An emergency evacuation simulation diagnosis driven road widening optimization method and system and a storage medium

Abstract

The present application relates to the technical field of road reconstruction, in particular to a road widening optimization method and system driven by emergency evacuation simulation diagnosis and a storage medium. The present application simulates the moving speed of individuals on each road in the emergency evacuation process in the dense urban area in the simulation environment, identifies the congested road section based on the moving simulation speed, then formulates a widening scheme for the congested road section, updates the road width based on the widening scheme, simulates the evacuation performance corresponding to the widening scheme, and counts the construction cost generated by the widening scheme, and finally optimizes the widening scheme based on the evacuation performance and the construction cost. The present application locates the congested road section based on the moving simulation speed, which represents the road section that is congested in the real emergency evacuation process, so that the present application can formulate a widening scheme for the real congested road section that may occur, so that the road after widening can meet the road conditions required by emergency evacuation.

Description

Technical Field

[0001] This invention relates to the field of road reconstruction technology, specifically to a road widening optimization method, system, and storage medium driven by emergency evacuation simulation diagnosis. Background Technology

[0002] Dense urban areas, including urban villages, are characterized by extremely high building and population density, insufficient alleyway width, fragmented road networks, and complex connectivity. In the event of rapidly evolving disasters such as fires, emergency evacuation is necessary. However, these densely populated areas are prone to localized bottlenecks, congestion, queues at exits, and delays during emergency evacuations. Widening roads can facilitate rapid evacuation. Current technology analyzes the matching degree between the number of residents around each road and the road width, widening or reconstructing roads where the width doesn't match the population size. However, during emergency evacuations, road congestion depends not only on the number of people around the road but also on the speed and direction of movement. For example, roads where people concentrate their movement are more prone to congestion, requiring widening or reconstruction. Current technology, by widening roads solely based on population numbers, results in widened roads that do not meet the necessary conditions for emergency evacuation.

[0003] Therefore, existing technologies still need to be improved and enhanced. Summary of the Invention

[0004] To address the aforementioned technical problems, this invention provides a road widening optimization method, system, and storage medium driven by emergency evacuation simulation diagnosis, which solves the problem that existing road widening schemes are not suitable for emergency evacuation.

[0005] To achieve the above objectives, the present invention adopts the following technical solution:

[0006] In a first aspect, the present invention provides a road widening optimization method driven by emergency evacuation simulation diagnosis, comprising:

[0007] Obtain the passable area covered by roads in a dense urban area, simulate the movement speed of individuals in the dense urban area within the passable area during emergency evacuation, and obtain the simulated movement speed.

[0008] Based on the simulated movement speed, the congested sections of the road are identified.

[0009] Develop a widening plan for the congested road section, simulate the evacuation performance corresponding to the widening plan, and determine the construction cost of the widening plan.

[0010] Based on the evacuation performance and the construction cost, the widening scheme is optimized.

[0011] In one implementation, the simulated movement speed of individuals in the densely populated urban area during an emergency evacuation within the passable area is obtained, including:

[0012] The individuals are assigned to the accessible area to simulate their individual positions within the accessible area during an emergency evacuation.

[0013] Based on the individual's location, the density of people around the individual and the individual's kernel density gradient function are determined. The kernel density gradient function is used to characterize the direction of the compressive force experienced by the individual.

[0014] Based on the population density, determine the individual's movement rate;

[0015] Obtain the exit locations of the densely populated urban areas, and determine the evacuation navigation direction based on the individual locations and the exit locations;

[0016] Based on the kernel density gradient function and the evacuation navigation direction, the movement direction of the individual is estimated;

[0017] Based on the direction of movement and the speed of movement, the simulated speed of movement is obtained.

[0018] In one implementation, estimating the individual's movement direction based on the kernel density gradient function and the evacuation navigation direction includes:

[0019] The interaction pressure generated between the interactive individuals is determined based on the personnel density around each interactive individual and the kernel density gradient function.

[0020] The interactive extrusion force is normalized to obtain the direction of the interactive force experienced by the individual.

[0021] The individual's movement direction is obtained based on the evacuation navigation direction and the direction of the interaction force acting on the individual.

[0022] In one implementation, the movement direction of the individual is obtained based on the evacuation navigation direction and the direction of the interaction force experienced by the individual, including:

[0023] Based on the density of people around the individual, the weights of the evacuation navigation direction and the direction of the interaction force experienced by the individual are determined.

[0024] Based on their respective weights, the evacuation navigation direction and the direction of the interaction force experienced by the individual are weighted and calculated to obtain the individual's movement direction.

[0025] In one implementation, based on the simulated movement speed, the congested road segments on the road are determined, including:

[0026] The dense urban area is divided into several grids, and the average movement speed of the individual within the grid is determined based on the movement simulation speed.

[0027] Based on the average moving speed, the congestion time of the individual within the grid is calculated.

[0028] The average congestion density of the grid is calculated, and the congestion density is used to characterize the population density within the grid.

[0029] Based on the congestion duration and average congestion density of the grid, a congested grid is selected from several grids;

[0030] The congestion grid is mapped onto the road to obtain the congested road segments.

[0031] In one implementation, simulating the evacuation performance corresponding to the widening scheme includes:

[0032] The simulation measures the required evacuation time for the densely populated urban area based on the widening scheme.

[0033] During the evacuation simulation, the number of people remaining in the densely populated urban area at each evacuation moment within the evacuation duration is counted.

[0034] Based on the number of people who have not been evacuated, the evacuation performance corresponding to the widening scheme is obtained.

[0035] In one implementation, the widening scheme is optimized based on the evacuation performance and the construction cost, including:

[0036] The widening scheme is optimized with the goal of minimizing the weighted sum of the evacuation performance and the construction cost.

[0037] In one implementation, the densely populated urban area is an urban village.

[0038] Secondly, embodiments of the present invention also provide an emergency evacuation simulation diagnosis-driven road widening optimization system, wherein the system comprises the following components:

[0039] The speed simulation module is used to obtain the passable area of ​​the covered roads in the dense urban area, simulate the movement speed of individuals in the dense urban area in the passable area during emergency evacuation, and obtain the movement simulation speed.

[0040] The congested road segment filtering module is used to identify congested road segments on the road based on the simulated movement speed.

[0041] The evacuation effect cost statistics module is used to formulate a widening plan for the congested road section, simulate the evacuation performance corresponding to the widening plan, and determine the construction cost generated by the widening plan.

[0042] An optimization module is used to optimize the widening scheme based on the evacuation performance and the construction cost.

[0043] Thirdly, embodiments of the present invention also provide a computer-readable storage medium storing an emergency evacuation simulation diagnosis-driven road widening optimization program. When the emergency evacuation simulation diagnosis-driven road widening optimization program is executed by a processor, it implements the steps of the emergency evacuation simulation diagnosis-driven road widening optimization method described above.

[0044] Beneficial Effects: This invention simulates the movement speed of individuals on various roads during emergency evacuation in a densely populated urban area. Based on the simulated movement speed, congested road sections are identified. Widening plans are then developed for these congested sections, and the road widths are updated accordingly. Emergency evacuation is then simulated again in the densely populated urban area with the updated road widths to obtain the evacuation performance corresponding to the widened plan. This evacuation performance represents whether people can be evacuated from the widened urban area in a timely manner. The construction cost of the widening plan is also calculated. Finally, the widening plan is optimized based on the evacuation performance and construction cost. As can be seen from the above analysis, this invention locates congested road sections based on simulated movement speed. These congested sections represent road sections that would become congested during a real emergency evacuation, enabling the invention to develop widening plans for potentially congested sections, thus ensuring that the widened roads meet the road conditions required for emergency evacuation. Attached Figure Description

[0045] Figure 1 This is an overall flowchart of the present invention;

[0046] Figure 2 This is a schematic diagram of the building outline in an embodiment of the present invention;

[0047] Figure 3 This is a schematic diagram of the road network in an embodiment of the present invention;

[0048] Figure 4 This is a road network numbering statistics chart with a width of two meters in this embodiment of the invention;

[0049] Figure 5 This is a road network numbering statistics chart with a width of four meters in this embodiment of the invention;

[0050] Figure 6 The structural diagram of the road widening optimization system driven by emergency evacuation simulation diagnosis provided by this invention;

[0051] Figure 7 This is a block diagram illustrating the internal structure of a terminal device provided in an embodiment of the present invention. Detailed Implementation

[0052] The technical solutions of the present invention will be clearly and completely described below with reference to the embodiments and accompanying drawings. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0053] Research has revealed that densely populated urban areas, including urban villages, are characterized by extremely high building and population density, insufficient alleyway width, fragmented road networks, and complex connectivity. In the event of rapidly evolving disasters such as fires, emergency evacuation is necessary. However, these densely populated areas are prone to localized bottlenecks, congestion at exits, and delays during emergency evacuations. Widening roads can facilitate rapid evacuation. Current technology analyzes the matching degree between the number of residents around each road and the road width, widening or reconstructing roads where the width does not match the population size. However, during emergency evacuations, road congestion depends not only on the number of people around the road but also on the speed and direction of movement. For example, roads where people concentrate their movement are more prone to congestion, requiring widening or reconstruction. Current technology, by widening roads solely based on population numbers, results in widened roads that do not meet the necessary conditions for emergency evacuation.

[0054] To address the aforementioned technical problems, this invention provides a road widening optimization method, system, and storage medium driven by emergency evacuation simulation and diagnosis, solving the problem that existing road widening schemes are unsuitable for emergency evacuation. In specific implementation, firstly, the passable areas of roads covering dense urban areas are obtained, and the movement speed of individuals within these passable areas during emergency evacuation is simulated to obtain the simulated movement speed. Then, based on the simulated movement speed, congested road sections are identified. Widening schemes are then developed for these congested road sections, and the corresponding evacuation performance of the widening scheme is simulated, along with the construction cost incurred by the widening scheme. Based on the evacuation performance and construction cost, the widening scheme is optimized.

[0055] For example, a densely populated urban area might be a village within a city with three main roads. Existing technology is used to build a model of the village in a simulation environment, and existing technology is used to simulate the people within the village. After building the simulation environment, an emergency evacuation is conducted within the simulated village. During the evacuation, individuals move within the passable area including the main roads. The simulated movement speed of individuals on each main road is collected. If the simulated movement speed of individuals on a road segment is slow, it indicates that the road segment is too narrow for people to pass quickly, making it a congested segment. A widening plan is then developed for the congested segment to increase its width. The emergency evacuation is then re-simulated in the village after the roads have been widened. After the emergency evacuation simulation concludes, the evacuation performance of the village after the roads have been widened is determined. Finally, based on evacuation performance and construction cost, it is determined whether the widening plan is the optimal plan. If not, the widening plan for the congested road section is updated to update the width of the congested road section. Emergency evacuation is then carried out again in the urban village with the updated road width to obtain new evacuation performance. Based on the new evacuation performance and the construction cost incurred by the modified widening plan, it is evaluated whether the updated widening plan is the optimal widening plan. If not, the widening plan for the congested road section is updated again, and emergency evacuation is simulated again until the optimal widening plan is obtained.

[0056] The road widening optimization method driven by emergency evacuation simulation diagnosis in this embodiment can be applied to terminal devices, which can be terminal products with data processing capabilities, such as computers. In this embodiment, as... Figure 1 As shown, the road widening optimization method driven by emergency evacuation simulation diagnosis specifically includes the following steps:

[0057] S100: Obtain the passable area of ​​the covered roads in the dense urban area, simulate the movement speed of individuals in the dense urban area within the passable area during emergency evacuation, and obtain the movement simulation speed.

[0058] S200, based on the simulated movement speed, the congested road sections on the road are obtained;

[0059] S300, formulate a widening plan for the congested road section, simulate the evacuation performance corresponding to the widening plan, and determine the construction cost of the widening plan;

[0060] S400, based on the evacuation performance and the construction cost, optimize the widening scheme.

[0061] The road widening optimization method based on steps S100, S200, S300, and S400 can be applied to road widening in urban villages, as well as to road widening in densely populated urban areas such as shopping malls and train stations.

[0062] The passable area in step S100 is determined in the following way: the location of buildings and roads in the dense urban area is collected, and the area in the dense urban area that is near the road, excluding the location of buildings, is taken as the passable area.

[0063] Step S100 simulates the movement speed of individuals in the densely populated urban area during emergency evacuation within the passable area to obtain the simulated movement speed, including the following specific steps S101 to S109:

[0064] S101, the individual is assigned to the passable area to simulate the individual's location within the passable area during an emergency evacuation.

[0065] In the simulation environment, individuals are directly assigned to passable areas to obtain their positions, simulating their initial positions at the start of an emergency evacuation. In this embodiment, the initial positions of individuals are evenly distributed within the passable areas.

[0066] If the actual location of each individual in a dense urban area is obtained, the actual location can be directly used as the initial location of the individual in the passable area of ​​the simulation environment.

[0067] After initializing the individual's initial position, an emergency evacuation is simulated. During the emergency evacuation, the individual's real-time position within the passable area is statistically analyzed, and this real-time position is the individual's position.

[0068] S102, based on the individual's location, determine the density of people around the individual and the kernel density gradient function of the individual, wherein the kernel density gradient function is used to characterize the direction of the compressive force experienced by the individual.

[0069] use Representative of individuals The surrounding population density ,in, Representatives are individuals The surrounding group of people Represents the individuals in this group. quality or weight, Representative of individuals and individuals The distance between them , Representative of individuals The individual position, Representative of individuals The individual position, Represents kernel density function The effective computational radius. The calculation formula is as follows:

[0070] ;

[0071] use Represents the kernel density gradient function, which is... The gradient.

[0072] S103, based on the personnel density The movement rate of the individual is determined.

[0073] use Representative of individuals The speed of movement, ,in This represents the speed of a person in a state of free movement; this speed is the baseline speed. The baseline density of personnel is used to distinguish areas of overcrowding. Represents the exponential constant.

[0074] S104, obtain the exit location of the dense urban area, and determine the evacuation navigation direction based on the individual location and the exit location.

[0075] use Representative of individuals Evacuation navigation directions , This represents the distance between an individual's location and the nearest exit location. represent The gradient, when preceded by a symbol, represents the direction in which the distance is reduced the fastest. This direction is the direction in which evacuees move towards the exit, and it is the evacuation navigation direction. represent Normalization, To prevent constants with a denominator of zero.

[0076] S105, based on the personnel density and the kernel density gradient function around each interactive individual. To determine the interactive pressure generated between interacting individuals.

[0077] use Representative of individuals The interactive squeezing force, , Representative of individuals The pressure of crowding Representative of individuals The pressure of crowding and The calculation principle is the same, among which , This represents the pressure coefficient. Representative of individuals The density of people in the surrounding area.

[0078] because It has directionality, therefore It also has directionality.

[0079] S106, the interactive extrusion force Normalization is performed to obtain the direction of the interaction force experienced by the individual.

[0080] use Representative of individuals The direction of the interaction force received, , represent Size, A value set to prevent the denominator from being zero.

[0081] S107, based on the density of people around the individual, determine the weights of the evacuation navigation direction and the direction of the interaction force experienced by the individual.

[0082] use Represents evacuation navigation direction The weights, using represent ( That is, the weight of the direction of the interaction force experienced by the individual. , The lower limit of the representative personnel density value, The upper limit of the value representing population density. The representative will The value of is restricted to Functions between.

[0083] S108, based on their respective weights, the evacuation navigation direction and the direction of the interaction force experienced by the individual are weighted and calculated to obtain the movement direction of the individual.

[0084] use Representative of individuals The direction of movement, .

[0085] S109, based on the direction of movement and the speed of movement To obtain the simulated movement speed .

[0086] ;

[0087] The representation of is a vector. This represents the magnitude of the vector. To prevent The value set when the denominator of the expression is zero. (Movement simulation speed) It has both size and direction.

[0088] Step S200, based on the simulated movement speed, determines the congested road sections, including the following specific steps S201, S202, S203, S204, and S205:

[0089] S201, the dense urban area is divided into several grids, and the average movement speed of the individual within the grid is determined based on the movement simulation speed.

[0090] The dense urban area is divided into several grids, which means dividing the passable area covered by roads in the dense urban area into several grids. Some grids are located on the roads, and some grids are located in the area surrounding the roads.

[0091] The above-mentioned mobile simulation speed It covers the velocity of individuals within each grid at any time step, from the motion simulation speed. Extract the raster All individuals within the first each discrete time step The speed of the grid All individuals within the first each discrete time step The grid is obtained by averaging the speeds. Individuals within a discrete time step average moving speed .

[0092] S202, Based on the average moving speed, calculate the congestion time of the individual within the grid. :

[0093] ;

[0094] In the formula, The speed threshold for determining congestion, represent Less than or equal to The corresponding time step, when Less than or equal to hour, The value is 1, otherwise The value is 0. Represents satisfaction Less than or equal to The total number of time steps This represents the duration of each time step. Less than or equal to The time indicates that the movement speed is slow due to congestion.

[0095] S203, Calculate the average congestion density of the grid, whereby the congestion density is used to characterize the population density within the grid.

[0096] use Representative grid The average congestion density of people inside, The calculation formula is as follows:

[0097] ;

[0098] This represents a numerical stability term used to prevent the denominator from being zero. Represents in grid Within discrete time steps The population density at that time Representative grid Personnel density inside Satisfying greater than or equal to The total number of time steps Representative personnel density threshold, The corresponding value is 1, which means using Statistical raster Internal personnel density greater than or equal to The corresponding time step, Represents satisfaction The total number of time steps, multiplied by the total number of time steps. ( That is, the duration of each time step is the grid. Internal personnel density greater than or equal to The total duration.

[0099] S204, based on the congestion duration and the average congestion density of the grid, select a congested grid from several grids.

[0100] Will meet the congestion duration Greater than or equal to And average congestion density Greater than or equal to The grid is used as a congestion grid. This represents the time threshold used to determine whether there is congestion. This represents the density threshold used to determine congestion. Represents a congestion grid.

[0101] S205, map the congestion grid onto the road to obtain the congested road segment.

[0102] Congested regions are constructed by identifying the locations of adjacent congested grid cells. Cluster analysis is then performed on these congested regions to obtain multiple congested areas. Representing the By identifying congested areas and mapping them to the corresponding road centerlines or road segments, we obtain the congested road segments. In other words, each congested area is mapped to the nearest road, and the road segment where the mapped location of the congested area is located is the congested road segment.

[0103] Step S300 involves formulating a widening plan for the congested road segment, which estimates how much the congested road segment needs to be widened to alleviate congestion during emergency evacuation. After widening the congested road segment in a densely populated urban area according to the above widening plan, an emergency evacuation simulation is conducted again in the densely populated urban area to obtain the evacuation performance of the densely populated urban area after adopting the widening plan. At the same time, the widening plan is comprehensively evaluated by combining the evacuation performance and the construction cost of the widening plan. If the widening plan is not good, it is adjusted to the above widening plan formulated for the congested road segment. Then, based on the adjusted widening plan, an emergency evacuation simulation is conducted again in the densely populated urban area to obtain new evacuation performance and new construction costs, until the optimal widening plan corresponding to the optimal evacuation performance and construction cost is obtained. In the above process of optimizing the widening plan, the congested road segment remains a fixed road segment.

[0104] The simulation of the evacuation performance corresponding to the widening scheme in step S300 includes the following specific steps S301, S302, and S303:

[0105] S301, Simulate the evacuation time required for the dense urban area to be rebuilt based on the widening scheme.

[0106] In other words, the aforementioned congested road sections in densely populated urban areas are reconstructed based on a widening scheme in a simulation environment. After the congested road sections in densely populated urban areas are reconstructed, emergency evacuations are repeatedly carried out in the densely populated urban areas, and the start time of the emergency evacuation is recorded. and the completion time of emergency evacuation Emergency evacuation completion time This refers to the time when all people leave a densely populated urban area or the time when the remaining number of people in the area reaches a preset tolerance threshold.

[0107] use Representatives at the broadening plan The duration of evacuation .

[0108] S302, During the evacuation simulation, the number of people remaining in the densely populated urban area at each evacuation moment within the evacuation duration is counted.

[0109] This embodiment uses Representing each evacuation moment, using Representative expansion plan The number of people who were not evacuated as a result.

[0110] ;

[0111] In the formula, Representatives at the broadening plan In densely populated urban areas during evacuation The remaining population This represents the total number of people in a densely populated urban area at the start of an emergency evacuation.

[0112] S303, based on the number of people who have not been evacuated, obtain the evacuation performance corresponding to the widening scheme.

[0113] use Representative expansion plan The corresponding evacuation performance, ,in, The representative population stay penalty weighting coefficient; when hour, Degenerate to only consider evacuation duration; when hour, The representative considered the evacuation completion time and the effects of continued confinement.

[0114] The construction cost in step S300 is used express: ,in, Representative expansion plan The number of newly added passable grid cells, This represents the resolution of the raster, measured in meters. Represents the area of ​​the grid. The coefficient representing the construction cost per unit area.

[0115] Step S400, which optimizes the widening scheme based on the evacuation performance and the construction cost, includes: optimizing the widening scheme with the goal of minimizing the weighted sum of the evacuation performance and the construction cost.

[0116] use Represents evacuation performance and construction costs Weighted sum, The calculation formula is as follows:

[0117] ;

[0118] In the formula, This represents the cost weighting coefficient.

[0119] The optimization objective of the widening scheme is to make To obtain the minimum value, The broadening scheme that yields the minimum value is the optimal broadening scheme. This embodiment uses a genetic algorithm for search. The widening scheme corresponding to the minimum value.

[0120] This embodiment can also include and The widening scheme corresponding to the minimum value is taken as the optimal widening scheme.

[0121] The following describes the widening method of this embodiment, using a village in a certain city as an example of a densely populated urban area, including the following specific steps:

[0122] Step 1: Data preparation and multi-channel raster environment construction for the study area, which covers an area of ​​approximately 80 hectares.

[0123] (1) Overview of the study area: The study area has dense buildings with irregular shapes. The internal passage is mainly through narrow alleys, with a typical alley width of about 0.8–3 meters. The study area contains about 1,660 multi-story residential or mixed-use buildings, with a permanent or temporary population of about 50,000 and a peak population of up to 100,000.

[0124] (2) Acquisition of raw data: Acquiring building outlines (e.g.) Figure 2 As shown), the road network consists of study boundaries and roads (road network as shown). Figure 3 (As shown) and elements such as accessible space and exit location (boundary exit). Building outlines can be derived from remote sensing imagery and cadastral data, and then manually cleaned, including correcting misalignments and removing non-residential structures.

[0125] (3) Rasterization: Set the resolution of the raster to 0.1m to discretize the study area into a regular grid; rasterize the building outline into an obstacle mask and the remaining free space into a passable mask; at the same time, generate exit masks, such as generating 16 boundary exits.

[0126] (4) Multi-channel output: Output three-channel grids respectively, namely the grids corresponding to the passable area, the grids corresponding to the obstacles, and the grids corresponding to the exit, and label the coordinates of these grids.

[0127] (5) File organization example: manage each simulation project, each project contains input grids and configuration files, output trajectories and statistics files.

[0128] Step 2: Generation of multi-source BFS potential field and global navigation field, where BFS stands for Breadth-First Search.

[0129] (1) Definition of exit set: Determine the set of boundary exits (e.g., 16 exits) from planning data and on-site surveys or observations, and mark it as the source point set on the grid.

[0130] (2) Potential field calculation: Perform multi-source BFS on the passable domain with all exits as the source points, calculate the shortest step distance from each grid to the nearest “accessible exit”, and obtain the distance field; building grids are used as impassable obstacles.

[0131] (3) Navigation field calculation: Calculate the gradient of the range field and normalize it. To avoid numerical noise, the range field can be lightly smoothed or a discrete difference stabilizing operator can be used before calculating the gradient; a constant is added where the gradient of the range field is very small to prevent the gradient of the range field from being zero.

[0132] (4) Visualization and inspection: Output distance field heat map and streamline map to check whether the navigation field forms a consistent guidance "pointing to the nearest reachable exit" in complex connected alleys and does not cross obstacles.

[0133] Step 3: SPH evacuation simulation (navigation-interaction coupling, density-velocity calibration). SPH stands for Smoothed Particle Hydrodynamics.

[0134] (1) Crowd initialization: Set the total number of people (e.g., 50,000 individual pedestrians), and place the individuals inside the building or near the building entrances and exits according to the weight of building area / floor / population density, and assign them initial speed / direction; set a time step of 0.1s.

[0135] (2) Local density estimation: For each individual, the neighborhood radius (derived from the effective computational radius) is calculated. Decision) Calculation , and by Obtain the crowding pressure; the kernel function can be a Gaussian kernel and... Cut off.

[0136] (3) Local interaction direction acquisition: The interaction extrusion force is obtained from the pressure gradient. (Reflecting the trend of "moving away from high-pressure / high-density areas"), and normalized to ( Representative of individuals The direction of the interactive forces experienced); its physical meaning is: in extremely narrow alleys and high-density scenarios, an individual's immediate movement is more affected by local feasible space and the squeezing of neighbors.

[0137] (4) Global-local fusion: Calculate evacuation navigation direction and with Fusion The purpose of evacuation is to maintain global proximity to the exit during low-density phases and gradually shift to local collision avoidance or detours during high-density phases, thereby stably reproducing phenomena such as exit queuing and bottleneck compression waves, which can effectively simulate congestion.

[0138] (5) Speed ​​calibration: using Apply density decay constraints to the velocity.

[0139] (6) Output: Output at least all trajectories and velocity sequences; output the remaining number of people curve; output the grid-level density / velocity spatiotemporal field for subsequent congestion diagnosis.

[0140] Step 4: Baseline congestion diagnosis and automatic extraction of congested road sections.

[0141] (1) Indicator construction: Calculate the cumulative congestion time at the grid level With average congestion density , and These respectively reflect the "persistence of slow-moving traffic congestion" and the "intensity of high-density congestion".

[0142] (2) High-risk raster screening: Adaptive screening is achieved using quantile thresholds. If a raster's... Congestion duration greater than or equal to 80% of the grid and A grid with a congestion density greater than 80% is considered a high-risk grid, and the intersection of high-risk grids forms a candidate congestion area.

[0143] (3) Clustering of congested areas: DBSCAN is used to cluster candidate congested areas to obtain multiple spatially connected congested areas. DBSCAN stands for Density-Based Spatial Clustering of Applications with Noise. Density-Based Spatial Clustering of Applications with Noise represents density-based spatial clustering of applications with noise.

[0144] (4) Road segment mapping: Spatial matching of each congested area with the road skeleton / centerline to obtain several candidate bottleneck road segments.

[0145] Step 5: Discrete broadening simulation in-loop genetic optimization.

[0146] (1) Discrete coding: Discrete coding is used to widen the amount of each candidate bottleneck segment. This method avoids the occurrence of unrealistic arbitrary decimal widening.

[0147] (2) Map update operator: For any individual candidate bottleneck road segment, execute the deterministic operator of road segment widening → grid rewriting: expand the passable domain around the corresponding road segment according to the above widening amount, and update the conflict handling of obstacle / building mask at the same time, such as the engineering constraint strategy of road priority or building priority.

[0148] (3) Simulation evaluation: After widening the passable area by the expansion amount, run the evacuation simulation to obtain the results. Modifying the number of raster cells And construct evacuation capabilities. and construction costs .

[0149] (4) Evolutionary search: Perform selection, crossover, mutation, and elite retention iterations on discrete road widths; each discrete road width requires a complete simulation evaluation to form a closed-loop optimization of "simulation in the loop".

[0150] (5) Output and verification: Output the optimal widening scheme and perform verification simulation under the same initial conditions as the baseline to quantify congestion dissipation and efficiency improvement.

[0151] (6) Example Results: Compared to the baseline scheme without widening, the optimized scheme reduces the total evacuation time by 143 seconds (approximately 14.3%); reduces the evacuation score by approximately 9.48%; and has a construction cost of 347.46 (currency unit), corresponding to an additional road reconstruction area of ​​approximately [missing information]. .

[0152] The optimal widening scheme involves widening each congested road segment by 0, 2, 0, 0, 0, 0, 0, 2, 0, 0, 4, 0, 0, meaning only 3 congested road segments are widened, exhibiting the characteristics of "sparse but effective" micro-modification.

[0153] Step 6: Explainable output and planning implementation (segment frequency, hotspot dissipation, comparison report). (1) Segment selection frequency: Statistically, each segment is selected as the 2m / 4m widening frequency during the genetic algorithm evaluation process, forming evidence of "sensitive segments"; those with high frequency are usually key bottlenecks or systemic bridging segments.

[0154] (2) Hot zone comparison: Output the cumulative congestion time / density heat map and congestion zone cluster boundary before and after optimization to verify whether the bottleneck hot zone has been significantly reduced or migrated.

[0155] (3) Deliverables: Generate a report consisting of “renovation list (road section number, location, widening amount) + cost estimate + evacuation improvement indicators + maps (at least 6-8 maps recommended)” for planning demonstration and communication with multiple parties. Figure 4 The image shows the road segment number, which is 2 meters wide. Figure 5 The image shows the road segment number, which is 4 meters wide. Figure 4 and Figure 5 The horizontal axis represents the road segment number, and the vertical axis represents the selection frequency of the road segment during the genetic algorithm optimization process, that is, the frequency at which the road segment is selected for widening during the optimization iteration process.

[0156] In summary, this invention simulates the movement speed of individuals on various roads during emergency evacuation in a densely populated urban area. Based on the simulated movement speed, congested road sections are identified. Widening plans are then developed for these congested sections, and the road widths are updated accordingly. Emergency evacuation is then simulated again in the densely populated urban area with the updated road widths to obtain the evacuation performance corresponding to the widened plan. This evacuation performance represents whether people can be evacuated from the widened densely populated urban area in a timely manner. The construction cost of the widening plan is also calculated. Finally, the widening plan is optimized based on the evacuation performance and construction cost. As the above analysis shows, this invention locates congested road sections based on simulated movement speed. These congested sections represent road sections that would become congested during a real emergency evacuation, enabling the invention to develop widening plans for potentially congested road sections, ensuring that the widened roads meet the road conditions required for emergency evacuation.

[0157] This embodiment also provides a road widening optimization system driven by emergency evacuation simulation diagnosis, such as... Figure 6 As shown, the system includes the following components:

[0158] Speed ​​simulation module 01 is used to obtain the passable area of ​​the covered roads in the dense urban area, simulate the movement speed of individuals in the dense urban area in the passable area during emergency evacuation, and obtain the movement simulation speed.

[0159] The congested road segment screening module 02 is used to obtain the congested road segments on the road based on the simulated movement speed;

[0160] The evacuation effect cost statistics module 03 is used to formulate a widening plan for the congested road section, simulate the evacuation performance corresponding to the widening plan, and determine the construction cost generated by the widening plan.

[0161] Optimization module 04 is used to optimize the widening scheme based on the evacuation performance and the construction cost.

[0162] Based on the above embodiments, the present invention also provides a terminal device, the principle block diagram of which can be as follows: Figure 7As shown, the terminal device includes a processor, memory, network interface, and display screen connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides the environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The network interface is used to communicate with external terminals via a network connection. When the computer program is executed by the processor, it implements a road widening optimization method driven by emergency evacuation simulation diagnosis. The display screen of the terminal device can be a liquid crystal display (LCD) or an e-ink display.

[0163] Those skilled in the art will understand that Figure 7 The schematic diagram shown is only a partial structural diagram related to the present invention and does not constitute a limitation on the terminal device to which the present invention is applied. The specific terminal device may include more or fewer components than shown in the figure, or combine certain components, or have different component arrangements.

[0164] In one embodiment, a terminal device is provided, comprising a memory, a processor, and an emergency evacuation simulation diagnosis-driven road widening optimization program stored in the memory and executable on the processor. When the processor executes the emergency evacuation simulation diagnosis-driven road widening optimization program, it implements the following operation instructions:

[0165] Obtain the passable area covered by roads in a dense urban area, simulate the movement speed of individuals in the dense urban area within the passable area during emergency evacuation, and obtain the simulated movement speed.

[0166] Based on the simulated movement speed, the congested sections of the road are identified.

[0167] Develop a widening plan for the congested road section, simulate the evacuation performance corresponding to the widening plan, and determine the construction cost of the widening plan.

[0168] Based on the evacuation performance and the construction cost, the widening scheme is optimized.

[0169] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided by this invention can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0170] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A road widening optimization method driven by emergency evacuation simulation diagnosis, characterized in that, include: Obtain the passable area covered by roads in a dense urban area, simulate the movement speed of individuals in the dense urban area within the passable area during emergency evacuation, and obtain the simulated movement speed. Based on the simulated movement speed, the congested sections of the road are identified. Develop a widening plan for the congested road section, simulate the evacuation performance corresponding to the widening plan, and determine the construction cost of the widening plan. Based on the evacuation performance and construction cost, the widening scheme is optimized; The simulated movement speed of individuals within the passable area during an emergency evacuation in the densely populated urban area is obtained, including: The individuals are assigned to the accessible area to simulate their individual positions within the accessible area during an emergency evacuation. Based on the individual's location, the density of people around the individual and the individual's kernel density gradient function are determined. The kernel density gradient function is used to characterize the direction of the compressive force experienced by the individual. Based on the population density, determine the individual's movement rate; Obtain the exit locations of the densely populated urban areas, and determine the evacuation navigation direction based on the individual locations and the exit locations; The interaction pressure generated between the interactive individuals is determined based on the personnel density around each interactive individual and the kernel density gradient function. The interactive extrusion force is normalized to obtain the direction of the interactive force experienced by the individual. The individual's direction of movement is obtained based on the evacuation navigation direction and the direction of the interaction force acting on the individual. Based on the direction of movement and the speed of movement, the simulated speed of movement is obtained.

2. The road widening optimization method driven by emergency evacuation simulation diagnosis as described in claim 1, characterized in that, Based on the evacuation navigation direction and the direction of the interaction force acting on the individual, the individual's movement direction is obtained, including: Based on the density of people around the individual, the weights of the evacuation navigation direction and the direction of the interaction force experienced by the individual are determined. Based on their respective weights, the evacuation navigation direction and the direction of the interaction force experienced by the individual are weighted and calculated to obtain the individual's movement direction.

3. The road widening optimization method driven by emergency evacuation simulation diagnosis as described in claim 1, characterized in that, Based on the simulated movement speed, the congested road sections on the road are identified, including: The dense urban area is divided into several grids, and the average movement speed of the individual within the grid is determined based on the movement simulation speed. Based on the average moving speed, the congestion time of the individual within the grid is calculated. The average congestion density of the grid is calculated, and the congestion density is used to characterize the population density within the grid. Based on the congestion duration and average congestion density of the grid, a congested grid is selected from several grids; The congestion grid is mapped onto the road to obtain the congested road segments.

4. The road widening optimization method driven by emergency evacuation simulation diagnosis as described in claim 1, characterized in that, The simulation includes the evacuation performance corresponding to the widening scheme, including: The simulation measures the required evacuation time for the densely populated urban area based on the widening scheme. During the evacuation simulation, the number of people remaining in the densely populated urban area at each evacuation moment within the evacuation duration is counted. Based on the number of people who have not been evacuated, the evacuation performance corresponding to the widening scheme is obtained.

5. The road widening optimization method driven by emergency evacuation simulation diagnosis as described in claim 1, characterized in that, Based on the evacuation performance and construction cost, the widening scheme is optimized, including: The widening scheme is optimized with the goal of minimizing the weighted sum of the evacuation performance and the construction cost.

6. The road widening optimization method driven by emergency evacuation simulation diagnosis as described in any one of claims 1-5, characterized in that, The densely populated urban areas are urban villages.

7. A road widening optimization system driven by emergency evacuation simulation diagnosis, characterized in that, The system comprises the following components: The speed simulation module is used to obtain the passable area of ​​the covered roads in the dense urban area, simulate the movement speed of individuals in the dense urban area in the passable area during emergency evacuation, and obtain the movement simulation speed. The congested road segment filtering module is used to identify congested road segments on the road based on the simulated movement speed. The evacuation effect cost statistics module is used to formulate a widening plan for the congested road section, simulate the evacuation performance corresponding to the widening plan, and determine the construction cost generated by the widening plan. An optimization module is used to optimize the widening scheme based on the evacuation performance and the construction cost; The simulated movement speed of individuals within the passable area during an emergency evacuation in the densely populated urban area is obtained, including: The individuals are assigned to the accessible area to simulate their individual positions within the accessible area during an emergency evacuation. Based on the individual's location, the density of people around the individual and the individual's kernel density gradient function are determined. The kernel density gradient function is used to characterize the direction of the compressive force experienced by the individual. Based on the population density, determine the individual's movement rate; Obtain the exit locations of the densely populated urban areas, and determine the evacuation navigation direction based on the individual locations and the exit locations; The interaction pressure generated between the interactive individuals is determined based on the personnel density around each interactive individual and the kernel density gradient function. The interactive extrusion force is normalized to obtain the direction of the interactive force experienced by the individual. The individual's direction of movement is obtained based on the evacuation navigation direction and the direction of the interaction force acting on the individual. Based on the direction of movement and the speed of movement, the simulated speed of movement is obtained.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a road widening optimization program driven by emergency evacuation simulation diagnosis. When the emergency evacuation simulation diagnosis road widening optimization program is executed by a processor, it implements the steps of the road widening optimization method driven by emergency evacuation simulation diagnosis as described in any one of claims 1-6.

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