation mode in the initial state and can be obtained through historical passenger flow data statistics. For example, for the rail subsystem, its modal share can be determined by the ratio of the total number of passengers entering and leaving rail stations to the total number of trips; while for the public transport subsystem, it can be determined by the ratio of the total number of passengers boarding and alighting at bus stations to the total number of trips. Furthermore, determining the initial sub-modal traffic volume based on the initial modal share and the traffic volume can be the result of a weighted allocation of the total traffic volume according to the modal share of each mode.

[0083] Step S3022: Determine the demand conversion coefficient between the two subsystems based on the incremental travel cost between the two subsystems.

[0084] The increase in travel costs is the degree to which the overall cost of travel increases due to resource constraints in the transportation subsystem. It can be reflected in quantitative indicators after converting non-monetary costs such as longer travel time, increased expenses, or decreased comfort.

[0085] In one embodiment, the demand conversion coefficient between the two subsystems is determined based on the travel cost increment between the two subsystems. Specifically, this includes: determining the travel cost increment of the corresponding subsystem based on the time value of residents' travel and the travel cost of the corresponding transportation mode; the travel cost includes the time cost, economic cost and comfort cost of each transportation mode; and determining the demand conversion coefficient between the two subsystems based on the travel cost increment between the two subsystems using a preset demand conversion function.

[0086] The time value of residents' travel is the economic value corresponding to a unit of travel time, reflecting an individual's subjective evaluation of time costs. In one specific embodiment, the time value of residents' travel can be obtained through questionnaires, wage level estimation, or backcalibration using discrete choice models. Furthermore, the time value of residents' travel includes one or more of the following: the time value of commuters, the time value of business travelers, and the time value of flexible travelers.

[0087] The travel cost of a specific transportation mode is the comprehensive cost incurred in completing a trip using that mode. It can be used to characterize the service level performance of a transportation mode and support comparisons of the relative attractiveness between different modes. For example, the travel cost of a specific transportation mode is obtained by integrating dimensions such as time consumption, expenses, and comfort loss, and quantifying them using a unified unit. Furthermore, the travel cost of a specific transportation mode consists of time cost, economic cost, and comfort cost, serving as the basic input for calculating the increment of travel costs.

[0088] Time cost refers to the opportunity cost incurred due to the time spent during travel, and can be determined by the ratio of travel distance to the average travel speed of each mode of transportation. In this embodiment, time cost includes, but is not limited to, one or more of the following: in-vehicle travel time cost, waiting time cost, and transfer connection time cost.

[0089] Economic cost refers to the monetary expenses directly paid during the trip. In one embodiment, economic cost is obtained by statistically analyzing or calculating actual expenditure data such as ticket price, fuel cost, parking fee, and toll fee. Furthermore, economic cost can be reflected in terms of ticketing expenses, vehicle usage costs, and ancillary service charges.

[0090] Comfort costs refer to the hidden costs incurred by users due to differences in environmental, spatial, and service conditions during transportation. Generally, the lower the comfort level, the higher the hidden costs. For example, comfort costs can be mapped to equivalent time or monetary costs through subjective satisfaction ratings, or estimated based on objective indicators such as vehicle density. Furthermore, comfort costs can include one or more of the following: crowding-related comfort costs, vibration and noise-related costs, and air quality-related costs.

[0091] Incremental travel costs refer to the increase in costs incurred by a traveler for completing a trip under specific transportation scenarios (such as before and after a mode of transportation change, route adjustment, or transportation system optimization) compared to a baseline state. This can be determined by first converting all time, economic, and comfort costs of each mode into equivalent time units and then subtracting them, or by normalizing all costs to monetary units using the time value of residents' travel as a coefficient before calculating the difference. This ensures comparability of multi-dimensional costs and accurately reflects the actual changes in costs incurred by users during mode switching.

[0092] Specifically, when subsystem m1 lacks sufficient spatiotemporal resources, its travel demand may shift to subsystem m2. In this case, the corresponding inter-mode demand conversion coefficient can be calculated by calling the demand conversion function. The expression for the demand transformation function is: ,in For the increase in travel costs of subsystem m1, This represents the increase in travel costs for subsystem m2.

[0093] The incremental travel cost between the two subsystems is determined based on a preset demand conversion function to establish the demand conversion coefficient between the two subsystems, and the corresponding conversion intensity value is output. This establishes a nonlinear mapping relationship from objective cost changes to subjective behavioral responses, making demand conversion more realistic and reasonable.

[0094] For example, in the scenario of assessing the adaptability of urban rail transit under sudden failures, the urban transportation carrying capacity assessment method in this embodiment can be as follows: During the morning rush hour, a subway main line experiences a temporary shutdown, and the system initiates an emergency carrying capacity assessment process. First, it collects travel time value data for residents in the affected area and calculates the travel costs for four modes: subway, regular bus, shared bicycle, and ride-hailing. This includes the average travel time, fare or usage fee for each mode, and the comfort loss estimated based on passenger density. The high delay state after the subway service interruption is considered a significant increase in travel costs, and pairwise comparisons are made with other modes to obtain the travel cost increments between alternative routes. Using a pre-demand conversion function, the cost increment is mapped to a demand conversion coefficient, simulating a high proportion of original subway passengers switching to express buses and shared bicycles connecting to rail stations. Based on this dynamic conversion result, travel demand is redistributed, generating new sub-modal traffic volumes, and it is found that some bus lines are about to be overloaded. Based on this, it is recommended to temporarily add short-distance trains and strengthen pedestrian guidance, which can effectively alleviate the risk of secondary congestion.

[0095] Step S3023: If the initial sub-mode traffic volume exceeds the corresponding maximum carrying capacity, the new sub-mode traffic volume is obtained by adjusting the difference between the initial sub-mode traffic volume and the corresponding maximum carrying capacity according to the demand conversion coefficient.

[0096] Step S3024: The new sub-mode traffic volume that meets the preset allocation rules is taken as the sub-mode traffic volume of the corresponding subsystem.

[0097] In steps S3023 and S3024, the maximum carrying capacity is the maximum number of trips a transportation subsystem can handle within a specific time period, and can be used to determine whether demand overflow has occurred. In this embodiment, a new sub-modal traffic volume is obtained by adjusting the difference between the initial sub-modal traffic volume and the maximum carrying capacity according to the demand conversion coefficient. When a subsystem is overloaded, the excess demand is redistributed to other alternative transportation modes according to the conversion coefficient.

[0098] After the transfer and allocation, it is necessary to further verify and adjust the total traffic volume of each subsystem according to the allocation rules to ensure that it equals the total traffic volume. If this condition is not met, the demand conversion coefficient should be adjusted by 5%-10% until it meets the allocation rules. By introducing a dynamic response mechanism to reflect the mode-shifting behavior of travelers under system overload, the demand allocation results can reflect the actual overflow and rebalancing in the multimodal transportation system.

[0099] For example, in a scenario simulating the redistribution of traffic pressure during peak hours in the city center, the urban traffic carrying capacity assessment method in this embodiment can be as follows: During the morning peak hours in the city center business district, the total number of rail transit trips is calculated based on the trip generation and attraction volume of each traffic zone, and the initial sub-modal traffic trip volume of the subway subsystem is determined in conjunction with the initial value of the modal share. After verification, it is found that the trip volume exceeds the maximum carrying capacity of the subway's key sections, indicating serious overloading. At this point, based on the increase in travel costs between the subway and other modes of transportation (such as regular buses, shared bicycles, and walking) (such as the reduction in comfort due to overcrowding in the vehicle being converted into a time penalty), the dynamic demand conversion coefficient between each mode is calculated. Subsequently, the portion of the trip volume exceeding the subway's carrying capacity is redistributed to the bus and slow-moving systems according to the conversion coefficient, forming a new sub-modal traffic trip volume. The final output of the adjusted demand distribution is closer to the actual observed behavior of "some passengers abandoning the subway and choosing to walk and ride shared bicycles during the morning peak hours," providing a reliable basis for optimizing bus scheduling and non-motorized vehicle lane configuration.

[0100] Step S303: Calculate the traffic demand of each subsystem based on the traffic volume of each subsystem and the travel distance and travel demand intensity coefficient of the corresponding subsystem.

[0101] The sub-modal traffic demand is the sum of the products of the sub-modal traffic trip volume of all "origin-destination pairs" in the subsystem and the corresponding travel distance and travel demand intensity coefficient.

[0102] Travel distance is the spatial length of a route from the origin to the destination along a specific transportation mode. In this embodiment, travel distance can be obtained by calculating the shortest or optimal path distance using a road network or rail network path search algorithm.

[0103] The travel demand intensity coefficient is a parameter that quantifies the strength of traffic demand within a specific time and space range. It is used to accurately characterize the relative strength of travel demand of residents or vehicles within a region.

[0104] For subsystem m, the formula can be used. Calculate the corresponding traffic demand by mode. ,in This represents the modal traffic trip volume of subsystem m. For travel distance, This is the demand intensity coefficient (e.g., 1.2 for commuting, 1.0 for shopping, 0.8 for leisure, and 1.1 for business).

[0105] Taking the assessment of traffic pressure around a large transportation hub as an example, the urban traffic carrying capacity assessment method in this embodiment can be to collect data on the number of residents and employment positions in multiple traffic zones within a 5-kilometer radius of a newly built high-speed rail station, and estimate their respective travel generation and attraction volumes. A dual-constraint gravity model is used to calculate the total traffic volume between each zone, forming a cross-regional travel distribution matrix. Combining the initial values ​​of the current modal share of each mode of transportation, and introducing the calibrated inter-modal demand conversion coefficient (such as the increase in demand for taxis and connecting buses due to the reduction in high-speed rail services), the traffic volume is dynamically allocated to the road subsystem, rail subsystem, and public transport subsystem to obtain the sub-modal traffic volume of each mode. Furthermore, by combining the actual average travel distance and travel time of each mode, the travel volume is converted into sub-modal traffic demand in "person-hours". It is found that during peak hours, the actual demand load of the connecting bus subsystem is far greater than expected due to long waiting times. Based on this, strategies for increasing the frequency of connecting buses and optimizing scheduling are proposed to effectively improve the hub's distribution efficiency.

[0106] Total traffic demand is the sum of the traffic demands of all sub-modes within a multimodal transportation system. It represents the actual load level of the entire transportation system and is used for balance analysis with total supply. In one embodiment, total traffic demand can be obtained by summing the traffic demands of each sub-mode within a unified unit of measurement. Furthermore, total traffic demand is the sum of the traffic demands of each sub-mode, and together with total spatiotemporal resource supply, it determines the carrying capacity index. This can be achieved by converting trip volumes of different modes to standard passenger-trip units before summing, or by weighting and combining them by travel distance. This forms a total indicator reflecting overall travel pressure, which can be used for system-level comparison with total supply.

[0107] Step S40: Determine the multimodal transportation carrying capacity index based on the total spatiotemporal resource supply and total transportation demand, and assess the transportation carrying capacity status of the target urban area based on the multimodal transportation carrying capacity index.

[0108] The multimodal transportation carrying capacity index is a comprehensive evaluation indicator of the supply and demand relationship of a multimodal transportation system. It can be determined by the ratio of total spatiotemporal resource supply to total transportation demand.

[0109] Traffic carrying capacity status refers to the operational level of a traffic system under current supply and demand conditions, and can be used to visually represent the pressure level of the traffic system. In a specific embodiment, traffic carrying capacity status can be determined based on the numerical range or threshold of the multimodal traffic carrying capacity index.

[0110] In one embodiment, assessing the traffic carrying capacity status of a target urban area based on a multimodal traffic carrying capacity index specifically includes: determining that the multimodal traffic system is in a low-load state when the multimodal traffic carrying capacity index is less than a first carrying capacity index threshold; and / or determining that the multimodal traffic system is in a suitable-load state when the multimodal traffic carrying capacity index is greater than the first carrying capacity index threshold and less than a second carrying capacity index threshold; and / or determining that the multimodal traffic system is in an overload state when the multimodal traffic carrying capacity index is greater than the second carrying capacity index threshold.

[0111] The first carrying capacity index threshold is a critical value used to determine whether a multimodal transportation system has exceeded a low-load state, and can be used as a boundary condition to distinguish between a low-load state and a suitable-load state. In one embodiment, the first carrying capacity index threshold can be determined based on statistical analysis of historical operating data or through traffic simulation calibration, representing the starting point at which the system begins to enter a normal load. In this embodiment, the first carrying capacity index threshold is preferably 0.8.

[0112] The low-load state is a state in which the supply of resources in a multimodal transportation system is significantly greater than the demand. It can be used to indicate that there is redundancy in the service capacity of the current transportation system, accompanied by insufficient resource utilization.

[0113] The second carrying capacity index threshold is a critical value used to identify whether a multimodal transportation system has entered an overloaded state. It can be used as a criterion to distinguish between a suitable and overloaded state, marking a key warning line for system operating pressure. In a specific embodiment, the second carrying capacity index threshold can be determined through system service capacity limit testing or retrospective analysis of actual congestion events, reflecting the maximum reasonable load limit that the system can withstand. In this embodiment, the second carrying capacity index threshold is preferably 1.2.

[0114] Among them, the load-suitable state is the operating state of a multimodal transportation system where the supply and demand relationship is within a reasonable matching range. It can be used to reflect that the system operates with high efficiency, the load of various modes of transportation is moderate, and the service stability is good.

[0115] Overload is a state in which the demand of a multimodal transportation system as a whole or a key subsystem exceeds its supply capacity. It can be used to warn of operational pressure on the transportation system, potentially leading to congestion, delays, or a decline in service quality. For example, overload includes, but is not limited to, one or more of the following: local node overload, main road overload, and network-wide spreading overload.

[0116] Taking the monthly assessment and early warning of urban traffic operation as an example, if the carrying capacity index of the city's central area reaches 1.35, exceeding the second carrying capacity index threshold of 1.2, the system judges it as an overloaded state; further decomposition shows that the full load rate of rail transit sections is too high and pedestrian congestion in connecting passages, while the index of the surrounding areas is only 0.6, lower than the first threshold of 0.8, and is in a low-load state; based on this, comprehensive suggestions are made to strengthen the employment layout of the outer clusters, optimize rail transit flow control and increase public transport capacity.

[0117] By mapping the continuous multi-modal traffic carrying capacity index to discrete traffic carrying capacity states through a hierarchical threshold mechanism, the operation status of urban traffic systems can be classified and managed. This enables the state discrimination to reflect the total balance relationship and embed the influence of mode coordination and transformation behavior. It can identify different load scenarios at the system-wide level, prompting insufficient resource utilization, identifying efficient operating intervals, and warning of service capacity exceeding limits. This enhances the decision-making orientation of the assessment results and provides a scientific basis for dynamic monitoring, early warning response, and multi-modal coordinated regulation, thereby improving the refinement and intelligence of urban traffic management.

[0118] In one embodiment, if the multimodal transportation system is in an overloaded state, the corresponding subsystem is recorded as the overloaded subsystem. The intermodal demand conversion coefficient is adjusted to transfer the traffic demand of the overloaded subsystem, and the ratio of the spatiotemporal resource supply of each subsystem to the submodal traffic demand is controlled to be greater than or equal to the first carrying capacity index threshold.

[0119] An overloaded subsystem refers to a traffic subsystem in a multimodal transportation system where the demand for traffic in a particular mode exceeds the spatiotemporal resource supply capacity, resulting in a local carrying capacity index exceeding the standard.

[0120] The inter-modal demand conversion coefficient can be modified by 2%-5% to shift traffic demand from overloaded subsystems, encouraging travelers to switch to other modes of transportation. By ensuring that the ratio of spatiotemporal resource supply to sub-modal traffic demand in each subsystem is greater than or equal to the first carrying capacity index threshold of 0.8, it can be guaranteed that each subsystem in a multimodal transportation system, operating independently, can cover at least 80% of the corresponding mode's traffic demand, preventing supply-demand imbalances in a single subsystem from dragging down overall traffic efficiency.

[0121] When adjusting the inter-mode demand conversion coefficient, the current demand conversion coefficient between the overloaded subsystem and the non-overloaded subsystem can be calculated using the demand conversion function in step S3022. Then, the demand conversion coefficient from the overloaded subsystem to the non-overloaded subsystem is increased by 10% to 20% of the current value, and the increased conversion coefficient must not exceed the first carrying capacity index threshold of 0.8. Next, the demand conversion coefficient from the non-overloaded subsystem to the overloaded subsystem is decreased by 15% to 25% of the current value, and the decreased conversion coefficient must be no less than 0.1. Then, based on the adjusted demand conversion coefficient, the traffic demand of each subsystem is redistributed through multiple rounds of iterative allocation to approximate a new equilibrium state or by using a dynamic loading method. Finally, the ratio of the sub-mode traffic demand to the spatiotemporal resource supply of each subsystem after redistribution is calculated. If this ratio of the overloaded subsystem is still greater than the second carrying capacity index threshold of 1.2, the inter-mode demand conversion coefficient is repeatedly readjusted until the ratio of the overloaded subsystem drops below the second carrying capacity index threshold.

[0122] For example, in a scenario simulating overload response of rail transit during peak hours, the urban traffic carrying capacity assessment method in this embodiment can be as follows: if the full load rate of a subway main line in a city reaches 130% during the morning peak, it is determined to be an overloaded subsystem; the average load rate of surrounding regular bus lines is 65%, belonging to a non-overloaded subsystem; the system automatically calculates the current demand conversion coefficient from subway to bus at 0.35, increases it to 0.42 (an increase of 20%), and at the same time decreases the conversion coefficient from bus back to subway from 0.40 to 0.30 (a decrease of 25%); based on the adjusted coefficients, a random user equilibrium allocation method is used to redistribute commuter travel volume, and the result shows that the subway section load has decreased to 118%; since it is still higher than the 1.2 threshold, the system iterates again to adjust the coefficients and redistribute, and finally, after two rounds of optimization, the subway load is reduced to below 1.15, achieving the goal of alleviating the pressure on the core line through inter-mode guidance.

[0123] By using a traffic volume allocation method driven by an adjusted demand conversion coefficient to achieve demand redistribution, and by continuously optimizing the allocation results through ratio judgment and iteration mechanisms, demand can be dynamically reorganized in a multimodal transportation system, so that the load of the overloaded subsystem can be gradually reduced to below the safety threshold, thereby improving the overall operational balance and control response capability of the system.

[0124] In summary, the urban transportation carrying capacity assessment method provided in this embodiment achieves systematic modeling of the urban transportation system by dividing the target urban area into blocks and constructing a multi-modal transportation system. This solves the problem of traditional methods focusing only on road networks while neglecting multi-modal coordination, expanding resource supply assessment from a single dimension to a system-wide dimension, and improving the comprehensiveness and accuracy of supply potential measurement. By introducing the concept of travel matching degree to reflect the accessibility, convenience, and user preference among different transportation modes, the total amount of traffic trips is allocated among modes, simulating mode switching behavior in real travel, significantly enhancing the realism of demand distribution. A multi-modal transportation carrying capacity index is constructed based on the ratio of total supply to total demand. This index comprehensively reflects the supply and demand balance of the urban transportation system, not only identifying congestion bottlenecks but also supporting cross-modal capacity allocation and facility connection optimization. This achieves a leap from static single-mode assessment to dynamic multi-dimensional assessment, comprehensively improving the scientificity and practicality of transportation planning decisions.

[0125] Based on the same inventive concept as the above embodiments, this embodiment also provides an urban traffic carrying capacity assessment system, including a processor and a memory; wherein, the memory stores a computer program, which is used by the processor to load and execute the urban traffic carrying capacity assessment method as described above.

[0126] like Figure 2 As shown, based on the same inventive concept as the above embodiments, this embodiment also provides a computer-readable storage medium storing instructions for loading and executing by a processor the urban traffic carrying capacity assessment method described above.

[0127] The embodiments of the mobile terminal and computer-readable storage medium provided in this application include all the technical features of the embodiments of the above control method. The extended and explanatory content of the specification is basically the same as that of the embodiments of the above method, and will not be repeated here.

[0128] This application also provides a computer program product, which includes computer program code. When the computer program code is run on a computer, it causes the computer to perform the methods described in the various possible implementations above.

[0129] This application also provides a chip, including a memory and a processor. The memory is used to store a computer program, and the processor is used to call and run the computer program from the memory, so that a device with the chip installed performs the methods described in the various possible implementations above.

[0130] The sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0131] In this application, the same or similar terms, concepts, technical solutions and / or application scenario descriptions are generally described in detail only when they appear for the first time. When they appear again, they are generally not repeated for the sake of brevity. When understanding the technical solutions and other contents of this application, the same or similar terms, concepts, technical solutions and / or application scenario descriptions that are not described in detail later can be referred to their previous relevant detailed descriptions.

[0132] In this application, the descriptions of the various embodiments have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0133] The technical features of the present application can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of the present application.

[0134] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in the above-mentioned storage medium and includes several instructions to cause a terminal device to execute the methods of each embodiment of this application. The above are only preferred embodiments of this application and do not limit the patent scope of this application. All equivalent structural or equivalent procedural transformations made using the content of this application's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of this application. It should be noted that the various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to mutually.

[0135] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the appended claims.

Claims

1. A method for assessing urban traffic carrying capacity, characterized in that, include: The target city area is divided into several traffic zones, and a multimodal transportation system for the target city area is constructed. The multimodal transportation system has multiple transportation subsystems based on different transportation modes; Based on the basic parameters of each transportation subsystem, the corresponding spatiotemporal resource supply is determined, and the spatiotemporal resource supply of each transportation subsystem is superimposed to obtain the total spatiotemporal resource supply of the multimodal transportation system. The traffic volume of traffic zones is allocated based on the travel matching degree between different traffic modes, the traffic demand of each traffic subsystem is determined by sub-mode, and the traffic demand of each traffic subsystem by sub-mode is superimposed to obtain the total traffic demand of the multi-modal transportation system. Based on the total spatiotemporal resource supply and the total traffic demand, a multimodal transportation carrying capacity index for the multimodal transportation system is determined, and the traffic carrying capacity status of the target urban area is assessed based on the multimodal transportation carrying capacity index.

2. The urban traffic carrying capacity assessment method according to claim 1, characterized in that, The specific steps of dividing the target urban area into several traffic zones include: A comprehensive overlay analysis map is generated by spatially overlaying the urban land use map, the road network vector map of the road subsystem, the rail subsystem route and station distribution map, and the bus subsystem route and station distribution map. The zoning type of the comprehensive overlay analysis map is determined based on the consistency of land use. According to the preset spatial boundary rules and scale control threshold rules, the blocks of the corresponding partition types are partitioned in the comprehensive overlay analysis diagram to obtain several traffic partitions.

3. The urban traffic carrying capacity assessment method according to claim 1, characterized in that, The transportation subsystem includes a road subsystem, a rail subsystem, and a public transportation subsystem; the determination of the corresponding spatiotemporal resource supply based on the basic parameters of each transportation subsystem specifically includes: Based on the equivalent passenger capacity per lane, service level coefficient, assessment period duration, total mileage, single-lane departure interval, average delay time, and spatiotemporal resource utilization efficiency coefficient of the road subsystem, the first spatiotemporal resource supply of the road subsystem is determined according to a preset first spatiotemporal resource formula; and / or Based on the vehicle capacity, maximum load factor, evaluation period duration, total mileage, departure interval, and average dwell time of the track subsystem, the spatiotemporal resource supply of the track subsystem is determined according to a preset second spatiotemporal resource formula; and / or The third spatiotemporal resource supply of the public transport subsystem is determined according to the preset third spatiotemporal resource formula based on the vehicle's rated passenger capacity, load factor, evaluation period duration, total mileage, departure interval, average stop time, and utilization efficiency coefficient.

4. The urban traffic carrying capacity assessment method according to claim 1, characterized in that, The allocation of traffic volume to traffic zones based on the travel matching degree between different traffic modes, and the determination of the sub-mode traffic demand of each traffic subsystem, specifically includes: Based on the trip generation and trip attraction of each traffic zone, the traffic trip volume between each traffic zone is calculated using a preset trip volume determination formula. Based on the initial modal share of each traffic mode and the inter-modal demand conversion coefficient, the traffic trip volume is allocated to obtain the modal traffic trip volume of each traffic subsystem. Based on the traffic volume of each subsystem and the travel distance and travel demand intensity coefficient of the corresponding subsystem, the traffic demand of each subsystem is calculated.

5. The urban traffic carrying capacity assessment method according to claim 4, characterized in that, The expression for the preset travel volume determination formula is: Among them, the For traffic volume; the aforementioned The average daily trip generation for the traffic zone; The average daily travel attraction of the traffic zone; To partition To partition The impedance function corresponding to the travel cost is expressed as follows: ,in The impedance coefficient has a value range of 0.01-0.

05. To partition To partition The travel cost is expressed as: , This is the time cost coefficient, with a value ranging from 1.0 to 2.

0. To partition To partition Average travel time This is the economic cost coefficient, with a value ranging from 0.5 to 1.

0. For partitioning To partition The average economic cost of travel.

6. The urban traffic carrying capacity assessment method according to claim 4, characterized in that, The allocation of traffic trips based on the initial modal share of each traffic mode and the inter-modal demand conversion coefficient yields the modal traffic trip volume for each traffic subsystem, specifically including: Determine the initial modal share of each traffic mode, and determine the initial modal traffic volume of each subsystem based on the initial modal share and the traffic volume. Determine the demand conversion coefficient between the two subsystems based on the incremental travel cost between the two subsystems; If the initial sub-mode traffic volume exceeds the corresponding maximum carrying capacity, a new sub-mode traffic volume is obtained by adjusting the difference between the initial sub-mode traffic volume and the corresponding maximum carrying capacity according to the demand conversion coefficient. The new traffic volume of different modes that meets the preset allocation rules will be used as the traffic volume of the corresponding subsystem.

7. The urban traffic carrying capacity assessment method according to claim 6, characterized in that, The determination of the demand conversion coefficient between the two subsystems based on the incremental travel cost between the two subsystems specifically includes: The incremental travel cost of the corresponding subsystem is determined based on the time value of residents' travel and the travel cost of the corresponding transportation mode; the travel cost includes the time cost, economic cost and comfort cost of each transportation mode. The incremental travel cost between the two subsystems is determined by the demand transformation coefficient between the two subsystems based on a preset demand transformation function.

8. The urban traffic carrying capacity assessment method according to claim 1, characterized in that, The assessment of the traffic carrying capacity status of the target urban area based on the multimodal traffic carrying capacity index specifically includes: When the multimodal traffic carrying capacity index is less than the first carrying capacity index threshold, the multimodal traffic system is determined to be in a low-load state; and / or When the multimodal transportation carrying capacity index is greater than a first carrying capacity index threshold and less than a second carrying capacity index threshold, the multimodal transportation system is determined to be in a suitable carrying capacity state; and / or When the multimodal traffic carrying capacity index is greater than the second carrying capacity index threshold, the multimodal traffic system is determined to be in an overloaded state.

9. The urban traffic carrying capacity assessment method according to claim 8, characterized in that, If a multimodal transportation system is in an overloaded state, the corresponding subsystem is recorded as an overloaded subsystem. The intermodal demand conversion coefficient is adjusted to transfer the traffic demand of the overloaded subsystem, and the ratio of the spatiotemporal resource supply of each subsystem to the submodal traffic demand is controlled to be greater than or equal to the first carrying capacity index threshold.

10. A system for assessing urban traffic carrying capacity, characterized in that, It includes a processor and a memory; wherein the memory stores a computer program for being loaded by the processor and executed as described in any one of claims 1-9.

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