An activity bearing capacity evaluation method for urban physical examination

Abstract

This invention discloses a method for assessing the activity carrying capacity of cities, enabling the evaluation of the activity carrying capacity of specific urban areas and the determination of the optimal distribution of activity demand within those areas. The evaluation process includes constructing a super-network of urban activities and travel, dynamically calculating resident activity and travel times, evaluating activity and travel utility, and calculating and evaluating activity carrying capacity based on a bi-level planning model. By assessing urban activity carrying capacity, this invention can serve applications such as travel policy evaluation, site selection for new activity facilities, activity strategy analysis, and TOD (Transit-Oriented Development) model verification, providing valuable information support for planners and travelers.

Description

Technical Field

[0001] This invention belongs to the field of transportation technology, and in particular relates to a method for assessing the activity carrying capacity of cities for physical examinations. Background Technology

[0002] As urban residents' quality of life continues to improve, their activity needs have increased significantly and diversified, including work, housing, cultural entertainment, social interaction, and health and exercise. This places enormous pressure on urban systems such as land, transportation, energy, and social services, and it remains to be seen whether these systems can support the activity needs of urban residents in the future. Therefore, urban planners and policymakers urgently need to understand whether existing infrastructure is sufficient to meet the rapidly growing activity needs of residents, making the adequacy assessment of resources such as transportation, housing, energy, and services a top priority for urban planning and development.

[0003] For urban health assessments, which include evaluating the adequacy and sustainability of urban resources, carrying capacity is a widely used fundamental indicator. The concept of carrying capacity originated in mechanics, referring to the maximum load an object can withstand without causing damage. Subsequently, this concept entered the field of ecology in the 1920s. In 1921, the concept of carrying capacity was proposed as the maximum limit to the number of individuals existing under specific environmental conditions. In recent years, research on carrying capacity has gradually expanded from natural ecosystems to multiple interdisciplinary fields such as environment, economy, society, and transportation. Concepts such as ecological carrying capacity, water resource carrying capacity, transportation carrying capacity, and environmental carrying capacity have emerged. The widespread application of the carrying capacity concept provides a useful approach for the quantitative assessment of urban activity facility supply. Currently, there is no relevant technology for researching urban activity carrying capacity. Summary of the Invention

[0004] Purpose of the invention: In order to solve the problems existing in the prior art, the present invention provides a method for assessing the activity carrying capacity of urban physical examinations.

[0005] Technical Solution: This invention provides a method for assessing the activity carrying capacity of urban health checkups, specifically including the following steps:

[0006] Step 1: Obtain basic urban data, which includes the location of nodes in the road network, the activity attributes of nodes, road segment locations, public transportation routes, the capacity of active nodes, private car travel costs, private car travel time value, public transportation travel costs, and public transportation travel time value.

[0007] Step 2: Construct a multimodal activity-travel network for the city The city's multimodal mobility network includes a private car network. and public transport subnetwork , where N represents the set of nodes during private car activities, including intersections, activity points, or pick-up and drop-off locations; This is a collection point for the activity areas of private cars. This represents the set of nodes in a public transportation route, including: stations and activity points; This refers to a set of public transport routes, including those connected to the network. Outbound network section Transfer sections Operating section With the activity section ; This represents the set of fixed routes for public buses; , ;

[0008] Step 3: Divide the day into T time intervals evenly and calculate the residents' dynamic activities - travel time, which includes the total travel time of travelers using private cars and the total travel time of travelers using public transportation.

[0009] Step 4: Calculate the traveler's activity on route r during the i-th time interval - total travel utility. ; , =1,2,3,…R1, where R1 represents… The total number of all road segments in the region;

[0010] Step 5: Construct a multi-modal activity-trip allocation model based on the principle of maximizing utility, and use this model as the lower-level model;

[0011] Step 6: Construct an activity carrying capacity model and use this model as the upper-level model;

[0012] Step 7: Combine the upper-level model and the lower-level model into a two-level planning model, and use the sensitivity analysis algorithm to solve the two-level planning model to obtain the city's activity carrying capacity.

[0013] Furthermore, in step 3, the total activity-trip time of the private car is the sum of the intervals during which the private car enters each road segment according to the activity-trip plan, and enters the path within the i-th time interval. Choose activity mode Private car activities - travel time is :

[0014]

[0015] in, For the first The travel / activity time of travelers using private cars to enter road segment r1 within a time interval. A variable that is 0 or 1, if it enters the path during the i-th time interval. And select the activity mode Private car travelers in the first Entering the road section during the time interval 1, then It equals 1, otherwise =0, Let OD be the set of paths for all private cars in w;

[0016] Public transport activity – total travel time includes time from origin to station, time on bus / metro train, transfer time, activity time, and entry into the path within the i-th time interval. Choose activity mode Public transport users' activities - travel time :

[0017]

[0018] in, A variable that is either 0 or 1, if it enters the path during the i-th time interval. Choose activity mode Public transport users in the If a person enters road segment r2 within a certain time interval, then ,otherwise , Let be the set of all public transport routes in OD to w; The expression is:

[0019] in, It refers to the traveler's internet access / logout / transfer route during the i-th time interval. Travel time and These represent the subway or bus speeds on the route during the i-th time interval. runtime on The table shows that travelers used public transportation to travel at node n during the i-th time interval. Duration, For variables, if node Located on the road section At the end, Otherwise, it equals 0.

[0020] Furthermore, in step 4, the traveler's activity on road segment r is reduced to the total travel utility. The expression is:

[0021]

[0022] in, This indicates that the traveler participated in the activity at node n during the i-th time interval. The utility, where A represents the set of activities. Let be a variable, if the traveler chooses to travel at node in the i-th time interval. Participate in the event And nodes Located on the road section The end position, then =1, otherwise ; For travelers using private cars during the i-th time interval, the route is... Total travel utility During the i-th time interval, travelers use public transportation on the road segment. Total travel utility; , as well as The expression is as follows:

[0023]

[0024]

[0025]

[0026] in, This indicates that during the i-th time interval, the traveler is at node [node name missing]. Participate in the event The marginal utility gained at that time and These represent the positive and negative utility of crowding activities. This indicates the value of travel / activity time when travelers use private vehicles. For travelers using private cars during the i-th time interval, the route is... Operating costs on For travelers entering the road segment by private car during the i-th time interval Travel / activity time; - It is a coefficient that converts the travel time of different types of public transportation routes into monetary units. For the cost of taking public transportation;

[0027] Furthermore, the multimodal activity-trip allocation model in step 5 is specifically as follows:

[0028]

[0029]

[0030]

[0031]

[0032]

[0033] in, Let m be the traffic of activity mode m in path p during the i-th time interval. OD corresponds to the activity mode in w within the i-th time interval. The travel demand, M represents the set of OD pairs, and M represents the set of activity patterns; A variable that is 0 or 1, if it enters the path during the i-th time interval. In the activity mode The travelers in the If a user enters path r within a certain time interval, then... ,otherwise .

[0034] Furthermore, the expression for the active bearing capacity model in step 6 is as follows:

[0035]

[0036]

[0037]

[0038]

[0039]

[0040] in, A variable that is either 0 or 1, indicating whether the traveler enters the activity mode of OD to w during the i-th time interval. Including activities ,but otherwise ; Indicates that in the known In the case of The value, Indicates the lower limit value. This indicates the upper limit value.

[0041] Furthermore, step 6 specifically includes:

[0042] Step 1: Initialization: Let Set the initial values ​​of the decision variables in the upper-level model. At this point, e=0.

[0043] Step 2: Perform iterative calculations to solve the lower-level model: based on With the constraint that residents' dynamic activities and travel times meet preset requirements, the lower-level activity-travel allocation model is solved to obtain the solution of the lower-level decision variables. ;

[0044] Step 3: Sensitivity Analysis: Obtain the derivatives of the upper-level decision variables with respect to the lower-level decision variables through sensitivity analysis of the lower-level model. ;

[0045] Step 4: Local linear approximation: using sensitivity analysis results By making a local linear approximation to the bi-level programming model, a single-level convex optimization problem is obtained.

[0046] Step 5: Solving the upper-level model: Solve the approximate convex optimization problem to obtain new auxiliary variables for the upper-level model. ;

[0047] Step 6: Update the values ​​of the decision variables in the upper-level model: ;

[0048] Step 7: Determine convergence: If the convergence condition is met... If the iteration terminates, then the iteration ends; otherwise, it will... Apply the result to the next iteration and increment the iteration count by 1; then return to step 2. This is the preset threshold.

[0049] Beneficial effects:

[0050] 1) Based on the concept and assessment method of activity carrying capacity of the present invention, the maximum activity demand that a specific urban area can accommodate under the constraints of land use, various resources and infrastructure can be obtained, as well as the optimal activity distribution, providing useful information for urban planning, land use, construction scheme evaluation, road network performance assessment and other purposes from the perspective of activities.

[0051] 2) This invention uses activity demand as an indicator to characterize activity carrying capacity. Activity demand (such as activity purpose, activity time, and activity area) is directly affected by land use. By combining land properties and individualized needs to analyze travel motivation, the traveler's travel purpose, time, and route can be accurately reconstructed.

[0052] 3) The traditional concept of traffic carrying capacity can obtain the optimal OD demand configuration for different regions to guide land use (such as development scale, development intensity, etc.), but it cannot provide information from the perspective of individual travel motivation, and cannot provide decision-making basis for refined schemes such as land use planning, site selection of activity facilities, and regional population control. However, the activity carrying capacity proposed in this invention can achieve this. Attached Figure Description

[0053] Figure 1 This is a schematic diagram of the process of the present invention.

[0054] Figure 2 This is a diagram illustrating a multimodal activity-travel super network.

[0055] Figure 3 This is a diagram illustrating travel timestamps.

[0056] Figure 4 This is a schematic diagram of different activity modes for the same OD point.

[0057] Figure 5 The diagram shows the activity carrying capacity and number of participants under different site selection schemes: (a) represents the activity carrying capacity and number of participants under different site selection locations; (b) represents the proportion of participants at nodes 2 and 3 to the total number of participants under different site selection locations.

[0058] Figure 6 This diagram illustrates the active carrying capacity values ​​under different TOD (Transit-Oriented Development) models. Detailed Implementation

[0059] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0060] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0061] The concept of activity carrying capacity refers to the maximum total amount of activity that a specific urban area can accommodate, given constraints on land use, activity structure, transportation systems, and activity time, and in accordance with travelers' activity preferences. Assessing activity carrying capacity answers two core questions: ① Given fixed land use, resources, and infrastructure, what is the maximum activity demand that a specific urban area can accommodate? This helps predict the limits of future development in that area, assisting planners in striking a balance between urban development and resource constraints, and formulating more sustainable development strategies. ② The optimal activity distribution in the area, i.e., what activity distribution maximizes the utilization of various resources.

[0062] like Figure 1 As shown, it includes the following steps:

[0063] (1) Acquisition of basic urban data;

[0064] (2) Construction of urban multimodal activity-travel network;

[0065] (3) Calculation of residents' dynamic activities - travel time;

[0066] (4) Calculation of residents' activity-travel utility;

[0067] (5) Construction of a multi-modal activity-trip allocation model based on user equilibrium;

[0068] (6) Construction of activity carrying capacity model;

[0069] (7) Assessment and output of activity carrying capacity.

[0070] In step (1), the data acquired includes the location of road network topology nodes, node attributes, road segment location, road segment attributes, public transportation routes, node activity attributes (for a node, its basic attribute is the road attribute, such as an intersection or a transportation hub. The node activity attribute is based on the transportation attribute, with the addition of activity attributes, such as a node being both a rail transit hub and a commercial point, meaning that the area around the hub is a commercial concentration area), public transportation capacity, departure frequency, capacity of active nodes, private car travel cost, private car travel time value, public transportation travel cost, public transportation travel time value, land use nature of active points, etc. The above data can be obtained through actual surveys, online collection, and public disclosure by planning departments. Examples of the attributes of active road segments and travel road segments are shown below:

[0071] Table 1. Example of Activity - Travel Route Attributes

[0072] 1 20 15 1400 -- -- 2 30 23 1800 -- -- 3 35 26 1400 -- -- 4 45 30 1800 -- -- 5 20 15 -- 600 12 6 45 23 -- 600 12 7 35 30 -- 600 12 8 20 22 -- 2250 6 9 25 30 -- 2250 6 10 10 0.5 10000 --

[0073] In step (2), let... This represents a multimodal activity-travel network, where This refers to a collection of nodes, road segments, and public transportation routes. Nodes here must be transportation nodes, such as intersections / transportation hubs, and may have additional activity attributes, such as commercial or leisure nodes. The multimodal activity-travel network can be divided into two subnetworks: a private car subnetwork. and public transport subnetwork ,in , On private car websites In the middle, location nodes This can be an intersection, an event, or a pick-up / drop-off point, with node 0 and node 0' representing the start and end nodes, respectively. Let... , Represents road network segments, The representative activity segment, i.e., the execution process of the activity, indicates where the traveler stops at the node to carry out the activity; within the public transportation subnetwork In China, public transportation routes These are fixed routes; buses / subways operate on these routes, and there are bus terminals. Indicates a station or activity point on the route. (Let) This represents a collection of public transport segments, including inbound segments, outbound segments, transfer segments, operating segments, and activity segments. An example of a multimodal activity-travel network is... Figure 2 As shown.

[0074] In step (3), the residents' dynamic activity-travel time includes the total interval time of private cars entering each road segment according to the activity-travel plan, such as... Figure 2 As shown, different activity choices, different modes of transportation, and different time choices will all lead to differences in travel time. Therefore, within the time interval... Enter OD pair path Select Activity Mode ( The activities and travel times of private car users can be represented as follows:

[0075]

[0076] in, For the first The travel / activity time of private cars entering road segment r1 within each time interval. A variable that is 0 or 1, if it enters the path during the i-th time interval. And select the activity mode Private car travelers in the first Entering the road section during the time interval 1, then It equals 1, otherwise =0, Let w be the set of paths for all private cars in OD pair; where activity mode refers to the available activity plans between OD point pairs, such as in Figure 1 In the process, between node 1 (home) and node 4 (work unit), there are two activity modes: one is home → shopping → work, and the other is home → work. The difference lies in whether or not you go to the mall to shop.

[0077] Residents' dynamic activities - travel time also includes the activities of public transport users - total travel time, which includes: time from origin to station, time on bus / metro trains, transfer time, and activity time. (At time intervals...) Enter OD pair path Select Activity Mode Public transport traveler activity - travel time can be represented as:

[0078]

[0079] in, A variable that is either 0 or 1, if it enters the path during the i-th time interval. Choose activity mode Public transport users in the If a person enters road segment r2 within a certain time interval, then ,otherwise , for Let OD be the set of all public transport routes in w; such as Figure 3 As shown, public transport activity - travel time component As shown below:

[0080]

[0081] In the formula, It refers to the section of the route where you go online / offline / transfer during the i-th time interval. Travel time It is the subway section during the i-th time interval. Train travel time. Definition This refers to the time interval during which, at node i... Conduct activities The duration of a node in a public transport network refers to its duration. It is a 0-1 variable, if the node It is a section of road If the value is the last point, then the value is 1; otherwise, it is 0.

[0082] A schematic diagram of the activity patterns between identical OD pairs is shown below. Figure 4 As shown, although the OD points of each activity plan are the same, the travel mode, activity pattern, and activity location of each plan are different. Therefore, this information should be generated when generating activity-travel routes.

[0083] In step (4), the costs incurred by individual activities—travel—are expressed in the form of generalized utility. Let... For travelers in the i-th time interval, on the road segment Activity - Total Travel Utility Let be the total utility of the activity-trip when entering OD pair 𝑤 and selecting activity mode 𝑚 path 𝑝 within the i-th time interval. The calculation principle is the sum of activity utility and trip utility.

[0084]

[0085]

[0086] In the formula, For travelers in the i-th time interval at node Participate in the event ( The utility of A (where A is the set of all activities). It is a variable that is either 0 or 1. If the traveler chooses to stay at node i in the i-th time interval... Participate in the event And nodes It is a section of road If the value ends at the end of the string, the value is 1; otherwise, it is 0. A variable that is either 0 or 1, if the path is selected in the i-th time interval. Travelers at the node Participate in the event ,and In the path If the value is above, then the value is 1; otherwise, it is 0. and These are the traveler's private car / public transportation routes during the i-th time interval. Total travel utility and These are the private car / public transportation users' OD pair during the i-th time interval. path Select Activity Mode The total utility of travel. Let W be the set of all activity patterns, and let W be the set of all OD pairs.

[0087] The utility of an activity depends on its location and time, as well as the level of congestion at that location, within a time interval of [missing information]. At that time, travelers were at the node Participate in the event The utility can be expressed as:

[0088]

[0089] In the formula, For travelers in the i-th time interval at node Participate in the event The marginal utility gained at that time and These represent the positive and negative utilities of activity congestion, determined by the nodes. Conduct activities The number of people determines this.

[0090] Enter OD pair in the i-th time interval Select Activity Mode Private car travelers on the route The travel utility is the sum of the utilities of each route segment:

[0091]

[0092] The travel utility of private car users includes the negative utility of travel time and operating costs (such as fuel costs and tolls). Therefore, during the i-th time interval, private car users on the road segment... The travel utility on the platform is:

[0093]

[0094] In the formula , This indicates the value of travel / activity time for private car users; For private car travelers in the i-th time interval, on the road segment Operating costs. It refers to the road segment within the i-th time interval. The passage time on the road.

[0095] Enter OD pair during the i-th time interval Select Activity Mode Public transport passengers on the route The travel utility is the sum of the utilities of each route segment:

[0096]

[0097] Among them, the travel utility of the road segment It can be calculated using the following formula:

[0098]

[0099] In the formula, - This is a coefficient that converts the travel time of different types of public transportation routes into monetary units; the cost of taking public transportation is... . It refers to the public transportation route within the i-th time interval. The passage time on the road.

[0100] Based on the method in step (4), the activity-trip utility of each path in the example road network of this embodiment can be calculated.

[0101] In step (5), the established activity-trip allocation model serves the calculation of activity carrying capacity. Referring to the user equilibrium theory of traditional traffic allocation, it is assumed that the behavior of travelers in the activity-trip network satisfies Wardrop's first principle, that is, travelers are perfectly rational, can fully perceive the cost of each activity-trip option, and always choose the path with the lowest activity-trip cost, i.e., the path with the highest utility. The model is as follows:

[0102] Lower-level model

[0103]

[0104]

[0105]

[0106]

[0107] In the formula, For the OD pair entering during the i-th time interval path Select Activity Mode Traffic, It is the OD pair in the i-th time interval Select activity mode The demand for travel.

[0108] In step (6), the constructed activity carrying capacity model is a two-level programming model. The upper-level model aims to maximize activity demand, while the lower-level model is a multi-modal activity-trip assignment (ATA) model based on the user equilibrium principle. By passing the decision variables (activity demand) of the upper-level model to the lower-level model, the lower-level model can output different activity-trip schemes and time-segment traffic flow. Through iterative calculations of the upper and lower-level models, the maximum activity demand can be obtained. The activity carrying capacity model is as follows:

[0109]

[0110]

[0111]

[0112]

[0113]

[0114] In the formula, This refers to the maximum activity demand that the area can accommodate. It is the OD pair in the i-th time interval Inter-activity mode The demand for activities. It is the OD pair in the i-th time interval Select activity mode The travel demand, It is a 0-1 variable; if the OD pair in the i-th time interval... Inter-activity mode There are activities in China If the value is 1, then the value is 1; otherwise, it is 0. The OD pair in the i-th time interval Inter-activity mode The formula, which includes the number of activities, describes the relationship between activity demand and travel demand. Refers to activities Total demand and It is an event The upper and lower limits of demand reflect the restrictions on land use and activity structure. For example, the total demand for shopping cannot exceed the capacity limit of commercial facilities, and the demand for schooling cannot exceed 30% of the total demand for activities (related to the age structure of the population).

[0115] In step (7), for the activity carrying capacity model, a customized sensitivity analysis algorithm is developed to approximate the two-layer programming model as a single-layer linear programming model for solution.

[0116] The algorithm for solving the activity bearing capacity based on sensitivity analysis is as follows:

[0117] Step 1: Initialization: Let Set the initial values ​​of the decision variables in the upper-level model. At this point, e=0.

[0118] Step 2: Solving the lower-level model: Based on By taking the requirement that the dynamic activities and travel times of the people meet the preset requirements as a constraint, the lower-level activity-travel allocation model is solved to obtain the solution of the lower-level decision variables. The lower-level activity-trip assignment model can be solved by path-based traffic assignment algorithms, such as gradient projection algorithms.

[0119] Step 3: Sensitivity Analysis: Obtain the derivatives of the upper-level decision variables with respect to the lower-level decision variables through sensitivity analysis of the lower-level model. .

[0120] Step 4: Local linear approximation: using sensitivity analysis results By making a local linear approximation to the bilevel programming problem, we obtain a single-level convex optimization problem.

[0121] Step 5: Solving the upper-level model: Solve the approximate convex optimization problem to obtain new auxiliary variables for the upper-level model. .

[0122] Step 6: Determine if the solution satisfies the constraints of the upper-level model: Due to the convex optimization problem There may be no solution, leading to Problems that may deviate from the feasible region, according to Search for feasible higher-level decision variables, including step size. Take 1, 2, ... in sequence until based on The results of the lower-level activity-trip assignment model satisfy the constraints of the upper-level land use, road segment capacity, and activity structure.

[0123] Step 7: Determine convergence: If the convergence condition is met... The iteration terminates if the condition is met. The preset threshold will be used; otherwise, [the threshold will be set]. Apply the result to the next iteration and increment the iteration count by 1; then return to step 2.

[0124] Through the above steps, the activity carrying capacity, i.e. the maximum activity demand, of the example city area in this embodiment can be obtained through iteration.

[0125] The concept and calculation method of activity carrying capacity proposed in this embodiment can be used to evaluate the merits of site selection for activity facilities, such as... Figure 5 As shown, the activity carrying capacity and number of participants vary significantly under different site selection schemes. During the site selection process for new facilities, the impact of the construction scheme on activity carrying capacity and activity distribution should be fully considered to select the most suitable scheme for urban planning. In addition, it can also be used to evaluate the merits of urban planning policies (such as the TOD model), such as... Figure 6 As shown, the activity carrying capacity of a region varies depending on the distance between the residence and public transportation stops. By developing appropriate TOD (Transit-Oriented Development) schemes, the convenience of public transportation can be improved, activity carrying capacity can be maximized, and travel structure and activity distribution can be optimized.

[0126] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A method for evaluating activity carrying capacity of urban health examination, characterized in that, Specifically, the steps include the following: Step 1: Obtain basic urban data, which includes the location of nodes in the road network, the activity attributes of nodes, road segment locations, public transportation routes, the capacity of active nodes, private car travel costs, private car travel time value, public transportation travel costs, and public transportation travel time value. Step 2: Constructing the city multimodal activity-travel network The city multimodal activity-travel network includes a private car subnetwork and a public transit subnetwork , where N represents a node set in private car activity, including intersections, activity points or pick-up and drop-off locations; is a private car activity link set, represents a node set in public transit lines, including stations and activity points; represents a public transit link set, including network entry links , network exit links , transfer links , running links and activity links ; represents a fixed route set of private cars; , ; Step 3: Divide the day into I time intervals and calculate the residents' dynamic activities - travel time, which includes the total travel time of travelers using private cars and the total travel time of travelers using public transportation. Step 4: Calculate the traveler's activity on route r during the i-th time interval - total travel utility. ; , =1,2,3,…R1, where R1 represents… The total number of all road segments in the region; Step 5: Construct a multi-modal activity-trip allocation model based on the principle of maximizing utility, and use this model as the lower-level model; Step 6: Construct an activity carrying capacity model and use this model as the upper-level model; Step 7: Combine the upper-level model and the lower-level model into a two-level planning model, and use the sensitivity analysis algorithm to solve the two-level planning model to obtain the city's activity carrying capacity; The multimodal activity-trip allocation model in step 5 is specifically as follows: ； ； ； ； in, Let m be the traffic of activity mode m in path p during the i-th time interval. For the activity mode of OD in w during the i-th time interval The travel demand, M represents the set of OD pairs, and M represents the set of activity patterns; A variable that is 0 or 1, if it enters the path in the i-th time interval. In the activity mode The travelers in the If a user enters path r within a certain time interval, then... ,otherwise ; For travelers in the i-th time interval, on the road segment Activity - Total Travel Utility Let OD be the set of paths for all private cars in w. Let be the set of all public transport routes in OD to w. A set of activity modes; The expression for the active bearing capacity model in step 6 is as follows: ； ； ； ； in, A variable that is either 0 or 1, indicating whether the traveler enters the activity mode of OD to w during the i-th time interval. Including activities ,but otherwise ; Indicates that in the known In the case of The value, Indicates the lower limit value. This indicates the upper limit value.

2. The method for assessing the activity carrying capacity of urban physical examinations according to claim 1, characterized in that, In step 3, the total activity-trip time for private cars is the sum of the intervals between the private cars entering each road segment according to the activity-trip plan, and the time taken to enter the path within the i-th time interval. Choose activity mode Private car activities - travel time is : ； in, For the first The travel / activity time of travelers using private cars to enter road segment r1 within a time interval. A variable that is 0 or 1, if it enters the path during the i-th time interval. And select the activity mode Private car travelers in the first Entering the road section during the time interval 1, then It equals 1, otherwise =0, Let OD be the set of paths for all private cars in w; Public transport activity – total travel time includes time from origin to station, time on bus / metro train, transfer time, activity time, and entry into the path within the i-th time interval. Choose activity mode Public transport users' activities - travel time : ； in, A variable that is either 0 or 1, if it enters the path during the i-th time interval. Choose activity mode Public transport users in the If a person enters road segment r2 within a certain time interval, then ,otherwise , Let be the set of all public transport routes in OD to w; The expression is: ； in, It refers to the traveler's internet access / logout / transfer route during the i-th time interval. Travel time and These represent the subway or bus speeds on the route during the i-th time interval. runtime on This indicates that during the i-th time interval, travelers use public transportation to conduct activities at node n. Duration, For variables, if node Located on the road section At the end, Otherwise, it equals 0.

3. The method for assessing the activity carrying capacity of urban physical examinations according to claim 2, characterized in that, In step 4, the traveler's activities on road segment r and the total travel utility are discussed. The expression is: ； in, This indicates that the traveler participated in the activity at node n during the i-th time interval. The utility, where A represents the set of activities. Let be a variable, if the traveler chooses to travel at node in the i-th time interval. Participate in the event And nodes Located on the road section The end position, then =1, otherwise ; For travelers using private cars during the i-th time interval, the route is... Total travel utility This indicates that during the i-th time interval, travelers used public transportation on the road segment. Total travel utility; , as well as The expression is as follows: ； ； ； in, This indicates that during the i-th time interval, the traveler is at node [node name missing]. Participate in the event The marginal utility gained at that time and These represent the positive and negative utility of crowding activities. This indicates the value of travel / activity time when travelers use private vehicles. For travelers using private cars during the i-th time interval, the route is... Operating costs on For travelers entering the road segment by private car during the i-th time interval Travel / activity time; - It is a coefficient that converts the travel time of different types of public transportation routes into monetary units. Cost of taking public transportation.

4. The method for assessing the activity carrying capacity of urban physical examinations according to claim 1, characterized in that, Step 6 specifically involves: Step 1: Initialization: Let Set the initial values ​​of the decision variables in the upper-level model. At this point, e=0. Step 2: Perform iterative calculations to solve the lower-level model: based on With the constraint that residents' dynamic activities and travel times meet preset requirements, the lower-level activity-travel allocation model is solved to obtain the solution of the lower-level decision variables. ; Step 3: Sensitivity Analysis: Obtain the derivatives of the upper-level decision variables with respect to the lower-level decision variables through sensitivity analysis of the lower-level model. ; Step 4: Local linear approximation: using sensitivity analysis results By making a local linear approximation to the bi-level programming model, a single-level convex optimization problem is obtained. Step 5: Solving the upper-level model: Solve the approximate convex optimization problem to obtain new auxiliary variables for the upper-level model. ; Step 6: Update the values ​​of the decision variables in the upper-level model: ; Step 7: Determine convergence: If the convergence condition is met... If the iteration terminates, then the iteration ends; otherwise, it will... Apply this to the next iteration calculation and increment the iteration count by 1; Then return to step 2; This is the preset threshold.

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