Urban multi-modal traffic network state simulation deduction method and device based on monte carlo simulation

Abstract

The application discloses a city multimodal traffic network state simulation deduction method and device based on Monte Carlo simulation, and the method comprises the following steps: constructing a city multimodal traffic network model and a disturbance scene model; randomly extracting a disturbance scene from the disturbance scene model based on Monte Carlo random sampling, and correcting the network state of the city multimodal traffic network model based on the extracted disturbance scene; based on the corrected city multimodal traffic network model, performing batch simulation deduction and collecting network running state data; based on the collected network running state data, performing multi-dimensional evaluation on the running result of the city multimodal traffic network model, and identifying a traffic network bottleneck. The application can face the multimodal traffic network and can perform dynamic simulation deduction and optimization decision under the disturbance condition.

Description

Technical Field

[0001] This invention belongs to the field of intelligent transportation technology, specifically relating to a method and apparatus for simulating and extrapolating the state of urban multimodal transportation networks based on Monte Carlo simulation. Background Technology

[0002] With the continuous development of urban integrated transportation systems, the coupling between road traffic, rail transit, conventional buses, ride-hailing services, and slow-moving transportation is constantly increasing. Urban residents often need to choose or transfer between multiple modes of transportation during their actual travels. Therefore, the operational status of multimodal transportation networks has become an important factor affecting urban traffic efficiency, travel reliability, and emergency response capabilities.

[0003] Existing traffic analysis methods are mostly based on deterministic parameters or finite scenario assumptions, typically reflecting only operational results under fixed conditions. They struggle to accurately characterize the comprehensive impact of uncertainties such as extreme weather, traffic accidents, road construction, equipment failures, temporary traffic controls, and surges in passenger flow during large events on traffic networks. Particularly in multimodal transportation scenarios, significant synergistic effects exist between different modes of transport: a decrease in the capacity of a road segment can trigger bus delays, congestion at rail stations, an imbalance between taxi supply and demand, and increased detours by pedestrians, ultimately leading to systemic congestion. Traditional static models or single-optimization models are ill-suited to effectively characterize such dynamic evolutionary processes.

[0004] Monte Carlo simulation can generate a large number of disturbance scenarios through random sampling, and perform statistical analysis on the system's operating results under uncertain environments, making it suitable for handling stochastic problems in complex transportation systems. Introducing Monte Carlo simulation into the state extrapolation of urban multimodal transportation networks allows for repeated simulation of traffic operation processes under various disturbance combinations, thereby evaluating the adaptability, reliability, and resilience of pre-set traffic organization schemes. Therefore, it is necessary to propose a method capable of dynamic simulation extrapolation and optimization decision-making under disturbance conditions for multimodal transportation networks. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a method and apparatus for simulating and extrapolating the state of urban multimodal transportation networks based on Monte Carlo simulation. This method is capable of simulating and extrapolating the state of multimodal transportation networks and can perform dynamic simulation and optimization decision-making under disturbance conditions.

[0006] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0007] Firstly, a method for simulating and extrapolating the state of urban multimodal transportation networks based on Monte Carlo simulation is provided, including: constructing an urban multimodal transportation network model and a disturbance scenario model; randomly sampling disturbance scenarios from the disturbance scenario model based on Monte Carlo random sampling, and correcting the network state of the urban multimodal transportation network model based on the sampled disturbance scenarios; performing batch simulation extrapolation and collecting network operation status data based on the corrected urban multimodal transportation network model; and conducting multidimensional evaluation of the operation results of the urban multimodal transportation network model based on the collected network operation status data, and identifying traffic network bottlenecks.

[0008] Furthermore, the urban multimodal transportation network model includes at least: road traffic network, rail transit network, conventional public transport network, taxi / ride-hailing service network, slow traffic network, and multimodal transfer node network; wherein, the parameters of the urban multimodal transportation network model include at least: road segment length, design speed, basic capacity, departure interval, platform service capacity, transfer time, node throughput capacity, parking space capacity, and generalized travel cost parameters.

[0009] Furthermore, the disturbance scenario model includes at least natural disturbances, human-caused disturbances, and technical disturbances; among which, natural disturbances include: rainstorms and water accumulation, road icing, and blizzards; human-caused disturbances include: traffic accidents, road construction, temporary traffic control, and surges in passenger flow during large-scale events; and technical disturbances include: signal failures, track section failures, and station equipment failures.

[0010] Furthermore, the network state of the urban multimodal transportation network model is corrected based on the extracted disturbance scenarios, including: determining whether the extracted disturbance scenarios occur, when they occur, their location, duration, and degree of impact; and dynamically correcting the road segment capacity, node service capacity, operating speed, departure interval, transfer efficiency, queuing capacity, and traffic organization rules in the transportation network according to the sampling results, so as to obtain the dynamic transportation network state under the corresponding disturbance scenarios.

[0011] Furthermore, based on the revised urban multimodal transportation network model, batch simulations are performed and network operation status data is collected, including: under the extracted disturbance scenarios, inputting the OD traffic demand matrix, time-period passenger / vehicle flow distribution, travel mode ratio, capacity allocation scheme, signal timing scheme, and existing traffic organization scheme; under the dynamic traffic network state, batch simulations are performed on traffic demand allocation, route selection, traffic flow operation, transfer connection, queue propagation, and congestion evolution processes; when it is detected that the preset traffic organization scheme does not meet the time limit, capacity, or risk threshold requirements, dynamic route reconstruction, capacity reallocation, signal timing adjustment, or traffic organization optimization are triggered; network operation status data under each disturbance scenario is collected, and the network operation status data includes at least: total travel time, average delay, road segment flow, road segment saturation, node congestion, number of transfers, transfer cost, capacity utilization rate, overload probability, network connectivity index, and network resilience index.

[0012] Furthermore, under the extracted perturbation scenario, with the goal of minimizing the expected total generalized travel cost, the objective function is expressed as:

[0013]

[0014] in, Indicates a disturbance scenario The probability of occurrence, Representation of travel unit In a disturbed scenario Next in time step Choose mode of transportation Passing section Decision variables, Representation of travel unit At the node Is a transfer required at this location? Indicates the basic driving time. This indicates the additional delay caused by the disturbance. This represents the generalized cost per unit road segment. Indicates transfer time. This indicates the penalty for transferring. This represents the set of all perturbation scenarios. This represents the set of all travel units. This represents the set of all simulation time steps. Represents the set of all road segments in a transportation network. Represents the set of all modes of transportation. This represents the set of all nodes in the transportation network;

[0015] The constraints include:

[0016] (1) Path connectivity constraints:

[0017]

[0018] in, The flow conservation parameter is set to 1 at the starting point, -1 at the ending point, and 0 at the other nodes. Representation of travel unit In a disturbed scenario Next in time step Choose mode of transportation Passing section Decision variables;

[0019] (2) Road segment capacity constraints:

[0020]

[0021] in, For the perturbation scene Lower section At time step Effective traffic capacity;

[0022] (3) Node service capacity constraints:

[0023]

[0024] in, For the perturbation scene Next node At time step Service capabilities;

[0025] (4) Passenger capacity constraints:

[0026]

[0027] in, For travel units The corresponding customer flow For transportation In a disturbed scenario Downward Passage Maximum carrying capacity;

[0028] (5) Arrival time limit constraint:

[0029]

[0030] in, For travel units In a disturbed scenario Total travel time below For travel units Arrival time limit;

[0031] (6) Variable value constraints:

[0032] .

[0033] Furthermore, when simulation results show that the average delay, node congestion, or road segment saturation under a certain disturbance scenario exceeds a preset threshold, an adaptive large neighborhood search algorithm is used to reconstruct the path scheme. In the destruction phase, path segments that have reached the set conditions due to the disturbance scenario are removed from the current scheme. These path segments are selected based on a random strategy, a worst-case cost strategy, or a congestion sensitivity strategy. In the repair phase, based on the principles of shortest time, lowest generalized cost, or minimum congestion spread, the segments to be repaired are reinserted into the feasible network to form a new path scheme. By iteratively executing the destruction and repair process, an optimized scheme that meets the constraints and has a low expected generalized travel cost is obtained.

[0034] Furthermore, based on the collected network operation status data, the operation results of the urban multimodal transportation network model are evaluated in multiple dimensions, and traffic network bottlenecks are identified, including: assessing the average travel time, average delay, transfer cost, road segment saturation, node congestion, network accessibility, and network resilience of the urban multimodal transportation network model; identifying key congested nodes, key bottleneck road segments, and high-risk evolution paths through statistical analysis, sensitivity analysis, or cluster analysis, and forming evaluation results; and outputting traffic organization optimization schemes or emergency control schemes based on the evaluation results.

[0035] In a second aspect, a device for simulating and extrapolating the state of a multimodal urban transportation network based on Monte Carlo simulation is provided, comprising a storage medium and a processor; the storage medium is used to store instructions; the processor is used to operate according to the instructions to execute the method for simulating and extrapolating the state of a multimodal urban transportation network based on Monte Carlo simulation described in the first aspect.

[0036] Thirdly, a computer-readable storage medium is provided, on which a computer program is stored, characterized in that, when the computer program is executed by a processor, it implements the method for simulating and extrapolating the state of urban multimodal transportation networks based on Monte Carlo simulation as described in the first aspect.

[0037] Compared with existing technologies, the beneficial effects achieved by this invention are as follows: This invention constructs an urban multimodal transportation network model and a disturbance scenario model; randomly extracts disturbance scenarios from the disturbance scenario model based on Monte Carlo random sampling, and corrects the network state of the urban multimodal transportation network model based on the extracted disturbance scenarios; performs batch simulation and collects network operation status data based on the corrected urban multimodal transportation network model; performs multidimensional evaluation of the operation results of the urban multimodal transportation network model based on the collected network operation status data, and identifies traffic network bottlenecks; it can repeatedly perform dynamic simulation and optimization decisions for multimodal transportation networks under various disturbance combinations, thereby evaluating the adaptability, reliability, and risk resistance of preset traffic organization schemes. Attached Figure Description

[0038] Figure 1 This is a schematic diagram of the main process of a method for simulating and extrapolating the state of a multimodal urban transportation network based on Monte Carlo simulation, provided in an embodiment of the present invention.

[0039] Figure 2 This is an example diagram of the partially optimized timing representation output in an embodiment of the present invention. Detailed Implementation

[0040] The present invention will be further described below with reference to the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and should not be used to limit the scope of protection of the present invention.

[0041] Example 1

[0042] like Figure 1 , Figure 2 As shown, a method for simulating and extrapolating the state of a multimodal urban transportation network based on Monte Carlo simulation includes: constructing a multimodal urban transportation network model and a disturbance scenario model; randomly selecting disturbance scenarios from the disturbance scenario model based on Monte Carlo random sampling, and correcting the network state of the multimodal urban transportation network model based on the selected disturbance scenarios; performing batch simulation and extrapolating based on the corrected multimodal urban transportation network model and collecting network operation status data; and conducting multidimensional evaluation of the operation results of the multimodal urban transportation network model based on the collected network operation status data, and identifying traffic network bottlenecks.

[0043] Step S1: Construct a multimodal urban transportation network model and a disturbance scenario model.

[0044] Construct a multimodal urban transportation network model, which includes at least: road transportation network, rail transit network, conventional public transport network, taxi / ride-hailing service network, slow traffic network, and multimodal transfer node network.

[0045] The parameters of the urban multimodal transportation network model include at least: road segment length, design speed, basic traffic capacity, departure interval, platform service capacity, transfer time, node throughput capacity, parking space capacity, and generalized travel cost parameters.

[0046] Construct a disturbance scenario model, which includes at least natural disturbances, human-induced disturbances, and technical disturbances.

[0047] Natural disturbances include scenarios such as heavy rain and water accumulation, road icing, and blizzards; human disturbances include scenarios such as traffic accidents, road construction, temporary traffic control, and surges in passenger flow during large-scale events; and technical disturbances include scenarios such as signal failures, track section failures, and station equipment failures.

[0048] The probability parameters of the disturbance scenario model are determined by combining historical traffic data, monitoring data and expert experience, forming the distribution of scenario occurrence probability, duration, scope of impact and intensity of impact.

[0049] Specifically, based on urban GIS vector topology data and on-site traffic survey data, an integrated urban multimodal transportation network model was constructed. This model integrates road traffic, rail transit, regular buses, taxi / ride-hailing services, pedestrian and bicycle traffic, and multimodal transfer nodes into a unified directed weighted topology network. Connectivity points between different modes of transportation were merged into standardized transfer nodes according to industry standards for transfer convenience. Basic connection parameters such as transfer walking distance and escalator and stairwell efficiency were simultaneously recorded. Model parameters were calibrated using a combination of industry-standard specifications and measured data. Road segment length and design speed were directly extracted from GIS vector data. Basic road capacity was determined according to urban road engineering design specifications. Rail and bus departure intervals were calculated using the average of actual operating timetables. Station service capacity was calculated according to industry-standard service specifications. Parking capacity was obtained from on-site survey data. The generalized travel cost was integrated, encompassing time cost, monetary cost, and transfer penalty cost. Time cost was converted using the transportation industry's standard time value, and transfer penalty cost was set to a reasonable value consistent with industry norms. Simultaneously, disturbance scenario models covering natural, human, and technological types are constructed. Each type of disturbance is quantitatively characterized from four dimensions: probability of occurrence, duration, spatial range of impact, and intensity reduction factor. Based on urban historical traffic monitoring and event statistics, the probability distribution of various disturbances is fitted using the maximum likelihood estimation method. Different disturbance types, intensity of impact, and spatiotemporal range are cross-combined to generate standardized disturbance scenarios that meet sampling requirements, forming a complete set of disturbance scenarios that can be used for Monte Carlo random sampling.

[0050] Step S2: Randomly extract disturbance scenarios from the disturbance scenario model based on Monte Carlo random sampling, and correct the network state of the urban multimodal transportation network model based on the extracted disturbance scenarios.

[0051] Based on the disturbance scenario model established in step S1, multiple disturbance scenarios are randomly selected using the Monte Carlo random sampling method. For each disturbance scenario, at least the occurrence, occurrence time, location, duration, and degree of impact of the disturbance scenario are determined.

[0052] Based on the sampling results, the traffic capacity of road segments, the service capacity of nodes, the operating speed, the departure interval, the transfer efficiency, the queuing capacity and the traffic organization rules in the traffic network are dynamically corrected to obtain the dynamic traffic network state under the corresponding disturbance scenario.

[0053] Specifically, a stable pseudo-random number generator is used to perform Monte Carlo random sampling. Weighted random sampling is achieved based on the probability of occurrence of disturbance scenarios. A sufficient number of sampling times is set to ensure that the sampling results meet the statistical significance requirements. Each sampling explicitly outputs the occurrence time, spatial coordinates, duration, and impact intensity coefficient of the disturbance scenario. Based on the disturbance scenario parameters obtained from the sampling, the operational status of the urban multimodal transportation network is dynamically corrected. First, the effective capacity of road segments and the service capacity of nodes are reduced according to the disturbance impact intensity coefficient. Simultaneously, the operating speed of road segments, the departure interval of traffic modes, and the transfer efficiency are adjusted. Different types of disturbances correspond to different intensity reduction standards. Natural disturbances, human-induced disturbances, and technical disturbances are each given reasonable reduction intervals according to their own impact characteristics. Using the time step conventional in the traffic simulation industry as a benchmark, network parameters and traffic organization rules are gradually updated to complete the dynamic adjustment of road segment capacity, node throughput, operating speed, transfer efficiency, queuing capacity, and traffic control rules. Finally, the dynamic traffic network state under the corresponding disturbance scenario is generated, providing a suitable basic network environment for subsequent simulations.

[0054] Step S3: Based on the revised urban multimodal transportation network model, perform batch simulation and collect network operation status data.

[0055] In the extracted disturbance scenario, the inputs include the OD traffic demand matrix, time period passenger / vehicle flow distribution, travel mode ratio, capacity configuration scheme, signal timing scheme, and existing traffic organization scheme.

[0056] In a dynamic traffic network, batch simulations are conducted to analyze traffic demand allocation, route selection, traffic flow, transfer connections, queue propagation, and congestion evolution.

[0057] When a preset traffic organization plan is detected to not meet the time limit, capacity or risk threshold requirements, dynamic route reconstruction, capacity reallocation, signal timing adjustment or traffic organization optimization is triggered.

[0058] Collect network operation status data under various disturbance scenarios. The network operation status data includes at least: total travel time, average delay, road segment traffic, road segment saturation, node congestion, number of transfers, transfer cost, capacity utilization rate, overload probability, network connectivity indicators, and network resilience indicators.

[0059] Under the extracted perturbation scenario, the optimization objective is to minimize the expected total generalized travel cost, and the objective function is expressed as:

[0060]

[0061] in, Indicates a disturbance scenario The probability of occurrence, Representation of travel unit In a disturbed scenario Next in time step Choose mode of transportation Passing section Decision variables, Representation of travel unit At the node Is a transfer required at this location? Indicates the basic driving time. This indicates the additional delay caused by the disturbance. This represents the generalized cost per unit road segment. Indicates transfer time. This indicates the penalty for transferring. This represents the set of all perturbation scenarios. This represents the set of all travel units. This represents the set of all simulation time steps. Represents the set of all road segments in a transportation network. Represents the set of all modes of transportation. This represents the set of all nodes in the transportation network;

[0062] The constraints include:

[0063] (1) Path connectivity constraints:

[0064]

[0065] in, The flow conservation parameter is set to 1 at the starting point, -1 at the ending point, and 0 at the other nodes. Representation of travel unit In a disturbed scenario Next in time step Choose mode of transportation Passing section Decision variables;

[0066] (2) Road segment capacity constraints:

[0067]

[0068] in, For the perturbation scene Lower section At time step Effective traffic capacity;

[0069] (3) Node service capacity constraints:

[0070]

[0071] in, For the perturbation scene Next node At time step Service capabilities;

[0072] (4) Passenger capacity constraints:

[0073]

[0074] in, For travel units The corresponding customer flow For transportation In a disturbed scenario Downward Passage Maximum carrying capacity;

[0075] (5) Arrival time limit constraint:

[0076]

[0077] in, For travel units In a disturbed scenario Total travel time below For travel units Arrival time limit;

[0078] (6) Variable value constraints:

[0079]

[0080] When simulation results show that the average delay, node congestion, or road segment saturation under a certain disturbance scenario exceeds a preset threshold, dynamic path reconstruction is triggered. Preferably, an adaptive large neighborhood search algorithm is used to reconstruct the path scheme.

[0081] During the disruption phase, some path segments severely affected by the disturbance are removed from the current scheme. These path segments are selected based on a random strategy, a worst-case strategy, or a congestion-sensitive strategy.

[0082] During the repair phase, based on the principles of shortest time, lowest generalized cost, or least congestion spread, the segment to be repaired is reinserted into the feasible network to form a new path scheme.

[0083] By iteratively executing the destruction and repair process, an optimized solution that satisfies the constraints and has a low expected generalized travel cost is obtained.

[0084] Specifically, under the corrected dynamic traffic network state, the input is an OD traffic demand matrix generated based on multi-source traffic data inversion. The matrix's time resolution meets the conventional requirements for urban traffic analysis. Simultaneously, time-period passenger and vehicle flow distribution, the proportion of residents' travel modes, capacity allocation schemes, signal timing schemes, and existing traffic organization schemes are imported. Calculations are performed using a professional traffic simulation engine adapted for multi-mode collaborative simulation, with the simulation time step set in accordance with industry standards for traffic flow evolution analysis. During the simulation, a dynamic user optimal allocation algorithm is used to simulate traveler path selection behavior, fully reproducing the entire process of traffic demand allocation, path selection, traffic flow operation, transfer connections, queue propagation, and congestion evolution. Single-scenario simulations cover the core peak hours of urban traffic, and batch simulations are performed for each sampled disturbance scenario. During the simulation, key network operation indicators are monitored in real time. When core operation indicators exceed the preset threshold of the traffic system's service level, a dynamic optimization process is automatically triggered. An adaptive large neighborhood search algorithm is used to reconstruct the path scheme. In the disruption phase, path segments significantly affected by disturbances are removed based on a congestion sensitivity strategy. In the repair phase, based on the principle of minimum generalized cost, a classic shortest path algorithm is used to search for feasible paths and re-insert path segments. Iterative calculations are performed until the objective function meets the convergence condition, ultimately yielding an optimized scheme that satisfies all constraints. During the simulation, network operation status data is collected at the regular frequency of traffic condition monitoring, comprehensively collecting indicators such as total travel time, average delay, segment flow, segment saturation, node congestion, number of transfers, transfer costs, capacity utilization, overload probability, network connectivity, and network resilience. All indicators are calculated and statistically analyzed according to standard methods in the field of intelligent transportation.

[0085] Step S4: Based on the collected network operation status data, conduct a multi-dimensional evaluation of the operation results of the urban multimodal transportation network model and identify traffic network bottlenecks.

[0086] Based on the network operation status data collected in step S3, the simulation results under all disturbance scenarios are statistically analyzed to obtain the average travel time, average delay, average number of transfers, road segment saturation distribution, node congestion distribution, and network resilience index, and then the results are evaluated.

[0087] Through statistical analysis, sensitivity analysis, or cluster analysis, the key congestion nodes, key bottleneck sections, and high-risk evolution paths that have the most significant impact on system performance are identified, and evaluation results are formed.

[0088] Based on the assessment results, traffic organization optimization plans or emergency control plans will be output, such as: setting up detour plans, increasing backup bus capacity, adjusting departure intervals, optimizing intersection signal control, and strengthening on-site traffic management at key nodes.

[0089] Specifically, based on the full network operation data collected through batch simulations, a comprehensive evaluation is conducted from four dimensions: operational efficiency, service level, system resilience, and transfer experience. Operational efficiency focuses on analyzing average travel time and average delay; service level focuses on statistically analyzing road segment saturation and node congestion; system resilience focuses on calculating network connectivity and overload probability; and transfer experience focuses on monitoring the number of transfers and transfer costs. After scientifically removing outliers from multiple sampling simulation results, the mean and variance of each indicator are calculated to ensure the robustness and reliability of the evaluation results. Relying on statistical analysis, sensitivity analysis, and cluster analysis, traffic network bottlenecks are identified. All network nodes are clustered according to congestion characteristics, and nodes with the most significant congestion characteristics are selected as key congestion nodes. Road segments with long congestion durations and consistently excessive service levels are marked as key bottleneck road segments. Sensitivity analysis identifies paths with large traffic fluctuations under disturbances and classifies them as high-risk congestion evolution paths. Finally, based on the bottleneck identification results, feasible traffic organization optimization and emergency control schemes are output. The schemes include four core components: detour route planning, backup capacity allocation, signal timing adjustment, and key node guidance. The signal timing uses industry-classic algorithms to recalculate control parameters and rationally allocate right-of-way according to traffic flow distribution. Backup bus capacity is rationally allocated according to passenger flow demand in bottleneck sections. Rail departure intervals are dynamically optimized based on station passenger flow pressure, forming a complete traffic control strategy adaptable to various disturbance scenarios.

[0090] Example 2

[0091] Based on the Monte Carlo simulation-based urban multimodal traffic network state simulation and deduction method described in Embodiment 1, this embodiment provides a Monte Carlo simulation-based urban multimodal traffic network state simulation and deduction device, including a storage medium and a processor; the storage medium is used to store instructions; the processor is used to operate according to the instructions to execute the Monte Carlo simulation-based urban multimodal traffic network state simulation and deduction method described in Embodiment 1.

[0092] Example 3

[0093] Based on the method for simulating and extrapolating the state of a multimodal urban transportation network based on Monte Carlo simulation as described in Embodiment 1, this embodiment provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it implements the method for simulating and extrapolating the state of a multimodal urban transportation network based on Monte Carlo simulation as described in Embodiment 1.

[0094] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application 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.

[0095] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. 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... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0096] 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.

[0097] 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.

[0098] The embodiments of the present invention have been described above with reference to the accompanying drawings. However, the present invention is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of the present invention without departing from the spirit and scope of the claims. All of these forms are within the protection scope of the present invention.

Claims

1. A method for simulating and extrapolating the state of urban multimodal transportation networks based on Monte Carlo simulation, characterized in that, include: Construct a multimodal urban transportation network model and a disturbance scenario model; Based on Monte Carlo random sampling, disturbance scenarios are randomly selected from the disturbance scenario model, and the network state of the urban multimodal transportation network model is corrected based on the selected disturbance scenarios. Based on the revised urban multimodal transportation network model, batch simulations were performed and network operation status data were collected. The operation results of the urban multimodal transportation network model are evaluated in multiple dimensions based on the collected network operation status data, and traffic network bottlenecks are identified. Among them, the network state of the urban multimodal transportation network model is corrected based on the extracted disturbance scenarios, including: Determine whether the extracted disturbance scenario occurred, when it occurred, its location, duration, and degree of impact; Based on the sampling results, the traffic capacity of road segments, node service capacity, operating speed, departure interval, transfer efficiency, queuing capacity and traffic organization rules in the traffic network are dynamically corrected to obtain the dynamic traffic network state under the corresponding disturbance scenario. Based on the revised urban multimodal transportation network model, batch simulations were performed and network operation status data were collected, including: In the extracted disturbance scenario, the inputs are the OD traffic demand matrix, time period passenger / vehicle flow distribution, travel mode ratio, capacity configuration scheme, signal timing scheme and existing traffic organization scheme; In a dynamic traffic network, batch simulations are conducted to analyze traffic demand allocation, route selection, traffic flow, transfer connections, queue propagation, and congestion evolution. When a preset traffic organization plan is detected to not meet the time limit, capacity or risk threshold requirements, dynamic route reconstruction, capacity reallocation, signal timing adjustment or traffic organization optimization is triggered. Collect network operation status data under various disturbance scenarios. The network operation status data includes at least: total travel time, average delay, road segment traffic, road segment saturation, node congestion, number of transfers, transfer cost, capacity utilization rate, overload probability, network connectivity index, and network resilience index. Under the extracted perturbation scenario, the optimization objective is to minimize the expected total generalized travel cost, and the objective function is expressed as: in, Indicates a disturbance scenario The probability of occurrence, Representation of travel unit In a disturbed scenario Next in time step Choose mode of transportation Passing section Decision variables, Representation of travel unit At the node Is a transfer required at this location? Indicates the basic driving time. This indicates the additional delay caused by the disturbance. This represents the generalized cost per unit road segment. Indicates transfer time. This indicates the penalty for transferring. This represents the set of all perturbation scenarios. Represents the set of all travel units. This represents the set of all simulation time steps. This represents the set of all road segments in the transportation network. Represents the set of all modes of transportation. This represents the set of all nodes in the transportation network.

2. The method for simulating and extrapolating the state of urban multimodal transportation networks based on Monte Carlo simulation according to claim 1, characterized in that, The urban multimodal transportation network model includes at least: road transportation network, rail transit network, conventional public transport network, taxi / ride-hailing service network, slow traffic network, and multimodal transfer node network; The parameters of the urban multimodal transportation network model include at least: road segment length, design speed, basic traffic capacity, departure interval, platform service capacity, transfer time, node throughput capacity, parking space capacity, and generalized travel cost parameters.

3. The method for simulating and extrapolating the state of urban multimodal transportation networks based on Monte Carlo simulation according to claim 1, characterized in that, The disturbance scenario model includes at least natural disturbances, human-induced disturbances, and technical disturbances; Natural disturbances include: rainstorms and flooding, road icing, and blizzards; human disturbances include: traffic accidents, road construction, temporary traffic control, and surges in passenger flow during large-scale events; and technical disturbances include: signal failures, track section failures, and station equipment failures.

4. The method for simulating and extrapolating the state of urban multimodal transportation networks based on Monte Carlo simulation according to claim 1, characterized in that, The constraints of the objective function include: (1) Path connectivity constraints: in, The flow conservation parameter is set to 1 at the starting point, -1 at the ending point, and 0 at the other nodes. Representation of travel unit In a disturbed scenario Next in time step Choose mode of transportation Passing section Decision variables; (2) Road segment capacity constraints: in, For the perturbation scene Lower section At time step Effective traffic capacity; (3) Node service capacity constraints: in, For the perturbation scene Next node At time step Service capabilities; (4) Passenger capacity constraints: in, For travel units The corresponding customer flow For transportation In a disturbed scenario Downward Passage Maximum carrying capacity; (5) Arrival time limit constraint: in, For travel units In a disturbed scenario Total travel time below For travel units Arrival time limit; (6) Variable value constraints: 。 5. The method for simulating and extrapolating the state of urban multimodal transportation networks based on Monte Carlo simulation according to claim 4, characterized in that, When the simulation results show that the average delay, node congestion or road segment saturation under a certain disturbance scenario exceeds the preset threshold, the adaptive large neighborhood search algorithm is used to reconstruct the path scheme. During the disruption phase, path segments that meet set conditions due to the disturbance scenario are removed from the current scheme. These path segments are selected based on a random strategy, a worst-case strategy, or a congestion-sensitive strategy. During the repair phase, based on the principles of shortest time, lowest generalized cost, or least congestion spread, the segment to be repaired is reinserted into the feasible network to form a new path scheme. By iteratively executing the destruction and repair process, an optimized solution that satisfies the constraints and has a low expected generalized travel cost is obtained.

6. The method for simulating and extrapolating the state of urban multimodal transportation networks based on Monte Carlo simulation according to claim 1, characterized in that, Based on the collected network operation status data, a multidimensional evaluation of the operation results of the urban multimodal transportation network model is conducted, and traffic network bottlenecks are identified, including: The average travel time, average delay, transfer cost, road saturation, node congestion, network accessibility, and network resilience of the urban multimodal transportation network model are evaluated. Through statistical analysis, sensitivity analysis, or cluster analysis, key congestion nodes, key bottleneck sections, and high-risk evolution paths are identified to form assessment results; Based on the assessment results, traffic organization optimization plans or emergency control plans will be generated.

7. A simulation and deduction device for urban multimodal transportation network status based on Monte Carlo simulation, characterized in that, Including storage media and processor; The storage medium is used to store instructions; The processor is configured to operate according to the instructions to execute the urban multimodal transportation network state simulation and deduction method based on Monte Carlo simulation as described in any one of claims 1 to 6.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the method for simulating and extrapolating the state of urban multimodal transportation networks based on Monte Carlo simulation as described in any one of claims 1 to 6.

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