Road deicing time decision method based on road network operation resilience expected loss

Abstract

The present application relates to a kind of road deicing and snowing opportunity decision method based on road network operation resilience expected loss, belong to intelligent transportation and emergency management technical field.The method includes: based on road network operation performance function establishes road network operation resilience quantification model;Establish opportunity decision model, specifically is based on the deicing and snowing equipment demand of service section, determines the optimal opportunity and timing of each section deicing operation;Optimization goal is based on the minimum expected resilience loss and deicing and snowing operation cost, while considering the resilience benefit of early deicing and snowing start and system recovery time, under the constraint condition, the operation opportunity of deicing and snowing equipment such as road emergency resource is optimized.The present application can optimize the operation opportunity of various deicing and snowing equipment such as road emergency resource, cope with the decline of road network resilience under various scenarios of ice and snow weather.

Description

Technical Field

[0001] This invention belongs to the field of intelligent transportation and emergency management technology, and relates to a method for determining the timing of road de-icing and snow removal based on the expected loss of road network operational resilience. Background Technology

[0002] Road network resilience refers to the ability of a transportation system to maintain basic operational performance and quickly recover to a stable state after being subjected to external disturbances (such as natural disasters or traffic accidents). During snow and ice disasters, road network resilience is directly related to urban operational safety, emergency response efficiency, and residents' travel security.

[0003] For a long time, ensuring road traffic safety during low-temperature rain, snow, and ice disasters has been a major challenge faced by multiple departments, including transportation management, road administration, and traffic police. Currently, the deployment and timing of ice-breaking and snow-removal equipment in response to such disasters exhibit the following characteristics and shortcomings:

[0004] (1) The existing snow and ice removal operation scheduling is mostly based on the experience of road administration personnel. Due to the lack of detailed real-time road network operation data, the selection of operation timing is often delayed or blind.

[0005] (2) Different road sections of different levels and locations in the road network have different sensitivities to ice and snow and different importance in the road network structure. The optimal snow removal time varies significantly in different areas. The traditional "one-size-fits-all" or simple administrative order approach cannot effectively take into account the overall efficiency.

[0006] (3) If key road sections that contribute significantly to the performance of the road network cannot be accurately identified and prioritized, the overall performance of the road network will recover slowly, resulting in a huge loss of resilience.

[0007] (4) Snow and ice removal operations involve a large amount of emergency resources (such as vehicles, de-icing agents, manpower, etc.). Existing decision-making models often struggle to find the optimal balance between "minimizing road network resilience loss" and "minimizing operating costs".

[0008] In conclusion, optimizing the operation of snow and ice removal equipment to improve the efficiency of post-disaster road network recovery and reduce emergency costs is a key issue that urgently needs to be addressed in the field of intelligent transportation and emergency management. Summary of the Invention

[0009] In view of this, the purpose of this invention is to provide a road de-icing and snow removal timing decision method based on the expected loss of road network operational resilience, establish a quantitative model of road network operational resilience, and optimize the timing of emergency resources such as de-icing and snow removal equipment based on this model, thereby improving the post-disaster road network recovery efficiency and reducing emergency costs.

[0010] To achieve the above objectives, the present invention provides the following technical solution: A method for determining the timing of road de-icing and snow removal based on the expected loss of road network operational resilience, specifically including: A quantitative model of road network operational resilience is established based on the road network operational performance function. Establish a timing decision model: Based on the demand for snow and ice removal equipment in the service sections, determine the optimal timing and sequence of snow and ice removal operations for each section; the optimization objective is to optimize the timing of road emergency resources such as snow and ice removal equipment under constraints, based on minimizing the expected resilience loss and snow and ice removal operation costs, while also considering the resilience benefits of starting snow and ice removal operations in advance and the system recovery time. The constraints include job time logic constraints, trigger condition constraints, response time constraints, priority timing constraints, path dependency constraints, resource capacity constraints, job time window constraints, and system recovery time calculation constraints.

[0011] Furthermore, the establishment of the road network operational resilience quantification model specifically includes: a road network operational performance function:

[0012] 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: ,

[0013] Build operational resilience metrics:

[0014] in, To enhance the resilience of the road network operation, This is the time required for the road network to recover its operational performance. This represents the average recovery rate. The moment when system performance reaches its lowest point. ; Road segment performance degradation model:

[0015] 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, .

[0016] Furthermore, the objective function of the timing decision model is to minimize the expected resilience loss and the cost of snow and ice removal operations, while also considering the resilience benefits of starting snow and ice removal operations earlier. and system recovery time :

[0017] in, The objective function is... For road network map, For a set of nodes, It is a directed edge set; Equipment type set: 1-Snowplow, 2-Rescue vehicle, 3-Drone; For road section At any moment Is snow and ice removal work in progress? 1 represents yes, 0 represents no. The duration of snowy or icy weather; For the first Number of equipment of this type; For the first The hourly operating cost of this type of equipment; , , , These are weighting coefficients used to balance the importance of different resilience indicators; For road section in advance The resilience benefits brought by starting construction after ice and snow; For road section The de-icing operation ends at this time; The decision to begin snow and ice removal, among which This is the threshold for triggering performance degradation. This represents the area of ​​toughness loss.

[0018] Furthermore, the logical constraint on the operation time is as follows:

[0019]

[0020] in, For road section Length; ; For the first De-icing efficiency of this type of equipment.

[0021] Furthermore, the triggering condition constraint is as follows:

[0022] in, For road section Whether the de-icing trigger condition has been met, 1 represents yes, 0 represents no.

[0023] Furthermore, the response time constraint is:

[0024] in, This sets the upper limit for emergency response time for road de-icing and snow removal.

[0025] Furthermore, the priority timing constraint is as follows:

[0026] in, For road section Priority level; Furthermore, the path dependency constraints specifically include: for road segments with connectivity, the timing of road vehicle clearing and road surface de-icing needs to be considered.

[0027] in, For road section A set of prerequisite road sections that must be completed before the operation can begin.

[0028] Furthermore, the resource capability constraints are as follows:

[0029] in For road section The length.

[0030] Furthermore, the operation time window constraint is as follows:

[0031] in, This is the width of the task time window.

[0032] Furthermore, the system recovery time calculation constraint is as follows: .

[0033] The beneficial effects of this invention are as follows: 1) This invention improves the resilience and recovery efficiency of road network operations. Specifically, it is reflected in the following ways: By establishing a quantitative model of road network operational resilience, it can effectively address the decline in road network performance under icy and snowy weather; the established timing decision model incorporates "expected recovery time" as a penalty term into the objective function, which can significantly compress the cycle of road network recovery from a disaster-stricken state to a stable state; by optimizing the timing of operations, it minimizes the "resilience loss area" enclosed by the performance degradation curve and the time axis, ensuring the overall operational level of the road network; the objective function includes a "reward for expected resilience recovery speed," which enables rapid rebound of road network performance after a disaster through scientific equipment scheduling.

[0034] 2) It changes the traditional road administration dispatch model, which relies on experience and lacks timeliness. It fully considers the differences in resilience contribution and performance degradation rate among different areas and sections of the road network, tailoring the optimal snow and ice removal timing for each section. Through priority level constraints, it ensures that critical road sections with significant impact on road network performance are addressed promptly, avoiding the paralysis of the entire road network due to local delays. The timing decision model introduces the calculation of "resilience benefits from early snow and ice removal commencement," quantifying the resilience gains brought by early operations to secure the best "time window" for emergency response.

[0035] 3) Under the premise of ensuring safety, the allocation of emergency resources and operating costs were optimized, achieving a balance between social and economic benefits. Specifically, the objective function comprehensively considered the hourly operating costs of various equipment (snowplows, rescue vehicles, drones, etc.), avoiding ineffective resource input and waste. The timing decision-making model rigorously considered the matching relationship between the number of equipment, operational intensity, and road length, ensuring that the decision-making plan has extremely high feasibility under existing resource constraints. The introduction of path dependency constraints scientifically handled the sequential logic of "road clearing" and "road de-icing," improving the coordination efficiency of multi-department collaborative operations.

[0036] 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

[0037] 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 the road de-icing and snow removal timing decision method based on the expected loss of road network operational resilience of the present invention. Detailed Implementation

[0038] 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.

[0039] Please see Figure 1 This invention provides a method for determining the timing of road de-icing and snow removal based on the expected loss of road network operational resilience. It establishes a quantitative model of road network operational resilience and optimizes the timing of emergency resources such as de-icing and snow removal equipment based on minimizing the expected resilience loss and the cost of de-icing and snow removal operations.

[0040] 1. Real-time resilience quantification of road network operation 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.

[0041] Road network operation performance functions:

[0042] 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.

[0043] To facilitate cross-scenario comparisons, normalized performance is defined as follows: ,

[0044] Build operational resilience metrics:

[0045] 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. .

[0046] Road segment performance degradation model:

[0047] 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. .also, .

[0048] 2. Timing Decision Model Based on serviceable road sections The Number of devices Road section Equipment requirements To determine the optimal timing and sequence of de-icing operations for each road section.

[0049] 1) Decision variables Road section De-icing operation begins; Road section The de-icing operation ends at this time; Road section At any moment Is de-icing work in progress? 1 represents yes, 0 represents no. Road section Priority level; Road section Whether the de-icing trigger condition has been met, 1 represents yes, 0 represents no.

[0050] 2) Objective function Minimize the expected resilience loss and de-icing operation costs, while also considering the resilience benefits of starting de-icing operations earlier. and system recovery time :

[0051] in, For road network map, For a set of nodes, It is a directed edge set; Equipment type set: 1-Snowplow, 2-Rescue vehicle, 3-Drone; For the first The hourly operating cost of this type of equipment; The duration of snowy or icy weather. , , , These are weighting coefficients used to balance the importance of different resilience indicators. For road section in advance The resilience benefits brought by starting construction during the snow and ice season. For the timing, among which This is the threshold for triggering performance degradation.

[0052] 3) Constraints (1) Logical constraints on task time:

[0053]

[0054] in, For road section Length; .

[0055] (2) Triggering condition constraints:

[0056] (3) Response time constraint:

[0057] in, This sets the upper limit for emergency response time for road de-icing and snow removal.

[0058] (4) Priority timing constraints:

[0059] (5) Path dependency constraints: For road sections with connectivity, the sequence of road clearing and de-icing needs to be considered:

[0060] in, For road section A set of prerequisite road sections that must be completed before the operation can begin.

[0061] (6) Resource capacity constraints:

[0062] (7) Job time window constraints:

[0063] in, This is the width of the task time window.

[0064] (8) Constraints on system recovery time calculation:

[0065] This invention can optimize the operation of various road emergency resources such as snow and ice removal equipment, and cope with the decline in road network resilience under various snow and ice weather scenarios.

[0066] 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 determining the timing of road de-icing and snow removal based on the expected loss of road network operational resilience, 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, specifically including: Road network operational 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, System recovery time; This represents the average recovery rate. The moment when system performance reaches its lowest point. ; 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, ; Establish a timing decision model: Based on the demand for snow and ice removal equipment in the service sections, determine the optimal timing and sequence of snow and ice removal operations for each section; the optimization objective is to optimize the timing of snow and ice removal equipment operation under constraints, based on minimizing the expected resilience loss and snow and ice removal operation costs, while considering the resilience benefits of starting snow and ice removal operations earlier and the system recovery time. The constraints include job time logic constraints, trigger condition constraints, response time constraints, priority timing constraints, path dependency constraints, resource capacity constraints, job time window constraints, and system recovery time calculation constraints.

2. The method for determining the timing of road de-icing and snow removal according to claim 1, characterized in that, The objective function of the timing decision model is to minimize the expected resilience loss and the cost of snow and ice removal operations, while also considering the resilience benefits of starting snow and ice removal operations earlier. and system recovery time : 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 road section At any moment Is snow and ice removal work in progress? 1 represents yes, 0 represents no. The duration of snowy or icy weather; For the first Number of equipment of this type; For the first The hourly operating cost of this type of equipment; , , , These are weighting coefficients used to balance the importance of different resilience indicators; For road section in advance The resilience benefits brought by starting construction after ice and snow; For road section The de-icing operation ends at this time; The decision to begin snow and ice removal, among which This is the threshold for triggering performance degradation. This represents the area of ​​toughness loss.

3. The method for determining the timing of road de-icing and snow removal according to claim 2, characterized in that, The logical constraint for the task time is: in, For road section Length; ; For the first De-icing efficiency of this type of equipment.

4. The method for determining the timing of road de-icing and snow removal according to claim 2, characterized in that, The triggering condition constraint is as follows: in, For road section Whether the de-icing trigger condition has been met, 1 represents yes, 0 represents no.

5. The method for determining the timing of road de-icing and snow removal according to claim 2, characterized in that, The response time constraint is: in, This sets the upper limit for emergency response time for road de-icing and snow removal.

6. The method for determining the timing of road de-icing and snow removal according to claim 2, characterized in that, The priority timing constraint is as follows: in, For road section Priority level; The path dependency constraints specifically include: for road segments with connectivity, the timing of road vehicle clearance and road surface de-icing needs to be considered. in, For road section A set of prerequisite road sections that must be completed before the operation can begin.

7. The method for determining the timing of road de-icing and snow removal according to claim 2, characterized in that, The resource capacity constraints are as follows: in, For road section Length; For the first De-icing efficiency of this type of equipment.

8. The method for determining the timing of road de-icing and snow removal according to claim 2, characterized in that, The constraint for the task time window is: in, This is the width of the task time window.

9. The method for determining the timing of road de-icing and snow removal according to claim 2, characterized in that, The constraint for calculating the system recovery time is: .

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