Road network expected operation toughness-based road emergency resource allocation method in ice and snow weather

Abstract

The invention relates to an ice and snow weather road emergency resource allocation method based on road network expected operation toughness, and belongs to the technical field of traffic transportation emergency management. The method comprises the steps of establishing a road network operation toughness quantification model based on a road network operation performance function; a static configuration optimization model is established, and optimal configuration of road emergency resources such as various ice and snow removing equipment, obstacle removing rescue vehicles and unmanned aerial vehicles at road network nodes is determined under constraint conditions by taking a weighted target of minimizing expected total cost and maximizing road network toughness coverage as well as considering a toughness loss area and a peak impact depth at the same time. According to the method, the configuration of road emergency resources such as various ice and snow removing equipment, obstacle removing rescue vehicles and unmanned aerial vehicles in the road network can be optimized, so that the emergency resources can cope with the road network toughness reduction in various scenes in ice and snow weather at a certain cost.

Description

Technical Field

[0001] This invention belongs to the field of emergency management technology for transportation, and relates to a method for allocating emergency road resources in icy and snowy weather based on the expected operational resilience of the road network. Background Technology

[0002] Resilience refers to a system's ability to maintain its basic functions and quickly recover to a stable state after being subjected to external disturbances. In the field of traffic engineering, road network operational resilience is mainly reflected in the road network's ability to maintain stable traffic flow and quickly restore traffic performance after a sudden snow and ice disaster.

[0003] Frequent low-temperature rain, snow, and freezing disasters in winter pose a severe challenge to the coordinated efforts of multiple departments, including transportation management, road administration, and traffic police. However, existing emergency management models for snow and ice weather still have the following significant problems:

[0004] (1) When encountering severe snow and ice disasters, relevant departments often adopt a "one-size-fits-all" approach to road closures or simply use police cars to drive through the roads to control speed. Although this passive management model avoids accidents to some extent, it leads to a rapid decline in the efficiency of the road network. The system lacks the "resilience" to maintain its performance under disturbances, which can easily cause large-scale and long-term traffic paralysis.

[0005] (2) Currently, the road administration department mainly relies on historical experience to deploy emergency resources such as ice-breaking and snow removal equipment, obstacle clearing and rescue vehicles, and drones at fixed locations. Due to the lack of precise quantitative analysis, the physical locations and quantities of resources deployed are often not scientific enough and are difficult to adapt to the randomness and variability of road network disasters.

[0006] (3) Due to the poor matching between resource reserve points and core disaster-stricken road sections, the commuting time for emergency equipment to reach the site is long. During the "disturbance-recovery" process, the long response time directly restricts the recovery speed of the road network performance, making the actual operational resilience of the road network far lower than expected.

[0007] In summary, existing measures for responding to snow and ice weather tend to be reactive and based on subjective decisions, lacking a holistic plan from the systemic perspective of "resilience enhancement." How to establish a scientific quantitative model of road network operational resilience and optimize the allocation of diverse emergency resources based on resilience expectations to achieve rapid recovery of road network performance is a critical issue that urgently needs to be addressed in the field of road traffic emergency management. Summary of the Invention

[0008] In view of this, the purpose of this invention is to provide a method for allocating road emergency resources in icy and snowy weather based on the expected operational resilience of the road network, to establish a quantitative model of road network operational resilience, and to optimize the allocation of various road emergency resources such as de-icing and snow removal equipment, obstacle clearing and rescue vehicles and drones in the road network based on the expected resilience value.

[0009] To achieve the above objectives, the present invention provides the following technical solution: A method for allocating emergency road resources in icy and snowy weather based on the expected operational resilience of the road network, specifically including: A quantitative model of road network operational resilience is established based on the road network operational performance function. A static configuration optimization model is established to minimize the weighted objective of minimizing the expected total cost and maximizing the road network resilience coverage. The model also considers the resilience loss area and peak impact depth to determine the optimal configuration of various road emergency resources such as snow and ice removal equipment, obstacle clearing and rescue vehicles, and drones at road network nodes under constraints. The constraints include total budget constraints, logical constraints on equipment configuration, coverage constraints, mandatory coverage constraints for critical road sections, coordination constraints on equipment types, geographical distribution constraints, and upper and lower bounds on the number of equipment.

[0010] Furthermore, the establishment of the road network operational resilience quantification model specifically includes: a road network operational performance function:

[0011] in, Indicates time The overall performance of the road network operation For road section The importance weight of resilience, For road section Traffic capacity, Let be the set of directed edges in the road network graph; For the initial operating performance of the road network; To facilitate cross-scenario comparisons, normalized performance is defined as follows: ,

[0012] Build operational resilience metrics:

[0013] in, To enhance the resilience of the road network operation, This is the time required for the road network to recover its operational performance. Road segment performance degradation model:

[0014] in, For a moment Section The performance degradation rate, For the first Such equipment in road sections Recovery efficiency This represents the maximum performance degradation rate. For road section The moment when performance begins to degrade To reach the moment of maximum degradation, and This is a parameter representing the degradation rate. For road section The moment when performance begins to recover; therefore, yes and The function, i.e. ;also, .

[0015] Furthermore, the objective function of the static configuration optimization model is:

[0016] in, The objective function is... For road network map, For a set of nodes, It is a directed edge set; For device type set: k =1 indicates a snowplow. k =2 indicates a rescue vehicle. k =3 indicates a drone; For the first Equipment procurement costs; For the first Annual maintenance cost of this type of equipment; Indicates at node Configuration number The number of such devices; Indicates the first Can such equipment effectively cover road sections? 0 indicates no, and 1 indicates yes; Represents a node Whether to set it as a device parking point, 0 indicates no, 1 indicates yes; , , , These are weighting coefficients used to balance the importance of the four resilience indicators; Area of ​​toughness loss; peak impact depth ; This represents the average recovery rate. The moment when system performance reaches its lowest point. ; Road segment resilience contribution:

[0017] in, For road section The resilience contribution, The duration of snowy or icy weather.

[0018] Furthermore, the overall budget constraint is:

[0019] in, For the total budget.

[0020] Furthermore, the device configuration logic constraints are as follows:

[0021] in, Large numbers are used for logical constraints.

[0022] Furthermore, the coverage capability constraint is as follows:

[0023] in, For road section A set of driving directions; For nodes to section of road Distance from the midpoint; For the first Service radius of similar equipment; This is an indicator function; it is 1 if the condition is met, and 0 otherwise.

[0024] Furthermore, the mandatory coverage constraint for the key road segment is as follows:

[0025] in, This is a collection of key road sections. The threshold value is used.

[0026] Furthermore, the device type coordination constraint is as follows:

[0027] This constraint ensures that the configuration of drones does not exceed that of ground equipment, thus guaranteeing collaborative operation capabilities.

[0028] Furthermore, the geographical distribution constraints are as follows:

[0029] in, For nodes To the node Distance (km); The minimum coverage radius of the equipment parking points determined by the management department can be adjusted according to actual conditions.

[0030] Furthermore, the upper and lower bounds of the number of devices are:

[0031]

[0032] in, For the first Maximum number of devices of this type.

[0033] The beneficial effects of this invention are as follows: 1) This invention takes "resilience" as the core optimization objective and establishes a quantitative model for road network operational resilience. Unlike traditional methods that only focus on local traffic capacity, this method can quantitatively assess the road network's ability to maintain function and recover after snow and ice disturbances. Through optimized configuration, it can effectively reduce the area of ​​road network performance loss, decrease the peak impact depth, and significantly improve the average recovery speed, ensuring the operational stability of the road network under extreme weather conditions.

[0034] 2) This invention solves the problem of traditional configuration models relying on experience, leading to unscientific configuration locations and quantities. Specifically, it comprehensively considers equipment procurement and maintenance costs, resilience coverage benefits, loss area penalties, and recovery speed rewards in the model, achieving a balance between economic costs and protection effectiveness. By setting service radius and coverage capacity constraints, it ensures that various emergency equipment can reach the disaster-stricken road sections within a specified time, shortening response time.

[0035] 3) This invention not only configures a single snow removal equipment, but also takes into account diverse resources such as obstacle clearing and rescue vehicles and drones. Specifically, it incorporates equipment type coordination constraints to ensure that the number of drones matches the number of ground equipment, fully leveraging the synergistic advantages of drones in monitoring and patrols with ground equipment in actual operations. By identifying key road sections (RC indicators) and setting mandatory coverage constraints, priority restoration of the traffic backbone network is guaranteed.

[0036] 4) Optimize infrastructure layout and reduce long-term operating costs through scientific site selection. Specifically, the model determines the optimal node configuration and parking location, which, combined with geographical constraints (such as a 15km radius requirement in urban areas), constructs a more rational and efficient emergency support network. While meeting total budget constraints, precise calculations of the number of various types of equipment avoid excessive redundancy or insufficient allocation of resources, thereby reducing the management and maintenance costs throughout the entire lifecycle.

[0037] 5) By defining normalized performance indicators, this invention enables the configuration method to be applied to road network scenarios of different scales and traffic characteristics, and has strong technical universality and promotion value.

[0038] Other advantages, objectives, and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination, or may be learned from practice of the invention. The objectives and other advantages of the invention can be realized and obtained through the following description. Attached Figure Description

[0039] To make the objectives, technical solutions, and advantages of the present invention clearer, the preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings, wherein: Figure 1 This is a flowchart of a road emergency resource allocation method based on the expected operational resilience of the road network in icy and snowy weather. Detailed Implementation

[0040] The following specific examples illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of the present invention. Unless otherwise specified, the following embodiments and features can be combined with each other.

[0041] Please see Figure 1 This invention provides a method for allocating road emergency resources in icy and snowy weather based on the expected operational resilience of the road network. It establishes a quantitative model of road network operational resilience and optimizes the allocation of various road emergency resources such as snow and ice removal equipment, obstacle clearing and rescue vehicles, and drones on the road network based on the expected resilience value.

[0042] 1. Establish a quantitative model for road network operational resilience. In the context of icy and snowy weather, road network operational resilience is defined as the ability of a road network to maintain and recover its functions when subjected to icy and snowy disturbances. This invention employs a resilience quantification method based on road network operational performance losses.

[0043] Road network operation performance functions:

[0044] in, Indicates time The overall performance of the road network operation This represents the initial operational performance of the road network. For road section The importance weight of resilience, For road section Traffic capacity. Let be the set of directed edges in the road network graph.

[0045] To facilitate cross-scenario comparisons, normalized performance is defined as follows: ,

[0046] Build operational resilience metrics:

[0047] in, This is the time required for the road network to recover its operational performance. Area of ​​toughness loss; peak impact depth ; This represents the average recovery rate. The moment when system performance reaches its lowest point. .

[0048] Road segment performance degradation model:

[0049] in, For the first Such equipment in road sections Recovery efficiency This represents the maximum performance degradation rate. For road section The moment when performance begins to degrade To reach the moment of maximum degradation, and This is the degradation rate parameter. This is the moment when performance begins to recover. Therefore, yes and The function, i.e. .

[0050] Road segment resilience contribution:

[0051] in, The duration of snowy or icy weather.

[0052] 2. Establish a static configuration optimization model The model determines the optimal configuration of various snow and ice removal equipment, obstacle clearing and rescue vehicles, and drones at road network nodes.

[0053] (1) Decision variables Decision variables include , indicating at node Configuration number The number of such devices; Indicates the first Can such equipment effectively cover road sections? 0 indicates no, and 1 indicates yes; Represents a node Whether to set it as a device parking point, 0 indicates no, 1 indicates yes. , indicating road segment direction Required number Number of devices of this type.

[0054] (2) Objective function The weighted objective is to minimize the expected total cost and maximize the road network resilience coverage, while also considering the area of ​​resilience loss. and peak impact depth :

[0055] in, For road network map, For a set of nodes, It is a directed edge set; For device type set: k =1 indicates a snowplow. k =2 indicates a rescue vehicle. k =3 indicates a drone; For the first Equipment procurement costs; For the first Annual maintenance cost of this type of equipment; , , , These are weighting coefficients used to balance the importance of different resilience indicators.

[0056] (3) Constraints 1) Overall Budget Constraint:

[0057] in, For the total budget.

[0058] 2) Device configuration logic constraints:

[0059] in, Large numbers are used for logical constraints.

[0060] 3) Coverage constraints:

[0061] in, For road section A set of driving directions; For nodes to section of road Distance from the midpoint; For the first Service radius of similar equipment; This is an indicator function; it is 1 if the condition is met, and 0 otherwise.

[0062] 4) Mandatory coverage constraints for critical road sections:

[0063] in, This is a collection of key road sections. The threshold value is used.

[0064] 5) Equipment type coordination constraints:

[0065] This constraint ensures that the configuration of drones does not exceed that of ground equipment, thus guaranteeing collaborative operation capabilities.

[0066] 6) Geographical distribution constraints:

[0067] in, For nodes To the node Distance (km). Ensure there is at least one equipment parking point within 15km of the urban node; 15km can be adjusted according to actual conditions.

[0068] 7) Upper and lower limits of equipment quantity:

[0069]

[0070] in, For the first Maximum number of devices of this type.

[0071] This invention can optimize the allocation of various road emergency resources such as snow and ice removal equipment, obstacle clearing and rescue vehicles, and drones on the road network, enabling emergency resources to cope with the decline in road network resilience under various snow and ice weather scenarios at a certain cost.

[0072] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for allocating emergency road resources in icy and snowy weather based on the expected operational resilience of the road network, characterized in that, The method specifically includes: A quantitative model of road network operational resilience is established based on the road network operational performance function. A static configuration optimization model is established to minimize the weighted objective of the total expected cost and maximize the road network resilience coverage, while considering the resilience loss area and peak impact depth, to determine the optimal configuration of road emergency resources at road network nodes under constraints. The constraints include total budget constraints, logical constraints on equipment configuration, coverage constraints, mandatory coverage constraints for critical road sections, coordination constraints on equipment types, geographical distribution constraints, and upper and lower bounds on the number of equipment.

2. The method for allocating emergency road resources in icy and snowy weather according to claim 1, characterized in that, The establishment of the road network operation resilience quantification model specifically includes: road network operation performance function: in, Indicates time The overall performance of the road network operation For road section The importance weight of resilience, For road section Traffic capacity, Let be the set of directed edges in the road network graph; For the initial operating performance of the road network; Define normalized performance: ， Build operational resilience metrics: in, To enhance the resilience of the road network operation, This is the time required for the road network to recover its operational performance. Road segment performance degradation model: in, For a moment Section The performance degradation rate, For the first Such equipment in road sections Recovery efficiency This represents the maximum performance degradation rate. For road section The moment when performance begins to degrade To reach the moment of maximum degradation, and This is a parameter representing the degradation rate. For road section The moment when performance begins to recover; therefore, yes and The function, i.e. ;also, .

3. The method for allocating emergency road resources in icy and snowy weather according to claim 1, characterized in that, The objective function of the static configuration optimization model is: in, The objective function is... For road network map, For a set of nodes, It is a directed edge set; For device type set; For the first Equipment procurement costs; For the first Annual maintenance cost of this type of equipment; Indicates at node Configuration number The number of such devices; Indicates the first Can such equipment effectively cover road sections? 0 indicates no, and 1 indicates yes; Represents a node Whether to set it as a device parking point, 0 indicates no, 1 indicates yes; , , , These are the weighting coefficients; Area of ​​toughness loss; peak impact depth ; This represents the average recovery rate. The moment when system performance reaches its lowest point. ; Road segment resilience contribution: in, For road section The resilience contribution, The duration of snowy or icy weather.

4. The method for allocating emergency road resources in icy and snowy weather according to claim 3, characterized in that, The overall budget constraint is: in, For the total budget.

5. The method for allocating emergency road resources in icy and snowy weather according to claim 3, characterized in that, The device configuration logic constraints are as follows: in, Large numbers are used for logical constraints.

6. The method for allocating emergency road resources in icy and snowy weather according to claim 3, characterized in that, The coverage capability constraint is: in, For road section A set of driving directions; For nodes to section of road Distance from the midpoint; For the first Service radius of similar equipment; This is an indicator function; it is 1 if the condition is met, and 0 otherwise.

7. The method for allocating emergency road resources in icy and snowy weather according to claim 3, characterized in that, The mandatory coverage constraint for the critical road segment is as follows: in, This is a collection of key road sections. The threshold value is used.

8. The method for allocating emergency road resources in icy and snowy weather according to claim 3, characterized in that, The device type coordination constraint is as follows: This constraint ensures that the configuration of drones does not exceed that of ground equipment, thus guaranteeing collaborative operation capabilities.

9. The method for allocating emergency road resources in icy and snowy weather according to claim 3, characterized in that, The geographical distribution constraints are: in, For nodes To the node The distance; The minimum coverage radius for equipment parking points determined by the management department.

10. The method for allocating emergency road resources in icy and snowy weather according to claim 3, characterized in that, The upper and lower bounds of the number of devices are: in, For the first Maximum number of devices of this type.

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