Expressway closed state monitoring method, device and equipment and storage medium

Abstract

The application discloses a highway closed state monitoring method, device, equipment and storage medium. Including: based on the difference between the real-time flow and the historical flow at the monitoring node on the highway, determining the detection area; statistics the real-time flow of nodes and road sections in the detection area, and taking the nodes and road sections whose real-time flow meet the flow closure characteristics as the alternative closed nodes and alternative closed road sections; performing multi-data source fusion verification and spatial correlation verification on the alternative closed nodes and alternative closed road sections to obtain closed nodes and closed road sections; performing full life cycle management on the closed nodes and closed road sections until the closed state is released. Based on the flow characteristics on the highway, the application can quickly and accurately monitor the highway infrastructure closing, utilize two kinds of data feature fusion verification and space-time correlation verification, can avoid false positives, and through life cycle management, the road closure information is completely tracked and monitored.

Description

Technical Field

[0001] This application relates to the field of intelligent transportation technology, and more specifically, to a method, device, equipment, and storage medium for monitoring the closed status of highways. Background Technology

[0002] Currently, methods for detecting road closures using floating car GPS data have limitations. Since GPS data is sampled data, the calculation results are subject to error. Detecting road closures based on toll data suffers from coarse-grained calculations due to the large distances between gantry (toll booths), making it difficult to obtain high-precision road operation status. Furthermore, problems in data acquisition and transmission can lead to erroneous extraction of road closure information. Objectively, there are issues with untimely, inaccurate, and incomplete information reporting, necessitating the development of technical capabilities for rapid discovery, accurate assessment, and full lifecycle management of infrastructure opening and closing information. Summary of the Invention

[0003] This application provides a method, apparatus, equipment, and storage medium for monitoring the closure status of highways, in order to at least solve the technical problem in the related art of the difficulty in quickly and accurately detecting the closure of highway infrastructure.

[0004] According to one aspect of the embodiments of this application, a method for monitoring the closure status of a highway is provided, comprising:

[0005] Based on the difference between real-time and historical traffic flow at monitoring nodes on highways, detection zones are determined.

[0006] The real-time traffic flow of nodes and road segments within the detection area is statistically analyzed, and nodes and road segments whose real-time traffic flow meets the traffic closure characteristics are selected as candidate closure nodes and candidate closure road segments.

[0007] The candidate closed nodes and candidate closed road segments are subjected to multi-data source fusion verification and spatial correlation verification to obtain the closed nodes and closed road segments.

[0008] The closed nodes and closed road sections are managed throughout their entire lifecycle until the closure is lifted.

[0009] In one embodiment, a detection zone is determined based on the difference between real-time and historical traffic flow at monitoring nodes on a highway, including:

[0010] Calculate the difference between the real-time flow value and the historical flow characteristic value at the monitoring node;

[0011] When the difference is greater than a preset threshold, the monitoring node is designated as the node to be detected.

[0012] Aggregate nodes to be detected that have connectivity relationships with their associated road segments to obtain one or more detection zones.

[0013] In one embodiment, before determining the detection zone based on the difference between real-time and historical traffic flow at monitoring nodes on the highway, the method further includes:

[0014] The total traffic flow passing through the gantry nodes and toll station nodes within a preset time period is counted, as well as the OD traffic flow from the gantry nodes and toll station nodes to each downstream gantry node and toll station node.

[0015] The total traffic flow passing through the current road segment within a preset time period, as well as the OD traffic flow from the current road segment to downstream road segments, are recorded.

[0016] The date is divided into multiple characteristic days, and the characteristic days are further divided into multiple time slices with the preset duration to obtain the total node flow, node OD flow, total road segment flow, and road segment OD flow corresponding to each time slice in each characteristic day;

[0017] Based on the mathematical expectation of the total node flow, node OD flow, total road segment flow, and road segment OD flow for multiple characteristic days and time slices, the historical flow characteristic value corresponding to each time slice in each characteristic day is obtained.

[0018] In one embodiment, before calculating the real-time traffic flow of nodes and road segments within the detection area, the method further includes:

[0019] The total traffic volume of all toll station nodes for all statistical periods of the previous day is used as the first sample set;

[0020] If the total traffic of the current toll station node is greater than or equal to the median of the first sample set, then the current toll station node is a high-value toll station node.

[0021] If the total traffic of the current toll station node is less than the median of the first sample set, then the current toll station node is a low-value node.

[0022] Use the total flow of all gantry nodes for all statistical periods of the previous day as the second sample set;

[0023] If the total flow of the current gantry node is greater than or equal to the median of the second sample set, then the current gantry node is a high-value gantry node.

[0024] If the total flow of the current gantry node is less than the median of the second sample set, then the current gantry node is a low-value node.

[0025] In one embodiment, nodes and road segments whose real-time traffic meets the traffic closure characteristics are selected as candidate closure nodes and candidate closure road segments, including:

[0026] When the real-time total traffic of a high-value node exhibits zeroing or sudden drop characteristics, the high-value node is determined to meet the traffic closure characteristic, with the closure type being complete closure.

[0027] When the real-time total traffic of a low-value node has a zeroing characteristic, the low-value node is determined to meet the traffic closure characteristic, and the closure type is completely closed.

[0028] The system sequentially determines whether the OD flow from the current node to each downstream node has a zero-flow characteristic. Nodes with zero-flow characteristics are identified as satisfying the flow closure characteristic; the closure type is partial closure.

[0029] When the total flow through the current road segment to the downstream has a zeroing characteristic, the current road segment is determined to meet the flow closure characteristic, and the closure type is completely closed.

[0030] The system sequentially determines whether the OD flow from the current road segment to each downstream road segment has a zero-flow characteristic. Road segments with zero-flow characteristics are identified as meeting the flow closure characteristics, and the closure type is partial directional closure.

[0031] In one embodiment, the candidate closed nodes and candidate closed road segments are subjected to multi-data source fusion verification and spatial correlation verification to obtain the closed nodes and closed road segments, including:

[0032] Obtain a set of candidate closed nodes and candidate closed road segments that are directly related to the topology. If both candidate closed nodes and candidate closed road segments are in a completely closed state, or both are in a partially closed state and the closed passage direction is the same, then both are determined to be closed nodes and closed road segments through data source fusion verification.

[0033] Get any gantry closed node, get the gantry closed node in the upstream and downstream of the highway. If the adjacent gantry node is a target candidate node that has not passed the data source fusion verification, then determine that the target candidate node has passed the spatial correlation test and take the target candidate node as the closed node.

[0034] Obtain any closed node of a toll station, and obtain the adjacent toll station nodes upstream and downstream of the closed node of the toll station on the highway. If the adjacent toll station node is a target candidate node that has not passed the data source fusion verification, then determine that the target candidate node has passed the spatial correlation test, and designate the target candidate node as a closed node.

[0035] In one embodiment, full lifecycle management of the closed nodes and closed road segments includes:

[0036] The detection, updating, splitting, merging, and dissipation processes of the closed nodes and closed road segments are monitored to obtain various lifecycle information of the closed nodes and closed road segments;

[0037] Send the lifecycle information of closed nodes and closed road sections to the application platform.

[0038] According to another aspect of the embodiments of this application, a highway closure status monitoring device is provided, comprising:

[0039] The detection zone extraction module is used to determine the detection zone based on the difference between real-time and historical traffic flow at monitoring nodes on highways.

[0040] The detection module is used to count the real-time traffic flow of nodes and road segments within the detection area, and to designate nodes and road segments whose real-time traffic flow meets the traffic closure characteristics as candidate closure nodes and candidate closure road segments.

[0041] The verification module is used to perform multi-data source fusion verification and spatial correlation verification on the candidate closed nodes and candidate closed road segments to obtain the closed nodes and closed road segments.

[0042] The management module is used to manage the closed nodes and closed road sections throughout their entire lifecycle, until the closure is lifted.

[0043] According to another aspect of the embodiments of this application, an electronic device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to execute the above-described highway closure status monitoring method through the computer program.

[0044] According to another aspect of the embodiments of this application, a computer-readable storage medium is also provided, wherein a computer program is stored in the computer program, which is configured to execute the above-described highway closure status monitoring method when running.

[0045] The technical solutions provided in this application embodiment may include the following beneficial effects:

[0046] The highway closure status monitoring method provided in this application quickly and accurately identifies closure nodes and road sections based on traffic flow change characteristics. Specifically, by constructing a detection zone, the sensing and calculation range is reduced, improving computational efficiency and meeting the needs of real-time calculation and monitoring. Utilizing methods such as feature fusion verification of node and road section traffic flow data, as well as spatiotemporal correlation verification, avoids false alarms caused by data collection and transmission issues, thus improving the accuracy of results. Furthermore, through full lifecycle management, complete tracking and monitoring of road closure information facilitates data management and analysis, enabling real-time perception of dynamic changes in closure information. Attached Figure Description

[0047] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0048] Figure 1 This is a flowchart of an optional highway closure monitoring method according to an embodiment of this application;

[0049] Figure 2 This is a flowchart of an optional highway closure monitoring method according to an embodiment of this application;

[0050] Figure 3 This is a schematic diagram of traffic flow changes according to an embodiment of this application;

[0051] Figure 4 This is a schematic diagram illustrating a Bollinger Band method for detecting a sudden drop in flow rate according to an embodiment of this application;

[0052] Figure 5 This is a schematic diagram of a detected closed road section and closed node according to an embodiment of this application;

[0053] Figure 6 This is a schematic diagram of a highway closure status monitoring device according to an embodiment of this application;

[0054] Figure 7 This is a schematic diagram of the structure of an optional electronic device according to an embodiment of this application. Detailed Implementation

[0055] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0056] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0057] The following is in conjunction with the appendix Figure 1 This application provides a detailed description of the highway closure monitoring method according to its embodiments. It should be noted that the following application scenarios are shown only to facilitate understanding of the spirit and principles of this application, and the implementation methods of this application are not limited in any way. On the contrary, the implementation methods of this application can be applied to any applicable scenario. Figure 1 As shown, the method mainly includes the following steps:

[0058] S101 determines the detection zone based on the difference between real-time and historical traffic flow at monitoring nodes on the highway.

[0059] The method in this application embodiment also includes constructing a historical traffic feature database and determining road sections that may be closed by monitoring the difference between real-time traffic and historical traffic.

[0060] In one embodiment, before extracting the node to be detected based on the difference between real-time traffic flow and historical traffic flow at monitoring nodes on the highway, a historical traffic flow feature library is also constructed.

[0061] Specifically, based on highway toll data, the total traffic flow passing through gantry nodes and toll station nodes within a preset time period is statistically analyzed, as well as the OD traffic flow from gantry nodes and toll station nodes to each downstream gantry node and toll station node.

[0062] In one implementation, given a statistical duration t sta The total number of vehicles passing through a given gantry or toll station within the preset time period is counted to obtain the total traffic flow f of that gantry or toll station. total Define the gantry and toll station as monitoring nodes, and specify the current monitoring node as node. cur Its downstream node set is Statistical analysis of t respectively sta Inside, by the current node curThe number of vehicles that travel to each downstream node yields the OD (Original Demand) flow set from that node to all downstream nodes.

[0063] Furthermore, based on the GPS floating car data matched to the road segment, the total traffic flow passing through the current road segment within a preset time period, as well as the OD traffic flow from the current road segment to downstream road segments, are calculated.

[0064] Given a target road segment link cur , statistics t sta Internal, pathway link cur And reach the total number of vehicles in its downstream link, thus obtaining the link. cur Total traffic flow f linkcur Count t separately sta Internal, pathway link cur And the number of vehicles reaching each downstream segment, to obtain the link. cur OD flow set

[0065]

[0066] Furthermore, the date is divided into multiple characteristic days, and each characteristic day is further divided into multiple time slices with a preset duration, to obtain the total node flow, node OD flow, total road segment flow, and road segment OD flow corresponding to each time slice in each characteristic day.

[0067] With a fixed statistical duration t sta By dividing each day into multiple slices and using curve denoising to reduce noise in the daily traffic, the traffic data for a given day can be obtained.

[0068]

[0069] Divide all dates into seven characteristic days and statutory holidays according to the weekdays (Monday to Sunday), denoted as follows: Day 8 is a holiday-related day. The multi-day traffic can then be recorded as... fday j i This represents the flow rate of the j-th time slice of the i-th feature day.

[0070] Based on the mathematical expectation of the total node flow, node OD flow, total road segment flow, and road segment OD flow for multiple characteristic days and time slices, the historical flow characteristic value corresponding to each time slice in each characteristic day is obtained.

[0071] Traffic flow for m slices with the same characteristics and the same time. Find its expected value.

[0072]

[0073] Understandably, by calculating the mathematical expectation of all time-slice traffic data, the historical traffic feature dataset can be obtained. By calculating the expected value of the total flow of nodes, the OD flow of nodes, the total flow of road segments, and the OD flow of road segments over a period of time, a complete historical flow characteristic database can be obtained.

[0074] Furthermore, this application adopts a method of pre-extracting the detection area and extracting closed information only within the detection area. This reduces the overhead of GPS matching to the road network and shortens the detection range, greatly improving computational efficiency by calculating only a portion of the road network and toll nodes.

[0075] In one embodiment, the difference between the real-time traffic value and the historical traffic characteristic value at the monitoring node is calculated; when the difference is greater than a preset threshold, the monitoring node is designated as a node to be detected.

[0076] Let the toll gantry and toll station be labeled as toll nodes, and let the total real-time traffic of node be f. cur_total Its corresponding historical flow is f his_total Given a traffic deviation threshold ε diff If f is satisfied his_total -f cur_total >ε diff If the value is zero, then that node is the node to be detected. Perform the above calculation on all nodes to obtain all nodes to be detected, denoted as _____.

[0077] Furthermore, nodes to be detected that have connectivity relationships and associated road segments are aggregated to obtain one or more detection zones.

[0078] Understandably, the toll collection nodes are connected, forming a typical directed connected graph. Based on node connectivity, we can... The nodes in the graph form different directed connected subgraphs. Then, in any directed connected subgraph, all nodes... and all associated links in the subgraph This constitutes a detection region graph. Based on this method, based on Extract all detection areas.

[0079] By extracting the detection area and performing real-time calculations only within that area, time overhead is reduced, thus meeting the requirements for real-time calculation and monitoring.

[0080] S102 statistically detects the real-time traffic flow of nodes and road segments within the monitoring area, and identifies nodes and road segments whose real-time traffic flow meets the traffic closure characteristics as candidate closure nodes and candidate closure road segments.

[0081] In one embodiment, the value of a highway segment varies depending on its geographical location and time of day. High-traffic nodes have a significant impact on the road, and their traffic flow changes after closure are clearly visible, allowing for rapid detection of closure information. Conversely, low-traffic nodes have a smaller impact from closures and their traffic flow changes are less noticeable, but these nodes constitute a large proportion and are widely distributed, requiring sufficient accuracy. Through traffic flow analysis, highway network toll collection nodes are categorized into high-value and low-value nodes.

[0082] Specifically, the total traffic volume for all statistical periods preceding all toll station nodes is used as the first sample set; the median of the sample set is selected as the threshold θ for determining traffic value. ts If the total traffic of the current toll station node is greater than or equal to the median of the first sample set, then the current toll station node is a high-value toll station node; if the total traffic of the current toll station node is less than the median of the first sample set, then the current toll station node is a low-value node.

[0083] The total flow of all gantry nodes for all statistical periods of the previous day is used as the second sample set; the median of the sample set is selected as the threshold θ for determining the flow value. gantry If the total flow of the current gantry node is greater than or equal to the median of the second sample set, then the current gantry node is a high-value gantry node; if the total flow of the current gantry node is less than the median of the second sample set, then the current gantry node is a low-value node.

[0084] By dividing high-value and low-value scenarios, differentiated perception of scenarios is achieved, which improves the perception speed of high-value road sections while ensuring the perception accuracy of low-value road sections.

[0085] Furthermore, the real-time traffic flow of nodes and road segments within the detection area is statistically analyzed to determine whether the real-time traffic flow meets the traffic closure characteristics.

[0086] Among these, flow closure characteristics include flow returning to zero and flow plummeting. For example... Figure 3 As shown, Figure 3 The left side shows a schematic diagram of the flow returning to zero. Figure 3 The diagram on the right illustrates a sudden drop in traffic flow.

[0087] When analyzing whether traffic exhibits a zero-return characteristic, statistical t is used. sta Real-time GPS traffic flow and real-time toll traffic flow at nodes within the internal detection zone are used to calculate its closure characteristics. Let the real-time traffic flow be f. cur The historical flow for the corresponding characteristic day and characteristic time period is f. his When f is satisfied cur =0 and f his When the value is greater than ε, the flow rate meets the zero-flow characteristic. ε is the historical flow rate threshold, which can be set according to the actual situation.

[0088] When analyzing the characteristics of a sudden drop in traffic flow, a curve trend analysis method is used to analyze the traffic flow over a certain period of time to determine whether it meets the characteristics of a sudden drop in traffic flow. Common curve trend analysis methods include derivatives, difference ratios, trend fitting, moving averages, etc.

[0089] Bollinger Bands algorithm uses the moving average of the curve as the middle band and selects the upper and lower bands based on statistical characteristics. The trend of the curve is determined by the distance between the current value and the upper and lower bands. For example... Figure 4 As shown, when the flow rate curve falls below the lower band, it can be determined that the curve exhibits a sharp downward trend. This application uses the Bollinger Bands method as an example to extract the sharp downward trend in flow rate as its characteristic feature:

[0090] Given a Bollinger Band time window of n, this means selecting n consecutive time slices of flow as the calculation object, where the last time slice is the current time slice. A moving average method is used to process the flow curve.

[0091]

[0092] Obtain the middle band of the Bollinger Bands. Calculate the average flow rate f within the time window ave :

[0093]

[0094] Calculate the standard deviation σ of the flow rate within the time window:

[0095]

[0096] The upper Bollinger Band is The lower rail is The current sudden drop in traffic is characterized by:

[0097] Furthermore, when the real-time total traffic of high-value nodes exhibits zeroing or sudden drop characteristics, the high-value nodes are determined to meet the traffic closure characteristics, with the closure type being fully closed; when the real-time total traffic of low-value nodes exhibits zeroing characteristics, the low-value nodes are determined to meet the traffic closure characteristics, with the closure type being fully closed.

[0098] The system sequentially checks whether the OD (Original Directional) traffic from the current node to each downstream node exhibits a zero-traffic characteristic. Nodes with zero-traffic characteristics are identified as meeting the traffic closure criteria; the closure type is partial closure. Nodes whose real-time traffic meets the traffic closure criteria are selected as candidate closure nodes.

[0099] Furthermore, when the total flow through the current road segment to the downstream has a zero-value characteristic, it is determined that the current road segment meets the flow closure characteristic, and the closure type is complete closure.

[0100] The system sequentially determines whether the OD flow from the current road segment to each downstream road segment has a zero-flow characteristic. Road segments with zero-flow characteristics are identified as meeting the flow closure characteristics, and the closure type is partial directional closure.

[0101] Road sections whose real-time traffic flow meets the traffic closure characteristics will be selected as candidate closure sections.

[0102] S103 performs multi-data source fusion verification and spatial correlation verification on the candidate closed nodes and candidate closed road segments to obtain the closed nodes and closed road segments.

[0103] In one implementation, multi-data source fusion verification is performed first.

[0104] Obtain a set of candidate closed nodes and candidate closed road segments that are directly related to the topology. If both candidate closed nodes and candidate closed road segments are in a completely closed state, or both are in a partially closed state and the closed traffic directions are consistent, then both are determined to be closed nodes and closed road segments through data source fusion verification.

[0105] Among them, the direct topological association relationship means that the node and the road segment are the closest neighbors on the same highway, and there are no other nodes between them.

[0106] By performing multi-data source fusion verification on all candidate closure nodes and candidate closure sections, valid closure nodes can be obtained.

[0107] Furthermore, spatial correlation verification is performed. Generally, the closed area of ​​a road segment is often quite large, and its influence extends further due to its transmission effect on upstream and downstream traffic flow. Moreover, road closures and toll station shutdowns rarely occur in isolation; specifically, multiple toll stations may be closed consecutively on a highway, or toll stations within a closed road segment may also be closed simultaneously. Therefore, spatial correlation analysis can serve as an important means of verifying the effectiveness of road closures.

[0108] Specifically, for any gantry closed node, the adjacent gantry nodes upstream and downstream of the gantry closed node on the highway are obtained. If the adjacent gantry node is a target candidate closed node that has not passed the data source fusion verification, then the target candidate closed node is determined to have passed the spatial correlation test and is used as the closed node.

[0109] Get any toll station closed node, and get the toll station closed node's adjacent toll station nodes upstream and downstream of the highway. If the adjacent toll station node is a target candidate closed node that has not passed the data source fusion verification, then the target candidate closed node is determined to have passed the spatial correlation test, and the target candidate node is taken as the closed node.

[0110] This application utilizes two data feature fusion verification methods and spatiotemporal correlation verification to avoid false alarms caused by data acquisition and transmission problems, thereby improving the accuracy of the results.

[0111] S104 implements full lifecycle management of closed nodes and closed road sections until the closure is lifted.

[0112] Road closures and tollbooth closures are often characterized by long durations and evolving reasons for closure. To achieve comprehensive monitoring and management of road closures and tollbooth closures, this application implements lifecycle management for both.

[0113] The lifecycle management of closed road sections includes processes such as detection, updating, splitting, merging, and dissipation.

[0114] Specifically, closed road segment detection includes: defining a closed road segment as a series of consecutive valid closed gantries on the same highway; extracting consecutive adjacent valid closed gantries on the same highway; obtaining the first road fork, merging point, or GPS candidate closed road segment outside the boundary gantry as the boundary of the closed road segment; and obtaining the nodes, consecutive road segments, and detection time within the boundary range, which constitute the detected closed road segment.

[0115] Specifically, on the same highway, a continuous road segment associated with consecutive effective closed gantries is defined as a closed road segment, such as... Figure 5 As shown.

[0116] Given an effective closed gantry node valid _ gantry By searching upstream and downstream along the highway where it is located, a continuous set of effective closed gantry frames can be obtained. Search By identifying the topologically adjacent toll stations of all gantries, and selecting the valid closed toll stations among them, the set of closed toll stations for that closed road segment can be obtained. like Figure 5 As shown, the search proceeds outward from the boundary gantry until a road fork or a candidate closed road segment extracted based on GPS traffic flow is encountered; this is the boundary of that closed road segment. All ordered road chains between the two boundaries constitute the road chain set of that closed road segment. The above three sets and the detection time t of the closed road section start Forming a complete closed road section

[0117]

[0118] By searching all valid closed gantries using the above method, all closed road sections can be detected.

[0119] Closed road segment update: Existing closed road segment boundary nodes are checked. If a boundary node is unclosed, it is deleted. Otherwise, adjacent nodes along the same highway are searched on the outer side. If a node is a valid closed node, it is included in the closed road segment range. Based on the updated closed nodes, the boundary positions are recalculated, and the road segment range is updated.

[0120] Specifically, given the closed road sections to be updated Check if the boundary gantry is a valid closed gantry. If so, search for adjacent consecutive valid closed gantry along the current high speed and include them. If not, continue searching inwards for unclosed gantry frames and move them from... Deleted. Based on the updated... Recalculate and Complete road close Update.

[0121] Closed segment splitting: If some nodes within a closed segment are unclosed, causing the segment to lose topological continuity, then those nodes are deleted, and the node set is split into two connected subsets. The extent of the two sub-closed segments is recalculated based on the two connected subsets.

[0122] Specifically, given the road section to be split and closed. like If the gantry on the China-Africa border is no longer closed, then the following splitting operation will be performed on it:

[0123] delete If there are k consecutive unclosed gantry states, then The gantry in the middle is no longer continuous, and splits into a set of upstream continuous gantry. and downstream continuous gantry assembly

[0124]

[0125] Based on respectively and By calculating its closed boundary, the set of closed toll booths, and the set of closed links, the two closed road segments after the split can be obtained. and

[0126]

[0127] Closure segment merging: If two closure segments are on the same highway and their boundary gantries are adjacent gantries, then a merging operation is performed on the two: the nodes of the two are merged and the scope of the closure segment is redefined based on the node set.

[0128] Specifically, given the closed road sections to be merged

[0129] If both are on the same highway and their boundary gantries are adjacent, then a merging operation will be performed on both: road c close =road a close ∪road c close ,in:

[0130]

[0131] The closed section after the merger is

[0132]

[0133] Closed road section dissipation: When the number of gate frame nodes within a closed road section is 0, the closed road section dissipates.

[0134] Specifically, given a closed road section If the closed road section meets the requirements at the current moment Once all gantries within the road have been released from their closed positions, the closed section of road will dissipate. close delete.

[0135] To monitor the entire lifecycle of toll station closures, the system sequentially records the start time, closure type, and closure information from the moment a toll station is detected as closed, until the station is reopened. Lifecycle information for each closure node and road segment is then sent to application platforms, such as vehicle management platforms, traffic management platforms, and vehicle terminals. This ensures rapid and timely sharing of highway closure information.

[0136] The highway closure status monitoring method provided in this application quickly and accurately identifies closure nodes and road sections based on traffic flow change characteristics. Specifically, by constructing a detection zone, the sensing and calculation range is reduced, improving computational efficiency and meeting the needs of real-time calculation and monitoring. Utilizing methods such as feature fusion verification of node and road section traffic flow data, as well as spatiotemporal correlation verification, avoids false alarms caused by data collection and transmission issues, thus improving the accuracy of results. Furthermore, through full lifecycle management, complete tracking and monitoring of road closure information facilitates data management and analysis, enabling real-time perception of dynamic changes in closure information.

[0137] To facilitate understanding of the methods in the embodiments of this application, the following description is provided in conjunction with the appendix. Figure 2 Further description.

[0138] Specifically, historical charging data and historical GPS data are acquired, historical traffic characteristics are extracted from the historical charging data and historical GPS data, and a historical traffic characteristic database is constructed based on the extracted historical traffic characteristics.

[0139] Acquire real-time billing data and real-time GPS data, extract real-time traffic characteristics based on the real-time billing data and real-time GPS data, and update the historical traffic characteristic database based on the real-time traffic characteristics.

[0140] Further, the detection area is acquired, node value is analyzed, and nodes are categorized into high-value and low-value nodes. Traffic closure features are extracted, and nodes and road segments whose real-time traffic meets these features are selected as candidate closure nodes and road segments. Feature fusion verification is then performed, involving multi-data source fusion verification and spatial correlation verification on the candidate closure nodes and road segments to obtain the closed nodes and road segments. Lifecycle management is implemented for closed road segments and closed toll stations. Closure information is stored in a closure information results database.

[0141] The method is characterized by the following features: by extracting the inspection area, real-time calculations are performed only within the inspection area, reducing time overhead and meeting the requirements of real-time calculation and monitoring; by fusing and verifying features extracted from two data sources, erroneous extraction caused by data anomalies is avoided; spatial correlation analysis further improves the accuracy of the results; by combining the relatively accurate spatial location characteristics of GPS data, the perception results are more accurate; and through lifecycle management, complete information monitoring of closed road sections and toll stations is achieved, which is beneficial to data management and analysis.

[0142] According to another aspect of the embodiments of this application, a highway closure status monitoring device for implementing the above-described highway closure status monitoring method is also provided. For example... Figure 6 As shown, the device includes:

[0143] The detection zone extraction module 601 is used to determine the detection zone based on the difference between real-time traffic flow and historical traffic flow at monitoring nodes on the highway.

[0144] The detection module 602 is used to count the real-time traffic of nodes and road segments within the detection area, and to select nodes and road segments whose real-time traffic meets the traffic closure characteristics as candidate closure nodes and candidate closure road segments.

[0145] The verification module 603 is used to perform multi-data source fusion verification and spatial correlation verification on the candidate closed nodes and candidate closed road segments to obtain the closed nodes and closed road segments.

[0146] Management module 604 is used to manage closed nodes and closed road sections throughout their entire lifecycle, until the closure is lifted.

[0147] It should be noted that the highway closure status monitoring device provided in the above embodiments is only illustrated by the division of the above functional modules when executing the highway closure status monitoring method. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the highway closure status monitoring device and the highway closure status monitoring method embodiments provided in the above embodiments belong to the same concept, and the implementation process is detailed in the method embodiments, which will not be repeated here.

[0148] According to another aspect of the present application, an electronic device corresponding to the highway closure status monitoring method provided in the foregoing embodiments is also provided to execute the highway closure status monitoring method described above.

[0149] Please refer to Figure 7 This illustrates a schematic diagram of an electronic device provided by some embodiments of this application. For example... Figure 7 As shown, the electronic device includes: a processor 700, a memory 701, a bus 702, and a communication interface 703. The processor 700, the communication interface 703, and the memory 701 are connected via the bus 702. The memory 701 stores a computer program that can run on the processor 700. When the processor 700 runs the computer program, it executes the highway closure status monitoring method provided in any of the foregoing embodiments of this application.

[0150] The memory 701 may include high-speed random access memory (RAM) or non-volatile memory, such as at least one disk storage device. Communication between this system network element and at least one other network element is achieved through at least one communication interface 703 (which can be wired or wireless), such as the Internet, wide area network, local area network, or metropolitan area network.

[0151] Bus 702 can be an ISA bus, PCI bus, or EISA bus, etc. Buses can be divided into address buses, data buses, control buses, etc. Memory 701 is used to store programs. After receiving execution instructions, processor 700 executes the programs. The highway closure status monitoring method disclosed in any of the aforementioned embodiments of this application can be applied to processor 700, or implemented by processor 700.

[0152] The processor 700 may be an integrated circuit chip with signal processing capabilities. In implementation, each step of the above method can be completed by the integrated logic circuitry in the hardware of the processor 700 or by instructions in software form. The processor 700 may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it may also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), an off-the-shelf programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this application. The general-purpose processor may be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this application can be directly embodied in the execution of a hardware decoding processor, or executed by a combination of hardware and software modules in the decoding processor. The software modules may reside in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. The storage medium is located in memory 701. Processor 700 reads the information in memory 701 and, in conjunction with its hardware, completes the steps of the above method.

[0153] The electronic device provided in this application embodiment and the highway closure status monitoring method provided in this application embodiment are based on the same inventive concept and have the same beneficial effects as the methods they adopt, operate or implement.

[0154] According to another aspect of the present application, a computer-readable storage medium corresponding to the highway closure status monitoring method provided in the foregoing embodiments is also provided, wherein a computer program (i.e., a program product) is stored thereon, and when the computer program is run by a processor, it executes the highway closure status monitoring method provided in any of the foregoing embodiments.

[0155] It should be noted that examples of computer-readable storage media may also include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other optical and magnetic storage media, which will not be elaborated here.

[0156] The computer-readable storage medium provided in the above embodiments of this application and the highway closure status monitoring method provided in the embodiments of this application are based on the same inventive concept and have the same beneficial effects as the methods adopted, run or implemented by the application stored therein.

[0157] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0158] The above embodiments merely illustrate several implementation methods of the present invention, and their descriptions are relatively specific and detailed, but they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these all fall within the protection scope of the present invention. Therefore, the protection scope of this patent should be determined by the appended claims.

Claims

1. A method for monitoring the closed status of a highway, characterized in that, include: Based on the difference between real-time and historical traffic flow at monitoring nodes on highways, detection zones are determined. The real-time traffic flow of nodes and road segments within the detection area is statistically analyzed, and nodes and road segments whose real-time traffic flow meets the traffic closure characteristics are selected as candidate closure nodes and candidate closure road segments. The candidate closed nodes and candidate closed road segments are subjected to multi-data source fusion verification and spatial correlation verification to obtain closed nodes and closed road segments. This includes: obtaining a set of candidate closed nodes and candidate closed road segments with direct topological association; if both candidate closed nodes and candidate closed road segments are completely closed, or both are partially closed in the same direction with the same direction of passage, then both pass the data source fusion verification and are determined as closed nodes and closed road segments; obtaining any gantry closed node, and obtaining the gantry nodes upstream and downstream of the gantry closed node on the highway; if the adjacent gantry nodes are target candidate nodes that have not passed the data source fusion verification, then the target candidate node is determined to have passed the spatial correlation test and is designated as a closed node; obtaining any toll station closed node, and obtaining the toll station nodes upstream and downstream of the toll station closed node on the highway; if the adjacent toll station nodes are target candidate nodes that have not passed the data source fusion verification, then the target candidate node is determined to have passed the spatial correlation test and is designated as a closed node. The closed nodes and closed road segments are managed throughout their entire lifecycle until the closure is lifted. This includes: monitoring the detection, updating, splitting, merging, and dissipation processes of the closed nodes and closed road segments to obtain various lifecycle information of the closed nodes and closed road segments; and sending the various lifecycle information of the closed nodes and closed road segments to the application platform.

2. The method according to claim 1, characterized in that, Based on the difference between real-time and historical traffic flow at monitoring nodes on highways, detection zones are determined, including: Calculate the difference between the real-time flow value and the historical flow characteristic value at the monitoring node; When the difference is greater than a preset threshold, the monitoring node is designated as the node to be detected. Aggregate nodes to be detected that have connectivity relationships with their associated road segments to obtain one or more detection zones.

3. The method according to claim 1, characterized in that, Before determining the detection zone, based on the difference between real-time and historical traffic flow at monitoring nodes on highways, the following steps are also included: The total traffic flow passing through the gantry nodes and toll station nodes within a preset time period is counted, as well as the OD traffic flow from the gantry nodes and toll station nodes to each downstream gantry node and toll station node. The total traffic flow passing through the current road segment within a preset time period, as well as the OD traffic flow from the current road segment to downstream road segments, are recorded. The date is divided into multiple characteristic days, and the characteristic days are further divided into multiple time slices with the preset duration to obtain the total node flow, node OD flow, total road segment flow, and road segment OD flow corresponding to each time slice in each characteristic day; Based on the mathematical expectation of the total node flow, node OD flow, total road segment flow, and road segment OD flow for multiple characteristic days and time slices, the historical flow characteristic value corresponding to each time slice in each characteristic day is obtained.

4. The method according to claim 1, characterized in that, Before calculating the real-time traffic flow of nodes and road segments within the detection area, the following steps are also included: The total traffic volume of all toll station nodes for all statistical periods of the previous day is used as the first sample set; If the total traffic of the current toll station node is greater than or equal to the median of the first sample set, then the current toll station node is a high-value toll station node. If the total traffic of the current toll station node is less than the median of the first sample set, then the current toll station node is a low-value node. Use the total flow of all gantry nodes for all statistical periods of the previous day as the second sample set; If the total flow of the current gantry node is greater than or equal to the median of the second sample set, then the current gantry node is a high-value gantry node. If the total flow of the current gantry node is less than the median of the second sample set, then the current gantry node is a low-value node.

5. The method according to claim 4, characterized in that, The nodes and road segments whose real-time traffic meets the traffic closure characteristics are selected as candidate closure nodes and candidate closure road segments, including: When the real-time total traffic of a high-value node exhibits zeroing or sudden drop characteristics, the high-value node is determined to meet the traffic closure characteristic, with the closure type being complete closure. When the real-time total traffic of a low-value node has a zeroing characteristic, the low-value node is determined to meet the traffic closure characteristic, and the closure type is completely closed. The system sequentially determines whether the OD flow from the current node to each downstream node has a zero-flow characteristic. Nodes with zero-flow characteristics are identified as satisfying the flow closure characteristic; the closure type is partial closure. When the total flow through the current road segment to the downstream has a zeroing characteristic, the current road segment is determined to meet the flow closure characteristic, and the closure type is completely closed. The system sequentially determines whether the OD flow from the current road segment to each downstream road segment has a zero-flow characteristic. Road segments with zero-flow characteristics are identified as meeting the flow closure characteristics, and the closure type is partial directional closure.

6. A highway closure status monitoring device, characterized in that, include: The detection zone extraction module is used to determine the detection zone based on the difference between real-time and historical traffic flow at monitoring nodes on highways. The detection module is used to count the real-time traffic flow of nodes and road segments within the detection area, and to designate nodes and road segments whose real-time traffic flow meets the traffic closure characteristics as candidate closure nodes and candidate closure road segments. The verification module is used to perform multi-data source fusion verification and spatial correlation verification on the candidate closed nodes and candidate closed road segments to obtain closed nodes and closed road segments. This includes: acquiring a set of candidate closed nodes and candidate closed road segments with direct topological association; if both candidate closed nodes and candidate closed road segments are completely closed, or both are partially closed in the same direction with the same direction of passage, then both pass the data source fusion verification and are determined as closed nodes and closed road segments; acquiring any gantry closed node, and acquiring the gantry nodes upstream and downstream of the gantry closed node on the highway; if the adjacent gantry nodes are target candidate nodes that have not passed the data source fusion verification, then the target candidate node is determined to have passed the spatial correlation test and is designated as a closed node; acquiring any toll station closed node, and acquiring the toll station nodes upstream and downstream of the toll station closed node on the highway; if the adjacent toll station nodes are target candidate nodes that have not passed the data source fusion verification, then the target candidate node is determined to have passed the spatial correlation test and is designated as a closed node. The management module is used to perform full lifecycle management of the closed nodes and closed road segments until the closed state is lifted. This includes: monitoring the detection, updating, splitting, merging, and dissipation processes of the closed nodes and closed road segments to obtain various lifecycle information of the closed nodes and closed road segments; and sending the various lifecycle information of the closed nodes and closed road segments to the application platform.

7. An electronic device, characterized in that, It includes a processor and a memory storing program instructions, the processor being configured to perform the highway closure monitoring method as described in any one of claims 1 to 5 when executing the program instructions.

8. A computer-readable medium, characterized in that, It stores computer-readable instructions, which are executed by a processor to implement a highway closure monitoring method as described in any one of claims 1 to 5.

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