A method and system for evaluating the state of bridge clusters

Abstract

This application relates to a method and system for evaluating the state of bridge clusters, specifically in the field of bridge cluster load effect evaluation. The method includes establishing a main road traffic distribution network and a traffic distribution mathematical model; inputting the observed ratio of inbound and outbound traffic flows from the ramps of node bridges into the mathematical model to solve for the outbound traffic flows from the ramps and the mainline; constructing dynamic random traffic flows and truck mixing rates for each road segment; applying the random traffic flows to the finite element model of the bridge cluster within the road segment to calculate the loading effect; and evaluating the traffic load level and overload risk of the bridge cluster within the road segment based on the number of times the loading effect exceeds the standard threshold and the truck mixing ratio. This application, based on the conservation of traffic flow at node bridges, uses the known traffic flow on the known node bridges to estimate the traffic flow of other unknown road segments, and uses this estimate to evaluate the traffic load level of all bridge clusters within the road segment, further achieving the goal of evaluating the state of bridge clusters on main roads.

Description

Technical Field

[0001] This application relates to the field of bridge cluster load effect evaluation technology, and in particular to a bridge cluster state evaluation method and system. Background Technology

[0002] As one of the main external forces acting on bridges, the long-term effects of vehicles can affect the usability, durability, and safety of bridges.

[0003] Current methods for evaluating the condition of bridges under vehicle loads mostly involve using dynamic weighing sensors placed on the bridge to acquire data on passing vehicles and apply this data to the bridge structure to assess its load-bearing capacity and fatigue reliability.

[0004] With the development of the transportation industry, bridges have gradually evolved from individual structures scattered at major traffic points to clusters of main road bridges densely distributed along road networks, waterways, or elevated roads. These bridges are connected into a whole through key node bridges or interchanges in the road.

[0005] Due to limited monitoring resources, dynamic weighing systems can only be installed on typical key overpasses on main roads. Therefore, evaluating the condition of bridges on non-monitored sections (i.e., sections without dynamic weighing systems) on main roads using limited traffic monitoring points is a practical engineering challenge. Summary of the Invention

[0006] This application provides a method and system for evaluating the status of bridge clusters under the macroscopic traffic flow estimation of main roads. It uses the traffic flow on bridges at limited known nodes on main roads to estimate the traffic flow and truck mixing rate of other unknown road sections, thereby realizing the evaluation of traffic load level of main roads and the evaluation of overload risk of bridge clusters.

[0007] A method for evaluating the state of bridge clusters, comprising:

[0008] A traffic distribution network for the main road is established based on the location of the node bridges in the main road and the direction of the main road, and vehicles on the main road enter and leave at the node bridges.

[0009] Observe the inbound traffic flow of all bridge ramps at all nodes;

[0010] Statistics on the proportion of vehicles exiting bridge ramps to vehicles entering the main line at all nodes;

[0011] A mathematical model for traffic distribution on the main road is established based on the conservation of traffic flow at the node bridges, and the exit traffic flow at the node bridge ramps and the exit traffic flow on the main line are solved.

[0012] Obtain vehicle load information observed within the first time period;

[0013] Based on vehicle load information, the proportion of vehicle types entering each bridge node during the second time period is dynamically calculated, and the proportion of vehicle types entering each road segment and the truck mixing ratio during the second time period are obtained.

[0014] Congestion status of random traffic flow is constructed for each road segment, and the traffic load level of bridge clusters on main roads is evaluated.

[0015] In some embodiments, the nodal bridge is an interchange or a straight bridge with on-ramps and off-ramps;

[0016] The traffic distribution network consists of multiple nodes and multiple road segments. Each node corresponds to a node bridge, and the main road between two adjacent nodes is a road segment.

[0017] In some embodiments, the specific steps for observing the inbound traffic flow of all node bridge ramps are as follows:

[0018] By using vehicle observation stations pre-set at the on-ramp of all node bridges, the traffic flow entering the ramps of all node bridges is observed;

[0019] The vehicle observation station is either a vehicle dynamic weighing system or a bridge dynamic weighing system.

[0020] In some embodiments, the specific steps for calculating the proportion of outbound traffic from all bridge ramps to inbound traffic on the main line are as follows:

[0021] Drones were used to continuously film the bridge nodes to obtain traffic flow videos;

[0022] Image processing methods were used to process the traffic flow video to obtain the aforementioned ratio of the traffic flow leaving the node bridge ramps to the traffic flow entering the main road.

[0023] In some embodiments, the traffic distribution mathematical model is expressed by the following formula:

[0024]

[0025] in,

[0026] Used to represent the traffic flow leaving the main line of the bridge at node i+1;

[0027] Used to represent the traffic flow entering the main line of the bridge at node i+1;

[0028] Used to represent the traffic flow leaving the bridge ramp at node i+1;

[0029] Used to represent the traffic flow entering the bridge ramp at node i+1;

[0030] The proportion of traffic leaving the bridge ramp at node i+1 to traffic entering the main line;

[0031] m represents the total number of nodes in the main road traffic distribution network.

[0032] In some embodiments, for the first and last junction nodes in a closed-loop arterial road, the traffic distribution mathematical model is expressed by the following formula:

[0033]

[0034] in,

[0035] Used to indicate the traffic flow leaving the main line of the bridge at node 1;

[0036] Used to indicate the traffic flow entering the main line of the bridge at node 1;

[0037] Used to indicate the departure traffic flow of the bridge ramp at node 1;

[0038] Used to indicate the traffic flow entering the bridge ramp at node 1;

[0039] The proportion of traffic exiting the bridge ramp at node 1 to traffic entering the main line;

[0040] m represents the total number of nodes in the main road traffic distribution network.

[0041] In some embodiments, the first time period is the past three months;

[0042] The vehicle load information includes the weight distribution, length distribution, and axle load ratio of different vehicle models.

[0043] In some embodiments, the specific steps for dynamically calculating the proportion of vehicle types entering each bridge node during the second time period based on vehicle load information, and obtaining the proportion of vehicle types entering each road segment during the second time period and the truck mixing ratio are as follows:

[0044] Based on vehicle load information, the proportion of vehicle types entering each bridge node during the second time period is dynamically calculated.

[0045] For the main road in the closed loop, the proportion of vehicle types entering each of the first three road segments in the second time period is obtained by weighting the proportion of vehicle types entering the three node bridges in front of that road segment in the second time period. The proportion of vehicle types entering all other road segments except the first three road segments in the second time period is equal to the proportion of vehicle types entering the one node bridge in front of that road segment in the second time period.

[0046] For open-loop main roads, the proportion of vehicle types entering the second road segment during the second time period is obtained by weighting the proportion of vehicle types entering the two node bridges preceding the road segment during the second time period. The proportion of vehicle types entering the third road segment during the second time period is obtained by weighting the proportion of vehicle types entering the three node bridges preceding the road segment during the second time period. The proportion of vehicle types entering the second road segment in all other road segments except the second and third road segments is equal to the proportion of vehicle types entering the second time period of the one node bridge preceding the road segment.

[0047] Vehicles with three or more axles are defined as freight trucks. The freight truck mixing ratio for each road segment in the second time period is obtained based on the proportion of vehicle types entering each road segment in the second time period.

[0048] In some embodiments, the steps for constructing random traffic flows based on congestion conditions on each road segment and evaluating the traffic load level of bridge clusters on main roads are as follows:

[0049] Based on the traffic flow leaving the main line of each node bridge, the vehicle load information observed on the main road in the first time period, and the proportion of vehicle types entering each road segment in the second time period, the Monte Carlo method is used to randomly generate the congested random traffic flow of the main road. In the congested random traffic flow, the vehicle spacing is considered to be 1m to account for congestion.

[0050] The loading effect was obtained by loading the random traffic flow of congestion onto the finite element model of all bridges on the main road, and the number of times the threshold exceeded the standard calculation threshold was counted.

[0051] The traffic load level of the bridge cluster was determined by using the number of times the threshold was exceeded and the proportion of trucks entering each road segment during the second time period.

[0052] If the number of times exceeding the threshold is less than p and the truck mixing ratio is less than q, then it is determined that the traffic load level in the road segment is low at that moment and the overload level of the bridge cluster in the road segment is low.

[0053] If the number of times the threshold is exceeded is greater than p and the truck mixing ratio is less than q, then it is determined that the traffic load level in the road segment is moderate at that moment, and the bridge cluster in the road segment is at risk of overloading.

[0054] If the number of times the threshold is exceeded is greater than p and the truck mixing ratio is greater than q, it is determined that the traffic load level in the road segment is high at that moment, and the risk of overloading of the bridge cluster in the road segment is relatively high.

[0055] A bridge cluster status evaluation system, characterized in that it employs the aforementioned bridge cluster status evaluation method.

[0056] The beneficial effects of the technical solution provided in this application include:

[0057] Based on the conservation of traffic flow at node bridges, the traffic flow on other unknown road sections is estimated using the known traffic flow on node bridges, and then used to evaluate the traffic load level of all bridge clusters within the road section, thereby achieving the goal of evaluating the status of bridge clusters on main roads. Attached Figure Description

[0058] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0059] Figure 1 This is a flowchart of the bridge cluster status evaluation method in an embodiment of the present invention.

[0060] Figure 2 This is a schematic diagram of the main road traffic distribution network model in an embodiment of the present invention.

[0061] Figure 3 This is a schematic diagram of the arrangement of vehicle observation stations on the node bridge in an embodiment of the present invention.

[0062] Figure 4 This is a schematic diagram of traffic flow statistics for a node bridge based on image recognition, as described in an embodiment of the present invention.

[0063] Figure 5 This is a schematic diagram illustrating the estimation of traffic flow leaving bridges at main road nodes and traffic flow on road sections in an embodiment of the present invention.

[0064] Figure 6 This is a schematic diagram of the process for evaluating the status of road segment bridge clusters in an embodiment of the present invention.

[0065] Detailed implementation method.

[0066] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0067] Based on the problems existing in the prior art, this invention proposes a method for evaluating the state of bridge clusters under macroscopic traffic flow estimation of main roads. This method includes establishing a main road traffic distribution network based on the location of node bridges and the road orientation; establishing a main road traffic distribution mathematical model based on the conservation of node bridge traffic flow; inputting the observed ratio of on-ramp and off-ramp traffic flow into the mathematical model to solve for on-ramp and mainline off-ramp traffic flow; constructing dynamic random traffic flow and truck mixing rate for each road segment; loading the random traffic flow onto the finite element model of the bridge cluster within the road segment to calculate the loading effect; and evaluating the traffic load level and overload risk of the bridge cluster within the road segment based on the number of times the loading effect exceeds the standard threshold and the truck mixing ratio.

[0068] In this embodiment, based on the conservation of traffic flow on node bridges, the traffic flow on other unknown road sections is estimated using the known traffic flow on node bridges, and this estimation is used to evaluate the traffic load level of all bridge clusters within the road section, thereby further achieving the goal of evaluating the status of bridge clusters on the main road.

[0069] Specifically, such as Figure 1 As shown, the method for evaluating the state of bridge clusters under macroscopic traffic flow estimation of main roads includes:

[0070] Step S1: Establish a main road traffic distribution network based on the location of the node bridge in the main road and the direction of the main road, where vehicles on the main road enter and leave at the node bridge.

[0071] Step S2: Observe the traffic flow entering the bridge ramps at all nodes.

[0072] Step S3: Calculate the proportion of traffic leaving the bridge ramps at all nodes to traffic entering the main line.

[0073] Step S4: Establish a mathematical model for traffic distribution on the main road based on the conservation of traffic flow at the node bridges, and solve for the exit traffic flow at the node bridge ramps and the exit traffic flow on the main line.

[0074] Step S5: Obtain the vehicle load information observed during the first time period.

[0075] Step S6: Based on the vehicle load information, dynamically calculate the proportion of vehicle types entering each node bridge during the second time period, and obtain the proportion of vehicle types entering each road segment and the truck mixing ratio during the second time period.

[0076] Step S7: Construct random traffic flow for each road segment under congestion conditions, and evaluate the traffic load level of the bridge clusters on the main roads.

[0077] In a preferred embodiment, in step S1, the traffic distribution network consists of nodes and road segments, such as... Figure 2 As shown, the node bridges are numbered sequentially as follows: Where m is the number of bridge nodes, and a road segment is represented by two nodes, such as... The road segment refers to the main road between the bridge at node i and the bridge at node i+1. The traffic flow on each bridge node originates from the inbound traffic flow via the ramps. Traffic flow leaving the ramp Traffic flow on the main line and the main line leaving the traffic flow composition.

[0078] The main roads are highways or urban ring roads that allow entry and exit only via key node bridges. Key node bridges can be interchanges or straight bridges with on / off ramps. Traffic flow is sampled hourly.

[0079] In a preferred embodiment, in step S2, due to limited vehicle monitoring resources, vehicle observation stations are only deployed on the on-ramp of all node bridges in the main road network, and a vehicle monitoring system (hereinafter referred to as the system) is established. Figure 3 The diagram shows a typical layout of vehicle monitoring stations on an interchange. The traffic flow entering section (17-01) via the bridge ramp at this node is equal to the sum of the traffic flows at vehicle monitoring stations WIM-01 and WIM-03. The vehicle monitoring stations are not limited to vehicle dynamic weighing systems (WIM) and bridge dynamic weighing systems (BWIM) other vehicle monitoring methods.

[0080] In a preferred embodiment, in step S3, a drone continuously captures video of traffic flow on the bridge node, and image processing methods are used to perform grayscale transformation, Gaussian filtering, vehicle edge detection, etc., on the video images to detect and count vehicles. Figure 4 As shown, the traffic flow of the main line entering the bridge node is statistically obtained. and off-ramp traffic flow And calculate the departure ratio. It should be noted that if there are multiple ramps leading off the main road, the number of vehicles exiting the main road via these ramps should be considered. This represents the total number of vehicles that have left the bridge. Furthermore, while the departure of individual vehicles from the bridge node has a degree of randomness, it represents a significant proportion of the total number of vehicles leaving the bridge node. It also has statistical stability.

[0081] In a preferred embodiment, in step S4, when the main road is an open loop, the traffic distribution mathematical model is represented by the following formula 1:

[0082] (1)

[0083] in, Used to represent the traffic flow leaving the main line of the bridge at node i+1. Used to represent the traffic flow entering the main line of the bridge at node i+1. Used to represent the traffic flow leaving the bridge ramp at node i+1. The traffic flow entering the bridge ramp at node i+1 can be obtained in real time by the vehicle monitoring system fixed in step S2, with a sampling time of one hour. The proportion of traffic leaving the bridge ramp at node i+1 to traffic entering the main line is obtained from big data statistics in step S3. m represents the total number of nodes in the main road traffic distribution network.

[0084] In step S4, when the main road is a closed loop, the traffic distribution mathematical model for all nodes except the first and last junction nodes is represented by the above formula 1.

[0085] For the first and last intersection node (bridge) in a closed-loop arterial road, the traffic distribution mathematical model is expressed by the following formula 2:

[0086] (2)

[0087] in, Used to indicate the traffic flow leaving the main line of the bridge at node 1. Used to indicate the traffic flow entering the main line of the bridge at node 1. Used to indicate the number of vehicles leaving the bridge ramp at node 1. Used to indicate the traffic flow entering the bridge ramp at node 1. The percentage of traffic leaving the bridge ramp at node 1 relative to the traffic entering the main line. m represents the total number of nodes in the main road traffic distribution network.

[0088] The system of linear equations established by Equations 1 and 2 has a number of equations equal to the number of unknowns. and The number of [variables] indicates that the mathematical model has a unique solution; therefore, the traffic flow of all road segments and the departure traffic flow from nodes on the main road can be obtained by solving the system of linear equations. For example... Figure 5 The figure shows the estimated traffic flow at various locations on the main ring road.

[0089] The above establishes a one-way traffic model for main roads. A two-way model can be composed of two independent one-way models.

[0090] In a preferred embodiment, in step S5, the system periodically collects vehicle load information monitored over the past three months and statistically analyzes the global characteristic parameters of the main roads, such as vehicle weight distribution, vehicle length distribution, and axle load ratio, according to vehicle type classification.

[0091] Among them, the weight and length distribution functions of different vehicle models are fitted using log-normal distribution and Gaussian mixture distribution, and the fitting formula is as follows:

[0092] = + (3)

[0093] Where n is the vehicle type identifier, n=2,3,4,5,6 correspond to two-axle, three-axle, four-axle, five-axle and six-axle vehicles respectively. For different vehicle models, this refers to the vehicle weight or length. It follows a log-normal distribution, where s is the number of distributions and j is the j-th distribution. It follows a Gaussian distribution, where t is the number of distributions and k is the k-th distribution.

[0094] The axle load percentage of different vehicle models is recorded as follows: Where n is the vehicle model identifier, The percentage of the axle load on the first axle of the n-type vehicle. This represents the percentage of the axle load on the sixth axle of the n-type vehicle.

[0095] In a preferred embodiment, in step S6, the proportion of vehicle types entering the bridge at each node is calculated based on the hourly sampling time. Where i is the node bridge number, The percentage of two-axle vehicles on the bridge at node i is given, and so on for the others.

[0096] To ensure a more reasonable proportion of vehicle types traveling on each section of the main road, the vehicle type proportions of the first three bridge nodes of each section are taken and weighted accordingly. Therefore, the vehicle type proportions for that section are... , where R is the weighting coefficient.

[0097] For the main road in the closed loop, the first section That is, the proportion of vehicle types entering the main road between the first and second node bridges within one hour is... ,in , and They are respectively , , The weights are typically set to: The second section That is, the proportion of vehicle types entering the main road between the second and third node bridges within one hour is... ,in , and They are respectively , , The weights are typically set to: The third section That is, the proportion of vehicle types entering the main road between the third and fourth node bridges within one hour is... ,in , and They are respectively , , The weights are typically set to: Apart from the first three road sections, the percentage of vehicle types entering other sections of the main roads within one hour was as follows: ,in , and They are respectively , , The weights, i≥3, are typically taken as: For the main open-loop road, the first section That is, the proportion of vehicle types entering the main road between the first and second node bridges within one hour is... The second section That is, the proportion of vehicle types entering the main road between the second and third node bridges within one hour is... ,in , They are respectively , The weight of the third segment. That is, the proportion of vehicle types entering the main road between the third and fourth node bridges within one hour is... ,in , and They are respectively , , The weighting. Besides the first three road sections, the percentage of vehicle types entering other sections of the main roads within one hour is... ,in , and They are respectively , , The weights, i≥3, are typically taken as: .

[0098] , and The value can be adjusted as needed.

[0099] If we define three-axle or higher vehicles as freight trucks, then what is the freight truck mixing rate on a road segment? ,in The percentage of vehicle types within the section from the bridge at node i to the (i+1)th road segment. The percentage of the i-th type of vehicle.

[0100] In a preferred embodiment, in step S7, as follows: Figure 6 As shown, the hourly traffic flow of each section of the main line obtained from step S4 The percentage of vehicle types on the road segment obtained in step S6 Local feature parameters, such as vehicle weight distribution, vehicle length distribution, and axle load ratio of various vehicle types obtained in step S5, are used to randomly generate congested random traffic flows for the road segment using the Monte Carlo method, with vehicle spacing assumed to be 1m to account for congestion. The random traffic flows are then applied to the finite element models of all bridges within the road segment to obtain the loading effect, and the number of times the load exceeds the calculated threshold is counted. .

[0101] Number of times used Mixing rate with trucks Two comprehensive indicators are used to determine the traffic load level of bridge clusters:

[0102] (1) When the threshold is exceeded a certain number of times And the rate of trucks mixed in This indicates that the traffic load level in the road section is low at that time, and the overload level of the bridge cluster in the road section is low.

[0103] (2) When the threshold number is exceeded And the rate of trucks mixed in This indicates that the traffic load level in the road section is moderate at this time, and the bridge cluster in the road section is at risk of overloading.

[0104] (3) When the threshold number is exceeded And the rate of trucks mixed in This indicates that the traffic load level in the road section is high at this time, and the risk of overloading of the bridge cluster in the road section is relatively high.

[0105] The thresholds for the number of times the threshold is exceeded and the mixing rate are not limited to 3 and 0.5, and any other classification criteria similar to this form are within the scope of this patent invention.

[0106] This invention also proposes a bridge cluster status evaluation system, such as... Figure 4 As shown, all of the above methods can be applied to this system.

[0107] In the description of this application, it should be noted that the terms "upper," "lower," etc., indicating the orientation or positional relationship are based on the orientation or positional relationship shown in the accompanying drawings, and are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of this application. Unless otherwise expressly specified and limited, the terms "installed," "connected," and "linked" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication between two elements. For those skilled in the art, the specific meaning of the above terms in this application can be understood according to the specific circumstances.

[0108] It should be noted that in this application, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0109] The above description is merely a specific embodiment of this application, enabling those skilled in the art to understand or implement this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features claimed herein.

Claims

1. A method for evaluating the state of bridge clusters, characterized in that, include: A traffic distribution network for the main road is established based on the location of the node bridges within the main road and the direction of the main road. Vehicles on the main road enter and exit at the node bridges. The node bridges are either interchanges or straight bridges with on- and off-ramps. The traffic distribution network consists of multiple nodes and multiple road segments. Each node corresponds to a node bridge, and the main road between two adjacent nodes is a road segment. Observe the inbound traffic flow of all bridge ramps at all nodes; Statistics on the proportion of traffic exiting bridge ramps at all nodes to the total traffic entering the main line of the bridge at that node. A mathematical model for traffic distribution on the main road is established based on the conservation of traffic flow at the node bridges, and the exit traffic flow at the node bridge ramps and the exit traffic flow on the main line are solved. Obtain vehicle load information observed within the first time period; The vehicle load information includes the weight distribution, length distribution, and axle load ratio of different vehicle models; Based on vehicle load information, the proportion of vehicle types entering each bridge node during the second time period is dynamically calculated, and the proportion of vehicle types entering each road segment and the truck mixing ratio during the second time period are obtained. Construct random traffic flow patterns for each road segment under congestion conditions, and evaluate the traffic load level of bridge clusters on main roads; The traffic distribution mathematical model is expressed by the following formula: in, Used to represent the traffic flow leaving the main line of the bridge at node i+1; Used to represent the traffic flow entering the main line of the bridge at node i+1; Used to represent the traffic flow leaving the bridge ramp at node i+1; Used to represent the traffic flow entering the bridge ramp at node i+1; The proportion of traffic leaving the bridge ramp at node i+1 to traffic entering the main line; m represents the total number of nodes in the main road traffic distribution network.

2. The bridge cluster status evaluation method as described in claim 1, characterized in that, The specific steps for observing the inbound traffic flow of all bridge ramps at all nodes are as follows: By using vehicle observation stations pre-set at the on-ramp of all node bridges, the traffic flow entering the ramps of all node bridges is observed; The vehicle observation station is either a vehicle dynamic weighing system or a bridge dynamic weighing system.

3. The bridge cluster status evaluation method as described in claim 1, characterized in that, The specific steps for calculating the proportion of outbound traffic from all bridge ramps to inbound traffic on the main line are as follows: Drones were used to continuously film the bridge nodes to obtain traffic flow videos; Image processing methods were used to process the traffic flow video to obtain the aforementioned ratio of the traffic flow leaving the node bridge ramps to the traffic flow entering the main road.

4. The bridge cluster status evaluation method as described in claim 1, characterized in that, For the first and last junction nodes in a closed-loop arterial road, the traffic distribution mathematical model is expressed by the following formula: in, Used to indicate the traffic flow leaving the main line of the bridge at node 1; Used to indicate the traffic flow entering the main line of the bridge at node 1; Used to indicate the departure traffic flow of the bridge ramp at node 1; Used to indicate the traffic flow entering the bridge ramp at node 1; The proportion of traffic exiting the bridge ramp at node 1 to traffic entering the main line; m represents the total number of nodes in the main road traffic distribution network.

5. The bridge cluster status evaluation method as described in claim 1, characterized in that, The first time period refers to the past three months.

6. The bridge cluster status evaluation method as described in claim 1, characterized in that, The process involves dynamically calculating the proportion of vehicle types entering each bridge node during the second time period based on vehicle load information, and obtaining the proportion of vehicle types and the truck mixing ratio for each road segment during the second time period. The specific steps are as follows: Based on vehicle load information, the proportion of vehicle types entering each bridge node during the second time period is dynamically calculated. For the main road in the closed loop, the proportion of vehicle types entering each of the first three road segments in the second time period is obtained by weighting the proportion of vehicle types entering the three node bridges in front of that road segment in the second time period. The proportion of vehicle types entering all other road segments except the first three road segments in the second time period is equal to the proportion of vehicle types entering the one node bridge in front of that road segment in the second time period. For open-loop main roads, the proportion of vehicle types entering the second road segment during the second time period is obtained by weighting the proportion of vehicle types entering the two node bridges preceding the road segment during the second time period. The proportion of vehicle types entering the third road segment during the second time period is obtained by weighting the proportion of vehicle types entering the three node bridges preceding the road segment during the second time period. The proportion of vehicle types entering the second road segment in all other road segments except the second and third road segments is equal to the proportion of vehicle types entering the second time period of the one node bridge preceding the road segment. Vehicles with three or more axles are defined as freight trucks. The freight truck mixing ratio for each road segment in the second time period is obtained based on the proportion of vehicle types entering each road segment in the second time period.

7. The bridge cluster status evaluation method as described in claim 1, characterized in that, The specific steps for constructing random traffic flows based on congestion conditions on each road segment and evaluating the traffic load level of bridge clusters on main roads are as follows: Based on the traffic flow leaving the main line of each node bridge, the vehicle load information observed on the main road in the first time period, and the proportion of vehicle types entering each road segment in the second time period, the Monte Carlo method is used to randomly generate the congested random traffic flow of the main road. In the congested random traffic flow, the vehicle spacing is considered to be 1m to account for congestion. The loading effect was obtained by loading the random traffic flow of congestion onto the finite element model of all bridges on the main road, and the number of times the threshold exceeded the standard calculation threshold was counted. The traffic load level of the bridge cluster was determined by using the number of times the threshold was exceeded and the proportion of trucks entering each road segment during the second time period. If the number of times exceeding the threshold is less than p and the truck mixing ratio is less than q, then it is determined that the traffic load level in the road segment is low at that moment and the overload level of the bridge cluster in the road segment is low. If the number of times the threshold is exceeded is greater than p and the truck mixing ratio is less than q, then it is determined that the traffic load level in the road segment is moderate at that moment, and the bridge cluster in the road segment is at risk of overloading. If the number of times the threshold is exceeded is greater than p and the truck mixing ratio is greater than q, it is determined that the traffic load level in the road segment is high at that moment, and the risk of overloading of the bridge cluster in the road segment is relatively high.

8. A bridge cluster status evaluation system, characterized in that, The bridge cluster status evaluation method according to any one of claims 1-7 is adopted.

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