An urban agglomeration comprehensive passenger transport network emergency evacuation path planning method and system

Abstract

The application discloses a kind of urban group comprehensive passenger transport network emergency evacuation path planning methods, comprising the following steps: step 1: urban group comprehensive passenger transport network model construction;Step 2. urban group comprehensive passenger transport network evacuation stage division;Step 3. ant colony algorithm is solved urban group comprehensive passenger transport network emergency evacuation planning problem.The application has the advantages that: when modeling, the situation of urban traffic is considered, so that the urban group comprehensive passenger transport network model constructed is more in line with the actual traffic network form;The evacuation process is divided into stages, the travel characteristics and conditions of each stage are studied, and the complete evacuation process from generation to dissipation is established;When constructing the heuristic function of ant colony algorithm, the dynamic heuristic function is set according to the travel conditions of different evacuees, thereby improving the efficiency and authenticity of simulation operation.

Description

Technical Field

[0001] This invention relates to the field of network emergency evacuation route planning technology, and in particular to an emergency evacuation route planning method for urban agglomeration integrated passenger transport networks. Background Technology

[0002] With the continuous development of the economy and society, individual cities, limited by their space, population, and resources, are increasingly unable to serve as a driving force for development. Therefore, as the highest spatial organization form in the mature stage of urban development, urban agglomerations have experienced rapid growth in recent years. This has led to a surge in passenger transport demand within urban agglomerations, with a large number of travelers traversing the region via urban and intercity transport routes. Due to the complexity of urban agglomeration passenger transport networks, in the event of emergencies, travelers within the network need to be rapidly evacuated from the source of the emergency to their destination. How to guide travelers within the urban agglomeration passenger transport network to utilize various modes of transportation and achieve evacuation in the shortest possible time has become an urgent research issue.

[0003] Regarding the issue of evacuation routes within transportation networks, the current state of research and technology both domestically and internationally is mainly as follows:

[0004] The establishment of transportation network models is fundamental to the study of passenger flow dispersal within the network. Currently, there has been some research on transportation network modeling both domestically and internationally. (Pi et al.) [1] A comprehensive transportation network has been established, encompassing multiple modes of transport, various routes, and transfer facilities. (Shi Wenjing) [2] This study investigated the public transport and subway systems in Xi'an, constructing a two-layer bus-subway composite network and examining cascading failure phenomena by adjusting parameters. (De Regt Robin et al.) [3] This paper models the public transport networks of several British cities, analyzing and classifying their topological characteristics, spatial features, and stability. (Xu Min et al.) [4] A node evaluation model considering time parameters was established, and the relationship between node importance and the network was analyzed.

[0005] Ant colony optimization (ACO) is a biomimetic algorithm for swarm intelligence, widely used due to its excellent global search capabilities and ease of integration with other intelligent algorithms. In evacuation path planning, SUN... [5] This paper applies a degree convolutional neural network to a congested environment, using crowd density maps to determine congested locations and combining this with an ant colony algorithm to find the optimal path from the congested area to the evacuation exit. (Cao Xianghong) [6] By combining the algorithm with Dijkstra's algorithm, the initial path is first solved using Dijkstra's algorithm, and then the dynamic evacuation path is optimized under fire scenarios using the ant colony algorithm. WHANG ZD [7]In the improvement of the ant colony algorithm, a direction factor was introduced to reduce the number of inflection points in the generated path and to automatically adjust the pheromone distribution, thereby improving the computational efficiency of the algorithm. ZHANG SC et al.[8] used a pheromone diffusion model to analyze the ant colony algorithm. * The algorithm generates paths with high pheromone concentrations, which greatly improves the retrieval capability of the ant colony algorithm. LUO et al.[9] improved the ant colony algorithm by introducing a dynamic penalty method and using adaptive rules for node selection, which improved the convergence efficiency of the algorithm. Baioletti et al.

[10] proposed a high-energy pheromone model in the ant colony algorithm and introduced pheromone interaction in heuristic search. Yang Liwei et al.

[11] proposed an improved ant colony algorithm strategy that considers multiple factors, which solved the problem that the ant colony algorithm is difficult to apply in a variable and complex environment.

[0006] In summary, while there is considerable research both domestically and internationally on the application of ant colony algorithms in evacuation, its scope is primarily limited to small-scale evacuations or evacuations within a single transportation network, failing to meet the characteristics of large-scale, multi-modal transportation in urban agglomerations. Furthermore, most studies employ fixed weights when assigning network weights, neglecting to consider the weight differences arising from varying travel purposes among different travelers on the same route.

[0007] References

[0008] [1]Xidong Pi,Wei Maa,Zhen(Sean)Qiana.A general formulation for multi-modal dynamic traffic assignment considering multi-class vehicles,publictransit and parking[J].Transportation Research Part C,2019,104:369-389;

[0009] [2] Shi Wenjing. A Study on the Robustness of Urban Public Transportation Systems from the Perspective of Two-Layer Complex Networks [D]. Chang'an University, 2021;

[0010] [3]De Regt R,Von Ferber C,Holovatch Y,Leboaka M.Public transportation in Great Britain viewed as a complex network[J].Transportmetrica A-TransportScience,2019,15(2):722-748;

[0011] [4] Xu Min. Evaluation method of importance of subway network nodes based on passenger flow [J]. Journal of Shandong Jiaotong University, 2021, 29(04):39-45;

[0012] [5]SUN SL,ZHAO Q,XIE W Z.Study on safe evacuation routes based oncrowd density map of shopping mall[J].IEEE Access,2020,8:153981-153992;

[0013] [6] Cao Xianghong, Li Xinyan, Wei Xiaoge, et al. Dynamic planning of emergency evacuation routes based on Dijkstra-ACO hybrid algorithm [J]. Journal of Electronics and Information Technology, 2020, 42(06):1502-1509;

[0014] [7]WANG ZD,WU CH,XU J,et al.Research on path planning ofcleaningrobot based on an improved ant colony algorithm[J].MATEC Web of Conferences,2021336:07005-07005;

[0015] [8] ZHANG SC, PU JX, SI Y N. An adaptive improved ant colony system based on population information entropy for path planning of mobile robot [J]. IEEE Access, 2021, (99): 1-1;

[0016] [9]LUO Q,WANG H,ZHENG Y,et al.Research on path planning of mobilerobot based on improved ant colony algorithm[J].Neural Computing andApplications,2020,32(6):1555-1566;

[0017]

[10] Baioletti M, Milani A, Poggioni V, et al. Experimental evaluation of pheromone models in ACOPlan[J]. Annals of Mathematics and Artificial Intelligence, 2011, 62(3):187-217;

[0018]

[11] Yang Liwei, Fu Lixia, Guo Ning, et al. Path planning based on multi-factor improved ant colony algorithm [J / OL]. Computer Integrated Manufacturing Systems, 2021(11):1-18. Summary of the Invention

[0019] This invention aims to overcome the defects and shortcomings of the existing technology and provide a method for planning emergency evacuation routes in urban agglomeration integrated passenger transport networks. By optimizing network modeling, refining the analysis of the entire evacuation process, and improving the ant colony algorithm, emergency evacuation routes in urban agglomeration integrated passenger transport networks in the event of an incident can be obtained.

[0020] To achieve the above-mentioned objectives, the technical solution adopted by the present invention is as follows:

[0021] A method for planning emergency evacuation routes in an integrated passenger transport network of an urban agglomeration includes the following steps:

[0022] Step 1: Construction of a comprehensive passenger transport network model for urban agglomerations;

[0023] Step 1 includes the following sub-steps:

[0024] Step 1.1: All bus stations, train stations, airports, and ports within the urban agglomeration are designated as network nodes and numbered. Public transportation connecting lines between any network nodes are used as edges to construct single-mode passenger transport network models for the urban agglomeration.

[0025] Step 1.2: Delineate the central urban area of ​​the city as a transportation zone, and fully couple and link the nodes of various transportation modes that are geographically close within the transportation zone; all trips originate and terminate within the transportation zone; trips within the transportation zone are undertaken by urban public transportation, and the three single-mode passenger transport networks of the urban agglomeration are connected through a small transportation network composed of urban transportation, thus constructing a multi-mode passenger transport network model for the urban agglomeration.

[0026] Step 1.3: Outside the traffic zone, there are some stations with different modes of transportation such as urban transportation and walking that have a transfer time of less than 10 minutes. In order to reflect the transfer relationship, coupling edges are added between these nodes with transfer relationship, which become adjacent associated nodes.

[0027] Step 1.4: Define the edge weights of the multi-modal transportation connection network in urban agglomerations;

[0028] Step 1.5: Define the edge weights of the passenger transport network for a single mode of transport within the urban agglomeration;

[0029] Step 1.6: Couple the single-mode passenger transport network and the multi-mode passenger transport network of the urban agglomeration to construct a comprehensive passenger transport network for the urban agglomeration;

[0030] Step 2. Division of the evacuation phases of the integrated passenger transport network in urban agglomerations; the entire process of travelers evacuating within the network is divided into the following four phases:

[0031] (1) The start-up phase refers to the process by which evacuees reach the first passenger network node in the city from their starting position, which consists of randomly generated traveler locations and evacuation time within the city.

[0032] (2) Intercity travel phase, which refers to the phase in which travelers move within the single-mode passenger transport network of the urban agglomeration during the dispersal process;

[0033] (3) Transfer stage, which refers to the stage in which travelers transfer from one mode of transportation to another during the evacuation process, that is, the stage of movement in the urban cluster connecting passenger transport network, and refers to the time spent moving between different transport nodes in the city.

[0034] (4) Arrival stage, which refers to the stage after the traveler arrives at the evacuation destination and is evacuated from the transportation node in the destination area to the destination location in the city.

[0035] Step 3. Solve the emergency evacuation planning problem of the urban agglomeration integrated passenger transport network using the ant colony algorithm, including the following sub-steps:

[0036] Step 3.1: Initialize the algorithm parameter values, including: the number of ants m for the ant colony algorithm, the pheromone importance factor α, the heuristic information importance factor β, the initial path pheromone concentration, and the pheromone volatility factor ρ;

[0037] Step 3.2: Assign all ants to the initial evacuation nodes in the urban agglomeration integrated passenger transport network and perform path search; use roulette wheel to determine the ant's path selection method, and the selection probability P of each ant when selecting a path is simultaneously affected by the pheromone concentration of the path and the heuristic function;

[0038] Step 3.3: After completing one iteration, update the pheromone concentration of all paths in the network; the change in pheromone concentration is determined by the increase in pheromone during this iteration and the pheromone evaporated during the iteration.

[0039] Step 3.4: Determine the difference between the number of iterations N and the maximum number of iterations N. max The relationship, if N < N maxThen proceed to step 3.2; if N = N max Then proceed to step 3.5;

[0040] Step 3.5: Complete the calculation and output the optimal evacuation route and evacuation time.

[0041] Further, step 1.1 includes the following sub-steps:

[0042] Step 1.1.1: Obtain the locations of all bus stations, train stations, airports, and ports within the city cluster, and group and number them to construct a network node set S = {S1, S2, S3, S4}.

[0043] Step 1.1.2: Obtain the public transportation connectivity routes between nodes within the urban agglomeration, group them, and construct the network edge set L = {L1, L2, L3, L4}, where... L2, L3, and L4 are similar in shape to L1;

[0044] Step 1.1.3: Construct the urban agglomeration road passenger transport network, urban agglomeration rail passenger transport network, urban agglomeration waterway passenger transport network, and urban agglomeration air passenger transport network N1, N2, N3, N4 respectively.

[0045] Further, step 1.2 specifically includes:

[0046] Urban public transportation forms a small network within a traffic zone. Nodes of different modes of transport within the zone are connected by urban public transportation. Public transportation lines constitute the edges of the multi-mode transport connection network of the urban agglomeration. Nodes in the single-mode passenger transport network within the traffic zone constitute the nodes of the multi-mode transport connection network of the urban agglomeration.

[0047] Furthermore, in step 1.4, the edge weights of the multi-modal transportation connection network in the urban agglomeration are defined as follows:

[0048]

[0049] in, Let l represent the edge l of traveler x within traffic zone n. ij Dynamic weights during passage; This indicates that traveler x is within the traffic zone n on edge l. ij The distance traveled; v n Let v represent the operating speed of public transportation within traffic zone n, and v represent the operating speed of public transportation.

[0050] Furthermore, the edge weights of the urban agglomeration single-mode passenger transport network in step 1.5 are as follows:

[0051]

[0052] in, S represents the single-mode passenger transport network of an urban agglomeration. n Line l on ij Right of way; S represents the single-mode passenger transport network of an urban agglomeration. n Line l on ij distance; v n S represents the single-mode passenger transport network of an urban agglomeration. n The average operating speed of the corresponding transportation vehicle;

[0053] Furthermore, in step 3.2, when ants are choosing a path and facing the decision of selecting the next node, they use a roulette wheel to determine their transition probability. That is, the probability that the m-th ant will move from node i to node j at a certain moment is:

[0054]

[0055] Where, τ ij (t), τ is (t) represents the pheromone content on paths ij and is at time t; α and β are the pheromone importance factor and the heuristic information importance factor, respectively; η is (t), η ij (t) is the heuristic function; N m q0 represents the set of target nodes that ant m can choose to move to next when it is at node i; q0 is a continuously increasing random number set to prevent the algorithm from converging too quickly, q0∈[0,1].

[0056] Furthermore, in step 3.2, the heuristic function η ij It can be expressed as the following formula:

[0057]

[0058] Where d is the distance of the path;

[0059] Construct heuristic functions as follows:

[0060]

[0061]

[0062] in, This represents the distance from the evacuee's randomly generated starting point within the traffic zone to their destination transportation node within the zone; It represents the distance an evacuee moves between two nodes in a single-mode passenger transport network within an urban agglomeration; It represents the distance from a transport node within the transportation zone to the destination transport node for evacuees during transfers; This represents the distance from the evacuee's destination transportation node to a randomly generated destination location within the community; These represent the operating speeds of public transportation along the corresponding routes; t o The waiting time for evacuees is randomly generated.

[0063] Furthermore, in step 3.3, after completing one iteration, the pheromone concentration of all paths within the network is updated; the change in pheromone value is determined by the increase in pheromone during this iteration and the pheromone evaporated during the iteration, as expressed by the following formula:

[0064] τ ij (t+1)=(1-ρ)τ ij (t)+ρΔτ ij (t+1) (7)

[0065]

[0066]

[0067] Where, τ ij (t+1) represents the amount of pheromone on path ij at time t+1; Δτ ij (t+1) represents the pheromone increment on path ij at time t+1; Let λ be the amount of pheromone left by the m-th ant on path ij; λ is a control coefficient, which is 1 if ant m passes through path ij, and 0 if ant m does not pass through path ij; Q is a constant representing the intensity of the pheromone.

[0068] This invention discloses an emergency evacuation route planning system for an urban agglomeration integrated passenger transport network. This system can be used to implement the above-mentioned emergency evacuation route planning method for an urban agglomeration integrated passenger transport network. Specifically, it includes: an urban agglomeration integrated passenger transport network module, an urban agglomeration integrated passenger transport network evacuation stage division module, and an urban agglomeration integrated passenger transport network emergency evacuation planning solution module.

[0069] The integrated passenger transport network module for urban agglomerations includes: a single-mode passenger transport network for urban agglomerations and a multi-mode transport connection network for urban agglomerations;

[0070] All bus stations, train stations, airports, and ports within the urban agglomeration are treated as network nodes and numbered. Public transportation connecting lines between any network nodes are used as edges to construct single-mode passenger transport network models for the urban agglomeration.

[0071] The city center is designated as a transportation zone, and nodes of various transportation modes that are geographically close within the transportation zone are fully coupled and linked. All trips originate and terminate within the transportation zone. Trips within the transportation zone are handled by urban public transportation. A small transportation network composed of urban transportation connects the passenger transport networks of three urban agglomerations with single transportation modes, thus constructing a passenger transport network model for connecting multiple transportation modes in urban agglomerations.

[0072] The urban agglomeration integrated passenger transport network evacuation phase division module is used to divide the entire process of travelers evacuating within the network into the following four phases:

[0073] (1) The start-up phase refers to the process by which evacuees reach the first passenger network node in the city from their starting position, which consists of randomly generated traveler locations and evacuation time within the city.

[0074] (2) Intercity travel phase, which refers to the phase in which travelers move within the single-mode passenger transport network of the urban agglomeration during the dispersal process;

[0075] (3) Transfer stage, which refers to the stage in which travelers transfer from one mode of transportation to another during the evacuation process, that is, the stage of movement in the urban cluster connecting passenger transport network, and refers to the time spent moving between different transport nodes in the city.

[0076] (4) Arrival stage, which refers to the stage after the traveler arrives at the evacuation destination and is evacuated from the transportation node in the destination area to the destination location in the city.

[0077] The emergency evacuation planning and solution module for the urban agglomeration integrated passenger transport network is used to: assign all ants of the ant colony algorithm to the initial evacuation nodes in the urban agglomeration integrated passenger transport network and perform path search; determine the path selection method of the ants using roulette wheel; update the pheromone concentration of all paths in the network after completing the iterative calculation; and complete the calculation and output the optimal evacuation path and evacuation time.

[0078] Compared with the prior art, the advantages of the present invention are as follows:

[0079] 1. The modeling process considers urban traffic conditions, making the constructed integrated passenger transport network model for urban agglomerations more consistent with actual traffic network morphology. Previous studies on urban agglomeration traffic behavior often neglected the specific traffic conditions within cities due to the large geographical area spanned and the complexity of the network. This invention models the internal transportation network of cities by defining the city's central urban area as a traffic zone and constructing a passenger transport network connecting various modes of transport, such as long-distance bus stations, train stations, and airports. Urban traffic is combined through single-mode passenger transport networks to construct an integrated passenger transport network model for urban agglomerations.

[0080] 2. The evacuation process was divided into stages, and the travel characteristics and situations of each stage were studied, establishing a complete evacuation process from generation to dissipation. Based on a thorough analysis of the entire emergency evacuation process in urban agglomerations, the evacuation process was divided into the initiation stage, intercity travel stage, transfer stage, and arrival stage. Each stage and its associated time and costs were defined, accurately describing the evacuation process of evacuees in the urban agglomeration's integrated passenger transport network during emergencies. This provides a theoretical basis for using the ant colony algorithm to solve the emergency evacuation path planning problem in urban agglomeration integrated passenger transport networks.

[0081] 3. When constructing the heuristic function for the ant colony algorithm, a dynamic heuristic function is set according to the different evacuation stages of evacuees, thereby improving the efficiency and realism of the simulation. Based on the characteristics of traffic evacuation behavior, a heuristic function based on evacuation time is set. Due to different travel stages, the time spent by different evacuees moving within the same traffic zone or road segment may vary. By establishing a dynamic heuristic algorithm based on evacuation stage division, the path selection process of evacuees is described more accurately, improving the efficiency and realism of the simulation algorithm. Attached Figure Description

[0082] Figure 1 This is a flowchart of an emergency evacuation route planning method for an integrated passenger transport network in an urban agglomeration, according to an embodiment of the present invention.

[0083] Figure 2 This is a schematic diagram of a partial model of the integrated passenger transport network in an urban agglomeration according to an embodiment of the present invention;

[0084] Figure 3 This is a modeling diagram of the integrated passenger transport network of an urban agglomeration according to an embodiment of the present invention;

[0085] Figure 4 This is a flowchart illustrating the simulation and analysis of the ant colony algorithm for solving the emergency evacuation route planning problem of an urban agglomeration integrated passenger transport network according to an embodiment of the present invention.

[0086] Figure 5 This is a convergence analysis diagram of the ant colony algorithm for solving the emergency evacuation route planning problem of urban agglomeration integrated passenger transport network according to an embodiment of the present invention;

[0087] Figure 6 This invention provides an embodiment of the ant colony algorithm for solving the problem of emergency evacuation route planning in urban agglomeration integrated passenger transport networks, which yields the optimal evacuation route image.

[0088] Figure 7 This invention relates to an ant colony algorithm for solving the problem of emergency evacuation route planning in urban agglomeration integrated passenger transport networks, which calculates the optimal evacuation route cost. Detailed Implementation

[0089] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and examples.

[0090] like Figure 1 As shown, a method for planning emergency evacuation routes in an integrated passenger transport network of an urban agglomeration includes the following steps:

[0091] Step 1. Modeling the integrated passenger transport network of the urban agglomeration;

[0092] Step 2. Division of the evacuation phases of the integrated passenger transport network in urban agglomerations;

[0093] Step 3. Solve the emergency evacuation planning problem of the urban agglomeration integrated passenger transport network using the ant colony algorithm.

[0094] Step 1 includes the following sub-steps:

[0095] Step 1.1: All bus stations, train stations, airports, and ports within the urban agglomeration are designated as network nodes and numbered. Public transportation connecting lines between any nodes are used as edges to construct single-mode passenger transport network models for the urban agglomeration.

[0096] Step 1.1 includes the following sub-steps:

[0097] Step 1.1.1: Obtain the locations of all bus stations, train stations, airports, and ports within the city cluster, and group and number them to construct a network node set S = {S1, S2, S3, S4}.

[0098] Step 1.1.2: Obtain the public transportation connectivity routes between nodes within the urban agglomeration, group them, and construct the network edge set L = {L1, L2, L3, L4}, where... L2, L3, and L4 are similar in shape to L1;

[0099] Step 1.1.3: Construct the urban agglomeration road passenger transport network, urban agglomeration rail passenger transport network, urban agglomeration waterway passenger transport network, and urban agglomeration air passenger transport network N1, N2, N3, N4 respectively.

[0100] Step 1.2: The central urban area is designated as a transportation zone, where all trips originate and terminate within the transportation zone. Trips within the zone are handled by urban public transportation. A small-scale transportation network composed of urban public transportation connects the three single-mode passenger transport networks of the urban agglomeration, constructing a multi-mode passenger transport network model for the urban agglomeration.

[0101] Urban public transportation forms a small network within a traffic zone. Nodes of different modes of transport within the zone are connected by urban public transportation. Public transportation lines constitute the edges of the multi-mode transport connection network of the urban agglomeration. Nodes in the single-mode passenger transport network within the traffic zone constitute the nodes of the multi-mode transport connection network of the urban agglomeration.

[0102] Step 1.3: For two nodes that are geographically close and have convenient transfers outside the traffic zone, in order to reflect the transfer relationship, a coupling edge is added between these nodes with transfer relationship, thus making them adjacent associated nodes;

[0103] Step 1.4: Define the edge weights of the multi-modal transportation connection network in urban agglomerations;

[0104] Because the area within a traffic zone is relatively small and the mode of transportation is relatively simple, the public transportation speed *v* on all lines within the network can be considered to be the same. Furthermore, since both the origin and destination points are generated within the traffic zone, different travelers still need to take urban public transportation to reach their specific final destination after arriving at the transportation node in the destination zone. Therefore, the edge weights of the multi-modal transportation connection network in the urban agglomeration are defined as follows:

[0105]

[0106] in, Let l represent the edge l of traveler x within traffic zone n. ij Dynamic weights during passage. This indicates that traveler x is within the traffic zone n on edge l. ij The distance traveled. v n This indicates the operating speed of public transportation within traffic zone n.

[0107] Step 1.5: Define the edge weights of the passenger transport network for a single mode of transport within the urban agglomeration;

[0108] Unlike roads within a traffic zone, intercity public transport lines within a city cluster only involve point-to-point transport, meaning travel from one node to another. Therefore, all travelers on the same transport route cover the same distance. Consequently, the edge weights of a single-mode passenger transport network within a city cluster are:

[0109]

[0110] in, S represents the single-mode passenger transport network of an urban agglomeration. n Line l on ij Right of way. S represents the single-mode passenger transport network of an urban agglomeration. n Line l on ij The distance. v nS represents the single-mode passenger transport network of an urban agglomeration. n The average operating speed of the corresponding transportation vehicle.

[0111] Step 1.6: Couple the urban agglomeration's passenger transport network and multi-modal transport connection network to construct a comprehensive urban agglomeration passenger transport network, such as... Figure 2 , Figure 3 As shown.

[0112] In step 2, based on the actual travel situation, the entire process of travelers dispersing within the network is divided into the following four stages:

[0113] (1) The start-up phase refers to the process by which evacuees arrive at the first passenger network node in the city from their starting position. It consists of randomly generated traveler locations, evacuation time within the city, and waiting time.

[0114] (2) Intercity travel phase, which refers to the phase in which travelers move within the single-mode passenger transport network of the urban agglomeration during the dispersal process.

[0115] (3) Transfer stage, which refers to the stage in which travelers transfer from one mode of transportation to another during the evacuation process, that is, the stage of movement in the urban cluster connecting passenger transport network, which refers to the time spent moving between different transport nodes in the city and the waiting time.

[0116] (4) Arrival stage, which refers to the stage after the traveler arrives at the evacuation destination and is evacuated from the transportation node in the destination area to the destination location in the city.

[0117] like Figure 4 As shown, step 3 includes the following sub-steps:

[0118] Step 3.1: Initialize algorithm parameters, including the number of ants m, initial parameter values, including the number of ants m, pheromone importance factor α, heuristic information importance factor β, initial path pheromone concentration, and pheromone volatility factor ρ, etc.

[0119] Step 3.2: All ants are assigned to initial evacuation nodes in the urban agglomeration integrated passenger transport network and path search is performed; a roulette wheel method is used to determine the ants' path selection, and the selection probability P of each ant is simultaneously affected by the pheromone concentration of the path and the heuristic function; the roulette wheel method described in Step 3.2 is characterized by:

[0120] When ants choose their path and face the decision of which node to move to, they use a roulette wheel to determine their transition probability. That is, the probability that the m-th ant will move from node i to node j at a given time is:

[0121]

[0122] Where, τ ij (t), τ is (t) represents the pheromone content on paths ij and is at time t; α and β are the pheromone importance factor and the heuristic information importance factor, respectively; η is (t), η ij (t) is the heuristic function; N m Let q0 represent the set of target nodes that ant m can choose to move to next when it is at node i; q0 is a continuously increasing random number set to prevent the algorithm from converging too quickly, q0∈[0,1];

[0123] Heuristic function η ij This is a crucial factor influencing ants' path selection in ant colony optimization; in function design, it is typically taken as the reciprocal of the distance.

[0124]

[0125] Where d is the distance of the path.

[0126] However, in the route planning process for emergency evacuation of urban agglomeration integrated passenger transport networks, two objective factors need to be considered when constructing heuristic functions. First, during evacuation activities, due to the impact of emergencies, evacuees prioritize the time required to reach their destination over the distance traveled. Second, given the complexity of transportation routes and the vast geographical scope of urban agglomeration integrated passenger transport networks, it is necessary to consider the characteristics of evacuees at different stages of their journey and construct heuristic functions accordingly.

[0127]

[0128]

[0129] in, This represents the distance from the evacuee's randomly generated starting point within the traffic zone to their destination transportation node within the zone; It represents the distance an evacuee moves between two nodes in a single-mode passenger transport network within an urban agglomeration; It represents the distance from a transport node within the transportation zone to the destination transport node for evacuees during transfers; This represents the distance from the evacuee's destination transportation node to a randomly generated destination location within the community. These represent the operating speeds of public transportation along the corresponding routes. o The waiting time for evacuees is randomly generated.

[0130] Step 3.3: After completing one iteration, update the pheromone concentration for all paths within the network. The change in pheromone concentration is determined by the increase in pheromone during this iteration and the pheromone evaporated during the iteration.

[0131] The pheromone concentration update method described in step 3.3 is characterized by:

[0132] τ ij (t+1)=(1-ρ)τ ij (t)+ρΔτ ij (t+1) (25)

[0133]

[0134]

[0135] Where, τ ij (t+1) represents the amount of pheromone on path ij at time t+1; Δτ ij (t+1) represents the pheromone increment on path ij at time t+1; Let λ be the amount of pheromone left by the m-th ant on path ij; λ is a control coefficient, which is 1 if ant m passes through path ij, and 0 if ant m does not pass through path ij; Q is a constant representing the intensity of the pheromone.

[0136] Step 3.4: Determine the difference between the number of iterations N and the maximum number of iterations N. max The relationship, if N < N max Then proceed to step 3.2; if N = N max Then proceed to step 3.5;

[0137] Step 3.5: Complete the calculation and output the optimal evacuation route and evacuation time.

[0138] This embodiment, in conjunction with the Hohhot-Baotou-Ordos-Yulin urban agglomeration, includes the following steps:

[0139] Step 1: Obtain the specific details of each transportation node and the public transportation routes between nodes within the Hohhot-Baotou-Ordos-Yulin urban agglomeration. Abstract the stations as network nodes and the public transportation routes as network edges. Construct the single-mode passenger transport network model, the multi-mode passenger transport network model, and the comprehensive passenger transport network model of the Hohhot-Baotou-Ordos-Yulin urban agglomeration in sequence.

[0140] Step 2: Due to the near absence of waterway transportation within the Hohhot-Baotou-Ordos-Yulin urban agglomeration and the extremely low proportion of air transport, it is difficult to construct a single-mode passenger transport network. Therefore, the single-mode passenger transport network only constructs a road transport sub-network model N1 and a rail transport sub-network model N2. The road transport sub-network N1 contains 83 nodes, and the rail transport sub-network N2 contains 18 nodes, i.e., n 1＝83 ,n 2＝18 The total number of nodes is 101.

[0141] Step 3: Due to the relatively small number of medium and large cities within the Hohhot-Baotou-Ordos-Yulin urban agglomeration, and the small size of most counties and banners (excluding a few larger cities), the traffic zones within the Hohhot-Baotou-Ordos-Yulin urban agglomeration are divided into four: Hohhot Traffic Zone T1, Baotou Traffic Zone T2, Ordos Traffic Zone T3, and Yulin Traffic Zone T4. Where n1 = 5, n2 = 8, n3 = 6, and n4 = 4.

[0142] Step 4: Utilize GIS technology to obtain the actual distances between nodes within the passenger transport network connecting the Hohhot, Baotou, Ordos, and Yulin urban agglomerations. Obtain the average operating speed of public transportation within Hohhot, Baotou, Ordos, and Yulin through surveys. Based on the above data, determine the right-of-way for the urban agglomeration connecting passenger transport network within the four traffic zones.

[0143] Step 5: By combining field surveys, document reviews, and actual operational data, obtain the distances between various transportation nodes within the Hohhot-Baotou-Ordos-Yulin urban agglomeration and the average operating speeds of various transportation modes. Based on the above data, determine the right-of-way for single-mode passenger transport networks within the Hohhot-Baotou-Ordos-Yulin urban agglomeration.

[0144] Step 6: Initialize the ant colony algorithm parameters. Based on the previous experimental simulation data, set the parameter values ​​to α = 1.5, β = 2, ρ = 0.4, and m = 200 for calculation.

[0145] Step 7: Set the initial evacuation location as the urban area of ​​Hohhot and the evacuation destination as the urban area of ​​Yulin. Add the city numbers to the taboo table and calculate the heuristic information matrix.

[0146] Step 8: Each evacuee begins searching for evacuation routes from Hohhot City, and determines the node selection for the next destination through a roulette wheel method. After selection, the taboo table is updated according to the selected city number.

[0147] Step 9: Determine whether the evacuees have reached Yulin City. If they have, stop the iteration and record the evacuation route and time spent. Otherwise, return to step 7.

[0148] Step 10: Update pheromones, clear the tabu table, and proceed to the next iteration until the maximum number of iterations is reached. Output the optimal algorithm convergence result, optimal path cost, and optimal evacuation path, such as... Figure 5-7 As shown.

[0149] A method for planning emergency evacuation routes in an integrated passenger transport network of an urban agglomeration includes the following steps:

[0150] Step 1: Construction of a comprehensive passenger transport network model for urban agglomerations;

[0151] Step 1 includes the following sub-steps:

[0152] Step 1.1: All bus stations, train stations, airports, and ports within the urban agglomeration are designated as network nodes and numbered. Public transportation connecting lines between any network nodes are used as edges to construct single-mode passenger transport network models for the urban agglomeration.

[0153] Step 1.2: Delineate the central urban area of ​​the city as a transportation zone, and fully couple and link the nodes of various transportation modes that are geographically close within the transportation zone; all trips originate and terminate within the transportation zone; trips within the transportation zone are undertaken by urban public transportation, and the three single-mode passenger transport networks of the urban agglomeration are connected through a small transportation network composed of urban transportation, thus constructing a multi-mode passenger transport network model for the urban agglomeration.

[0154] Step 1.3: Outside the traffic zone, there are some stations with different modes of transportation such as urban transportation and walking that have a transfer time of less than 10 minutes. In order to reflect the transfer relationship, coupling edges are added between these nodes with transfer relationship, which become adjacent associated nodes.

[0155] Step 1.4: Define the edge weights of the multi-modal transportation connection network in urban agglomerations;

[0156] Step 1.5: Define the edge weights of the passenger transport network for a single mode of transport within the urban agglomeration;

[0157] Step 1.6: Couple the single-mode passenger transport network and the multi-mode passenger transport network of the urban agglomeration to construct a comprehensive passenger transport network for the urban agglomeration;

[0158] Step 2. Division of the evacuation phases of the integrated passenger transport network in urban agglomerations; the entire process of travelers evacuating within the network is divided into the following four phases:

[0159] (1) The start-up phase refers to the process by which evacuees reach the first passenger network node in the city from their starting position, which consists of randomly generated traveler locations and evacuation time within the city.

[0160] (2) Intercity travel phase, which refers to the phase in which travelers move within the single-mode passenger transport network of the urban agglomeration during the dispersal process;

[0161] (3) Transfer stage, which refers to the stage in which travelers transfer from one mode of transportation to another during the evacuation process, that is, the stage of movement in the urban cluster connecting passenger transport network, and refers to the time spent moving between different transport nodes in the city.

[0162] (4) Arrival stage, which refers to the stage after the traveler arrives at the evacuation destination and is evacuated from the transportation node in the destination area to the destination location in the city.

[0163] Step 3. Solve the emergency evacuation planning problem of the urban agglomeration integrated passenger transport network using the ant colony algorithm, including the following sub-steps:

[0164] Step 3.1: Initialize the algorithm parameter values, including: the number of ants m for the ant colony algorithm, the pheromone importance factor α, the heuristic information importance factor β, the initial path pheromone concentration, and the pheromone volatility factor ρ;

[0165] Step 3.2: Assign all ants to the initial evacuation nodes in the urban agglomeration integrated passenger transport network and perform path search; use roulette wheel to determine the ant's path selection method, and the selection probability P of each ant when selecting a path is simultaneously affected by the pheromone concentration of the path and the heuristic function;

[0166] Step 3.3: After completing one iteration, update the pheromone concentration of all paths in the network; the change in pheromone concentration is determined by the increase in pheromone during this iteration and the pheromone evaporated during the iteration.

[0167] Step 3.4: Determine the difference between the number of iterations N and the maximum number of iterations N. max The relationship, if N < N max Then proceed to step 3.2; if N = N max Then proceed to step 3.5;

[0168] Step 3.5: Complete the calculation and output the optimal evacuation route and evacuation time.

[0169] In another embodiment of the present invention, an emergency evacuation route planning system for an urban agglomeration integrated passenger transport network is provided. This system can be used to implement the above-mentioned emergency evacuation route planning method for an urban agglomeration integrated passenger transport network. Specifically, it includes: an urban agglomeration integrated passenger transport network module, an urban agglomeration integrated passenger transport network evacuation stage division module, and an urban agglomeration integrated passenger transport network emergency evacuation planning solution module.

[0170] The integrated passenger transport network module for urban agglomerations includes: a single-mode passenger transport network for urban agglomerations and a multi-mode transport connection network for urban agglomerations;

[0171] All bus stations, train stations, airports, and ports within the urban agglomeration are treated as network nodes and numbered. Public transportation connecting lines between any network nodes are used as edges to construct single-mode passenger transport network models for the urban agglomeration.

[0172] The city center is designated as a transportation zone, and nodes of various transportation modes that are geographically close within the transportation zone are fully coupled and linked. All trips originate and terminate within the transportation zone. Trips within the transportation zone are handled by urban public transportation. A small transportation network composed of urban transportation connects the passenger transport networks of three urban agglomerations with single transportation modes, thus constructing a passenger transport network model for connecting multiple transportation modes in urban agglomerations.

[0173] The urban agglomeration integrated passenger transport network evacuation phase division module is used to divide the entire process of travelers evacuating within the network into the following four phases:

[0174] (1) The start-up phase refers to the process by which evacuees reach the first passenger network node in the city from their starting position, which consists of randomly generated traveler locations and evacuation time within the city.

[0175] (2) Intercity travel phase, which refers to the phase in which travelers move within the single-mode passenger transport network of the urban agglomeration during the dispersal process;

[0176] (3) Transfer stage, which refers to the stage in which travelers transfer from one mode of transportation to another during the evacuation process, that is, the stage of movement in the urban cluster connecting passenger transport network, and refers to the time spent moving between different transport nodes in the city.

[0177] (4) Arrival stage, which refers to the stage after the traveler arrives at the evacuation destination and is evacuated from the transportation node in the destination area to the destination location in the city.

[0178] The emergency evacuation planning and solution module for the urban agglomeration integrated passenger transport network is used to: assign all ants of the ant colony algorithm to the initial evacuation nodes in the urban agglomeration integrated passenger transport network and perform path search; determine the path selection method of the ants using roulette wheel; update the pheromone concentration of all paths in the network after completing the iterative calculation; and complete the calculation and output the optimal evacuation path and evacuation time.

[0179] In another embodiment of the present invention, a terminal device is provided, comprising a processor and a memory. The memory stores a computer program, which includes program instructions. The processor executes the program instructions stored in the computer storage medium. The processor may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing and control core of the terminal, suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions to achieve a corresponding method flow or corresponding function. The processor described in this embodiment of the present invention can be used in the operation of an emergency evacuation route planning method for an urban agglomeration integrated passenger transport network, including the following steps:

[0180] Step 1. Modeling the integrated passenger transport network of the urban agglomeration;

[0181] Step 2. Division of the evacuation phases of the integrated passenger transport network in urban agglomerations;

[0182] Step 3. Solve the emergency evacuation planning problem of the urban agglomeration integrated passenger transport network using the ant colony algorithm.

[0183] In another embodiment of the present invention, a storage medium is provided, specifically a computer-readable storage medium (Memory). This computer-readable storage medium is a memory device in a terminal device used to store programs and data. It is understood that the computer-readable storage medium here can include both the built-in storage medium in the terminal device and extended storage media supported by the terminal device. The computer-readable storage medium provides storage space that stores the terminal's operating system. Furthermore, this storage space also stores one or more instructions suitable for loading and execution by a processor. These instructions can be one or more computer programs (including program code). It should be noted that the computer-readable storage medium here can be high-speed RAM or non-volatile memory, such as at least one disk storage device.

[0184] One or more instructions stored in a computer-readable storage medium can be loaded and executed by a processor to implement the corresponding steps of the emergency evacuation route planning method for the urban agglomeration integrated passenger transport network in the above embodiments; one or more instructions in the computer-readable storage medium are loaded and executed by the processor in the following steps:

[0185] Step 1. Modeling the integrated passenger transport network of the urban agglomeration;

[0186] Step 2. Division of the evacuation phases of the integrated passenger transport network in urban agglomerations;

[0187] Step 3. Solve the emergency evacuation planning problem of the urban agglomeration integrated passenger transport network using the ant colony algorithm.

[0188] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0189] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0190] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0191] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0192] Those skilled in the art will recognize that the embodiments described herein are intended to help the reader understand the implementation methods of the present invention, and should be understood that the scope of protection of the present invention is not limited to such specific statements and embodiments. Those skilled in the art can make various other specific modifications and combinations based on the technical teachings disclosed in this invention without departing from the spirit of the invention, and these modifications and combinations are still within the scope of protection of the present invention.

Claims

1. A method for planning emergency evacuation routes in an integrated passenger transport network of an urban agglomeration, characterized in that, Includes the following steps: Step 1: Construction of a comprehensive passenger transport network model for urban agglomerations; Step 1 includes the following sub-steps: Step 1.1: All bus stations, train stations, airports, and ports within the urban agglomeration are designated as network nodes and numbered. Public transportation connecting lines between any network nodes are used as edges to construct single-mode passenger transport network models for the urban agglomeration. Step 1.2: Delineate the central urban area of ​​the city as a transportation zone, and fully couple and link the nodes of various transportation modes that are geographically close within the transportation zone; all trips originate and terminate within the transportation zone; trips within the transportation zone are undertaken by urban public transportation, and the three single-mode passenger transport networks of the urban agglomeration are connected through a small transportation network composed of urban transportation, thus constructing a multi-mode passenger transport network model for the urban agglomeration. Step 1.3: Outside the traffic zone, there are some stations with different modes of transportation such as urban transportation and walking that have a transfer time of less than 10 minutes. In order to reflect the transfer relationship, coupling edges are added between these nodes with transfer relationship, which become adjacent associated nodes. Step 1.4: Define the edge weights of the multi-modal transportation connection network in urban agglomerations; Step 1.5: Define the edge weights of the passenger transport network for a single mode of transport within the urban agglomeration; Step 1.6: Couple the single-mode passenger transport network and the multi-mode passenger transport network of the urban agglomeration to construct a comprehensive passenger transport network for the urban agglomeration; Step 2. Division of the evacuation phases of the integrated passenger transport network in urban agglomerations; the entire process of travelers evacuating within the network is divided into the following four phases: (1) The start-up phase refers to the process by which evacuees reach the first passenger network node in the city from their starting position, which consists of randomly generated traveler locations and evacuation time within the city. (2) Intercity travel phase, which refers to the phase in which travelers move within the single-mode passenger transport network of the urban agglomeration during the dispersal process; (3) Transfer stage, which refers to the stage in which travelers transfer from one mode of transportation to another during the evacuation process, that is, the stage of movement in the urban cluster connecting passenger transport network, and refers to the time spent moving between different transport nodes in the city. (4) Arrival stage, which refers to the stage after the traveler arrives at the evacuation destination and is evacuated from the transportation node in the destination area to the destination location in the city. Step 3. Solve the emergency evacuation planning problem of the urban agglomeration integrated passenger transport network using the ant colony algorithm, including the following sub-steps: Step 3.1: Initialize the algorithm parameter values, including: the number of ants m for the ant colony algorithm, the pheromone importance factor α, the heuristic information importance factor β, the initial path pheromone concentration, and the pheromone volatility factor ρ; Step 3.2: Assign all ants to the initial evacuation nodes in the urban agglomeration integrated passenger transport network and perform path search; use roulette wheel to determine the ant's path selection method, and the selection probability P of each ant when selecting a path is simultaneously affected by the pheromone concentration of the path and the heuristic function; The heuristic function is as follows: ； ； in, This represents the distance from the evacuee's randomly generated starting point within the traffic zone to their destination transportation node within the zone; It represents the distance an evacuee moves between two nodes in a single-mode passenger transport network within an urban agglomeration; It represents the distance from a transport node within the transportation zone to the destination transport node for evacuees during transfers; This represents the distance from the evacuee's destination transportation node to a randomly generated destination location within the community; , , , These represent the operating speeds of public transportation along the corresponding routes; The waiting time for evacuees is randomly generated; Step 3.3: After completing one iteration, update the pheromone concentration of all paths in the network; the change in pheromone concentration is determined by the increase in pheromone during this iteration and the pheromone evaporated during the iteration. Step 3.4: Determine the difference between the number of iterations N and the maximum number of iterations. If the relationship, Then proceed to step 3.2; if Then proceed to step 3.5; Step 3.5: Complete the calculation and output the optimal evacuation route and evacuation time.

2. The method for planning emergency evacuation routes in an urban agglomeration integrated passenger transport network according to claim 1, characterized in that: Step 1.1 includes the following sub-steps: Step 1.1.1: Obtain the locations of all bus stations, train stations, airports, and ports within the city cluster, and group and number them to construct a network node set. , , , , ; Step 1.1.2: Obtain the public transportation connectivity routes between nodes within the urban agglomeration, group them, and construct a set of network edges. ,in , , Form and shape ; Step 1.1.3: Construct the urban agglomeration road passenger transport network, urban agglomeration rail passenger transport network, urban agglomeration waterway passenger transport network, and urban agglomeration air passenger transport network respectively. .

3. The emergency evacuation route planning method for an integrated passenger transport network in an urban agglomeration, as described in claim 1, is characterized in that: Step 1.2 specifically includes: Urban public transportation forms a small network within a traffic zone. Nodes of different modes of transport within the zone are connected by urban public transportation. Public transportation lines constitute the edges of the multi-mode transport connection network of the urban agglomeration. Nodes in the single-mode passenger transport network within the traffic zone constitute the nodes of the multi-mode transport connection network of the urban agglomeration.

4. The emergency evacuation route planning method for an integrated passenger transport network in an urban agglomeration, as described in claim 1, is characterized in that: In step 1.4, the edge weights of the multi-modal transportation connection network of the urban agglomeration are defined as follows: (1)； in, Indicates the traveler In the traffic community internal edges Dynamic weights during passage; Indicates the traveler In the traffic community Internal edge The distance traveled; Indicates traffic community The operating speed of internal public transportation, Indicates the operating speed of public transportation.

5. The emergency evacuation route planning method for an integrated passenger transport network in an urban agglomeration, as described in claim 4, is characterized in that: In step 1.5, the edge weights of the urban agglomeration single-mode passenger transport network are: (2)； in, Indicates a single mode of transport passenger network within an urban agglomeration. On the line Right of way; Indicates a single mode of transport passenger network within an urban agglomeration. On the line The distance; Indicates a single mode of transport passenger network within an urban agglomeration. The average operating speed of the corresponding transportation vehicle.

6. The method for planning emergency evacuation routes in an integrated passenger transport network of an urban agglomeration according to claim 1, characterized in that: In step 3.2, when an ant is choosing its path and faces the decision of which node to move to, it uses a roulette wheel to determine its transition probability, i.e., the probability of its transition at a certain moment. Only ants from the node Transfer to node The probability is: (3)； in, , The path at time t , The amount of pheromones on the surface; , These are the pheromone importance factor and the heuristic information importance factor, respectively. , For heuristic functions; Ants At the node The set of target nodes that can be selected for the next step; This is a continuously increasing random number set to prevent the algorithm from converging too quickly. .

7. The emergency evacuation route planning method for an integrated passenger transport network in an urban agglomeration according to claim 1, characterized in that: In step 3.3, after completing one iteration, the pheromone concentration of all paths in the network is updated; the change in pheromone value is determined by the increase in pheromone during this iteration and the pheromone evaporated during the iteration, as shown in the following formula: (7)； (8)； (9)； in, Path at time t+1 The amount of pheromones on the body; In order to be in Path within a time period The increase in pheromones on the surface; For the first Only ants on the path The amount of pheromones left on the skin; As a control factor, if ants Path ,but If ants Path not visited ,but Q is a constant representing the intensity of the pheromone.

8. An emergency evacuation route planning system for an integrated passenger transport network in an urban agglomeration, characterized in that... This system can be used to implement the emergency evacuation route planning method for urban agglomeration integrated passenger transport network as described in any one of claims 1 to 7; The urban agglomeration integrated passenger transport network emergency evacuation route planning system includes: an urban agglomeration integrated passenger transport network module, an urban agglomeration integrated passenger transport network evacuation stage division module, and an urban agglomeration integrated passenger transport network emergency evacuation planning solution module. The integrated passenger transport network module for urban agglomerations includes: a single-mode passenger transport network for urban agglomerations and a multi-mode transport connection network for urban agglomerations; All bus stations, train stations, airports, and ports within the urban agglomeration are treated as network nodes and numbered. Public transportation connecting lines between any network nodes are used as edges to construct single-mode passenger transport network models for the urban agglomeration. The city center is designated as a transportation zone, and nodes of various transportation modes that are geographically close within the transportation zone are fully coupled and linked. All trips originate and terminate within the transportation zone. Trips within the transportation zone are handled by urban public transportation. A small transportation network composed of urban transportation connects the passenger transport networks of three urban agglomerations with single transportation modes, thus constructing a passenger transport network model for connecting multiple transportation modes in urban agglomerations. The urban agglomeration integrated passenger transport network evacuation phase division module is used to divide the entire process of travelers evacuating within the network into the following four phases: (1) The start-up phase refers to the process by which evacuees reach the first passenger network node in the city from their starting position, which consists of randomly generated traveler locations and evacuation time within the city. (2) Intercity travel phase, which refers to the phase in which travelers move within the single-mode passenger transport network of the urban agglomeration during the dispersal process; (3) Transfer stage, which refers to the stage in which travelers transfer from one mode of transportation to another during the evacuation process, that is, the stage of movement in the urban cluster connecting passenger transport network, and refers to the time spent moving between different transport nodes in the city. (4) Arrival stage, which refers to the stage after the traveler arrives at the evacuation destination and is evacuated from the transportation node in the destination area to the destination location in the city. The emergency evacuation planning and solution module for the urban agglomeration integrated passenger transport network is used to: assign all ants of the ant colony algorithm to the initial evacuation nodes in the urban agglomeration integrated passenger transport network and perform path search; determine the path selection method of the ants using roulette wheel; update the pheromone concentration of all paths in the network after completing the iterative calculation; and complete the calculation and output the optimal evacuation path and evacuation time.

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