Road administration and dispatching methods and equipment for multi-event concurrent scenarios on highways

Abstract

This invention discloses a road administration scheduling method and device for concurrent multi-event scenarios on highways, belonging to the field of intelligent transportation and highway operation management technology. It addresses the technical problems in existing highway operation management where road administration resource scheduling in concurrent multi-event scenarios relies heavily on manual experience, struggles to comprehensively consider the urgency of events and the dynamic state of the road network, and suffers from low resource allocation efficiency. The method includes: mapping management demand nodes and road administration resource nodes to a weighted directed graph of the road network; calculating the management demand priority for traffic events in concurrent multi-event scenarios under dynamic road network conditions; performing collaborative scheduling calculations for road administration resource nodes to minimize the overall traffic impact and the economic cost of resource scheduling; and performing dynamic rolling optimization calculations on the road administration resource allocation scheme based on the generation of new traffic events and / or changes in road network traffic conditions to obtain the optimal road administration resource allocation scheme.

Description

Technical Field

[0001] This application relates to the fields of intelligent transportation and highway operation management, and in particular to road administration and dispatching methods and equipment for highway multi-event concurrent scenarios. Background Technology

[0002] With the continuous expansion of the expressway network and the sustained growth of traffic flow, sudden or abnormal events such as traffic accidents, vehicle breakdowns, road spills, road construction, and localized congestion occur frequently during expressway operation. When such events occur, it is usually necessary for management forces such as road patrol vehicles, clearing vehicles, and emergency response personnel to quickly arrive at the scene to handle the situation, restore road traffic capacity, and ensure traffic safety.

[0003] In actual operation and management, highway operation and management departments often need to handle multiple different types of management needs simultaneously. For example, within the same time period, different road sections may experience traffic accidents, vehicle breakdowns, debris spills, or severe congestion at the same time, creating a scenario of multiple concurrent management needs. In this situation, management departments need to rationally allocate patrol vehicles, clearing equipment, and emergency personnel under limited road administration resources to achieve timely handling of multiple incidents.

[0004] Current highway emergency management methods largely rely on manual experience for dispatch decisions. Dispatchers typically allocate road administration resources based on event location, experience, and simple distance principles. However, in scenarios with multiple concurrent events, different events vary significantly in terms of risk level, traffic impact range, and urgency of response. Furthermore, road network traffic conditions may dynamically change due to traffic congestion or accident impacts. Traditional manual dispatch methods struggle to comprehensively consider multiple factors such as event priority, real-time road network status, and resource constraints, easily leading to unreasonable resource allocation or low response efficiency.

[0005] Furthermore, existing dispatching methods typically lack dynamic update mechanisms. When new events occur or the road network status changes, it is difficult to optimize and adjust existing dispatching plans in a timely manner, thus affecting emergency response efficiency and the speed of road traffic restoration. Therefore, how to achieve coordinated dispatching and dynamic optimization of road administration forces under the condition of multiple concurrent events on highways, taking into account event priorities, dynamic road network status, and distribution of management resources, has become an urgent technical problem to be solved in the field of highway operation and management. Summary of the Invention

[0006] This application provides a road administration scheduling method and device for multi-event concurrent scenarios on highways, which solves the following technical problems: In the existing highway operation and management, the scheduling of road administration forces in multi-event concurrent scenarios mainly relies on manual experience, which makes it difficult to comprehensively consider the urgency of events and the dynamic status of the road network, resulting in low resource allocation efficiency.

[0007] The embodiments of this application adopt the following technical solutions: On one hand, embodiments of this application provide a road administration scheduling method for concurrent multi-event scenarios on highways, including: constructing management demand nodes and road administration resource nodes based on collected highway traffic operation data and schedulable road administration resource units; mapping the management demand nodes and the road administration resource nodes to a weighted directed graph of the road network constructed based on the set of road network nodes and the set of road connection relationships, and obtaining a dynamic road network state; and evaluating the dynamic road network state based on the comprehensive impact of traffic events corresponding to the management demand nodes and the time urgency index corresponding to the road administration resource nodes. In a multi-event concurrent scenario, the priority of management needs for traffic events is calculated to obtain the comprehensive handling priority of each management need node. Based on the comprehensive handling priority of each management need node, the road administration resource nodes are subjected to collaborative scheduling calculations to minimize the overall traffic impact and the economic cost of resource scheduling, outputting a road administration resource allocation scheme for multi-event concurrent scenarios. The road administration resource allocation scheme is then subjected to dynamic rolling optimization calculations based on the generation of new traffic events and / or changes in road network traffic status to obtain the optimal road administration resource allocation scheme adapted to the current high-speed traffic state.

[0008] This application's embodiments achieve unified and collaborative management and allocation of various traffic management needs generated during highway operation. It comprehensively considers factors such as event type, risk level, traffic congestion degree, and road network traffic status, prioritizing management need nodes. Based on this, a collaborative scheduling model is constructed between road administration resources and need nodes, enabling optimized allocation and route planning of road administration patrol vehicles, clearing equipment, and emergency response forces. Furthermore, it can quickly generate reasonable road administration force dispatch plans in the event of multiple concurrent events, improving emergency response efficiency, shortening event handling time, and reducing the impact of traffic congestion, thereby enhancing the intelligence and collaboration level of highway operation management.

[0009] In one feasible implementation, based on collected high-speed traffic operation data and schedulable road administration resource units, management demand nodes and road administration resource nodes are constructed respectively. Specifically, this includes: acquiring the high-speed traffic operation data through a high-speed traffic operation monitoring system; wherein the high-speed traffic operation data includes at least: video surveillance data, traffic detector data, ETC gantry data, road administration patrol reporting information, and traffic accident alarm information; performing anomaly detection and event identification based on traffic state change characteristics on the high-speed traffic operation data to obtain traffic events occurring on the highway; wherein the anomaly detection includes at least: traffic accidents, vehicle malfunctions, road debris, road construction encroachment, and abnormal congestion; generating corresponding management demand nodes based on the identified traffic events; wherein the management demand nodes are used to indicate the location of events requiring road administration resources to handle; generating road administration resource nodes based on road administration resource units and the corresponding schedulable resource quantity; wherein the road administration resource nodes are used to supply resources to the management demand nodes.

[0010] In one feasible implementation, the management demand nodes and the road administration resource nodes are mapped to a weighted directed graph of the road network constructed based on the set of road network nodes and the set of road connection relationships, and a dynamic road network state is obtained. Specifically, this includes: abstracting the highway road network into a weighted directed graph of the road network based on the set of road network nodes and the set of road connection relationships; constructing a dynamic road network weight matrix based on the basic travel time of road segments between road network nodes; mapping both the management demand nodes and the road administration resource nodes to the set of road network nodes, and obtaining the corresponding positions of the management demand nodes and the road administration resource nodes in the traffic network; and calculating the shortest travel path and estimated arrival time between the management demand nodes and the road administration resource nodes based on the corresponding positions using the dynamic road network weight matrix, recording and obtaining the dynamic road network state.

[0011] In one feasible implementation, before prioritizing the management needs of traffic events in a multi-event concurrent scenario within the dynamic road network state by using the comprehensive impact of traffic events corresponding to the management demand nodes and the time urgency indicators corresponding to the road administration resource nodes, the method further includes: extracting key factors affecting the urgency of traffic event handling from each management demand node; wherein, the key factors include: event severity, danger level, traffic congestion index, proportion of affected lanes, and scale of affected traffic flow; standardizing the key factors to obtain standardized key factor indicators; calculating the combined weight of the standardized key factor indicators using subjective and objective weights in AHP-entropy weighting to obtain key factor combined weights; and combining the key factor combined weights with the standardized key factor indicators to obtain the comprehensive impact of the traffic event.

[0012] In one feasible implementation, the management demand priority of traffic events in a multi-event concurrent scenario in the dynamic road network state is calculated by using the comprehensive impact of traffic events corresponding to the management demand nodes and the time urgency index corresponding to the road administration resource nodes, to obtain the comprehensive handling priority of each management demand node. Specifically, this includes: based on... The distance between the centers of each cluster based on the comprehensive impact of traffic events is obtained. ;in, For the current traffic incident The overall impact of traffic incidents; As the center of each cluster; according to To obtain the current traffic incident event level Where K represents the K clusters into which the historical comprehensive impact of historical traffic events is divided using K-means clustering; according to To obtain the current traffic event Number of road administration resource units required ;in, and All are mathematical constants; according to , obtain based on event level The anticipated collaborative handling of the current traffic incident Number of candidate resource nodes required ;in, The minimum number of road administration resource nodes required; This is the adjustment coefficient; This is the floor function; according to , to obtain based on the current traffic event Event processing coordination arrival time of the corresponding management requirement node ;in, The arrival time sorting item from any road administration resource node to the management demand node in the dynamic road network state; s is the number of shortest paths from any road administration resource node to the management demand node; according to The time urgency index corresponding to the road administration resource node in the event collaborative processing is obtained. ;in, Here, e is the time sensitivity coefficient; e is the Euler number; according to The overall processing priority of each management requirement node is obtained. ;in, The overall impact of the traffic incident is denoted as .

[0013] In one feasible implementation, based on the comprehensive handling priority of each management demand node, a collaborative scheduling calculation of road administration resources is performed on the road administration resource nodes to minimize the overall traffic impact and the economic cost of resource scheduling. This results in an output road administration resource allocation scheme for multi-event concurrent scenarios, specifically including: based on... The traffic impact cost function is obtained. ;in, The overall processing priority is set as follows; This refers to the start time of the collaborative event handling among multiple road administration resource nodes that first reach the management demand node; n is a constant; i is the label of the management demand node; according to The economic cost function of resource scheduling is obtained. ;in, Represents road administration resource nodes The unit scheduling cost of a single road administration resource unit; Represents road administration resource nodes To the current management requirement node The shortest path length; For road administration resource nodes To the current management needs node The number of resource units allocated; j is the number of the road administration resource node; according to The comprehensive optimization objective function for minimizing the overall impact of traffic and minimizing the economic cost of resource scheduling is obtained. ;in, The weight coefficients are 0. Based on the comprehensive optimization objective function, the optimal allocation of road administration resources is calculated for the scheduling model of road administration resources and demand nodes in the expressway network, resulting in the road administration resource allocation scheme for multi-event concurrent scenarios. The road administration resource allocation scheme includes: road administration resource number, corresponding demand node, recommended travel route, and estimated arrival time.

[0014] In one feasible implementation, by This is to fulfill the resource capacity constraint on the number of allocated resource units; wherein, For road administration resource nodes To the current management needs node The number of resource units allocated; n is a constant; _i_ is the resource capacity of the road administration resource node itself; _i_ is the label of the management demand node; _j_ is the label of the road administration resource node; through _j_... This is to ensure that the requirement for the number of allocated resource units is met; where m is a constant. For the current traffic incident The required number of road administration resource units; through This is to fulfill the non-negative integer constraint on the number of allocated resource units.

[0015] In one feasible implementation, the road administration resource allocation scheme is dynamically and continuously optimized based on the occurrence of new traffic events and / or changes in road network traffic conditions to obtain the optimal road administration resource allocation scheme to adapt to the current high-speed traffic conditions. Specifically, this includes: re-acquiring and processing the data sets of management demand nodes and road administration resource nodes based on the data update time under the occurrence of new traffic events and / or changes in road network traffic conditions to obtain new management demand nodes and new road administration resource nodes; updating the number of remaining schedulable road administration resource units in the road administration resource nodes; updating the comprehensive handling priority of each management demand node based on the new management demand nodes, the new road administration resource nodes, and the updated number of schedulable road administration resource units; performing a collaborative dynamic rolling update calculation of road administration resources for the road administration resource nodes according to the updated comprehensive handling priority; and solving and outputting the optimal road administration resource allocation scheme based on the new road administration resource and demand node scheduling model reconstructed by the rolling optimization.

[0016] In one feasible implementation, after performing dynamic rolling optimization calculations on the road administration resource allocation scheme based on the occurrence of new traffic events and / or changes in road network traffic status, the method further includes: sending the road administration resource allocation scheme to the corresponding mobile terminal and executing the road administration resource allocation scheme accordingly; if feedback update status is detected for management demand nodes and road administration resource nodes, then performing dynamic rolling optimization calculations on the road administration resource allocation scheme based on the occurrence of new traffic events and / or changes in road network traffic status to obtain the optimal road administration resource allocation scheme; and executing the resource scheduling information in the optimal road administration resource allocation scheme; wherein, the resource scheduling information includes at least: task number, target demand node location, recommended travel route, and estimated arrival time.

[0017] On the other hand, this application also provides a road administration and scheduling device for highway multi-event concurrent scenarios, including: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, so that the at least one processor can execute the road administration and scheduling method for highway multi-event concurrent scenarios described in any of the above embodiments.

[0018] This application provides a road administration and scheduling method and equipment for multi-event concurrent scenarios on highways. Compared with the prior art, the embodiments of this application have the following beneficial technical effects: 1. By using automated road administration and scheduling methods, reliance on manual experience is reduced, and the efficiency and accuracy of road administration and scheduling are improved.

[0019] 2. It can comprehensively consider the urgency of events and the dynamic status of the road network to optimize resource allocation and improve resource utilization efficiency.

[0020] 3. Based on the comprehensive impact and time urgency of traffic incidents, the system can also dynamically calculate the processing priority of each incident to ensure that emergencies are handled with priority.

[0021] 4. By constructing a dynamic road network status, the operation of the road network can be monitored in real time, providing real-time data support for scheduling decisions.

[0022] 5. It can effectively handle scenarios with multiple concurrent events, avoiding the impact on other events caused by improper handling of a single event.

[0023] 6. By minimizing the overall traffic impact and the economic cost of resource scheduling, intelligent and collaborative highway operation and management can be achieved.

[0024] 7. Dynamically optimize resource allocation plans based on new traffic events and / or changes in road network traffic conditions to ensure that the plans are always adapted to the current traffic situation. Attached Figure Description

[0025] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. In the drawings: Figure 1 A flowchart of a road administration and scheduling method for a multi-event concurrent scenario on a highway, provided as an embodiment of this application; Figure 2 This is a schematic diagram of the structure of a road administration and dispatching device for a multi-event concurrent scenario on a highway, provided as an embodiment of this application. Detailed Implementation

[0026] To enable those skilled in the art to better understand the technical solutions in this application, the technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this specification, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of this application.

[0027] This application provides a road administration and scheduling method for multi-event concurrent scenarios on highways, such as... Figure 1 As shown, the road administration and scheduling method for multi-event concurrent scenarios on highways specifically includes steps S101-S105: S101. Based on the collected high-speed traffic operation data and the dispatchable road administration resource units, management demand nodes and road administration resource nodes are constructed respectively.

[0028] Specifically, highway traffic operation data is acquired through the highway operation monitoring system. This data includes at least: video surveillance data, traffic detector data, ETC gantry data, information reported by road administration patrols, and traffic accident alarm information.

[0029] Furthermore, anomaly detection and event identification based on traffic state change characteristics are performed on highway traffic operation data to obtain traffic events occurring on highways. Anomaly detection includes at least: traffic accidents, vehicle malfunctions, road debris, road construction obstruction, and abnormal congestion.

[0030] In one embodiment, highway traffic operation data is acquired from a highway operation monitoring system. This data includes, but is not limited to, video surveillance data, traffic detector data, ETC gantry data, road administration patrol reports, and traffic accident alarm information. Next, anomaly detection and event identification are performed on the acquired highway traffic operation data. Traffic events occurring during highway operation, such as traffic accidents, vehicle breakdowns, road debris, road construction, and abnormal congestion, are identified based on traffic state change characteristics. Subsequently, corresponding management request nodes are generated based on the identified traffic events. Each management request node represents the location of an event requiring intervention from road administration personnel.

[0031] Furthermore, based on the identified traffic incidents, corresponding management demand nodes are generated. These management demand nodes represent the locations of incidents that require intervention from road administration authorities.

[0032] In one embodiment, the set of management requirement nodes constructed is as follows: ;in, Indicates the first Each management requirement node This represents the number of events requiring handling in the highway network at the current moment. Different management demand nodes require different numbers of road administration resource units (such as a road maintenance vehicle) due to the severity of the events and their adverse impact on traffic. Assume management demand nodes... The required number of road administration resource units is .

[0033] Furthermore, based on the road administration resource units and the corresponding number of schedulable resources, road administration resource nodes are generated. These road administration resource nodes are used to supply resources to nodes with management needs.

[0034] In one embodiment, the set of highway administration resource nodes is as follows: ,in: Indicates the first Each road administration resource node (such as the operation and maintenance center). This represents the number of schedulable road administration resource nodes. Different road administration resource nodes can provide different numbers of road administration resource units (such as a road administration rescue vehicle). Assuming road administration resource nodes... Can provide Each road administration resource unit.

[0035] S102. Map the management demand nodes and road administration resource nodes to the weighted directed graph of the road network constructed based on the set of road network nodes and the set of road connection relationships, and obtain the dynamic road network state.

[0036] Specifically, based on the set of road network nodes and the set of road connection relationships, the highway network is abstracted into a weighted directed graph of the road network.

[0037] In one embodiment, the highway network is abstracted as a weighted directed graph: ,in, This represents a set of road network nodes, which can correspond to highway toll stations, interchanges, service areas, or key nodes on road sections. This represents the set of road connections, where each edge represents a passable road between two adjacent nodes.

[0038] Furthermore, a dynamic road network weight matrix is ​​constructed based on the basic travel time of road segments between road network nodes.

[0039] In one embodiment, real-time traffic operation data for each road segment is obtained from a traffic operation monitoring system, including parameters such as average vehicle speed, traffic flow, lane occupancy, and road capacity, to calculate the travel time on each side of the road network. Let road segments be... Represents road network nodes With road network nodes The basic travel time for the road connections between them is: ;in, Indicates road segment Length, This indicates the road section The real-time average vehicle speed is then used to construct a dynamic road network weight matrix based on the traffic conditions of each road segment. This dynamic road network weight matrix is ​​used to represent the allocation of road administration resources from road network nodes. With road network nodes The cost of passage.

[0040] Furthermore, both management demand nodes and road administration resource nodes are mapped to the road network node set, and the corresponding positions of management demand nodes and road administration resource nodes in the traffic network are obtained.

[0041] Furthermore, by using a dynamic road network weight matrix, the shortest travel path and estimated arrival time between management demand nodes and road administration resource nodes are calculated based on their corresponding locations, and the dynamic road network status is recorded and obtained.

[0042] In one embodiment, based on the locations of the management demand nodes and road administration resource nodes obtained in step S101 above, the management demand nodes and road administration resource nodes are mapped to the road network node set. The system obtains the corresponding locations of management demand nodes and road administration resource nodes in the transportation network. Then, it uses the constructed dynamic road network weight matrix to calculate the values ​​of each road administration resource node. To each management requirement node Shortest travel route and estimated arrival time between This provides a road network status basis for subsequent optimization of road administration resource allocation. Finally, based on the various data obtained above, corresponding records are made in the transportation network to obtain the dynamic road network status.

[0043] S103. By using the comprehensive impact of traffic events corresponding to management demand nodes and the time urgency indicators corresponding to road administration resource nodes, the priority of management demand for traffic events in the scenario of multiple concurrent events in the dynamic road network is calculated to obtain the comprehensive handling priority of each management demand node.

[0044] Specifically, key factors affecting the urgency of traffic incident handling are extracted from each management demand node. These key factors include: incident severity, hazard level, traffic congestion index, proportion of affected lanes, and scale of affected traffic flow.

[0045] In one embodiment, firstly, the set of management requirement nodes obtained in step S101 is processed. For each management requirement node, key factors influencing the urgency of incident handling are extracted. These key factors include, but are not limited to: ① Severity of the incident. ① Severity index determined based on accident type or event level; ② Hazard level : Hazard level indicators determined based on whether hazardous materials vehicles or high-risk events are involved; ③ Traffic congestion index : Congestion index is calculated based on the ratio of the current average speed to the design speed of the road segment; ④ Proportion of affected lanes ⑤ The proportion of lanes closed or restricted due to the event to the total number of lanes; ⑥ The impact on traffic flow scale. The scale of traffic flow affected by the event.

[0046] Furthermore, the key factors are standardized to obtain standardized key factor indicators.

[0047] In one embodiment, the aforementioned key factors are standardized to eliminate differences in the dimensions of different indicators. For example, The standardization process is as follows: ,in Based on the method for calculating the severity of an event, the standardized indicators (standardized key factor indicators) can be obtained similarly: , , , , .

[0048] Furthermore, by using the subjective and objective weights in the AHP entropy weight combination weighting, the weight combination of standardized key factor indicators is calculated to obtain the key factor combination weight.

[0049] In one embodiment, the AHP entropy weight combination weighting method is used to determine the weights of each indicator. Specifically, the subjective weights of each standardized key factor indicator are obtained through the analytic hierarchy process, and are as follows: , , , , Objective weights were calculated using the entropy weight method based on historical traffic incident data, and were respectively... , , , , Then, the combined weights of the event severity are: ,in Similarly, the combined weights of the risk levels are obtained as follows: The combined weight of the traffic congestion index is The combined weight of the affected lane proportions is The combined weights affecting traffic flow scale are .

[0050] Furthermore, the combined weights of key factors and standardized key factor indicators are used to calculate the overall impact of the traffic incident. In other words, the constructed overall impact of the traffic incident describes the degree of comprehensive impact of the incident on the operational status of the highway. .

[0051] Furthermore, we assume that the set of the combined impact of historical traffic incidents is as follows: Then, K-means clustering is used to divide the events into K clusters: The centers of each cluster were obtained as follows: Then, regarding the current traffic incident... Based on its overall impact Calculate the distance to the center of each cluster: The distance between the centers of each cluster based on the comprehensive impact of traffic events is obtained. .in, For the current traffic incident The overall impact of traffic incidents; It serves as the center of each cluster.

[0052] Furthermore, according to To obtain the current traffic incident event level Where K represents the K clusters into which the historical comprehensive impact of historical traffic events is divided using K-means clustering; .

[0053] Furthermore, according to To obtain the current traffic event Number of road administration resource units required .in, and All of them are mathematical constants.

[0054] Furthermore, considering that the number of road administration resource units that a single management resource node can provide is limited, a single traffic incident may require the coordinated processing of multiple management resource nodes. This application, based on the event level... The estimated number of candidate road administration resource nodes required for coordinated handling of this incident. The formula is as follows: And then according to , obtain based on event level The anticipated collaborative handling of the current traffic incident Number of candidate resource nodes required .in, The minimum number of road administration resource nodes required; It is the adjustment coefficient, and The larger the value, the more road administration resources and nodes are needed to provide road administration services in response to major accidents. This is the floor function.

[0055] Furthermore, based on step S102, the shortest path algorithm is used to calculate any road administration resource node. To the management demand node Arrival time Connect all resource nodes to the management demand node (current traffic event). Arrival times are sorted in ascending order, i.e.: Then according to , to obtain based on the current traffic event Event processing coordination arrival time of the corresponding management requirement node .in, s represents the arrival time sorting item from any road administration resource node to the management demand node in the dynamic road network state; s represents the number of shortest paths from any road administration resource node to the management demand node.

[0056] Furthermore, the longer the time required for collaborative event processing, the greater the time urgency in providing management resources for that event. Simultaneously, to reflect the non-linear impact of arrival time on the urgency of event handling, this application uses an exponential decay function to define the arrival time urgency index, namely: based on... This yields the time urgency index corresponding to the road administration resource nodes used in event collaborative processing. .in, is the time sensitivity coefficient; e is the Euler number.

[0057] Furthermore, the greater the overall impact of a traffic incident, or the more urgent the time required for management resources to be available, the higher the priority of incident handling. Based on this, this application needs to construct a comprehensive priority evaluation function for management demand nodes: This allows us to obtain the comprehensive handling priority for each management requirement node. ;in, The overall impact of traffic incidents.

[0058] S104. Based on the comprehensive handling priority of each management demand node, perform collaborative scheduling calculations for road administration resources to minimize the overall traffic impact and the economic cost of resource scheduling, outputting a road administration resource allocation scheme for multi-event concurrent scenarios. That is, after obtaining the comprehensive handling priority of each management demand node... Then, by combining the distribution of road administration resources and the dynamic road network traffic status, the road administration forces are optimized and dispatched to achieve efficient handling in scenarios with multiple concurrent events.

[0059] It should be noted that the set of nodes based on management requirements is as follows: Management demand nodes The required number of road administration resource units is The set of road administration resource nodes is as follows: Road administration resource nodes Can provide Each road administration resource unit; then define resource allocation decision variables: This means that the road administration resource nodes are... To manage demand nodes The number of resource units allocated; then, based on the above data, a road administration resource and demand node scheduling model is constructed, which is a model of the scheduling relationship between road administration resources and demand nodes.

[0060] As a feasible implementation method, resource nodes To demand node Shortest path travel time When a demand node receives resource support from multiple resource nodes, the collaborative event handling start time is defined as follows: .

[0061] Specifically, the overall impact of traffic incidents on the road network operation needs to be considered first. Longer response times for high-priority events can lead to a greater risk of traffic congestion spreading. Therefore, a traffic impact cost function is constructed by coupling the overall priority of demand nodes with resource arrival times: The traffic impact cost function is obtained. .in, Prioritize comprehensive handling; Let be the start time of the coordinated event handling among multiple road administration resource nodes that first reaches the management demand node; n is a constant; and i is the label of the management demand node. Furthermore, this traffic impact cost function indicates that the higher the priority of a traffic event, the greater its overall impact on the traffic system if the response time is longer.

[0062] Furthermore, the economic costs incurred by resource scheduling also need to be considered. Moving road administration resources from road administration resource nodes to management demand nodes incurs vehicle operation costs and personnel costs, which are related to the length of the resource scheduling path. Therefore, the economic cost function for resource scheduling is defined as follows: The economic cost function of resource scheduling is obtained. .in, Represents road administration resource nodes The unit scheduling cost of a single road administration resource unit; Represents road administration resource nodes To the current management requirement node The shortest path length; For road administration resource nodes To the current management needs node The number of resource units allocated; j is the label of the road administration resource node.

[0063] As a feasible implementation method, through This is to fulfill the resource capacity constraint on the number of resource units allocated, that is: the number of resources allocated by each road administration resource node cannot exceed its own capacity. For road administration resource nodes To the current management needs node The number of resource units allocated; n is a constant; Here, represents the resource capacity of the road administration resource node itself; 'i' represents the label of the management demand node; and 'j' represents the label of the road administration resource node. Further processing is also required. This is to satisfy the constraint on the quantity of allocated resource units, namely: each management demand node requires a certain amount of road administration resources; where m is a constant. For the current traffic incident The required number of road administration resource units. Finally, through... This is to fulfill the non-negative integer constraint on the number of units of allocated resources.

[0064] Furthermore, according to The comprehensive optimization objective function for minimizing the overall impact of traffic and minimizing the economic cost of resource scheduling is obtained. .in, The weight coefficients are 0. In other words, after obtaining the shortest path travel time between road administration resource nodes and management demand nodes, as well as the comprehensive priority of each demand node, it is necessary to coordinate the scheduling of road administration resources in a multi-event concurrent scenario. To balance traffic operation efficiency and resource scheduling costs, this application constructs a comprehensive optimization objective function from two aspects: minimizing the overall traffic impact and minimizing the economic cost of resource scheduling. Finally, the above comprehensive optimization objective function is used... This can reduce the overall impact on traffic while decreasing resource scheduling costs, thereby enabling the coordinated and optimized allocation of road administration resources in scenarios with multiple concurrent events.

[0065] Furthermore, based on the comprehensive optimization objective function, the optimal allocation of road administration resources is calculated for the scheduling model of road administration resources and demand nodes in the expressway network, resulting in a road administration resource allocation scheme for multi-event concurrent scenarios. The road administration resource allocation scheme includes: road administration resource number, corresponding demand node, recommended travel route, and estimated arrival time.

[0066] As a feasible implementation method, an intelligent optimization algorithm is used to solve the above scheduling model to obtain the optimal resource allocation scheme. This optimization algorithm includes, but is not limited to, improved genetic algorithms, ant colony optimization algorithms, and particle swarm optimization algorithms. Then, the resource allocation matrix is ​​obtained through iterative calculation. This determines the amount of resources and corresponding paths allocated from road administration resource nodes to each demand node. Finally, based on the obtained resource allocation matrix... This generates a road administration resource allocation plan. The road administration resource allocation plan includes, but is not limited to: road administration resource number, corresponding demand node, recommended travel route, and estimated arrival time.

[0067] As a feasible implementation method, the particle swarm optimization algorithm is used to solve the above scheduling model, which can be summarized as follows: Step 1: Encoding and initialization: Encode each particle as a resource allocation scheme. Assume there are K road administration resource nodes and M demand nodes, based on the demand node number... Step 1: Randomly generate N particles (e.g., N=50) and randomly initialize the position and velocity of each particle. Step 2: Fitness calculation: Based on the particle position, resolve the demand node corresponding to each resource, calculate the shortest path travel time for each resource using road network data, and then calculate the fitness value according to the following comprehensive optimization objective function. Step 3: Individual and global optimal update: For each particle, if the current fitness is better than its historical optimal position, update the historical optimal position; then find the position with the best fitness among all particles as the global optimal position. Step 4: Velocity and position update: First update the velocity and position according to the standard particle swarm optimization formula. Step 5: Iteration termination: Continue to repeat steps 2-4 until the maximum number of iterations is reached (e.g., 200 generations) or the fitness no longer improves for 20 consecutive generations, and then output the resource allocation scheme corresponding to the global optimal particle; for example: Resource number: R1, demand node: B, recommended path: G15→G22, estimated arrival time: 12min; Resource number: R2, demand node: B, recommended path: G18→G22, estimated arrival time: 15min.

[0068] S105. Perform dynamic rolling optimization calculations on the road administration resource allocation scheme based on the occurrence of new traffic events and / or changes in road network traffic conditions to obtain the optimal road administration resource allocation scheme to adapt to the current high-speed traffic conditions.

[0069] It should be noted that after obtaining the initial road administration resource allocation plan, due to the significant dynamic changes in highway traffic conditions, the original scheduling plan needs to be dynamically updated when new traffic events occur or the road network traffic status changes. Therefore, this application introduces a rolling optimization mechanism to periodically update the road administration resource allocation plan.

[0070] Specifically, based on the data update time under the occurrence of new traffic events and / or changes in road network traffic status, the data sets of management demand nodes and road administration resource nodes are re-acquired and processed to obtain new management demand nodes and new road administration resource nodes.

[0071] In one embodiment, firstly, the highway traffic operation status is re-collected at fixed time intervals to update traffic event information and road network traffic status data. Let the... The next update time is: ;in: At the initial moment of the system, This refers to the number of updates. Secondly, at each update interval, the set of management requirement nodes is re-acquired. ,in, express The number of nodes required at any given time. Simultaneously update the road administration resource node set: ,in, Indicates time The number of schedulable resources is determined. Finally, the updated new management requirement nodes and new road administration resource nodes are obtained.

[0072] Furthermore, update the number of remaining schedulable road administration resource units in the road administration resource node. That is, update the number of remaining schedulable resource units in the road administration resource node: Represents road administration resource nodes At any moment The number of remaining resource units available.

[0073] Furthermore, based on the new management demand nodes, new road administration resource nodes, and the updated number of schedulable road administration resource units, the comprehensive processing priority of each management demand node is updated and calculated. On this basis, a new road administration resource and demand node scheduling model is reconstructed: .

[0074] Furthermore, based on the updated comprehensive handling priority, the road administration resource nodes undergo collaborative dynamic rolling update calculations for road administration resources. Then, based on the newly reconstructed road administration resource and demand node scheduling model using rolling optimization, the optimal road administration resource allocation scheme is solved and output. In other words, the original scheduling scheme (road administration resource allocation scheme) is dynamically adjusted through a rolling optimization mechanism, enabling the system to continuously generate optimal road administration resource allocation schemes that adapt to the current traffic conditions, thereby improving emergency management efficiency in multi-event concurrent scenarios.

[0075] Furthermore, the road administration resource allocation plan is sent to the corresponding mobile terminal, and the road administration resource allocation plan is executed accordingly.

[0076] Furthermore, if the management demand node and the road administration resource node are detected to have feedback update status, the road administration resource allocation scheme is dynamically and continuously optimized based on the occurrence of new traffic events and / or changes in road network traffic status to obtain the optimal road administration resource allocation scheme.

[0077] Furthermore, the resource scheduling information in the optimal road administration resource allocation plan is executed. This resource scheduling information includes at least: task number, target demand node location, recommended travel route, and estimated arrival time.

[0078] As a feasible implementation method, to ensure the effective execution of the dispatching plan, the obtained road administration resource allocation plan is sent to the terminals of road administration patrol vehicles, clearing vehicles, or emergency response personnel. The dispatching information includes the task number, the location of the target demand node, the recommended travel route, and the estimated arrival time. Then, during the execution of road administration resource tasks, the resource location and task execution status are acquired in real time through vehicle-mounted positioning devices or mobile terminals, and the status of resource nodes and demand nodes are updated. When a demand node is completed or the resource status changes, the updated information is fed back to the dispatching system, triggering the dynamic rolling optimization calculation in step S105 above, thereby obtaining the optimal road administration resource allocation plan. The resource dispatching information in the optimal road administration resource allocation plan is then executed, ultimately achieving dynamic coordination between road administration force dispatching and on-site handling.

[0079] As a feasible implementation method, this application realizes the intelligent allocation of road administration resources in complex traffic operation environments by constructing an integrated technical process that includes traffic event identification, road network dynamic state modeling, management demand priority calculation, and resource scheduling optimization. This method comprehensively considers multiple factors such as event severity, hazard level, traffic congestion, and the proportion of affected lanes to establish a multi-factor coupled demand priority evaluation model. This enables scheduling decisions to more accurately reflect the actual impact of traffic events on road network operation, thereby improving the rationality of resource allocation.

[0080] Meanwhile, this application incorporates real-time traffic operation status into the scheduling optimization process by constructing a dynamic road network status, enabling the route planning and task allocation of road administration resources to adapt to changes in traffic conditions, effectively shortening emergency response time and reducing the risk of traffic congestion spreading. Furthermore, this application introduces a dynamic rolling optimization mechanism and a scheduling feedback mechanism, allowing the system to update the scheduling plan in real time based on newly generated traffic events and resource execution status, thereby forming a closed-loop collaborative management model. Through the technical solutions described above, the efficiency of emergency response and the level of road network operation management under conditions of multiple concurrent events on highways can be significantly improved.

[0081] In addition, this application also provides a road administration and dispatching device for multi-event concurrent scenarios on highways, such as... Figure 2 As shown, the road administration and dispatching equipment 200 for multi-event concurrent scenarios on highways specifically includes: At least one processor 201; and a memory 202 communicatively connected to the at least one processor 201; wherein the memory 202 stores instructions executable by the at least one processor 201 to enable the at least one processor 201 to execute: Based on the collected high-speed traffic operation data and the dispatchable road administration resource units, management demand nodes and road administration resource nodes are constructed respectively. The management demand nodes and road administration resource nodes are mapped to a weighted directed graph of the road network constructed based on the set of road network nodes and the set of road connection relationships, and the dynamic road network state is obtained. By using the comprehensive impact of traffic events corresponding to management demand nodes and the time urgency indicators corresponding to road administration resource nodes, the priority of management demand for traffic events in a multi-event concurrent scenario in a dynamic road network is calculated, and the comprehensive handling priority of each management demand node is obtained. Based on the comprehensive handling priority of each management demand node, the road administration resource nodes are subjected to collaborative scheduling calculations to minimize the overall traffic impact and the economic cost of resource scheduling, and the road administration resource allocation scheme for multi-event concurrent scenarios is output. The road administration resource allocation scheme is dynamically and continuously optimized based on the occurrence of new traffic events and / or changes in road network traffic conditions to obtain the optimal road administration resource allocation scheme to adapt to the current high-speed traffic conditions.

[0082] This application's embodiments achieve unified and collaborative management and allocation of various traffic management needs generated during highway operation. It comprehensively considers factors such as event type, risk level, traffic congestion degree, and road network traffic status, prioritizing management need nodes. Based on this, a collaborative scheduling model is constructed between road administration resources and need nodes, enabling optimized allocation and route planning of road administration patrol vehicles, clearing equipment, and emergency response forces. Furthermore, it can quickly generate reasonable road administration force dispatch plans in the event of multiple concurrent events, improving emergency response efficiency, shortening event handling time, and reducing the impact of traffic congestion, thereby enhancing the intelligence and collaboration level of highway operation management.

[0083] The various embodiments in this application are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the device embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0084] The foregoing has described specific embodiments of this application. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in a different order than that shown in the embodiments and may still achieve the desired results. Furthermore, the processes depicted in the drawings do not necessarily require the specific or sequential order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0085] The above description is merely an embodiment of this application and is not intended to limit this application. For those skilled in the art, various modifications and variations can be made to the embodiments of this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principle of the embodiments of this application should be included within the scope of this specification.

Claims

1. A road administration and dispatching method for multi-event concurrent scenarios on highways, characterized in that, The method includes: Based on the collected high-speed traffic operation data and the dispatchable road administration resource units, management demand nodes and road administration resource nodes are constructed respectively. The management demand nodes and the road administration resource nodes are mapped to a weighted directed graph of the road network constructed based on the set of road network nodes and the set of road connection relationships, and the dynamic road network state is obtained. By using the comprehensive impact of traffic events corresponding to the management demand nodes and the time urgency index corresponding to the road administration resource nodes, the priority of management demand for traffic events in the multi-event concurrent scenario in the dynamic road network state is calculated to obtain the comprehensive handling priority of each management demand node. Based on the comprehensive handling priority of each management demand node, the road administration resource nodes are subjected to collaborative scheduling calculations to minimize the overall traffic impact and the economic cost of resource scheduling, and a road administration resource allocation scheme for multi-event concurrent scenarios is output. The road administration resource allocation scheme is dynamically and continuously optimized based on the occurrence of new traffic events and / or changes in road network traffic conditions to obtain the optimal road administration resource allocation scheme to adapt to the current high-speed traffic conditions.

2. The road administration and scheduling method for multi-event concurrent scenarios on highways according to claim 1, characterized in that, Based on the collected high-speed traffic operation data and the dispatchable road administration resource units, management demand nodes and road administration resource nodes are constructed, specifically including: The highway traffic operation data is obtained through the highway operation monitoring system; wherein the highway traffic operation data includes at least: video surveillance data, traffic detector data, ETC gantry data, road administration patrol report information, and traffic accident alarm information; The highway traffic operation data is subjected to anomaly detection and event identification based on traffic state change characteristics to obtain traffic events occurring on the highway; wherein, the anomaly detection includes at least: traffic accidents, vehicle malfunctions, road debris, road construction, and abnormal congestion; Based on the identified traffic incidents, corresponding management demand nodes are generated; wherein, the management demand nodes are used to indicate the location of the incidents that require handling by road administration forces; Based on the road administration resource unit and the corresponding number of schedulable resources, the road administration resource node is generated; wherein, the road administration resource node is used to supply resources to the management demand node.

3. The road administration and scheduling method for multi-event concurrent scenarios on highways according to claim 1, characterized in that, The management demand nodes and the road administration resource nodes are mapped to a weighted directed graph of the road network constructed based on the set of road network nodes and the set of road connection relationships, and the dynamic road network state is obtained, specifically including: Based on the set of road network nodes and the set of road connection relationships, the highway network is abstracted into a weighted directed graph of the road network; A dynamic road network weight matrix is ​​constructed based on the basic travel time of road segments between road network nodes; The management demand node and the road administration resource node are both mapped to the road network node set, and the corresponding positions of the management demand node and the road administration resource node in the traffic network are obtained. Using the dynamic road network weight matrix, the shortest travel path and estimated arrival time between the management demand node and the road administration resource node are calculated based on the corresponding location, and the dynamic road network status is recorded and obtained.

4. The road administration and scheduling method for multi-event concurrent scenarios on highways according to claim 1, characterized in that, Before prioritizing the management needs of traffic events in a multi-event concurrent scenario within the dynamic road network state by using the comprehensive impact of traffic events corresponding to the management demand nodes and the time urgency index corresponding to the road administration resource nodes, the method further includes: Extract key factors affecting the urgency of traffic incident handling from each of the management requirement nodes; wherein, the key factors include: incident severity, danger level, traffic congestion index, proportion of affected lanes, and scale of affected traffic flow; The key factors are standardized to obtain standardized key factor indicators. By using the subjective and objective weights in the AHP entropy weight combination weighting, the combined weights of the standardized key factor indicators are calculated to obtain the combined weights of the key factors. The combined weights of the key factors and the standardized key factor indicators are combined to calculate the overall impact of the traffic event.

5. The road administration and scheduling method for multi-event concurrent scenarios on highways according to claim 4, characterized in that, By using the comprehensive impact of traffic events corresponding to the management demand nodes and the time urgency index corresponding to the road administration resource nodes, the priority of management demands for traffic events in the multi-event concurrent scenario in the dynamic road network state is calculated to obtain the comprehensive handling priority of each management demand node, specifically including: according to The distance between the centers of each cluster based on the comprehensive impact of traffic events is obtained. ;in, For the current traffic incident The overall impact of traffic incidents; As the center of each cluster; according to To obtain the current traffic incident event level Where K represents the K clusters into which the historical comprehensive impact of historical traffic events is divided using K-means clustering; according to To obtain the current traffic event Number of road administration resource units required ;in, and All are mathematical constants; according to , obtain based on event level The anticipated collaborative handling of the current traffic incident Number of candidate resource nodes required ;in, The minimum number of road administration resource nodes required; This is the adjustment coefficient; This is the floor function; according to , to obtain based on the current traffic event Event processing coordination arrival time of the corresponding management requirement node ;in, s represents the arrival time sorting item from any road administration resource node to the management demand node in the dynamic road network state; s represents the number of shortest paths from any road administration resource node to the management demand node. according to The time urgency index corresponding to the road administration resource node in the event collaborative processing is obtained. ;in, is the time sensitivity coefficient; e is the Euler number; according to The overall processing priority of each management requirement node is obtained. ;in, The overall impact of the traffic incident is denoted as .

6. The road administration and scheduling method for multi-event concurrent scenarios on highways according to claim 1, characterized in that, Based on the comprehensive handling priority of each management demand node, a collaborative scheduling calculation of road administration resources is performed on the road administration resource nodes to minimize the overall traffic impact and the economic cost of resource scheduling. The result is an output road administration resource allocation scheme for multi-event concurrent scenarios, specifically including: according to The traffic impact cost function is obtained. ;in, The overall processing priority is set as follows; This represents the start time of the collaborative event handling among multiple road administration resource nodes that first reach the management demand node; n is a constant; i is the label of the management demand node; according to The economic cost function of resource scheduling is obtained. ;in, Represents road administration resource nodes The unit scheduling cost of a single road administration resource unit; Represents road administration resource nodes To the current management requirement node The shortest path length; For road administration resource nodes To the current management needs node The number of resource units allocated; j is the number of the road administration resource node; according to The comprehensive optimization objective function for minimizing the overall impact of traffic and minimizing the economic cost of resource scheduling is obtained. ;in, Let be the weighting coefficient, and satisfy 0 ; Based on the comprehensive optimization objective function, the optimal allocation of road administration resources is calculated for the scheduling model of road administration resources and demand nodes in the expressway network, resulting in the road administration resource allocation scheme for multi-event concurrent scenarios; wherein, the road administration resource allocation scheme includes: road administration resource number, corresponding demand node, recommended travel route and estimated arrival time.

7. The road administration and scheduling method for multi-event concurrent scenarios on highways according to claim 6, characterized in that, pass This is to fulfill the resource capacity constraint on the number of allocated resource units; wherein, For road administration resource nodes To the current management needs node The number of resource units allocated; n is a constant; is the resource capacity of the road administration resource node itself; i is the label of the management demand node; j is the label of the road administration resource node; pass This is to ensure that the requirement for the number of allocated resource units is met; where m is a constant. For the current traffic incident The required number of road administration resource units; pass This is to fulfill the non-negative integer constraint on the number of allocated resource units.

8. The road administration and scheduling method for multi-event concurrent scenarios on highways according to claim 1, characterized in that, The road administration resource allocation scheme is dynamically and continuously optimized based on the occurrence of new traffic events and / or changes in road network traffic conditions to obtain the optimal road administration resource allocation scheme to adapt to the current highway traffic conditions, specifically including: Based on the data update time under the occurrence of new traffic events and / or changes in road network traffic status, the data sets of management demand nodes and road administration resource nodes are re-acquired and processed to obtain new management demand nodes and new road administration resource nodes. Update the number of remaining schedulable road administration resource units in the road administration resource node; Based on the new management demand nodes, the new road administration resource nodes, and the updated number of schedulable road administration resource units, the comprehensive processing priority of each management demand node is updated and calculated. Based on the updated comprehensive handling priority, the road administration resource nodes are dynamically updated in a coordinated manner. Based on the new road administration resource and demand node scheduling model reconstructed by the rolling optimization, the optimal road administration resource allocation scheme is solved and output.

9. The road administration and scheduling method for multi-event concurrent scenarios on highways according to claim 1, characterized in that, After performing dynamic rolling optimization calculations on the road administration resource allocation scheme based on the occurrence of new traffic events and / or changes in road network traffic conditions, the method further includes: The road administration resource allocation plan is sent to the corresponding mobile terminal and executed accordingly; If a feedback update status is detected in the management demand node and the road administration resource node, the road administration resource allocation scheme is dynamically and continuously optimized based on the occurrence of new traffic events and / or changes in road network traffic status to obtain the optimal road administration resource allocation scheme. The resource scheduling information in the optimal road administration resource allocation scheme is executed; wherein, the resource scheduling information includes at least: task number, target demand node location, recommended travel route, and estimated arrival time.

10. A road administration and dispatching device for multi-event concurrent scenarios on highways, characterized in that, The device includes: At least one processor; and, A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, enabling the at least one processor to execute the road administration and scheduling method for multi-event concurrent scenarios on highways according to any one of claims 1-9.

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