Parking space planning method, system, device and medium based on parking flow analysis

Abstract

This invention discloses a parking space planning method, system, equipment, and medium based on parking flow analysis, applied in the field of data analysis technology. The method includes: determining the flow characteristics of a target parking location by performing location attribute analysis; extracting events affecting pedestrian flow based on the location attributes; predicting flow based on the flow characteristics and the events affecting pedestrian flow to obtain flow prediction information; collecting parking space distribution information of the target parking location to construct a parking space distribution interval map; determining the parking space change trend based on the flow prediction information; and determining a parking space planning and management scheme based on the parking space change trend and the parking space distribution interval map. This solves the technical problem in existing technologies where the comprehensiveness of parking data analysis results is low, leading to low intelligence in parking space planning and unreasonable planning.

Description

Technical Field

[0001] This invention relates to the field of data processing, and in particular to a parking space planning method, system, device, and medium based on parking flow analysis. Background Technology

[0002] Parking flow analysis is a method for statistically analyzing parking lot traffic. Current technologies often rely on statistical analysis of historical parking data to obtain data from different time periods. However, this data lacks a strong correlation with actual vehicle parking attributes, resulting in incomplete analysis and hindering targeted optimization of parking space management. Consequently, parking space planning suffers from low intelligence and inefficient planning.

[0003] Therefore, the comprehensiveness of parking data analysis results in existing technologies is relatively low, which leads to a low level of intelligence in parking space planning and the existence of technical problems such as unreasonable planning. Summary of the Invention

[0004] This application provides a parking space planning method, system, device, and medium based on parking flow analysis, which solves the technical problem that the comprehensiveness of parking data analysis results is low in the prior art, resulting in low intelligence in parking space planning and unreasonable planning.

[0005] This application provides a parking space planning method based on parking flow analysis, comprising: performing location attribute analysis on a target parking location to determine the location attribute flow characteristics; extracting events affecting pedestrian flow based on the location attributes; performing flow prediction based on the location attribute flow characteristics and the events affecting pedestrian flow to obtain flow prediction information; collecting parking space distribution information of the target parking location to construct a parking space distribution interval map; determining the parking space change trend based on the flow prediction information; and performing planning based on the parking space change trend and the parking space distribution interval map to determine a parking space planning and management scheme.

[0006] This application also provides a parking space planning system based on parking flow analysis, comprising: a flow characteristic acquisition module, used to perform location attribute analysis based on the target parking location to determine the location attribute flow characteristics; an event acquisition module, used to extract events affecting pedestrian flow based on the location attributes; a prediction information acquisition module, used to perform flow prediction based on the location attribute flow characteristics and the events affecting pedestrian flow to obtain flow prediction information; a parking space distribution acquisition module, used to collect parking space distribution information of the target parking location and construct a parking space distribution interval map; a parking space change acquisition module, used to determine the parking space change trend based on the flow prediction information; and a planning management module, used to perform planning based on the parking space change trend and the parking space distribution interval map to determine a parking space planning management scheme.

[0007] This application also provides an electronic device, including:

[0008] Memory, used to store executable instructions;

[0009] The processor, when executing executable instructions stored in the memory, implements the parking space planning method based on parking flow analysis provided in this application.

[0010] This application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements a parking space planning method based on parking flow analysis provided in this application.

[0011] This application proposes a parking space planning method and system based on parking flow analysis. The method involves analyzing the location attributes of a target parking location to determine its flow characteristics. Influencing events on pedestrian flow are extracted based on these location attributes. Flow prediction is performed based on the location attribute flow characteristics and the influencing events to obtain flow prediction information. Parking space distribution information of the target parking location is collected to construct a parking space distribution interval map. The parking space change trend is determined based on the flow prediction information. Planning is then performed based on the parking space change trend and the parking space distribution interval map to determine a parking space planning and management scheme. This method achieves accurate and comprehensive analysis of parking flow and intelligent parking space planning based on the analysis results, improving the efficiency and intelligence of parking space management. It solves the technical problem in existing technologies where the comprehensiveness of parking data analysis results is low, leading to low intelligence in parking space planning and unreasonable planning.

[0012] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, the following are specific embodiments of this application. Attached Figure Description

[0013] To more clearly illustrate the technical solutions of the embodiments of this disclosure, the accompanying drawings of the embodiments of this disclosure will be briefly described below. Obviously, the drawings described below only relate to some embodiments of this disclosure and are not intended to limit this disclosure.

[0014] Figure 1 A flowchart illustrating a parking space planning method based on parking flow analysis provided in this application embodiment;

[0015] Figure 2 A flowchart illustrating the process of obtaining location attribute flow characteristics using a parking space planning method based on parking flow analysis, provided in an embodiment of this application.

[0016] Figure 3A flowchart illustrating a parking space planning and management scheme based on parking flow analysis, provided for an embodiment of this application;

[0017] Figure 4 A schematic diagram of the system structure of a parking space planning method based on parking flow analysis provided in an embodiment of this application;

[0018] Figure 5 This is a schematic diagram of the structure of a system electronic device for a parking space planning method based on parking flow analysis, provided in an embodiment of the present invention.

[0019] Explanation of reference numerals in the attached figures: Flow characteristic acquisition module 11, Event acquisition module 12, Prediction information acquisition module 13, Parking space distribution acquisition module 14, Parking space change acquisition module 15, Planning management module 16, Processor 31, Memory 32, Input device 33, Output device 34. Detailed Implementation

[0020] Example 1

[0021] To make the objectives, technical solutions, and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings. The described embodiments should not be regarded as limitations on this application. All other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0022] In the following description, references are made to “some embodiments,” which describe a subset of all possible embodiments. However, it is understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict.

[0023] In the following description, the terms "first, second, third" are used merely to distinguish similar objects and do not represent a specific ordering of objects. It is understood that "first, second, third" may be interchanged in a specific order or sequence where permitted, so that the embodiments of this application described herein can be implemented in an order other than that illustrated or described herein.

[0024] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only.

[0025] While this application makes various references to certain modules of the system according to embodiments of this application, any number of different modules may be used and run on user terminals and / or servers. These modules are merely illustrative, and different aspects of the system and method may use different modules.

[0026] This application uses flowcharts to illustrate the operations performed by the system according to embodiments of this application. It should be understood that the preceding or following operations are not necessarily performed in exact order. Instead, various steps can be processed in reverse order or simultaneously, as needed. Furthermore, other operations can be added to these processes, or one or more steps can be removed from them.

[0027] like Figure 1 As shown in the figure, this application provides a parking space planning method based on parking flow analysis, the method including:

[0028] Based on the location attributes of the target parking location, determine the location attribute traffic characteristics;

[0029] Extract events affecting pedestrian flow based on the location attributes;

[0030] Traffic flow is predicted based on the location attributes and traffic flow characteristics, as well as the events affecting pedestrian flow, to obtain traffic flow prediction information.

[0031] Parking flow analysis is a method for statistically analyzing parking lot traffic. Current technologies often rely on statistical analysis of historical parking data to obtain data from different event periods. However, this data lacks a strong correlation with actual vehicle parking attributes, resulting in incomplete analysis and hindering targeted optimization of parking space management. Consequently, parking space planning suffers from low intelligence and inefficient planning. This new approach obtains the location information of the target parking lot and performs location attribute analysis to determine its traffic characteristics. Subsequently, events influencing pedestrian flow are extracted based on these location attributes, including traffic control events and traffic hotspot events. Traffic flow prediction is then performed based on the identified location attribute traffic characteristics and the events influencing pedestrian flow to obtain traffic prediction information.

[0032] like Figure 2 As shown, the method provided in this application embodiment further includes:

[0033] Based on the location attributes, determine the traffic flow range of the venue attributes;

[0034] Extract parking record information for a preset time period from the target parking location;

[0035] Traffic flow fluctuation analysis is performed based on the parking record information, and periodic features are extracted based on the traffic flow fluctuation information to obtain traffic flow periodic fluctuation features.

[0036] Based on the location attribute flow range and the flow cycle fluctuation characteristics, a flow characteristic fusion analysis is performed to determine the location attribute flow characteristics, which are used to characterize the changes in parking flow related to location attributes.

[0037] When determining the traffic flow characteristics of a parking lot, the location attributes of the target parking lot are obtained. These location attributes include the parking lot's location information and attribute information, such as shopping mall parking lots or office building parking lots. Based on the location information, the traffic flow range of the location attribute is determined. This traffic flow range is roughly determined based on the facility scale corresponding to the location information, such as the estimated traffic flow range for shopping malls or office buildings. Parking record information for a preset time period is extracted from the target parking lot. This preset time period can be in hours or minutes, depending on actual needs. Subsequently, traffic flow fluctuation analysis is performed based on the parking record information, and periodic features are extracted to obtain the traffic flow cycle fluctuation characteristics. By obtaining parking record information and performing parking traffic flow fluctuation analysis, parking traffic flow fluctuation information is obtained, and periodic features are extracted to obtain the traffic flow cycle fluctuation characteristics, i.e., the periods of traffic fluctuation in the parking record information, such as traffic increase cycles and traffic decrease cycles. Finally, a flow characteristic fusion analysis is performed based on the location attribute flow range and the flow cycle fluctuation characteristics. That is, the flow growth cycle and flow decline cycle are fused with the attribute flow range to determine the location attribute flow characteristics, that is, the flow attributes and changes of the location. The location attribute flow characteristics are used to characterize the changes in parking flow related to location attributes.

[0038] The method provided in this application also includes:

[0039] Based on the location attributes, determine the location traffic node information;

[0040] Extract traffic control events and high-traffic hotspot events at the current location;

[0041] Based on the traffic control event, the controlled traffic nodes are obtained, and combined with the location traffic node information, traffic-related overlapping nodes are determined;

[0042] When the aforementioned traffic-related overlapping nodes exist, the traffic control event will be treated as an event affecting pedestrian flow.

[0043] Based on the traffic hotspot events, determine the distance to the hotspot location;

[0044] Based on the traffic hotspot events, traffic flow is predicted, and the probability of traffic flow impact is predicted by combining the distance of the hotspot locations. When the probability of traffic flow impact reaches a preset threshold, the traffic hotspot events are regarded as the events affecting traffic flow.

[0045] Based on location attributes, location traffic node information is determined. These location traffic nodes are intersections of multiple roads, such as the four intersection nodes of a crossroads. Traffic control events and traffic flow hotspot events at these locations are extracted. These events include specific time ranges, event types, and event areas. Based on the traffic control events, the controlled traffic nodes are obtained based on the event areas. Combined with the location traffic node information, traffic-related overlapping nodes are identified. When such traffic-related overlapping nodes exist, traffic control is in effect at nearby traffic nodes, and these traffic control events are considered events affecting pedestrian flow. Based on the traffic flow hotspot events, the distance to the hotspot locations is determined. Pedestrian flow is predicted based on these hotspot events. During pedestrian flow prediction, the number of pedestrians at multiple similar traffic flow hotspot events is obtained through big data analysis. A weighted average is then calculated based on the pedestrian flow numbers at multiple similar traffic flow hotspot events. The weights of each traffic flow hotspot event are set by professionals or calculated using the same weights to obtain the pedestrian flow prediction results. The probability of pedestrian flow is predicted by combining the distance of the hotspot location. When the probability of pedestrian flow reaches a preset threshold, the impact on the parking lot is significant. The hotspot event is considered as the event affecting pedestrian flow. When the probability of pedestrian flow does not reach the preset threshold, the impact on the parking lot is small.

[0046] The method provided in this application also includes:

[0047] Determine the duration of the impact based on the aforementioned traffic control event;

[0048] Based on the aforementioned traffic hotspot events and the probability of pedestrian flow impact, the scope of pedestrian flow influence and the duration of its influence are determined.

[0049] Based on the impact time or the scope of the impact on the flow of people and the impact time of the flow, determine the information for predicting the impact on the flow of people;

[0050] The location attribute traffic characteristics are superimposed with the information affecting pedestrian flow prediction to determine the trend of pedestrian flow prediction changes as the traffic prediction information.

[0051] Based on the traffic control event, the time range of the traffic control event is obtained to determine the impact time. Since traffic control events will cause traffic obstruction, resulting in a decrease in pedestrian flow, the pedestrian flow impact range and its impact time are determined based on the traffic hotspot event and the pedestrian flow sweep probability. That is, the time range of the event is obtained based on the traffic hotspot event, the impact time is determined, and the pedestrian flow diversion of the traffic hotspot event is determined based on the pedestrian flow sweep probability. The pedestrian flow diversion is the product of the pedestrian flow prediction of the traffic hotspot event determined by the pedestrian flow sweep probability, thereby obtaining the pedestrian flow impact range. Based on the impact time or pedestrian flow impact range and its impact time, the impact pedestrian flow prediction information is determined. When obtaining the impact pedestrian flow prediction information, the impact time of the traffic control event is obtained to obtain the prediction time of the impact pedestrian flow prediction information. Based on the ratio of the number of traffic-related overlapping nodes to the number of location traffic nodes, the pedestrian flow loss ratio is obtained. Pedestrian flow change data is obtained based on the corresponding prediction time and added to the impact pedestrian flow prediction information. The impact pedestrian flow prediction information includes the prediction time and pedestrian flow change data. Alternatively, based on the scope of influence of pedestrian flow and the duration of its impact, the prediction time and pedestrian flow change data in the pedestrian flow prediction information can be determined. Finally, the location attribute traffic characteristics are overlaid with the pedestrian flow prediction information to determine the pedestrian flow prediction trend at the corresponding time as the traffic prediction information.

[0052] The method provided in this application also includes:

[0053] Based on the distance to the hotspot location, obtain relevant parking locations within that distance;

[0054] Based on the pedestrian flow prediction information and the relevant parking locations within the distance, diversion prediction information is obtained;

[0055] Based on the predicted pedestrian flow and the diversion prediction information, the probability of pedestrian flow impact is obtained.

[0056] When predicting pedestrian traffic based on the aforementioned traffic hotspot events, the process involves using big data to obtain the pedestrian flow data for multiple similar hotspot events. A weighted average is then calculated based on these pedestrian flow data, with the weights for each hotspot event set by professionals or calculated using the same weights. This yields the predicted pedestrian flow. When predicting the probability of pedestrian flow spread based on the distance to the hotspot location, the distance to the hotspot location is obtained, along with relevant parking spaces within that distance range (i.e., parking spaces within the distance from the hotspot location to the target parking lot). The pedestrian flow distribution weights for each parking space are determined based on its distance from the hotspot event; for example, a weight of 0.5 is assigned for a 100-meter radius, and 0.2 for a 500-meter radius. When multiple parking lots exist at a certain distance, the weight for each parking lot is the ratio of that distance's weight to the number of parking lots at that distance. These specific weights are pre-set by professionals. Finally, a weighted calculation is performed based on the obtained parking lot weights and the predicted pedestrian flow information to complete the acquisition of the predicted pedestrian flow. Based on the predicted pedestrian flow and the diversion prediction information, the probability of pedestrian flow impact is obtained, wherein the probability of pedestrian flow impact is the ratio of the diversion prediction information to the predicted pedestrian flow.

[0057] Collect parking space distribution information of the target parking area and construct a parking space distribution interval map;

[0058] Based on the traffic flow prediction information, the trend of parking space changes is determined;

[0059] Based on the parking space change trend and the parking space distribution interval map, a parking space planning and management scheme is determined.

[0060] The system collects parking space distribution information for the target parking area and constructs a parking space distribution interval map, which includes the location of parking spaces and the location of entrances and exits. Based on the traffic flow prediction information, it determines the parking space change trend, which includes specific attributes such as the time and amount of traffic flow change. Finally, based on the parking space change trend and the parking space distribution interval map, it performs planning to determine a parking space planning and management scheme. This achieves accurate and comprehensive analysis of parking traffic flow and intelligent parking space planning based on the analysis results, improving the efficiency and intelligence of parking space management.

[0061] like Figure 3 As shown, the method provided in this application embodiment further includes:

[0062] Based on the parking space distribution map, determine the parking space exit connection area;

[0063] Based on the parking space exit connection area, the parking space distribution is divided into multiple exit parking space management areas.

[0064] Based on the parking space change trend, a parking space trend analysis is performed to determine the parking space application attribute trend. The parking space application attribute trend is used to indicate the time-varying trend of parking space changes as a result of the location attribute traffic or events affecting pedestrian flow.

[0065] Based on the trend of parking space application attributes, the parking space demand is matched with the exit parking space management area to determine the open exit parking space management area.

[0066] Based on the trend of the parking space application attributes, corresponding to the open exit parking space management areas, multiple open management strategies for exit parking space management areas are generated as the parking space planning and management scheme.

[0067] When acquiring a parking space planning and management scheme, the parking space exit connection area is determined based on the parking space distribution interval map. The parking space distribution is then divided into multiple exit parking space management areas based on these exit connection areas. Subsequently, parking space trend analysis is performed based on the parking space change trends to determine the parking space application attribute trends. These trends represent the time-varying impact of parking space changes on location attribute traffic or events affecting pedestrian flow. Since the impact of attribute trends is generally short-lived and may involve instantaneous changes, these trends are crucial for accurate and timely implementation. Therefore, based on the trend of parking space application attributes, parking space demand is matched with the exit parking space management area. This involves determining the time and flow of the impact of parking space change trends. Time parameters are obtained based on the time impact range, and flow parameters are obtained based on the flow range. The time parameter is set based on the length of time; the shorter the time, the higher the time parameter, and vice versa. The flow parameter is obtained based on the flow range; the higher the flow range, the higher the flow parameter, and vice versa. Parameter calculations are performed based on the flow and time parameters to obtain the calculation results. The calculation results are then judged based on a pre-set threshold. When the calculation result meets the threshold (greater than or equal to the threshold), the exit parking space management area is determined to be open. When the calculation result does not meet the threshold (less than the threshold), the exit parking space management area is not opened. Each attribute trend corresponds to an exit parking space management area and a parking space demand matching calculation result. Finally, according to the open exit parking space management areas corresponding to the parking space application attribute trends, multiple open management strategies for exit parking space management areas are generated as the parking space planning and management scheme.

[0068] The technical solution provided by this invention analyzes the location attributes of a target parking location to determine its traffic flow characteristics. It then extracts events influencing pedestrian flow based on these location attributes. Traffic flow prediction is performed based on the location attribute traffic flow characteristics and the events influencing pedestrian flow to obtain traffic prediction information. Parking space distribution information of the target parking location is collected to construct a parking space distribution interval map. Based on the traffic prediction information, the trend of parking space changes is determined. Planning is then performed based on the parking space change trend and the parking space distribution interval map to determine a parking space planning and management scheme. This achieves accurate and comprehensive analysis of parking traffic flow and intelligent parking space planning based on the analysis results, improving the efficiency and intelligence of parking space management. It solves the technical problem in existing technologies where the comprehensiveness of parking data analysis results is low, leading to low intelligence in parking space planning and unreasonable planning.

[0069] Example 2

[0070] Based on the same inventive concept as the parking space planning method based on parking flow analysis in the foregoing embodiments, this invention also provides a system for a parking space planning method based on parking flow analysis. The system can be implemented in hardware and / or software, and is generally integrated into an electronic device to execute the method provided in any embodiment of this invention. For example... Figure 4 As shown, the system includes:

[0071] The traffic flow feature acquisition module 11 is used to perform location attribute analysis based on the target parking location and determine the location attribute traffic flow features.

[0072] Event acquisition module 12 is used to extract events affecting pedestrian flow based on the location attributes;

[0073] The prediction information acquisition module 13 is used to predict traffic flow based on the traffic flow characteristics of the location attributes and the events affecting the flow of people, and to obtain traffic flow prediction information.

[0074] The parking space distribution acquisition module 14 is used to collect parking space distribution information of the target parking area and construct a parking space distribution interval map.

[0075] The parking space change acquisition module 15 is used to determine the parking space change trend based on the traffic prediction information.

[0076] The planning and management module 16 is used to plan and determine the parking space planning and management scheme based on the parking space change trend and the parking space distribution interval map.

[0077] Furthermore, the traffic feature acquisition module 11 is also used for:

[0078] Based on the location attributes, determine the traffic flow range of the venue attributes;

[0079] Extract parking record information for a preset time period from the target parking location;

[0080] Traffic flow fluctuation analysis is performed based on the parking record information, and periodic features are extracted based on the traffic flow fluctuation information to obtain traffic flow periodic fluctuation features.

[0081] Based on the location attribute flow range and the flow cycle fluctuation characteristics, a flow characteristic fusion analysis is performed to determine the location attribute flow characteristics, which are used to characterize the changes in parking flow related to location attributes.

[0082] Furthermore, the event acquisition module 12 is also used for:

[0083] Based on the location attributes, determine the location traffic node information;

[0084] Extract traffic control events and high-traffic hotspot events at the current location;

[0085] Based on the traffic control event, the controlled traffic nodes are obtained, and combined with the location traffic node information, traffic-related overlapping nodes are determined;

[0086] When the aforementioned traffic-related overlapping nodes exist, the traffic control event will be treated as an event affecting pedestrian flow.

[0087] Based on the traffic hotspot events, determine the distance to the hotspot location;

[0088] Based on the traffic hotspot events, traffic flow is predicted, and the probability of traffic flow impact is predicted by combining the distance of the hotspot locations. When the probability of traffic flow impact reaches a preset threshold, the traffic hotspot events are regarded as the events affecting traffic flow.

[0089] Furthermore, the prediction information acquisition module 13 is also used for:

[0090] Determine the duration of the impact based on the aforementioned traffic control event;

[0091] Based on the aforementioned traffic hotspot events and the probability of pedestrian flow impact, the scope of pedestrian flow influence and the duration of its influence are determined.

[0092] Based on the impact time or the scope of the impact on the flow of people and the impact time of the flow, determine the information for predicting the impact on the flow of people;

[0093] The location attribute traffic characteristics are superimposed with the information affecting pedestrian flow prediction to determine the trend of pedestrian flow prediction changes as the traffic prediction information.

[0094] Furthermore, the event acquisition module 12 is also used for:

[0095] Based on the distance to the hotspot location, obtain relevant parking locations within that distance;

[0096] Based on the predicted pedestrian flow and the relevant parking locations within the distance, diversion prediction information is obtained;

[0097] Based on the predicted pedestrian flow and the diversion prediction information, the probability of pedestrian flow impact is obtained.

[0098] Furthermore, the planning management module 16 is also used for:

[0099] Based on the parking space distribution map, determine the parking space exit connection area;

[0100] Based on the parking space exit connection area, the parking space distribution is divided into multiple exit parking space management areas.

[0101] Based on the parking space change trend, a parking space trend analysis is performed to determine the parking space application attribute trend. The parking space application attribute trend is used to indicate the time-varying trend of parking space changes as a result of the location attribute traffic or events affecting pedestrian flow.

[0102] Based on the trend of parking space application attributes, the parking space demand is matched with the exit parking space management area to determine the open exit parking space management area.

[0103] Based on the trend of the parking space application attributes, corresponding to the open exit parking space management areas, multiple open management strategies for exit parking space management areas are generated as the parking space planning and management scheme.

[0104] The various units and modules included are divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of each functional unit are only for easy distinction between each other and are not used to limit the scope of protection of this invention.

[0105] Example 3

[0106] Figure 5 This is a schematic diagram of the structure of an electronic device provided in Embodiment 3 of the present invention, showing a block diagram of an exemplary electronic device suitable for implementing the embodiments of the present invention. Figure 5 The electronic device shown is merely an example and should not be construed as limiting the functionality or scope of the embodiments of the present invention. Figure 5 As shown, the electronic device includes a processor 31, a memory 32, an input device 33, and an output device 34; the number of processors 31 in the electronic device can be one or more. Figure 5 Taking a processor 31 as an example, the processor 31, memory 32, input device 33, and output device 34 in an electronic device can be connected via a bus or other means. Figure 5 Taking the example of a connection between China and Israel via a bus.

[0107] The memory 32, as a computer-readable storage medium, can be used to store software programs, computer-executable programs, and modules, such as the program instructions / modules corresponding to a parking space planning method based on parking flow analysis in this embodiment of the invention. The processor 31 executes various functional applications and data processing of the computer device by running the software programs, instructions, and modules stored in the memory 32, thereby realizing the aforementioned parking space planning method based on parking flow analysis.

[0108] Note that the above description is merely a preferred embodiment of the present invention and the technical principles employed. Those skilled in the art will understand that the present invention is not limited to the specific embodiments described herein, and various obvious changes, readjustments, and substitutions can be made without departing from the scope of protection of the present invention. Therefore, although the present invention has been described in detail through the above embodiments, the present invention is not limited to the above embodiments, and may include many other equivalent embodiments without departing from the concept of the present invention, the scope of which is determined by the scope of the appended claims.

Claims

1. A parking space planning method based on parking flow analysis, characterized in that, include: Based on the location attributes of the target parking location, determine the location attribute traffic characteristics; Extract events affecting pedestrian flow based on the location attributes; Traffic flow is predicted based on the location attributes and traffic flow characteristics, as well as the events affecting pedestrian flow, to obtain traffic flow prediction information. Collect parking space distribution information of the target parking area and construct a parking space distribution interval map; Based on the traffic flow prediction information, the trend of parking space changes is determined; Based on the parking space change trend and the parking space distribution interval map, a parking space planning and management scheme is determined. Based on the location attribute analysis of the target parking location, the location attribute traffic characteristics are determined, including: Based on the location attributes, determine the traffic flow range of the venue attributes; Extract parking record information for a preset time period from the target parking location; Traffic flow fluctuation analysis is performed based on the parking record information, and periodic features are extracted based on the traffic flow fluctuation information to obtain traffic flow periodic fluctuation features. Based on the location attribute flow range and the flow cycle fluctuation characteristics, a flow characteristic fusion analysis is performed to determine the location attribute flow characteristics, which are used to characterize the parking flow changes related to location attributes. Based on the location attributes, events affecting pedestrian flow are extracted, including: Based on the location attributes, determine the location traffic node information; Extract traffic control events and high-traffic hotspot events at the current location; Based on the traffic control event, the controlled traffic nodes are obtained, and combined with the location traffic node information, traffic-related overlapping nodes are determined; When the aforementioned traffic-related overlapping nodes exist, the traffic control event will be treated as an event affecting pedestrian flow. Based on the traffic hotspot events, determine the distance to the hotspot location; Based on the traffic hotspot events, traffic flow is predicted, and the probability of traffic flow impact is predicted by combining the distance of the hotspot locations. When the probability of traffic flow impact reaches a preset threshold, the traffic hotspot events are regarded as the events affecting traffic flow. Based on the parking space change trend and the parking space distribution map, a parking space planning and management scheme is determined, including: Based on the parking space distribution map, determine the parking space exit connection area; Based on the parking space exit connection area, the parking space distribution is divided into multiple exit parking space management areas. Based on the parking space change trend, a parking space trend analysis is performed to determine the parking space application attribute trend. The parking space application attribute trend is used to indicate the time-varying trend of parking space changes as a result of the location attribute traffic or events affecting pedestrian flow. Based on the trend of parking space application attributes, the parking space demand is matched with the exit parking space management area to determine the open exit parking space management area. Based on the trend of the parking space application attributes, corresponding to the open exit parking space management areas, multiple open management strategies for exit parking space management areas are generated as the parking space planning and management scheme.

2. The method as described in claim 1, characterized in that, Traffic flow prediction is performed based on the location attribute traffic characteristics and the events affecting pedestrian flow to obtain traffic prediction information, including: Determine the duration of the impact based on the aforementioned traffic control event; Based on the aforementioned traffic hotspot events and the probability of pedestrian flow impact, the scope of pedestrian flow influence and the duration of its influence are determined. Based on the impact time or the scope of the impact on the flow of people and the impact time of the flow, determine the information for predicting the impact on the flow of people; The location attribute traffic characteristics are superimposed with the information affecting pedestrian flow prediction to determine the trend of pedestrian flow prediction changes as the traffic prediction information.

3. The method as described in claim 2, characterized in that, Based on the aforementioned traffic hotspot events, pedestrian flow prediction is performed, and combined with the distance to the hotspot locations, pedestrian flow spillover probability prediction is performed, including: Based on the distance to the hotspot location, obtain relevant parking locations within that distance; Based on the predicted pedestrian flow and the relevant parking locations within the distance, diversion prediction information is obtained; Based on the predicted pedestrian flow and the diversion prediction information, the probability of pedestrian flow impact is obtained.

4. A parking space planning system based on parking flow analysis, used to execute the method of claim 1, characterized in that, include: The traffic flow characteristic acquisition module is used to perform location attribute analysis based on the target parking location and determine the location attribute traffic flow characteristics; The event acquisition module is used to extract events affecting pedestrian flow based on the location attributes; The prediction information acquisition module is used to predict traffic flow based on the traffic flow characteristics of the location attributes and the events affecting pedestrian flow, and to obtain traffic prediction information. The parking space distribution acquisition module is used to collect parking space distribution information of the target parking area and construct a parking space distribution interval map; The parking space change acquisition module is used to determine the parking space change trend based on the traffic prediction information. The planning and management module is used to plan and determine the parking space planning and management scheme based on the parking space change trend and the parking space distribution interval map.

5. An electronic device, characterized in that, The electronic device includes: Memory, used to store executable instructions; The processor, when executing executable instructions stored in the memory, implements the parking space planning method based on parking flow analysis as described in any one of claims 1 to 3.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by the processor, the program implements a parking space planning method based on parking flow analysis as described in any one of claims 1 to 3.

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