A green space service redemption ability measurement method based on activity chain time window

Abstract

This invention belongs to the field of urban public service assessment and spatial governance technology, specifically involving a method, system, device, and medium for measuring the service fulfillment capacity of green spaces based on activity chain time windows. The method acquires a set of established rigid activity anchor points, resident activity chains, and green space entrances within a target area; determines the available time for activity chain segments corresponding to adjacent rigid activity anchor points; and identifies candidate detour tolerance domains in the road network. Acceptable entrances are generated based on entrance detour ratios, direction matching coefficients, and traffic risk indices. The probability of achievable visits is determined by combining the service information of the corresponding green spaces, generating a dataset of achievable visit probabilities. Furthermore, the matching relationship between green space service demand and achievable service supply is determined, outputting the measurement results of green space service fulfillment capacity. This realizes the transformation of green space service demand from static spatial representation to real activity chain representation, improving the authenticity, reliability, and interpretability of urban green space service judgment results.

Description

Technical Field

[0001] This invention belongs to the field of urban public service assessment and spatial governance technology, specifically involving a method, system, equipment and medium for measuring the service delivery capability of green spaces based on activity chain time windows. Background Technology

[0002] Urban green spaces are an important component of the urban public service system and urban ecosystem, providing residents with services such as rest and social interaction, shade and cooling, psychological comfort, air purification, and urban microclimate regulation. As urban construction gradually enters the stage of stock renewal and refined governance, the evaluation of the service level of urban green spaces is no longer limited to green space area, spatial coverage, and the degree of balanced resource allocation, but is gradually required to reflect the possibility and stability of residents obtaining green space services in actual living scenarios.

[0003] Existing urban green space evaluation methods typically use administrative districts, blocks, plots, residential areas, workplaces, or regular grids as analysis units. They combine population data, socioeconomic data, ecological and environmental data, road traffic data, and green space data to evaluate the supply level, demand intensity, spatial accessibility, and equity of green spaces. For example, related technologies construct comprehensive green space supply indices, comprehensive green space demand indices, and indicators such as green space walkability and visual visibility to further generate a green space equity index, reflecting whether urban green space resource allocation is balanced. Such methods can reveal the supply and demand relationship and allocation equity of urban green spaces at a macro level, providing valuable reference for urban planning and public resource allocation.

[0004] However, the accessibility or equity of green space resources in a spatial statistical sense does not necessarily mean that residents can obtain corresponding services during actual travel. Existing evaluation methods usually judge the service level of green spaces based on service radius, buffer zone, road network accessibility, fixed walking time threshold, or supply and demand index. The evaluation results reflect more whether a certain area is within a predetermined coverage area or whether it has relatively high access to resources. Such methods do not adequately consider factors such as time constraints, route detour burdens, entrance and exit usage conditions, differences in opening hours, facility capacity occupancy, and short-term stay needs during residents' actual travel, which can easily lead to discrepancies between evaluation results and actual service access.

[0005] Especially in typical urban life scenarios such as short stops during weekday lunch breaks, between rail transit transfers, between school pick-ups and drop-offs, and between community service visits, residents' access to green spaces is often limited by both continuous travel schedules and short-term usage conditions. Even if a green space is geographically close on a map or within a fixed walking time range, it may not be able to be converted into a green space service that residents can actually access due to reasons such as high actual detour costs, inconvenient entrance and exit locations, complex access conditions, mismatched opening hours, insufficient stay time, or crowded service facilities. Summary of the Invention

[0006] Firstly, in view of the shortcomings of the existing technology, the purpose of this application is to provide a method for measuring the green space service fulfillment capability based on the activity chain time window, so as to solve at least one technical problem in the background technology.

[0007] The objective of this application can be achieved through the following technical solutions: A method for measuring the delivery capability of green space services based on activity chain time windows includes: The set of fixed rigid activity anchor points, the resident activity chain formed by the fixed activity anchor points, and the set of green space entrances within the target area are obtained. The fixed activity anchor points are the places where residents stay in their daily activities with time constraints and spatial stability. Based on two adjacent rigid activity anchors in the resident activity chain, the available time for the activity chain segment corresponding to the two adjacent rigid activity anchors is determined. The available time is used to characterize the time slack that can be used for green space access behavior without affecting the resident's completion of the activity chain segment. Based on the available time, candidate detour tolerance zones that can be used for green space access behavior are determined in the road network consisting of road nodes and road edges within the target area. The candidate detour tolerance zones include road nodes and / or road edges that satisfy the available time constraints. Based on the set of green space entrances, green space entrances located within the candidate detour tolerance zone are generated as candidate entrances. The entrance detour ratio, direction matching coefficient, and traffic risk index corresponding to the candidate entrances are obtained. An acceptable entrance is generated from the candidate entrances based on the entrance detour ratio, the direction matching coefficient, and the traffic risk index. Obtain service information of the green space corresponding to the acceptable entrance, the service information including one or more of the following: open status, minimum effective stay time, available capacity, queuing time and service utility; Based on the available time of the activity chain segment, the entrance detour ratio, direction matching coefficient and passage risk index corresponding to the acceptable entrance, and the service information, the achievable access probability corresponding to the activity chain segment is determined, and an achievable access probability dataset for the target area is generated. Based on the achievable visit probability dataset, the matching relationship between the demand for green space services and the supply of achievable services in the target area is determined, and the measurement result of the green space service fulfillment capability of the target area is generated according to the matching relationship. The green space service demand refers to the demand intensity corresponding to the activity chain segment within the target area, within the corresponding spatial unit and time slice, where the available time satisfies the minimum effective stay time corresponding to the residents' green space access behavior; the achievable service supply refers to the supply intensity obtained by calculating the service capacity of the green space corresponding to the acceptable entrance based on the achievable visit probability dataset.

[0008] Secondly, in view of the shortcomings of the existing technology, the purpose of this application is to provide a method for measuring the green space service fulfillment capability based on the activity chain time window, so as to solve at least one technical problem in the background technology.

[0009] The objective of this application can be achieved through the following technical solutions: A green space service delivery capability measurement system based on activity chain time windows includes: The data acquisition module is used to acquire the set of fixed rigid activity anchor points, the resident activity chain formed by the fixed activity anchor points, and the set of green space entrances within the target area. The fixed activity anchor points are the places where residents stay in their daily activities with time constraints and spatial stability. The available time determination module is used to determine the available time of the activity chain segment corresponding to the two adjacent rigid activity anchors in the resident activity chain. The available time is used to characterize the time slack that can be used for green space access behavior without affecting the resident's completion of the activity chain segment. The candidate detour tolerance domain determination module is used to determine, based on the available time, a candidate detour tolerance domain that can be used for green space access behavior in the road network composed of road nodes and road edges within the target area. The candidate detour tolerance domain includes road nodes and / or road edges that satisfy the available time constraint. An entrance screening module is used to generate candidate entrances for green space entrances located within the candidate detour tolerance zone based on the set of green space entrances, obtain the entrance detour ratio, direction matching coefficient and passage risk index corresponding to the candidate entrances, and generate acceptable entrances from the candidate entrances according to the entrance detour ratio, the direction matching coefficient and the passage risk index. The service information acquisition module is used to acquire service information of the green space corresponding to the acceptable entrance. The service information includes one or more of the following: open status, minimum effective stay time, available capacity, queuing time and service utility. An achievable visit probability generation module is used to determine the achievable visit probability corresponding to the activity chain segment based on the available time of the activity chain segment, the entrance detour ratio corresponding to the acceptable entrance, the direction matching coefficient and the passage risk index, and the service information, and to generate an achievable visit probability dataset for the target area. The measurement result generation module is used to determine the matching relationship between the demand for green space services and the supply of achievable services within the target area based on the achievable visit probability dataset, and to generate a measurement result of the green space service fulfillment capacity of the target area based on the matching relationship; wherein, the demand for green space services is the demand intensity corresponding to the activity chain segment in the corresponding spatial unit and time slice within the target area, where the available time satisfies the minimum effective stay time corresponding to the residents' green space access behavior; the supply of achievable services is the supply intensity obtained by converting the service capacity of the green space corresponding to the acceptable entrance based on the achievable visit probability dataset.

[0010] Thirdly, in view of the shortcomings of the existing technology, the purpose of this application is to provide a method for measuring the green space service fulfillment capability based on the activity chain time window, so as to solve at least one technical problem in the background technology.

[0011] The objective of this application can be achieved through the following technical solutions: A computer-readable storage medium storing a computer program, which, when executed by a processor, implements the first aspect of the method for measuring the fulfillment capability of green space services based on an activity chain time window.

[0012] Fourthly, in view of the shortcomings of the prior art, the purpose of this application is to provide a method for measuring the green space service fulfillment capability based on the activity chain time window, so as to solve at least one technical problem in the background art.

[0013] The objective of this application can be achieved through the following technical solutions: An electronic device includes a processor and a memory, the memory storing a computer program that, when executed by the processor, implements a method for measuring the fulfillment capability of green space services based on an activity chain time window, as described in the first aspect.

[0014] The beneficial effects of this application are: 1. To address the issue that service evaluation methods often rely on fixed service radii, static distances, or grid centroid determination, which fail to reflect residents' actual travel constraints, this invention identifies rigid activity anchor points based on residents' daily activity trajectories and calculates the net budget of activity chain time windows between adjacent anchor points. This transforms traditional green space service accessibility analysis into a service determination process under time budget constraints, obtaining contextualized service base data that reflects residents' actual disposable time conditions. This realizes the transformation of green space service demand from static spatial representation to real activity chain representation, improving the authenticity, reliability, and interpretability of urban green space service determination results.

[0015] 2. To address the problem that existing accessibility analyses ignore the actual road network structure and travel detour behavior, leading to significant deviations in service coverage identification results, this invention constructs a detour tolerance domain based on road network constraints. By combining the detour ratio, direction matching coefficient, and traffic risk index of green space entrances, an acceptability judgment rule is established for green space entrances. This allows for the selection of a set of candidate detour area nodes that meet time budget constraints, thereby enabling the identification of the actual accessible space range for residents and improving the accuracy and precision of green space service coverage.

[0016] 3. Addressing the issues that green space service evaluation is limited to coverage analysis, making it difficult to identify actual service failure areas, and lacking exploration of the causes of low fulfillment capacity and subsequent intervention applications, this invention calculates the service fulfillment probability to obtain a service fulfillment limitation index and intervention priority, enabling rapid measurement of urban green space service fulfillment capacity and determination of its causes. This improves the pertinence of measuring and determining the causes of urban green space service fulfillment capacity, thereby providing an application approach for evidence-based renewal of urban green spaces and promoting the refined development of urban green space governance. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a schematic diagram of the overall process of an embodiment of this application; Figure 2 This is a schematic diagram of the activity chain time window and the bypass tolerance domain principle of an embodiment of this application; Figure 3 This is a schematic diagram illustrating the service fulfillment capability measurement results of an embodiment of this application; Figure 4 This is a comparison chart of the probability of service access achieved by the sample in this application embodiment and the conventional method. Detailed Implementation

[0019] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0020] This embodiment relates to a method for measuring the service delivery capability of green spaces based on activity chain time windows. It belongs to the field of urban public service assessment and spatial governance technology, and is particularly suitable for measuring whether green space services can be actually used within a target area under the constraints of residents' real daily activity chains. The target area can be an urban area, a street area, a community area, a business district on the urban fringe, an area surrounding a rail transit transfer station, a school pick-up and drop-off area, a peripheral residential area, or other areas where the supply and demand relationship and service delivery capability of green space services need to be evaluated.

[0021] The measurement method proposed in this embodiment takes the target area as the object and transforms the evaluation of green space services from a static spatial coverage relationship into a service delivery process under the constraint of the time window of residents' actual activity chain. It uses the set of rigid activity anchor points, the resident activity chain formed by the rigid activity anchor points, the set of green space entrances, the road network, and green space service information in the target area to determine the available time, candidate detour tolerance domain, candidate entrance, acceptable entrance, achievable visit probability dataset, and green space service delivery capability measurement results of the activity chain segments in sequence. In this way, it identifies the matching relationship between green space service demand and achievable service supply under the constraint of the time window of residents' activity chain.

[0022] like Figure 1 As shown, the method of this embodiment may include the following steps: obtaining a set of fixed rigid activity anchor points, a resident activity chain formed by the fixed activity anchor points, and a set of green space entrances within the target area; determining the available time for corresponding activity chain segments based on two adjacent fixed activity anchor points in the resident activity chain; determining candidate detour tolerance zones in the road network based on the available time; generating candidate entrances based on the set of green space entrances and candidate detour tolerance zones, and generating acceptable entrances based on entrance detour ratio, direction matching coefficient, and traffic risk index; obtaining service information of the green space corresponding to the acceptable entrance; determining the probability of achievable visit based on available time, entrance evaluation parameters, and service information, and generating an achievable visit probability dataset; generating a green space service fulfillment capability measurement result based on the achievable visit probability dataset.

[0023] S1. Obtain the set of established rigid activity anchor points within the target area, the resident activity chain formed by the rigid activity anchor points, and the set of green space entrances. In this embodiment, the first step is to obtain a set of fixed rigid activity anchor points, a chain of resident activities formed by these fixed activity anchor points, and a set of green space entrances within the target area. The fixed activity anchor points are locations where residents stay during their daily activities and where there are both temporal constraints and spatial stability.

[0024] In this embodiment, rigid activity anchor points are locations where residents have time constraints and spatial stability in their daily activities. For example, rigid activity anchor points may include workplaces, residences, schools, fixed transfer points, fixed pick-up and drop-off points, and other locations where residents have strong time constraints and spatial stability in their daily activities. The time constraint means that the activity corresponding to the location has a relatively clear arrival time, departure time, start time, end time, or duration. The spatial stability means that the location has the spatial attribute of recurring or fixed occurrence in residents' daily activities.

[0025] The resident activity chain is formed by multiple rigid activity anchors arranged in chronological order. For users The resident activity chain can start from any two adjacent rigid activity anchor points. and These form a moving chain segment. This moving chain segment includes at least the preceding rigid moving anchor point. The second rigid movable anchor point The system considers the departure time of the preceding rigid activity anchor point, the arrival time of the following rigid activity anchor point, the travel buffer time between them, and the corresponding road network traffic constraints. By limiting green space access behavior to activity chain segments between two adjacent rigid activity anchor points, this implementation can reflect whether residents have a time slack available for green space access in their actual daily activity schedules.

[0026] The set of green space entrances includes the set of locations within the target area where residents can enter, approach, or use green space service units. These green space entrances can be park entrances, open green space entrances, street-side green space access points, public open space access points, or entrance locations connected to green space service units and capable of accommodating entry. The set of green space entrances is used for spatial matching with subsequent candidate detour tolerance domains to determine candidate entrances with accessibility potential under activity chain time windows and road network constraints.

[0027] To obtain the above-mentioned measurement input, population activity data D within the target area can be acquired. A Green space data D G and spatial access constraint data D R The population activity data D A Green space data D G and spatial access constraint data DR These are respectively used to form or verify the resident activity chains and their rigid activity anchors, green space entrance sets and service unit basic information required for subsequent measurements, and the road network composed of road nodes and roadsides; wherein, the population activity data D A It may include at least one of mobile phone signaling data, location trajectory data, public transportation card swipe data, and navigation log data; the green space data D G This can include vector data such as park boundaries, entrance locations, internal functional zones, opening hours, and facility distribution; the spatial access constraint data D R This can include vector data such as road networks, pedestrian networks, crossing facilities, slopes, fences, checkpoints, and accessibility facilities. Population activity data D A Green space data D G and spatial access constraint data D R The corresponding geographic coordinates and time information can be loaded into the city geographic information platform ArcGIS for unified processing to form the spatiotemporal data foundation required for subsequent activity chain time window calculation, candidate detour tolerance domain construction, entry point acceptability determination, and service fulfillment capability measurement.

[0028] During the data preparation process described above, population activity data can be analyzed. Green space data and spatial access constraint data Execution time standardization and spatial coordinate unification processing. For any original record The original record This can include position coordinates ,time and data source identifier Time standardization unifies records from different data sources to a unified time zone and time format for the target region, while spatial coordinate standardization transforms the spatial coordinates of different data sources to a unified coordinate reference used in the target region; for example, for any original record Perform time standardization and coordinate standardization, that is: In the formula, Used to unify time zones and time formats. Used to unify projected coordinate systems and spatial references; Indicates the first The location coordinates of the record; Indicates the first The time of each record; Indicates the first The original data source identifier for each record.

[0029] In one specific implementation, the target area can be divided into a set of regular grids. It is divided into time slice sets according to fixed time intervals. Regarding population activity data The activity records in the data are analyzed using spatial overlay statistical methods, such as mapping population activity records to corresponding grid cells and counting the number of activity records falling into each grid cell within each time slice. This allows for the calculation of the grid-time slice population activity intensity, using the following formula: In the formula, For data source The corresponding credibility weight; 1(A) is an indicator function, when condition A is true, 1(A)=1; when condition A is false, 1(A)=0.

[0030] To eliminate dimensional differences between different data sources and indicators, continuous variables can be standardized. For example, for the first... The element in the first Original values ​​on each grid cell The corresponding standardized results can be obtained. : In the formula, For the first The element in the first The original values ​​on each grid cell For the standardization results, To prevent extremely small quantities with a denominator of zero.

[0031] Based on the above data, resident activity chains can be formed from resident activity data, and activity chain segments corresponding to two adjacent rigid activity anchors can be obtained from these chains. Rigid activity anchors can originate from existing activity identification results or be obtained by identifying and aggregating dwell behaviors in resident activity trajectories. As a data preparation method, dwell behaviors can be identified based on spatial distance and dwell time thresholds on standardized resident trajectory data, and dwell clusters can be formed through spatial aggregation. .

[0032] For each cluster of stays Anchor point scores can be calculated based on dwell time (dwell(u,c), cross-day recurrence frequency (rec(u,c)), and the functional match value (func(c)) between dwell location and user activity functional requirements. When the anchor point score is not lower than the anchor point threshold eta and the dwell time is not lower than the dwell time threshold theta_d, the corresponding dwell cluster can be identified as a rigid active anchor point. For each dwell cluster... Anchor score The calculation formula is as follows: In the formula, dwell(u,c) is the dwell time, and rec(u,c) is the frequency of recurrence across days. The functional matching score is the score between the location of the dwell cluster and the user's functional needs. , , Each has three weights, and l 1+ l 2+ l 3=1; η in the anchor point determination condition represents the anchor point threshold. i d The dwell time threshold is defined as follows, where the subscript d represents the dwell time threshold. This threshold is set when the anchor point score is not less than η and the dwell time is not less than [a certain value]. i d At that time, the stationary cluster was identified as a rigid active anchor point. Through the above processing, we can obtain the set of fixed rigid activity anchors, the resident activity chains formed by the rigid activity anchors, and the set of green space entrances within the target area. These three objects serve as the basic inputs for subsequent calculations of available time for activity chain segments, construction of candidate detour tolerance domains, screening of green space entrances, and calculation of feasible visit probabilities.

[0033] S2. Based on two adjacent rigid activity anchors in the resident activity chain, determine the available time for each segment of the activity chain. After obtaining the set of fixed rigid activity anchors, the resident activity chains formed by these rigid activity anchors, and the set of green space entrances within the target area, the available time for each adjacent rigid activity anchor segment is determined based on these two adjacent rigid activity anchors. This available time characterizes the remaining time available for green space access without affecting residents' ability to complete the activity chain segment.

[0034] Specifically, for the resident activity chain of user u, for two adjacent rigid activity anchors in the resident activity chain... and Obtain the previous rigid active anchor point Departure time, the next rigid activity anchor point The arrival time and the corresponding travel buffer time for each activity chain segment are determined. The travel buffer time represents the time residents need to reserve when leaving the previous rigid activity anchor point, arriving at the next rigid activity anchor point, or completing an activity switch. Based on the departure time of the previous rigid activity anchor point, the arrival time of the next rigid activity anchor point, and the travel buffer time, the total available time for each activity chain segment is determined. .

[0035] Determine the total available time Subsequently, the previous rigid movable anchor point was further determined. To the latter rigid movable anchor point Minimum travel time between The minimum passage time This represents the necessary travel time required for a resident to reach a subsequent rigid activity anchor point from a previous rigid activity anchor point, without intervening in green space access activities. Based on the stated total available time... With the minimum passage time The difference between them generates the available time for the activity chain segment. .

[0036] In one specific implementation, the total available time between adjacent rigid moving anchor points can be calculated first. Then, based on the shortest path analysis of the urban road network, the minimum travel time between the two points is obtained. And based on this, the net budget Delta(u,k) of the activity chain window for this activity chain segment is obtained; where, The specific formula for calculating the total available time between two adjacent anchor points is as follows: In the formula, For users leaving the The time at each anchor point The time it takes for the user to reach the next anchor point. In the first The sum of travel buffer times in each activity chain.

[0037] A network topology is constructed based on the urban road network, and the minimum travel time between two anchor points is calculated through shortest path analysis, i.e.: In the formula, This represents the minimum travel time function between two points obtained based on the shortest path analysis of the urban road network.

[0038] D (u,k)= L u,k - t 0(u,k) This yields the net budget Δ(u,k) for the activity chain time window of this activity chain segment. In the decision criteria, mg represents the minimum acceptable stay threshold for population type g; when Δ(u,k) is not less than mg, it is denoted as an effective time window.

[0039] In one example, the study focuses on urban fringe business districts, rail transit transfer stations, and surrounding residential areas. Time zone unification and time format conversion are performed on activity data. Spatial positioning calibration is conducted for green space entrances, road networks, fence boundaries, and internal units. All data types are then loaded into the urban geographic information platform. The study area is divided into 200-meter regular grids, and activity data is uniformly sliced ​​in 15-minute time slices. Based on this, dwell cluster identification is performed on the standardized resident activity trajectories. Anchor point scores for dwell clusters are calculated from three dimensions: dwell duration, cross-day repetition frequency, and functional matching degree, thus identifying a set of rigid activity anchor points in residents' daily activity chains.

[0040] In Example Table 1, Sample W1 corresponds to a short stopover scenario in the CBD at midday, with the starting anchor point being office building A and the ending anchor point being conference building B, the time period being 12:08 to 12:40, the baseline travel time being 14 minutes, the anchor point buffer time being 10 minutes, and the available time for the activity chain segment being 8 minutes. Sample W2 corresponds to a short stopover scenario in the CBD at midday, with the starting anchor point being office building C and the ending anchor point being a restaurant D, the time period being 12:10 to 12:46, the baseline travel time being 19 minutes, the anchor point buffer time being 7 minutes, and the available time being 10 minutes. Sample W3 corresponds to a short stopover scenario involving rail transit transfers, with the starting anchor point being the subway station concourse and the ending anchor point being the office tower, the time period being 08:22 to 08:37, the baseline travel time being 8 minutes, the anchor point buffer time being 2 minutes, and the available time being 5 minutes. Sample W4 corresponds to a school drop-off / pick-up scenario, with the starting anchor point at the school's north gate and the ending anchor point at the community market, from 16:05 to 16:28. The baseline travel time is 13 minutes, the anchor point buffer time is 8 minutes, and the available time is 7 minutes. Sample W5 corresponds to a short stop scenario at a community service center, with the starting anchor point at the government service hall and the ending anchor point at the bus stop, from 10:18 to 10:41. The baseline travel time is 14 minutes, the anchor point buffer time is 6 minutes, and the available time is 7 minutes. These examples illustrate that even if different activity chain segments occur in the same target area, the available time will vary depending on the time arrangement and travel requirements.

[0041] Table 1. Output results of the activity chain time window The above examples illustrate that even if different activity chain segments occur in the same target area, they will have different available time due to differences in timing, preceding and following anchor point attributes, travel buffer time, and road traffic requirements. Therefore, this implementation does not directly assume that residents have access opportunities near green spaces, but rather first determines whether there are activity chain time windows in the actual activity chain that are sufficient to support green space access behavior.

[0042] After generating the available time for the activity chain segment, the available time is compared with the minimum effective stay time corresponding to a resident's green space access behavior. The minimum effective stay time is the shortest stay time required for a resident to complete one effective green space access behavior. When the available time is not less than the minimum effective stay time, the corresponding activity chain segment is added to the insertable activity chain segment set. Activity chain segments not added to the insertable activity chain segment set do not participate in the subsequent determination of candidate detour tolerance domains. This process avoids continuing to perform entry screening and achievable visit probability calculation for activity chain segments that clearly cannot support effective green space access behavior.

[0043] S3. Based on the available time, determine candidate detour tolerance zones that can be used for green space access behavior in the road network consisting of road nodes and roadsides within the target area. After determining the available time for the activity chain segment, candidate detour tolerance regions that can be used for green space access behavior are determined based on the available time in the road network consisting of road nodes and road edges within the target area; the candidate detour tolerance regions include road nodes and / or road edges that satisfy the available time constraints.

[0044] In one specific implementation, a road network consisting of road nodes and road edges can be constructed based on data from the road network, pedestrian network, pedestrian crossing facilities, fence boundaries, and slope constraints within the target area. The road nodes represent road intersections, pedestrian crossings, entrance connection points, path turning points, or other locations suitable for path calculation; the road edges represent passable road segments, pedestrian walkways, or connecting segments between adjacent road nodes. This road network is used not only to determine the minimum travel time between adjacent rigid activity anchors but also to determine whether residents still meet the available time constraints of the activity chain segment after passing through a certain road node, road edge, or green space entrance.

[0045] For any active chain segment in the set of insertable active chain segments, the two adjacent rigid active anchor points in the active chain segment are used as the start constraint and end constraint, respectively, to determine the road node and / or road edge to be judged in the road network. For each road node and / or road edge to be judged, the total detour time from the previous rigid active anchor point to the next rigid active anchor point via the road node and / or road edge to be judged is determined; the total detour time is used to represent the total travel time for residents to complete the active chain segment by passing the corresponding road node and / or road edge.

[0046] The additional travel time due to detours is determined based on the difference between the total detour travel time and the minimum travel time. This additional travel time represents the extra travel time a resident incurs relative to the minimum travel time to pass through a road node or roadside. When the additional travel time due to detours is not greater than the available time of the activity chain segment, the corresponding road node and / or roadside is added to the candidate detour tolerance domain corresponding to the activity chain segment.

[0047] In some examples, the network topology is constructed based on the urban road network, and the urban road network is built based on data such as road centerlines, pedestrian networks, crossing facilities, fence boundaries, and slope constraints. Let the urban road network be represented as... ,in, As a road network node, Gathering at the roadside.

[0048] Secondly, use the Spatial Connection tool in ArcGIS to connect the starting anchor points of the activity chain segments. and termination anchor point Match to adjacent nodes in the road network. For any road node in the road network... Calculate the distance from the starting anchor point to the road node. The shortest travel time, and from that road node Return the shortest travel time to the terminating anchor point. If the sum of the two travel times increases by no more than the minimum travel time of the active chain segment than the available time of the active chain segment, then the road node... The road edges that meet the time budget conditions are then included in the candidate detour tolerance domain. Subsequently, road edges that meet the time budget conditions are extracted according to road connectivity, and the corresponding road edges are extracted through GIS spatial connectivity and network adjacency rules to form the candidate detour tolerance domain under the activity chain time window conditions.

[0049] For any activity chain time window Define its candidate bypass region node set, namely: In the formula, Represents road network nodes; Indicates the distance from the starting anchor point to the node. Passage time; Indicates from node Returns the passage time to the terminating anchor point. Network node. A candidate detour is only included in the generated detour domain if the extra travel time from the starting anchor point to this node and back to the ending anchor point does not encroach on the minimum stay budget.

[0050] Next, candidate nodes that meet the time budget conditions are extracted according to road connectivity. Corresponding road edge sets are extracted using GIS spatial connectivity and network adjacency rules, thus forming candidate detour tolerance regions under the activity chain time window condition. The candidate detour tolerance region characterizes the potential travel range where residents can deviate from their original travel paths and access green spaces without disrupting the existing activity chain. Finally, the candidate detour tolerance region under the activity chain time window constraint is obtained. Figure 2 ).

[0051] The candidate detour tolerance domain includes a set of road nodes and / or a set of road edges associated with the activity chain segment, and each road node and / or road edge in the set of road nodes and / or road edges is written with a corresponding detour increase travel time. By writing the detour increase travel time into the data items corresponding to the road nodes and / or road edges, when the green space entrance is spatially matched with the candidate detour tolerance domain, the constraint relationship between the spatial location of the entrance and the activity chain time window can be obtained simultaneously.

[0052] S4. Based on the set of green space entrances, green space entrances located within the candidate detour tolerance zone are generated as candidate entrances. The detour ratio, direction matching coefficient, and traffic risk index corresponding to the candidate entrances are obtained. Acceptable entrances are generated from the candidate entrances according to the detour ratio, direction matching coefficient, and traffic risk index. After determining the candidate detour tolerance zone, based on the set of green space entrances, green space entrances located within the candidate detour tolerance zone are generated as candidate entrances. The entrance detour ratio, direction matching coefficient, and traffic risk index corresponding to the candidate entrances are obtained. An acceptable entrance is generated from the candidate entrances according to the entrance detour ratio, the direction matching coefficient, and the traffic risk index.

[0053] Specifically, when generating candidate entrances, the green space entrances in the green space entrance set are spatially matched with the road node set and / or roadside set in the candidate detour tolerance domain. This spatial matching does not simply determine the straight-line distance between the entrance and the activity anchor point, but rather determines whether the spatial location of the green space entrance falls within the candidate detour tolerance domain and whether the green space entrance is connected to the road nodes and / or roadsides in the candidate detour tolerance domain. For green space entrances that simultaneously meet the conditions of location inclusion and road connectivity, they are generated as candidate entrances and written into the candidate entrance set under the corresponding activity chain segment.

[0054] For any candidate entry in the candidate entry set Determine via the candidate entry point The entry detour time generated by completing the corresponding activity chain segment increases the travel time. This increased travel time is the additional travel time compared to the minimum travel time of the activity chain segment when a resident enters the green space via this candidate entry point. Based on the entry point... The ratio between the increased travel time due to detours and the available time of the activity chain segment is used to generate the entrance detour ratio for the candidate entrances. The entrance detour ratio Used to indicate the extent to which a candidate entry occupies the available time of an activity chain segment.

[0055] The specific formula for calculating the entrance detour ratio is as follows: .

[0056] Based on the degree of matching between the candidate entry and the direction of travel of the activity chain segment, a direction matching coefficient for the candidate entry is generated. Specifically, the original travel direction from the starting anchor point to the ending anchor point can be used as the reference direction, and the direction from the starting anchor point to the entrance of the green space can be used as the entrance direction. Direction vectors of the two paths can be extracted in a planar coordinate system, and the entrance direction matching coefficient can be calculated based on the relationship between the direction vectors. The closer the direction matching coefficient is to the positive direction, the more the entrance follows the residents' original travel direction; the closer the direction matching coefficient is to the opposite direction, the more the entrance deviates from the residents' original travel direction. That is: In the formula, The closer to 1, the more the entrance is in line with the original travel direction; the closer to -1, the more the entrance is away from the original travel direction.

[0057] According to the candidate entry The road traffic constraints involved in accessing green spaces are used to generate a traffic risk index for the candidate entrances. The aforementioned traffic risk index This is used to characterize the traffic safety and stability constraints when accessing green spaces via candidate entrances. The road traffic constraints may include high-risk pedestrian crossings, stair sections, path traffic uncertainty, and other road conditions affecting green space access behavior. In practice, along the candidate traffic path from the starting anchor point to the candidate entrance, the number of high-risk pedestrian crossings, stair lengths, and path traffic uncertainty along the path can be statistically analyzed, and a traffic risk index can be calculated accordingly. ,Right now: In the formula, The number of high-risk pedestrian crossings that need to be passed. This represents the length of the stairs that need to be traversed. This introduces uncertainty regarding the path's accessibility.

[0058] The entrance detour ratio, direction matching coefficient, and traffic risk index are written into the data items of the corresponding candidate entrances and compared with the corresponding thresholds respectively; when r u,k,e ≤ r g , f u,k,e ≥ f g and x u,k,e ≤ x g At that time, entry point e is determined to be an acceptable entry point within the activity chain's time window. Among these, r u,k,e The entrance detour ratio, f u,k,e For direction matching coefficients, x u,k,e For traffic risk index; r g , f g , x g These are the detour ratio threshold, direction matching threshold, and traffic risk threshold, respectively.

[0059] In this embodiment, sample W1 is used to filter the three entrances of the large park (Table 2). The extra detour time for the East Gate is 3.0 minutes, corresponding to a detour ratio of 0.375, a direction matching coefficient of 0.82, and a risk index of 0.11; the result is to retain it. The extra detour time for the Middle Gate is 5.0 minutes, corresponding to a detour ratio of 0.625, a direction matching coefficient of 0.74, and a risk index of 0.08; it is removed because the detour exceeds the threshold. The extra detour time for the West Gate is 2.0 minutes, corresponding to a detour ratio of 0.250, a direction matching coefficient of -0.21, and a risk index of 0.19; it is removed because the direction matching requirement is not met.

[0060] Table 2. Results of the Acceptability Assessment for Sample W1 S5. Obtain service information of the green space corresponding to the acceptable entrance. After an acceptable entry point is generated, the service information of the green space corresponding to that entry point is obtained. The service information includes one or more of the following: open status, minimum effective stay time, available capacity, queue waiting time, and service utility.

[0061] Specifically, based on the acceptable entrances in the set of acceptable entrances and the corresponding service units and service periods in the green space, the opening status, minimum effective stay time, available capacity, queuing time, and service utility of the green space during the corresponding service period are determined. The service unit is an area or facility unit within the green space that can provide specific service functions, and the service period is a time slice for evaluating or statistically analyzing the services provided by the green space. By defining service information through acceptable entrances, service units, and service periods, the entire green space can be avoided from being treated as a uniform, static service object with no capacity differences.

[0062] In some implementations, urban green spaces can be broken down into "entrance-service unit-service period" objects. This object can be represented as... , Where, in the formula, As the entrance, As a service unit, For service hours, For the minimum effective stay duration, For available capacity, The service feature vector is defined as follows: entrance represents the spatial location where residents enter or approach the green space service; service unit represents the specific spatial unit in the green space that carries the service; service period represents the time slice in which the service unit is in a specific open state, capacity state, and service state; and the service feature vector represents the environmental and facility characteristics of the service unit.

[0063] The "openness" refers to whether the corresponding green space or service unit is accessible during the corresponding service period. The "minimum effective stay time" refers to the shortest stay required for residents to obtain effective green space services at the corresponding service unit. The "available capacity" refers to the service capacity of the corresponding service unit to continue accommodating green space access during the corresponding service period. The "queue waiting time" refers to the potential waiting time for residents before accessing the corresponding service unit. The "service utility" refers to the degree of support the corresponding service unit provides for green space access.

[0064] When determining service utility, observational indicators for the corresponding service period can be extracted, including canopy coverage, seat density, perceived cooling value, accessibility score, and queuing reversal index, to form a service feature matrix. , Total number of service periods The number of evaluation indicators.

[0065] For the positive and negative indicators in the service feature matrix, standardization can be performed separately, and the comprehensive utility of the service period can be calculated by combining the information entropy and indicator weights of each indicator. ;like: Calculate the index weights Then, the weights of each indicator are weighted and superimposed with the standardized service feature matrix to obtain the comprehensive utility of the service period. q z The calculation formula is: In the formula, H n The information entropy of the nth indicator. oh n For the corresponding weights, r z,n For the standardized index value of service period z, q z For overall utility.

[0066] In this embodiment, calculations were performed for 12 service periods during the midday short-stay scenario, and the entropy weight method was used to objectively assign weights to five core indicators, resulting in a weight of 0.27 for canopy coverage, 0.31 for perceived cooling value, 0.18 for seat density, 0.11 for accessibility score, and 0.13 for queuing reversal index. The resulting comprehensive utility of service periods can characterize the usability differences of different green space micro-units under short-stay scenarios and provide input for subsequent calculations of the probability of visitation. This result indicates that the comprehensive utility of service periods can effectively characterize the usability differences of different green space micro-units under short-stay scenarios.

[0067] When determining queuing times, the demand intensity for each service period can be predicted based on the service unit's openness, available capacity, and historical activity intensity. The demand intensity is used to characterize the potential number of visits that may reach the service unit within a specific time period. In practice, the service demand intensity can be determined by combining the historical activity intensity, entrance detour ratio, and direction matching coefficient within the service period; the queuing time can be determined based on the service demand intensity and the available capacity within the service period. The calculation formula is: In the formula, d z For the predicted demand intensity during service period z, This is an indicator of open time and ingress connectivity conditions, set to 1 if the condition is met, and 0 otherwise; the summation range is the activity chain time window samples that satisfy the candidate relationship. e z For the corresponding entry point; , These are the entrance bypass ratio and the directional matching coefficient, respectively. l 1. l 2 represents the bypass impedance coefficient and the direction matching bonus coefficient, respectively.

[0068] Then, based on the predicted demand intensity for the service period, the queuing time is calculated according to the service unit capacity: Where ε is a very small positive number, used to avoid the denominator being zero; w z For queuing time, d z To predict demand intensity, c z For available capacity, m z Service hours Average turnover rate; when d z ≤ c z hour, w z Take 0.

[0069] In this embodiment, the predicted demand intensity for the tree-lined seating area in the pocket park next to the station during the midday period is 4.6, and the service unit capacity is 6, so there is basically no need to queue; the predicted demand intensity for the tree-lined seating area at the east gate of the large park during the period from 12:15 to 12:30 is 11.4, and the service unit capacity is 5, corresponding to a queuing waiting time of approximately 2.4 minutes.

[0070] When generating service information records, the acceptable entry point, service unit, and service time period are used as indexes. The open status, minimum effective stay time, available capacity, queue waiting time, and service utility are written into the corresponding indexes to generate service information records. These service information records clearly identify the entry point location, service unit, and service time period corresponding to each set of service information, thus providing a traceable data foundation for subsequent probability calculation of visits.

[0071] S6. Based on the available time of the activity chain segment, the entrance detour ratio, direction matching coefficient, and passage risk index corresponding to the acceptable entrance, and the service information, determine the achievable access probability corresponding to the activity chain segment, and generate an achievable access probability dataset for the target area. After obtaining the service information of the green space corresponding to the acceptable entrance, the feasible access probability corresponding to the activity chain segment is determined based on the available time of the activity chain segment, the entrance detour ratio, direction matching coefficient and passage risk index corresponding to the acceptable entrance, as well as the service information, and an feasible access probability dataset of the target area is generated.

[0072] When calculating the probability of achievable access, an open overlap coefficient is first determined based on the time range of the activity chain segment and the open status in the service information record. This open overlap coefficient indicates whether there is a valid overlap between the accessible time range corresponding to the activity chain segment and the green space service period. When the green space is not open during the corresponding service period, or when there is no valid overlap between the accessible time range of the activity chain segment and the service period, the open overlap coefficient does not support the generation of a positive probability of achievable access.

[0073] Then, the additional passage time for detours associated with the acceptable entry is obtained. This additional passage time for detours is already written into the data field of the corresponding candidate entry when generating candidate entry evaluation parameters, and it is included in subsequent probability calculations along with the acceptable entry. Based on the available time, the additional passage time for detours, the minimum effective dwell time, and the queuing waiting time, a dwell sufficiency coefficient is determined. The dwell sufficiency coefficient indicates whether the remaining time of the activity chain segment, after deducting the additional passage time for detours and the queuing waiting time, is sufficient to meet the minimum effective dwell time.

[0074] Based on the entrance detour ratio, direction matching coefficient, and traffic risk index, the accessibility impedance coefficient is determined. The accessibility impedance coefficient represents the combined hindering effect of entrance detour cost, direction matching degree, and road traffic risk on green space access behavior. The larger the entrance detour ratio, the more unfavorable the direction matching coefficient, and the higher the traffic risk index, the more significant the limiting effect of the accessibility impedance coefficient on the probability of achievable access.

[0075] Based on the available capacity and queuing time, a capacity fulfillment coefficient is determined. This coefficient indicates whether the corresponding service unit has sufficient capacity to handle green space access during the corresponding service period. If the available capacity is insufficient or the queuing time is too long, even if the entry point meets acceptable conditions and the activity chain segment has available time, the probability of a visit will still be limited.

[0076] When the available time is greater than zero, the open overlap coefficient is greater than zero, and the available capacity is greater than zero, a record of the probability of achievable visits corresponding to the activity chain segment is generated based on the open overlap coefficient, the sufficiency coefficient, the accessibility impedance coefficient, the capacity fulfillment coefficient, and the service utility. This record of the probability of achievable visits represents the likelihood of a resident actually completing a visit to the green space under specific activity chain segments, specific acceptable entry points, and specific service information records.

[0077] In some embodiments, first for any time window Service Hours Calculate the additional detour time relative to the baseline commuter link: The open overlap coefficient for service periods is further defined to characterize the degree of matching between the opportunity window and the actual open time of the service slot. If the open overlap time is insufficient to cover the minimum stay threshold, the coefficient is less than 1. The specific calculation formula is as follows: Further defining the sufficiency coefficient reflects whether the remaining time within the time window, after deducting detours and queuing, can truly support a valid stay, i.e.: Further defining the accessibility impedance coefficient characterizes the inhibition of travel intentions by additional travel costs and risks, namely: In the formula, α 1 represents the detour penalty coefficient. α 2 represents the risk penalty coefficient; the more convenient the entrance direction, the higher the probability of success.

[0078] The capacity realization factor is further defined by the following formula: Based on this, the probability of a visit during the service period is calculated using the following formula: Further defining the activity chain time window can achieve the service dose, with the specific formula as follows: In the formula, Dose(u,k) represents the actual service dose that residents can obtain under the time window constraint.

[0079] Using the activity chain segments, acceptable entry points, and service information records as indexes, the achievable visit probability records are written under the corresponding indexes to generate an achievable visit probability dataset for the target area. This achievable visit probability dataset at least reflects the correspondence between activity chain segments, acceptable entry points, service units, service periods, service information records, and achievable visit probability records. Through this dataset, subsequent aggregation of green space service demand and achievable service supply can be performed according to spatial units and time slices.

[0080] In this embodiment, the calculated visitability probabilities for the five samples are shown in Table 3. Sample W1 corresponds to a lunchtime office worker, with 8 minutes of available time in the activity chain segment, an additional 3.0 minutes of passage time added by detouring at the entrance, a direction matching coefficient of 0.82, an open overlap coefficient of 1.00, a queuing wait time of 2.4 minutes, a service utility of 0.74, and a visitability probability of 0.24. Sample W2 corresponds to a lunchtime office worker, with 10 minutes of available time, an additional 4.0 minutes of passage time added by detouring at the entrance, a direction matching coefficient of 0.76, an open overlap coefficient of 0.42, a queuing wait time of 1.2 minutes, a service utility of 0.61, and a visitability probability of 0.31. Sample W3 corresponds to short-stop passengers, with 5 minutes of available time. Detours at the entrance increase passage time by 2.0 minutes. The direction matching coefficient is 0.88, the open overlap coefficient is 1.00, the queuing time is 0.0 minutes, the service utility is 0.83, and the probability of arrival is 0.71. Sample W4 corresponds to parents picking up or dropping off their children, with 7 minutes of available time. Detours at the entrance increase passage time by 3.0 minutes. The direction matching coefficient is 0.18, the open overlap coefficient is 1.00, the queuing time is 0.8 minutes, the service utility is 0.67, and the probability of arrival is 0.18. Sample W5 corresponds to commuters during lunch break, with 7 minutes of available time. Detours at the entrance increase passage time by 2.0 minutes. The direction matching coefficient is 0.21, the open overlap coefficient is 1.00, the queuing time is 0.7 minutes, the service utility is 0.58, and the probability of arrival is 0.27. In summary, although W3 has a net time window budget of only 5 minutes, it still has a high probability of being visited due to its convenient entrance, no queues, complete overlap of open areas, and high micro-unit utility. W1, on the other hand, has a significantly lower probability of being visited due to the superposition of queues and dwell time barriers.

[0081] Table 3 shows the results of visit probability calculations achievable via the activity chain time window. S7. Based on the achievable visit probability dataset, determine the matching relationship between the demand for green space services and the supply of achievable services within the target area, and generate a measurement result of the green space service fulfillment capacity of the target area based on the matching relationship. After generating the feasible access probability dataset for the target area, the matching relationship between the demand for green space services and the supply of feasible services in the target area is determined based on the feasible access probability dataset, and the green space service fulfillment capability measurement result of the target area is generated according to the matching relationship.

[0082] Specifically, based on the activity chain segments in the achievable visit probability dataset, the spatial units and time slices corresponding to the activity chain segments are determined. The spatial units can be grids, blocks, traffic analysis zones, or other spatial division units within the target area used for statistical analysis of green space service demand and achievable service supply; the time slices can be time division units used for statistical analysis of activity chain time windows and green space service periods. The division of spatial units and time slices should be consistent with the statistical scale of the target area measurement task.

[0083] The green space service demand is generated by aggregating activity chain segments within the same spatial unit and time slice, where the available time is no less than the minimum effective stay time corresponding to residents' green space access behavior. This green space service demand is not simply population size or the number of people within the static service radius, but rather the demand intensity corresponding to activity chain segments that allow for the insertion of green space access behavior into the corresponding spatial unit and time slice.

[0084] Based on the achievable visit probability records within the same spatial unit and time slice, the service capacity of the green space corresponding to the acceptable entrance is calculated to generate achievable service supply. The achievable service supply is not the nominal supply area or static capacity of the green space, but rather the supply intensity after being calculated based on activity chain time windows, entrance acceptability, open status, available capacity, queuing time, and service utility constraints.

[0085] Based on the difference between the demand for green space services and the available supply of services, a matching relationship is generated between the demand for green space services and the available supply of services. This matching relationship represents the degree to which the demand for green space services is met by the available supply of services within a corresponding spatial unit and time slot. Based on this matching relationship, the degree to which the demand for green space services is met by the available supply of services within different spatial units and time slots in the target area is determined. Then, based on the degree of satisfaction, the green space service fulfillment capability within the target area is graded for different spatial units and time slots, and a green space service fulfillment capability measurement result is output, including spatial units, time slots, and corresponding service fulfillment capability levels.

[0086] In one alternative implementation, the matching relationship can be further represented as a service fulfillment constraint index. Specifically, green space service demand can be defined based on spatial units and time slices. And define the supply of achievable services based on the achievable visit probability dataset. Then, based on the service needs of green spaces and the ability to provide services The difference between the calculation service delivery restriction index .

[0087] For example, regarding each spatial unit Time slice Population types achievable visit probability Next, the level of service delivery will be further quantified and measured. First, the spatial unit... In time slice The following is a definition of the time window requirement for the target audience: In the formula, For in spatial units With Time Slice This belongs to the population type A collection of opportunity windows; For population type The minimum acceptable stay time; For population type Standard target service dose for a single time window.

[0088] Furthermore, the supply capacity that a service unit can fulfill during this period is defined as: Based on this, the Service Deliverability Limitation Index (FCI) is constructed, and the specific calculation formula is as follows: In the formula, To prevent tiny constants with a denominator of zero.

[0089] Through the above calculations, a service blind spot index dataset for each spatial unit under different time slices and population types can be obtained. Spatial traversal and threshold determination can then be performed on the dataset to identify service blind spots in urban green spaces.

[0090] In some embodiments, the following determination rule is established based on the size of the service fulfillment restriction index: when When the spatial unit is identified as a service fulfillment capability barrier area, it indicates that the service supply in the area is significantly insufficient within the corresponding time slice and intervention should be prioritized. when When identified as a potential service fulfillment capacity area, it indicates a certain mismatch between supply and demand, requiring local optimization to improve service fulfillment capacity; when When a region is identified as having stable service fulfillment capacity, it indicates that the overall supply and demand in the region are basically balanced, and only continuous monitoring is required. when When the time is reached, it is determined to be in the stable service fulfillment zone, indicating that the existing service units can effectively meet the demand.

[0091] In some embodiments, the reasons for visit failures to any spatial unit can be categorized, and failure reason codes can be assigned in priority order. These failure reason codes may include WINDOW_TIGHT, ENTRY_DIRECTION_MISMATCH, SLOT_CLOSED, STAY_TIME_TIGHT, QUEUE_OVERRUN, and SAFE_CROSSING_MISSING; where WINDOW_TIGHT indicates a tight activity chain window, ENTRY_DIRECTION_MISMATCH indicates a mismatch in entrance direction, SLOT_CLOSED indicates a mismatch in service hours, STAY_TIME_TIGHT indicates a tight dwell time, QUEUE_OVERRUN indicates excessively long queue times, and SAFE_CROSSING_MISSING indicates a lack of safe pedestrian crossing facilities. This reason categorization can serve as an extended output of the green space service fulfillment capability measurement results, used to explain the main constraints leading to service non-fulfillment.

[0092] In some embodiments, the service fulfillment limitation index after intervention can be recalculated for candidate intervention actions, and the intervention elasticity can be calculated based on the difference in the service fulfillment limitation index before and after intervention, the corresponding service demand, and the intervention cost. This allows for the prioritization of candidate interventions, generating a priority list that can be used by planning, design, and management personnel for further design updates and localized optimizations. Candidate interventions may include adding new entrances, extending opening hours, adding short-stop micro-units, improving pedestrian access, supplementing short-stop seating, or increasing available capacity; intervention flexibility... The specific calculation formula is as follows: In the formula, To realize the restricted index before intervention, For intervention costs.

[0093] In this embodiment, the FCI results obtained in step S4 are associated with a regular grid layer in ArcGIS, and grayscale symbols and linear extrusion heights are set according to FCI levels; terrain, road network, park boundaries, and entrance points are overlaid, and a three-dimensional service fulfillment capability measurement map is output using a fixed tilted view. Figure 3 This visualization is used to show that target grids that appear to be covered under conventional walk-through coverage analysis may still exhibit low-achievement-capability core areas or potentially low-achievement-capability areas under the constraints of the activity chain time window.

[0094] As shown in Table 4, the three target grids are considered to be covered under conventional walk-through coverage analysis, but in this invention they still appear as low-fulfillment-capability core areas or potential low-fulfillment-capability areas, respectively.

[0095] Table 4. Output Results of Blind Spot Identification and Intervention Priority This embodiment also provides a green space service fulfillment capability measurement system based on activity chain time windows. The system includes a data acquisition module, a available time determination module, a candidate detour tolerance domain determination module, an entry filtering module, a service information acquisition module, a visit probability generation module, and a measurement result generation module. Each module can be implemented through software programs, hardware circuits, or a combination of both. Without specifying a particular deployment environment, servers, cloud platforms, edge terminals, or mobile terminals are not listed as essential technical features.

[0096] The data acquisition module is used to acquire the set of fixed rigid activity anchor points, the resident activity chains formed by the fixed activity anchor points, and the set of green space entrances within the target area. Specifically, the data acquisition module can receive data that has undergone time standardization and spatial coordinate unification processing, as well as the set of rigid activity anchor points and resident activity chains formed by external systems. When data preparation is required, the data acquisition module can also read population activity data D_A, green space data D_G, and spatial access constraint data D_R, and provide this data to subsequent modules.

[0097] The available time determination module is used to determine the available time of an activity chain segment corresponding to two adjacent rigid activity anchors in the resident activity chain. Specifically, the available time determination module can read the departure time of the previous rigid activity anchor, the arrival time of the next rigid activity anchor, and the travel buffer time corresponding to the activity chain segment to determine the total available time of the activity chain segment; at the same time, it determines the minimum travel time between the previous rigid activity anchor and the next rigid activity anchor based on the road network, and generates the available time based on the difference between the total available time and the minimum travel time.

[0098] The candidate detour tolerance domain determination module is used to determine candidate detour tolerance domains that can be used for green space access behavior in the road network composed of road nodes and road edges within the target area, based on the available time. Specifically, the candidate detour tolerance domain determination module can, for any active chain segment in the set of insertable active chain segments, use two adjacent rigid active anchor points as the start constraint and the end constraint, respectively, traverse or filter the road nodes and / or road edges to be judged in the road network, determine the corresponding total detour time and the detour increase time, and write the road nodes and / or road edges that satisfy the available time constraints into the candidate detour tolerance domain corresponding to the active chain segment.

[0099] The entrance screening module is used to generate candidate entrances based on the set of green space entrances, which are located within the candidate detour tolerance domain. It then obtains the entrance detour ratio, direction matching coefficient, and traffic risk index corresponding to each candidate entrance, and generates acceptable entrances from the candidate entrances based on these parameters. Specifically, the entrance screening module can spatially match green space entrances with the set of road nodes and / or roadside sets in the candidate detour tolerance domain, and write the entrance detour ratio, direction matching coefficient, and traffic risk index for each candidate entrance. When a candidate entrance meets the detour ratio threshold, direction matching threshold, and traffic risk threshold, it is written into the acceptable entrance set.

[0100] The service information acquisition module is used to obtain service information of the green space corresponding to the acceptable entrance. Specifically, the service information acquisition module can match the acceptable entrances in the set of acceptable entrances with the service units and service periods in the corresponding green spaces. Using the acceptable entrance, service unit, and service period as index items, the module writes the open status, minimum effective stay time, available capacity, queue waiting time, and service utility under the corresponding index item to generate a service information record. The service information record serves as input for the subsequent visit probability generation module.

[0101] The feasible access probability generation module is used to determine the feasible access probability corresponding to the activity chain segment based on the available time, the entrance detour ratio corresponding to the acceptable entrance, the direction matching coefficient, the passage risk index, and the service information of the activity chain segment, and to generate an feasible access probability dataset for the target area. Specifically, the feasible access probability generation module can determine the open overlap coefficient, the sufficiency coefficient, the access resistance coefficient, and the capacity fulfillment coefficient, and generate an feasible access probability record based on the open overlap coefficient, the sufficiency coefficient, the access resistance coefficient, the capacity fulfillment coefficient, and the service utility when the available time is greater than zero, the open overlap coefficient is greater than zero, and the available capacity is greater than zero; subsequently, using the activity chain segment, the acceptable entrance, and the service information record as index items, the feasible access probability record is written under the corresponding index item to generate an feasible access probability dataset for the target area.

[0102] The measurement result generation module is used to determine the matching relationship between green space service demand and available service supply based on the available visit probability dataset, and generate a measurement result of green space service fulfillment capability. Specifically, the measurement result generation module can determine the corresponding spatial unit and time slice based on the activity chain segments in the available visit probability dataset, and summarize the activity chain segments that meet the minimum effective stay time requirement under the same spatial unit and the same time slice as green space service demand; at the same time, based on the available visit probability records under the same spatial unit and the same time slice, the service capacity of the green space corresponding to the acceptable entrance is converted to generate available service supply, and output the measurement result of green space service fulfillment capability based on the difference between the two.

[0103] In one embodiment of a computer-readable storage medium, the computer-readable storage medium stores a computer program that, when executed by a processor, implements the aforementioned method for measuring the service fulfillment capability of green spaces based on an activity chain time window. The processor performs data processing steps such as data reading, time determination, road network calculation, entrance screening, service information processing, probability calculation, and measurement result generation; it is not limited to the processor directly performing physical actions such as road construction, green space renovation, entrance setup, or on-site facility installation.

[0104] In one embodiment of the electronic device, the electronic device includes a processor and a memory. The memory stores a computer program, which, when executed by the processor, implements the aforementioned method for measuring the green space service fulfillment capability based on activity chain time windows. The electronic device can read the determined set of rigid activity anchor points, resident activity chains, green space entrance sets, road networks, and green space service information within the target area, and output the green space service fulfillment capability measurement results. The electronic device can output spatial units, time slices, green space service demands, available service supply, matching relationships, service fulfillment capability levels, and optional failure reasons and intervention priorities through a display interface, data interface, or file output.

[0105] Through the above implementation method, this application does not judge the service capacity of green space solely based on the static distance between green space and resident location, but rather measures green space access behavior within the time window of resident activity chain. By introducing the available time between adjacent rigid activity anchor points and determining the candidate detour tolerance domain based on the available time, it can reflect the actual time and spatial range available for resident access to green space without affecting the completion of the existing activity chain.

[0106] Because this implementation method further generates acceptable entrances from candidate entrances based on entrance detour ratio, direction matching coefficient, and traffic risk index, it avoids treating all green space entrances within a fixed distance range as usable entrances. Instead, it filters entrances based on detour cost, travel direction, and traffic risk, making the measurement of green space service capacity closer to the accessibility status during residents' actual travel. This process can identify entrances that are close in distance but have inconvenient directions, excessive detours, or high traffic risks, reducing the risk of service overestimation caused by static spatial coverage judgments.

[0107] Because this implementation further breaks down green space services into acceptable entrances, service units, and service periods, and uses these as indexes to generate service information records, it can reflect the differences in openness, minimum effective stay time, available capacity, queuing time, and service utility among different entrances, service units, and service periods. Therefore, this implementation avoids treating the entire green space as a homogeneous service provider, making the measurement results closer to the actual service acquisition process.

[0108] Because this implementation method also incorporates service information such as open status, minimum effective stay time, available capacity, queue waiting time, and service utility into the calculation of the probability of visit, it can reflect whether the green space service can be actually used during the corresponding service period. Even if a green space entrance meets the acceptable conditions, its probability of visit will still be limited if the corresponding service unit is not open, has insufficient capacity, has an excessively long queue waiting time, or insufficient stay time.

[0109] Because this implementation method determines the matching relationship between the demand for green space services and the supply of available services based on an achievable visit probability dataset, and outputs a measurement result of the green space service fulfillment capability, it can identify the degree to which green space services are actually fulfilled at both the spatial unit and time slice scales. This measurement result can not only show the difference between the demand for green space services and the supply of available services, but also explain the reasons for service non-fulfillment by incorporating factors such as entrance detours, direction matching, passage risks, opening status, capacity, and queuing, providing a basis for evaluating the supply and demand matching of green space services, identifying service shortcomings, and conducting subsequent optimization analysis.

[0110] In the description of this specification, the references to terms such as "an embodiment," "example," "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0111] The foregoing has shown and described the basic principles, main features, and advantages of this application. Those skilled in the art should understand that this application is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of this application. Various changes and modifications can be made to this application without departing from the spirit and scope thereof, and all such changes and modifications fall within the scope of the claims of this application.

Claims

1. A method for measuring the service delivery capability of green spaces based on activity chain time windows, characterized in that, include: The set of fixed rigid activity anchor points, the resident activity chain formed by the fixed activity anchor points, and the set of green space entrances within the target area are obtained. The fixed activity anchor points are the places where residents stay in their daily activities with time constraints and spatial stability. Based on two adjacent rigid activity anchors in the resident activity chain, the available time for the activity chain segment corresponding to the two adjacent rigid activity anchors is determined. The available time is used to characterize the time slack that can be used for green space access behavior without affecting the resident's completion of the activity chain segment. Based on the available time, candidate detour tolerance zones that can be used for green space access behavior are determined in the road network consisting of road nodes and road edges within the target area. The candidate detour tolerance zones include road nodes and / or road edges that satisfy the available time constraints. Based on the set of green space entrances, green space entrances located within the candidate detour tolerance zone are generated as candidate entrances. The entrance detour ratio, direction matching coefficient, and traffic risk index corresponding to the candidate entrances are obtained. An acceptable entrance is generated from the candidate entrances based on the entrance detour ratio, the direction matching coefficient, and the traffic risk index. Obtain service information of the green space corresponding to the acceptable entrance, the service information including one or more of the following: open status, minimum effective stay time, available capacity, queuing time and service utility; Based on the available time of the activity chain segment, the entrance detour ratio, direction matching coefficient and passage risk index corresponding to the acceptable entrance, and the service information, the achievable access probability corresponding to the activity chain segment is determined, and an achievable access probability dataset for the target area is generated. Based on the achievable visit probability dataset, the matching relationship between the demand for green space services and the supply of achievable services in the target area is determined, and the measurement result of the green space service fulfillment capability of the target area is generated according to the matching relationship. The green space service demand refers to the demand intensity corresponding to the activity chain segment within the target area, within the corresponding spatial unit and time slice, where the available time satisfies the minimum effective stay time corresponding to the residents' green space access behavior; the achievable service supply refers to the supply intensity obtained by calculating the service capacity of the green space corresponding to the acceptable entrance based on the achievable visit probability dataset.

2. The method for measuring the service delivery capability of green spaces based on activity chain time windows according to claim 1, characterized in that, The method of obtaining the available time of the activity chain segment corresponding to two adjacent rigid activity anchors in the resident activity chain includes: Obtain the departure time of the previous rigid activity anchor point, the arrival time of the next rigid activity anchor point, and the travel buffer time corresponding to the activity chain segment; The total available time of the activity chain segment is determined based on the departure time of the previous rigid active anchor point, the arrival time of the next rigid active anchor point, and the travel buffer time. Determine the minimum travel time between the previous rigid movable anchor point and the next rigid movable anchor point; The available time for the activity chain segment is generated based on the difference between the total available time and the minimum travel time.

3. The method for measuring the service delivery capability of green spaces based on activity chain time windows according to claim 2, characterized in that, After generating the available time for the activity chain segment, the available time is compared with the minimum effective stay time corresponding to the resident's green space access behavior. The minimum effective stay time is the shortest stay time required for a resident to complete one effective green space access behavior. When the available time is not less than the minimum effective dwell time, the corresponding activity chain segment is added to the set of insertable activity chain segments; For any active chain segment in the set of insertable active chain segments, the two adjacent rigid active anchor points in the active chain segment are used as the start constraint and the end constraint, respectively, to determine the road node and / or road edge to be judged in the road network. Determine the total travel time from the previous rigid active anchor point to the next rigid active anchor point via the road node to be judged and / or the roadside to be judged; The additional travel time due to the detour is determined based on the difference between the total detour travel time and the minimum travel time. When the increased travel time due to the detour is not greater than the available time of the active chain segment, the corresponding road node and / or roadside will be added to the candidate detour tolerance domain corresponding to the active chain segment. The candidate detour tolerance domain includes a set of road nodes and / or a set of road edges associated with the activity chain segment, and each road node and / or road edge in the set of road nodes and / or the set of road edges is associated with the detour increased travel time.

4. The method for measuring the service delivery capability of green spaces based on activity chain time windows according to claim 3, characterized in that, Based on the set of green space entrances, green space entrances located within the candidate detour tolerance domain are generated as candidate entrances. The detour ratio, direction matching coefficient, and traffic risk index corresponding to each candidate entrance are obtained. Acceptable entrances are then generated from the candidate entrances based on the detour ratio, direction matching coefficient, and traffic risk index, including: Spatial matching is performed between the green space entrances in the green space entrance set and the road node set and / or roadside set in the candidate detour tolerance domain; Green space entrances whose spatial location falls within the candidate detour tolerance domain and are connected to road nodes and / or roadsides in the candidate detour tolerance domain are generated as candidate entrances, and the candidate entrances are associated with the corresponding activity chain segments; The passage time is increased by detouring through the entrance generated by completing the corresponding activity chain segment via the candidate entrance. The entrance detour ratio of the candidate entrance is generated based on the ratio between the increased passage time due to the entrance detour and the available time of the activity chain segment. Based on the degree of matching between the candidate entry and the direction of travel of the activity chain segment, a direction matching coefficient for the candidate entry is generated; Based on the road traffic constraints involved when accessing green spaces via the candidate entrances, a traffic risk index is generated for each candidate entrance. The entrance detour ratio, direction matching coefficient, and traffic risk index are associated with the corresponding candidate entrances; When the entrance detour ratio is not greater than the detour ratio threshold, the direction matching coefficient is not less than the direction matching threshold, and the passage risk index is not greater than the passage risk threshold, the corresponding candidate entrance is added to the acceptable entrance set.

5. The method for measuring the service delivery capability of green spaces based on activity chain time windows according to claim 4, characterized in that, The process of obtaining service information for the green space corresponding to the acceptable entrance includes: Based on the acceptable entrances in the set of acceptable entrances and the corresponding service units and service periods in the green space, determine the opening status, minimum effective stay time, available capacity, queuing time and service utility of the green space during the corresponding service period. The open status, minimum effective stay time, available capacity, queuing time and service utility are associated with the corresponding acceptable entrance, service unit and service time period to generate service information records.

6. The method for measuring the service delivery capability of green spaces based on activity chain time windows according to claim 5, characterized in that, The method, based on the available time of the activity chain segment, the entrance detour ratio corresponding to the acceptable entrance, the direction matching coefficient, and the passage risk index, as well as the service information, determines the achievable access probability corresponding to the activity chain segment and generates an achievable access probability dataset for the target area, including: Based on the time range of the activity chain segment and the open status in the service information record, the open overlap coefficient is determined; Obtaining the entrance associated with the acceptable entrance increases the passage time by taking a detour; Based on the available time, the increased passage time due to entrance detour, the minimum effective stay time, and the queuing waiting time, a sufficient stay coefficient is determined. Based on the entrance detour ratio, direction matching coefficient, and passage risk index, the accessibility impedance coefficient is determined. Based on the available capacity and queuing time, determine the capacity fulfillment factor; When the available time is greater than zero, the open overlap coefficient is greater than zero, and the available capacity is greater than zero, a record of the probability of achievable visits corresponding to the activity chain segment is generated based on the open overlap coefficient, the sufficient stay coefficient, the access impedance coefficient, the capacity realization coefficient, and the service utility. Using the activity chain segment, acceptable entry point, and service information record as index items, the achievable visit probability record is written into the corresponding index item to generate an achievable visit probability dataset for the target area.

7. The method for measuring the service delivery capability of green spaces based on activity chain time windows according to claim 6, characterized in that, The step of determining the matching relationship between the demand for green space services and the supply of available services within the target area based on the feasible visit probability dataset, and generating a measurement result of the green space service fulfillment capacity of the target area based on the matching relationship, includes: Based on the activity chain segments in the achievable visit probability dataset, determine the spatial unit and time slice corresponding to the activity chain segment; The activity chain segments with available time of no less than the minimum effective stay time corresponding to the residents' green space access behavior are summarized to generate green space service demand. Based on the achievable visit probability record under the same spatial unit and the same time slice, the service capacity of the green space corresponding to the acceptable entrance is calculated to generate the achievable service supply. Based on the difference between the demand for green space services and the available supply of services, a matching relationship between the demand for green space services and the available supply of services is generated. Based on the matching relationship, determine the extent to which the green space service demand under different spatial units and time slices within the target area is met by the available service supply; Based on the degree of satisfaction, the ability to deliver green space services under different spatial units and time slices within the target area is classified. The output includes spatial units, time slices, and the corresponding service fulfillment capability level of the green space service fulfillment capability measurement results.

8. A green space service delivery capability measurement system based on activity chain time windows, characterized in that, include: The data acquisition module is used to acquire the set of fixed rigid activity anchor points, the resident activity chain formed by the fixed activity anchor points, and the set of green space entrances within the target area. The fixed activity anchor points are the places where residents stay in their daily activities with time constraints and spatial stability. The available time determination module is used to determine the available time of the activity chain segment corresponding to the two adjacent rigid activity anchors in the resident activity chain. The available time is used to characterize the time slack that can be used for green space access behavior without affecting the resident's completion of the activity chain segment. The candidate detour tolerance domain determination module is used to determine, based on the available time, a candidate detour tolerance domain that can be used for green space access behavior in the road network composed of road nodes and road edges within the target area. The candidate detour tolerance domain includes road nodes and / or road edges that satisfy the available time constraint. An entrance screening module is used to generate candidate entrances for green space entrances located within the candidate detour tolerance zone based on the set of green space entrances, obtain the entrance detour ratio, direction matching coefficient and passage risk index corresponding to the candidate entrances, and generate acceptable entrances from the candidate entrances according to the entrance detour ratio, the direction matching coefficient and the passage risk index. The service information acquisition module is used to acquire service information of the green space corresponding to the acceptable entrance. The service information includes one or more of the following: open status, minimum effective stay time, available capacity, queuing time and service utility. An achievable visit probability generation module is used to determine the achievable visit probability corresponding to the activity chain segment based on the available time of the activity chain segment, the entrance detour ratio corresponding to the acceptable entrance, the direction matching coefficient and the passage risk index, and the service information, and to generate an achievable visit probability dataset for the target area. The measurement result generation module is used to determine the matching relationship between the demand for green space services and the supply of achievable services within the target area based on the achievable visit probability dataset, and to generate a measurement result of the green space service fulfillment capacity of the target area based on the matching relationship; wherein, the demand for green space services is the demand intensity corresponding to the activity chain segment in the corresponding spatial unit and time slice within the target area, where the available time satisfies the minimum effective stay time corresponding to the residents' green space access behavior; the supply of achievable services is the supply intensity obtained by converting the service capacity of the green space corresponding to the acceptable entrance based on the achievable visit probability dataset.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the method for measuring the green space service fulfillment capability based on an activity chain time window as described in any one of claims 1 to 7.

10. An electronic device, characterized in that, It includes a processor and a memory, wherein the memory stores a computer program, which, when executed by the processor, implements the green space service fulfillment capability measurement method based on the activity chain time window as described in any one of claims 1 to 7.

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