Method for evaluating risk and toughness of urban traffic system during snowfall

Abstract

The invention relates to a method for evaluating the risk and toughness of an urban traffic system during snowfall. The method comprises the following steps: acquiring basic data of a target urban traffic system; based on the basic data, establishing a simulation model of the target urban traffic system; the simulation model at least comprises a road network sub-model and a travel demand sub-model; the road network sub-model is used for representing the connection relationship between different roads and the attribute of each road; the travel demand sub-model is used for representing a plurality of travelers and travel schemes of the travelers, and the travelers have the capability of autonomously deciding the travel schemes according to attributes of roads; simulating the operation state change of the target urban traffic system in different scenes by using the simulation model so as to evaluate the risk and toughness of the target urban traffic system during snowfall; the accumulated snow depths and / or traffic control strategies corresponding to different scenes are different. According to the method and the device, the risk and the toughness of the urban traffic network during the snowfall period are efficiently and intelligently evaluated based on dynamic simulation of multiple agents.

Description

TECHNICAL FIELD

[0001] The present disclosure relates to the technical field of disaster risk safety, and particularly relates to a risk and resilience assessment method for urban traffic system in snowfall. BACKGROUND

[0002] Global warming has become a recognized fact, although the overall snowfall may show a downward trend, the frequency and intensity of extreme snow events have increased significantly. These extreme snow disasters not only have a serious impact on agriculture and animal husbandry, but also pose a major challenge to urban infrastructure, economic activities and residents' lives, especially the urban traffic system. For example, in the northern region of China, heavy snow and freezing weather often cause road closures, traffic congestion, and even large-scale traffic paralysis, seriously affecting the operational efficiency and public safety of the urban traffic system. However, the existing urban traffic management system has problems such as delayed emergency response and insufficient snow removal resources, which further exacerbates the negative impact of snow disasters. SUMMARY

[0003] Therefore, the present disclosure provides a risk and resilience assessment method, device, electronic equipment, storage medium and computer program product for urban traffic system in snowfall.

[0004] According to an aspect of the present disclosure, a risk and resilience assessment method for urban traffic system in snowfall is provided, the method comprising:

[0005] obtaining basic data of a target urban traffic system; the basic data representing data related to urban traffic;

[0006] establishing a simulation model of the target urban traffic system based on the basic data; wherein the simulation model at least includes: a road network sub-model and a travel demand sub-model; the road network sub-model is used to represent the connection relationship between different roads and the attributes of each road; the travel demand sub-model is used to represent a plurality of travelers and the travel scheme of each traveler, and the traveler has the ability to autonomously decide the travel scheme according to the attributes of the road;

[0007] using the simulation model to simulate the change of the operating state of the target urban traffic system in different scenarios, so as to evaluate the risk and resilience of the target urban traffic system in snowfall; wherein the snow depth and / or traffic management strategy corresponding to different scenarios are different.

[0008] In one possible implementation, the basic data includes road network data and historical meteorological and traffic operation data; and the attributes of each road at least include the free flow speed of each road.

[0009] The method further comprises:

[0010] Based on the historical meteorological and traffic operation data, a free flow speed reduction model is established by regression analysis, which is used in the simulation process to determine the free flow speed of each road in the road network submodel under different snow depths; wherein the free flow speed reduction model represents the corresponding relationship between different snow depths and free flow speed reduction coefficients, and the free flow speed reduction coefficient is the ratio of the free flow speed of the road to the reference free flow speed.

[0011] In a possible implementation, the basic data includes: population and land use data, resident travel survey data;

[0012] Based on the basic data, the simulation model of the target city traffic system is established, which includes:

[0013] Based on the population and land use data, the trip generation and trip attraction of each region in the target city traffic system are estimated;

[0014] Based on the resident travel survey data, the trip generation and trip attraction of each region are adjusted to obtain the trip volume between different regions;

[0015] According to the trip volume between different regions, the plurality of travelers and the trip scheme of each traveler are generated.

[0016] In a possible implementation, the use of the simulation model to simulate the change of the operation state of the target city traffic system in different scenarios includes:

[0017] In the process of simulating the change of the operation state of the target city traffic system in the target scenario, based on the snow depth corresponding to the target scenario and / or the traffic management strategy corresponding to the target scenario, the attributes of each road in the road network submodel and the trip scheme of each traveler in the trip demand submodel are adjusted; the target scenario is any scenario in the different scenarios.

[0018] In a possible implementation, based on the snow depth corresponding to the target scenario and / or the traffic management strategy corresponding to the target scenario, adjusting the attributes of each road in the road network submodel and the trip scheme of each traveler in the trip demand submodel includes:

[0019] Based on the snow depth corresponding to the target scenario, the initial value of the attributes of each road in the road network submodel in the target scenario is determined;

[0020] Based on the traffic management strategy corresponding to the target scenario, at least one event in the target scenario and the occurrence time point of each event are set;

[0021] trigger the corresponding event at a time point of occurrence of the event, and adjust the attribute of each road in the road network sub-model;

[0022] based on the attribute of each road, each traveler in the travel demand sub-model adjusts the travel scheme of the traveler.

[0023] In a possible implementation, the adjusting of the travel scheme of each traveler in the travel demand sub-model based on the attribute of each road comprises:

[0024] The travel demand sub-model is iterated in multiple rounds, so that each traveler in the travel demand sub-model learns the optimal travel scheme in the target scenario; in each round of iteration, each traveler in the travel demand sub-model adjusts the travel scheme in the current round of iteration based on the result of the previous round of iteration.

[0025] In a possible implementation, the simulation model further comprises a public transport sub-model, the public transport sub-model being configured to represent the attribute of a bus line; the simulation of the change in the operating state of the target urban traffic system in different scenarios by using the simulation model comprises: in the process of simulating the change in the operating state of the target urban traffic system in the target scenario, based on the corresponding snow depth and / or the corresponding traffic management strategy of the target scenario, adjusting one or more of the attribute of each road in the road network sub-model, the travel scheme of each traveler in the travel demand sub-model, and the attribute of the bus line in the public transport sub-model.

[0026] According to another aspect of the present disclosure, a device for assessing the risk and resilience of an urban traffic system in snowfall is provided, the device comprising:

[0027] an acquisition module configured to acquire basic data of a target urban traffic system; the basic data representing data related to urban traffic;

[0028] a modeling module configured to establish a simulation model of the target urban traffic system based on the basic data; wherein the simulation model at least comprises a road network sub-model and a travel demand sub-model; the road network sub-model is configured to represent the connection relationship between different roads and the attribute of each road; the travel demand sub-model is configured to represent a plurality of travelers and the travel scheme of each traveler, and the traveler has the ability to autonomously decide the travel scheme according to the attribute of the road;

[0029] a simulation module configured to simulate the change in the operating state of the target urban traffic system in different scenarios by using the simulation model, so as to assess the risk and resilience of the target urban traffic system in snowfall; wherein the corresponding snow depth and / or the traffic management strategy of different scenarios are different.

[0030] According to another aspect of the present disclosure, there is provided an electronic device comprising a memory, a processor, and a computer program stored on the memory, the processor executing the computer program to implement the steps of the above method.

[0031] According to another aspect of the present disclosure, there is provided a non-transitory computer readable storage medium having stored thereon a computer program, the computer program, when executed by a processor, implementing the steps of the above method.

[0032] According to another aspect of the present disclosure, there is provided a computer program product comprising a computer program, or a non-transitory computer readable storage medium having a computer program stored thereon, the computer program, when executed by a processor, implementing the steps of the above method.

[0033] According to the aspects of the present disclosure, the basic data of a target urban traffic system is acquired; the basic data represents data related to urban traffic; based on the basic data, a simulation model of the target urban traffic system is established; wherein the simulation model at least comprises a road network sub-model and a travel demand sub-model; the road network sub-model is used to represent the connection relationship between different roads and the attributes of each road; the travel demand sub-model is used to represent a plurality of travelers and the travel scheme of each traveler, and the traveler has the ability to autonomously decide the travel scheme according to the attributes of the road; the running state changes of the target urban traffic system in different scenarios are simulated by using the simulation model to evaluate the risk and resilience of the target urban traffic system in snowfall; wherein the corresponding snow depth and / or traffic management strategy of different scenarios are different. In this way, based on the dynamic simulation of multiple agents (i.e. travelers), the running state of the target urban traffic system in the scenario of different snow depth and different traffic management strategy combination is comprehensively simulated at the micro (individual travel), meso (road traffic capacity), and macro (urban overall traffic flow) three levels, which can comprehensively depict the response behavior of the target urban traffic system in various scenarios; from the basic scenario of light snow without measures (i.e. without traffic management strategy) to the extreme scenario of heavy snow superimposed with multiple interventions (i.e. using multiple traffic management strategies), the running state covering all stages of the target urban traffic system can be obtained through simulation, so as to provide rich data support for the snow disaster risk and resilience analysis of the target urban traffic system, and further improve the accuracy of the risk and resilience evaluation of the urban traffic system during snowfall, realize efficient and intelligent evaluation of the risk and resilience of the urban traffic network during snowfall, so that the urban traffic system has dynamic prediction ability and intelligent collaborative scheduling mechanism when responding to snow, especially sudden snow disaster, and further can improve the emergency response ability and traffic resilience of the urban traffic system under extreme snow disaster conditions, and optimize the snow removal resource allocation ability.

[0034] Other features and aspects of the present disclosure will become apparent from a detailed description of exemplary embodiments with reference to the following drawings. BRIEF DESCRIPTION OF DRAWINGS

[0035] The accompanying drawings, which are incorporated in and constitute a part of the specification, illustrate exemplary embodiments, features, and aspects of the present disclosure and serve to explain the principles of the present disclosure.

[0036] Figure 1 a flow chart illustrating a method of risk and resilience assessment of urban transportation system during snowfall according to an embodiment of the present disclosure;

[0037] Figure 2 a flow chart illustrating another method of risk and resilience assessment of urban transportation system during snowfall according to an embodiment of the present disclosure;

[0038] Figure 3 a flow chart illustrating a simulation of changes in operating state of the target urban transportation system in different scenarios according to an embodiment of the present disclosure;

[0039] Figure 4 a flow chart illustrating a simulation of changes in operating state of the target urban transportation system in different scenarios according to an embodiment of the present disclosure;

[0040] Figure 5 a structural diagram of a device for risk and resilience assessment of urban transportation system during snowfall according to an embodiment of the present disclosure;

[0041] Figure 6 a block diagram of an electronic device 1900 according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0042] Various exemplary embodiments, features, and aspects of the present disclosure will be explained in detail below with reference to the accompanying drawings. Like reference numerals in the drawings denote like elements or components, and the drawings are not necessarily drawn to scale.

[0043] As used herein, the terms "include," "comprise," "have," or their variants are open-ended, and include one or more stated features, integers, elements, steps, components or functions but do not preclude the presence or addition of one or more other features, integers, elements, steps, components, functions or groups thereof.

[0044] When an element is referred to as being "connected," "coupled," "responsive," or "related" to another element, it can be directly connected, coupled, responsive, or related to the other element, or intervening elements can be present.

[0045] Although the terms first, second, third, etc. can be used herein to describe various elements / operations, such elements / operations should not be limited by these terms. These terms are only used to distinguish one element / operation from another. Thus, a first element / operation in some embodiments could be termed a second element / operation in other embodiments without departing from the teachings of the present inventive concept.

[0046] The word "exemplary" is used herein to mean "serving as an example, instance, or illustration." Any implementation described herein as "exemplary" is not necessarily to be construed as preferred or advantageous over other implementations.

[0047] In addition, for the purpose of convenience and brevity, detailed descriptions of well-known devices, methods, procedures, components, and circuits are not described in detail herein. It is appreciated that those skilled in the art will readily relate to the functions of the various articles and methods as they become more fully understood from the present description.

[0048] The present application proposes a risk and resilience assessment method for urban traffic system during snowfall (see detailed description below) based on the characteristics of urban traffic system. The method is based on dynamic simulation of multi-agent (i.e. travelers) to achieve comprehensive simulation of the running state of the target urban traffic system in different snow depth and different traffic management strategy combination scenarios at the micro (individual travel), meso (road capacity), and macro (overall traffic flow of the city) levels, which can fully depict the response behavior of the target urban traffic system in various scenarios. From the basic scenario of light snow without measures to the extreme scenario of heavy snow superimposed with multiple interventions, the simulation can obtain the running state covering all stages of the target urban traffic system, thereby providing rich data support for the snow disaster risk and resilience analysis of the target urban traffic system, and further improving the accuracy of the risk and resilience assessment of the urban traffic system during snowfall, achieving efficient and intelligent assessment of the risk and resilience of the urban traffic network during snowfall, so that the urban traffic system has dynamic prediction ability and intelligent coordination scheduling mechanism to respond to snowfall, especially sudden snow disasters, thereby improving the emergency response capability and traffic resilience of the urban traffic system under extreme snow disaster conditions, and optimizing the snow removal resource allocation capability.

[0049] Figure 1 A flowchart of a risk and resilience assessment method for urban traffic system during snowfall according to an embodiment of the present disclosure is shown. Exemplarily, the method can be executed by an electronic device with data processing capability, such as a computer, a server, a mobile phone, a tablet computer, etc. Figure 1 As shown, the method can include the following steps:

[0050] Step 101, obtaining the basic data of the target urban traffic system; the basic data represents data related to urban traffic.

[0051] The target city is a city that needs to evaluate the risk and resilience of the city's transportation system in a snowfall.

[0052] Exemplarily, the basic data can include population and land use data, resident travel survey data, road network data, historical meteorological and traffic operation data, bus route data, etc. For the target city, the basic data of the transportation system of the target city can be collected in advance.

[0053] The population and land use data can include population distribution, employment distribution, and land use type data. Exemplarily, the target city can be divided into different regions, and the size of each region can be divided according to the needs. For each region of the target city, the population distribution, employment distribution, land use type, and other population and land use data of the region are obtained.

[0054] The resident travel survey data can include travel purposes (such as shopping, commuting, etc.), occurrence times (such as occurrence time, duration, etc.), travel modes (such as car, bicycle, walking, bus, subway), travel routes, etc. Exemplarily, the OD survey (wherein “O” (Origin) refers to the origin of the traffic trip, and “D” (Destination) refers to the destination of the traffic trip) or travel diary of the residents of the target city can be collected.

[0055] The road network data can include the topological structure of the road network (the connection relationship between roads), the road level (trunk road, secondary trunk road, branch road, etc.), the length, the number of lanes, the design speed / limit speed (i.e. free flow speed, which can also be referred to as the maintainable free flow speed), the traffic capacity, the travel modes allowed to pass, etc. It should be noted that the urban road network usually includes urban internal roads and highway sections. Preferably, in the embodiments of the present disclosure, the road network data of the internal roads of the target city can be collected, and the data of the highway sections need not be collected, so as to highlight the characteristics of urban traffic.

[0056] The historical meteorological and traffic operation data can include snowfall meteorological data such as snowfall amount and snow depth change, and traffic operation data such as the driving speed (or average driving speed) of each road, the passing time, the congestion delay, etc. Exemplarily, the historical snowfall event meteorological data (such as snowfall amount and snow depth change) and the road traffic operation data (such as driving speed, passing time, and congestion delay) of the corresponding period of the target city can be sorted out. As an example, the driving speed of the road can include the free flow speed. For each snowfall event, the snow depth and the actual road free flow speed can be obtained by field measurement.

[0057] The bus line data can include: the number of bus lines, the direction of each bus line, the station, the timetable, etc. For example, the bus line data can be obtained from the bus operation diagram of the target city.

[0058] In step 102, a simulation model of the target city traffic system is established based on the basic data. The simulation model at least includes: a road network sub-model and a travel demand sub-model. The road network sub-model is used to represent the connection relationship between different roads and the attributes of each road. The travel demand sub-model is used to represent a plurality of travelers and the travel scheme of each traveler, and the traveler has the ability to autonomously decide the travel scheme according to the attributes of the road.

[0059] For example, different simulation platforms can be used for simulation, and no limitation is made. As an example, MAIsim can be used as a simulation platform for simulation, and in the MAIsim multi-agent simulation platform, the simulation model of the target city traffic system is established.

[0060] In one possible implementation, the simulation model of the target city traffic system is established based on the basic data, which can include: determining the connection relationship between different roads and the attributes of each road based on the road network data in the basic data; and then establishing the road network sub-model of the target city traffic system based on the connection relationship between different roads and the attributes of each road. The attributes of the road can include one or more of the following: road level, number of lanes, length, free flow speed, traffic capacity, and allowed travel mode. For example, the free flow data in the basic data can be the benchmark free flow speed, and the traffic capacity can be the benchmark traffic capacity. The benchmark free flow speed represents the free flow speed under the benchmark condition (such as dry and snow-free environment), and the benchmark traffic capacity represents the traffic capacity under the benchmark condition. The benchmark free flow speed and the benchmark traffic capacity can represent the traffic capacity of the road under the benchmark condition (such as dry and snow-free environment). The benchmark free flow speed V0 of each road can be the road design speed or the speed limit, and the benchmark traffic capacity can be set according to the number of lanes and / or the road level of the road (for example, the benchmark traffic capacity of 2000 vehicles per hour per lane).

[0061] Taking the MAIsim simulation platform as an example, the road network data in the basic data of the target urban traffic system can be imported into the MAIsim simulation platform to establish a road network submodel of the target urban traffic system in the MAIsim simulation platform. Each road is represented as a directed link (connecting two nodes) and each road is assigned corresponding attributes. It should be noted that in the process of constructing the road network submodel, the internal roads of the target city are preferentially modeled in detail: including the connection relationship between the main road, the secondary road, the branch road and the attributes of each road; and the highway part is not included in the main simulation range, so as to highlight the traffic characteristics of the urban road network. Compared with highways, urban road networks exhibit multi-modal characteristics and have the characteristics that residents' travel behavior is closely related to land use. In addition, for urban road intersections, the MATSim simulation platform simplifies the connection of each road, and the traffic effect at the road intersection is implicitly included in the traffic capacity and congestion mechanism of each road, without the need to explicitly model the traffic lights at the road intersection.

[0062] In a possible implementation, the simulation model can further include a public transport submodel for representing attributes of bus lines; for example, the attributes of the bus lines can include the number of bus lines, the direction of each bus line, the driving speed of the bus, the departure frequency, and the like. The establishment of the simulation model of the target urban traffic system based on the basic data further includes determining the attributes of the bus lines based on the bus line data in the basic data of the target urban traffic system, and further establishing a public transport submodel of the target urban traffic system.

[0063] Taking the MAIsim multi-agent simulation platform as an example, the bus line data in the basic data of the target urban traffic system can be imported into the MAIsim simulation platform to establish a public transport submodel of the target urban traffic system in the MAIsim simulation platform.

[0064] In a possible implementation, the establishment of the simulation model of the target urban traffic system based on the basic data includes: estimating the trip generation and trip attraction of each region in the target urban traffic system based on the population and land use data; adjusting the trip generation and trip attraction of each region based on the resident travel survey data to obtain the trip volume between different regions; and generating the plurality of travelers and the travel scheme of each traveler according to the trip volume between different regions. The traveler can also be referred to as a traffic participant, which is a subject that has the ability to autonomously decide a travel scheme according to the attributes of the road; the travel scheme (also referred to as a travel plan) can include one or more of a travel mode, a travel time, and a travel route.

[0065] Since the travel behaviors of residents in each region of the target urban traffic system are closely related to the land use of each region, for each region of the target urban traffic system, the trip generation and trip attraction of each region can be estimated according to the population and land use data of each region; meanwhile, the resident travel survey data can reflect the trip distribution information between different regions of the target city and the travel time, travel mode, travel route and other travel rules of the residents of the target city. Based on the resident travel survey data, the estimated trip generation and trip attraction of each region are adjusted, such as calibration and verification, so that the trip volume between different regions after adjustment conforms to the actual travel rules of the residents of the target city. Further, according to the trip volume between different regions after adjustment, based on the resident travel survey data, a plurality of travelers and the travel plan of each traveler are generated, so that the number of generated travelers matches the trip volume between different regions, and the travel plan of each traveler conforms to the travel rules of the residents of the target city. In this way, unlike the traffic modeling method based on traffic volume prediction in related technologies, in the embodiment of the present disclosure, the trip demand sub-model conforming to the actual situation of the target urban traffic system is established directly based on the population and land use data and the resident travel survey data in the basic data of the target urban traffic system.

[0066] Taking the MAIsim simulation platform as an example, the population and land use data and the resident travel survey data in the basic data of the target urban traffic system can be imported into the MAIsim simulation platform to establish the trip demand sub-model of the target urban traffic system in the MAIsim simulation platform. The trip volume between different regions can be represented by an OD (Origin-Destination, origin-destination) matrix. The OD matrix can be generated in the MAIsim simulation platform based on the population and land use data and the resident travel survey data. For example, the trip generation and trip attraction of each region can be determined based on the population and land use data of each region in the target urban traffic system, and the trip volume from region i to region j T ij is calculated based on the trip distribution information and the travel rules reflected by the resident travel survey data by using a gravity model or other methods.

[0067] T ij =O i ×D j ×f(c ij )

[0068] wherein O i represents the trip generation of region i, D j represents the trip attraction of region j, f(c ij ) represents a travel impedance function, c ij can be the average travel time or the average travel distance between region i and region j, and f(c ij ) changes with c ijdecreasing, for example, an exponential function f(c)=e -λc The average travel time or average travel distance distribution generated by the OD matrix is adjusted by adjusting the parameter λ to conform to the travel distribution law reflected by the travel survey data. At the same time, in order to meet the constraints of the OD matrix, a normalization coefficient or a balance factor can be introduced for the travel volume between different regions to ensure that j T ij =O i and∑ i T ij =D j ; that is, for any region, the sum of the travel volume from the region to each region is the travel generation of the region; at the same time, the sum of the travel volume from each region to the region is the travel attraction of the region.

[0069] Further, the OD matrix derived above is used to generate a corresponding number of travelers and travel plans for each traveler; in the MATSim simulation platform, the population (Population) is composed of a large number of agent-based models, each of which represents a traveler and has the ability to autonomously decide on a travel plan; by way of example, first, the MATSim simulation platform creates a simulated population through a synthetic population technique: according to the demographic characteristics embodied in the population and land use data, a certain number of virtual residents are generated, each of which is attached with attributes such as home address and work location, which can be sampled from the actual area and employment distribution in the population and land use data. By way of example, the MATSim simulation platform uses a fine-grained population generator, inputs macro population data, and samples micro population data to meet the requirements of desensitization. Then, the travel demand sub-model is refined to the individual level: for each pair of regions i->region j, a corresponding number of agents are generated according to the travel volume Tij from region i to region j, and an initial travel plan is formulated for the agents, wherein the initial travel route of each agent conforms to the travel route distribution of residents in the target city reflected in the resident travel survey data; the travel time of each agent can also be determined using the resident travel survey data to reproduce the time sequence characteristics of the morning and evening peaks in the travel time of each agent in the travel demand sub-model; the travel mode of each agent, in the embodiment of the present disclosure, focuses on urban transportation, so the car, bicycle, walking, bus, etc. travel mode scenarios are mainly simulated; by way of example, in order to evaluate the effect of public transportation scheduling strategies in subsequent simulation, public transportation travel modes (bus, subway) are introduced, in which case a portion of the agents can be set to choose public transportation travel modes such as bus and subway according to the survey statistics of the resident travel survey data; in subsequent traffic management strategy simulation, special strategies (such as priority snow removal for bus routes) can be implemented for public transportation travel modes to compare the differences in resident travel affected by snow disasters in different scenarios.

[0070] In this way, after the road network sub-model, the travel demand sub-model, and the public transportation sub-model of the target city traffic system are constructed, a basic simulation environment for micro-simulation of the target city traffic system is formed.

[0071] Step 103, using the simulation model, simulating the changes in the operating state of the target city traffic system in different scenarios to evaluate the risk and resilience of the target city traffic system during snowfall; wherein the snow depth and / or traffic management strategies corresponding to different scenarios are different.

[0072] In a possible implementation, the snow depth caused by snowfall can be divided into several representative grades to divide different scenarios; for example, it can include: light snow scenario (snow depth <5 cm), moderate snow scenario (snow depth in 5-15 cm), heavy snow scenario (snow depth in 15-30 cm), snow disaster scenario (snow depth > 30 cm). Among them, in the light snow scenario, although there is a small amount of snow, the road traffic is only slightly affected; in the moderate snow scenario, it corresponds to the common heavy snow weather, and the road is obviously slow; in the heavy snow scenario, it corresponds to the rare snowstorm, and the road traffic is almost paralyzed, and strong intervention is needed to restore traffic. In this way, through the snow depth grading simulation, the law of the deterioration of the performance of the urban traffic system (also referred to as the traffic service level) with the increase of the snow depth can be understood, and then the basis for the risk and resilience assessment of the target urban traffic system during snowfall is provided.

[0073] Taking the MAIsim multi-agent simulation platform as an example, the snowfall factor can be integrated into the simulation process by modifying the attributes of the roads in the road network sub-model of the target urban traffic system and setting the behavior rules in the MATSim simulation platform; for example, the free flow speed and the capacity of each road in the road network sub-model can be adjusted under different snow depth scenarios, so as to reflect the slow driving and limited traffic capacity caused by different snow depth.

[0074] Among them, the traffic management strategy, also referred to as the traffic management strategy, as a traffic intervention measure, will affect the traffic recovery efficiency of the target urban traffic system during snowfall. For example, according to the type of the strategy, the traffic management strategy can include one or more of the following: road snow removal sequence strategy, traffic control strategy, public transportation scheduling strategy.

[0075] The road snow removal sequence strategy, also referred to as the snow removal strategy; the priority of the snow removal operation is an important factor affecting the road traffic recovery. The types of the road snow removal sequence strategy can be set as needed, for example, the road snow removal sequence strategy can include one or more of the following: “main road priority snow removal” sub-strategy, “secondary road priority snow removal” sub-strategy, and “balanced snow removal” sub-strategy; among them, the “main road priority snow removal” sub-strategy can be to clean the urban main road and the backbone bus corridor first, to restore the traffic capacity of the main channel as soon as possible, and to process the branch road later; the “secondary road priority snow removal” sub-strategy can be to prioritize the secondary road and the residential area exit, to reduce the local travel obstruction, and to clean the main road later; the “balanced snow removal” sub-strategy can be to simultaneously divide the snow removal operation by the snow removal operation vehicle, and to restore the traffic capacity of the roads at all levels as synchronously as possible.

[0076] Traffic control strategy, which can also be referred to as traffic management measure; during snowfall, city managers often take traffic control strategies to reduce the risk of accidents and congestion. The types of traffic control strategies can be set as needed, for example, the traffic control strategy can include one or more of the following: a "close part of the road" sub-strategy, a "temporary speed limit" sub-strategy, a "intersection traffic intervention" sub-strategy, and a "travel demand management" sub-strategy; wherein the "close part of the road" sub-strategy can be to directly close the branch with large slope or frequent accidents in snow, and vehicles need to detour; the "temporary speed limit" sub-strategy can be to reduce the speed limit in the whole city or a specific area (such as uniformly to 30 km / h) to improve safety; the "intersection traffic intervention" sub-strategy can be to implement manual diversion or adjust signal timing at important intersections, and prioritize main road release to reduce congestion spread; the "travel demand management" sub-strategy can be to advocate flexible work schedule and staggered travel, or implement vehicle odd-even restriction to reduce the number of vehicles on the road, etc.

[0077] Public transportation scheduling strategy, which can also be referred to as bus strategy or bus coping strategy; the public transportation system is one of the key measures to improve the snow disaster resilience of the urban transportation system during snowfall, especially in the case of snow disaster. In the embodiments of the present disclosure, various public transportation scheduling strategies are evaluated through simulation. The types of public transportation scheduling strategies can be set as needed, for example, the public transportation scheduling strategy can include one or more of the following: an "increase bus frequency" sub-strategy, an "emergency bus line" sub-strategy, and a "bus priority" sub-strategy; wherein the "increase bus frequency" sub-strategy can include increasing the frequency of departure to alleviate passenger waiting time and attract more travelers to take the bus to reduce vehicles on the road; the "emergency bus line" sub-strategy can include setting up temporary shuttle buses between major bus corridors and residential areas to serve travelers who have difficulty detouring due to road closure; the "bus priority" sub-strategy can include prioritizing snow removal along bus routes when implementing the above-mentioned road snow removal sequence strategy, setting up bus-only lanes, etc.

[0078] Taking the MAIsim multi-agent simulation platform as an example, in order to simulate the dynamic snow removal and traffic recovery process under different traffic management strategies, the attributes of the roads in the road network sub-model of the target city traffic system can be updated in the simulation iteration process through the network event function (NetworkChangeEvent) of the MATSim simulation platform, and the attributes of the bus routes in the public transportation sub-model can also be updated. For example, a number of events can be pre-set in the time dimension of the simulation to represent the implementation of traffic management strategies, wherein any traffic management strategy can correspond to one or more events, and each event has a corresponding time point; in the simulation process, the events at each time point trigger changes in the specified road attributes in the road network sub-model, or trigger changes in the attributes of the bus routes in the public transportation sub-model.

[0079] In a possible implementation, the simulating, by using the simulation model, the change of the running state of the target urban traffic system in different scenarios includes: in the process of simulating the change of the running state of the target urban traffic system in a target scenario, adjusting one or more of the following: the attribute of each road in the road network submodel, the travel scheme of each traveler in the travel demand submodel, and the attribute of a bus line in the public traffic submodel, based on the corresponding snow depth and / or the corresponding traffic management strategy of the target scenario. The target scenario is any scenario in the different scenarios.

[0080] Exemplarily, taking the traffic management strategy as the traffic control strategy, the attribute of each road in the road network submodel can be adjusted in the MATSim simulation platform in the following manner to realize simulation of different types of traffic control strategies: for the "close part of the road" sub-strategy, the passing capacity of the relevant road can be set to 0 or a no-entry constraint is realized or the relevant road is temporarily removed from the road network submodel; for the "temporary speed limit" sub-strategy, the upper limit of the free-flow speed of the specified road can be reduced to simulate; for the "intersection traffic intervention" sub-strategy, the signal module of MATSim can be used to change the signal control scheme, or the signal module integrated in the MATSim simulation platform can be used to change the signal control scheme, or the artificial dredging effect can be approximately realized by increasing the intersection passing capacity; for the "travel demand management" sub-strategy, the travel scheme of part of the travelers (i.e., agents) can be directly reduced or the travel time distribution can be adjusted. Correspondingly, for different types of traffic control strategies, each traveler in the travel demand submodel can dynamically adjust the travel scheme as the attribute of the road in the road network submodel changes.

[0081] In a possible implementation, the simulating, by using the simulation model, the change of the running state of the target urban traffic system in different scenarios includes: in the process of simulating the change of the running state of the target urban traffic system in a target scenario, adjusting one or more of the following: the attribute of each road in the road network submodel, the travel scheme of each traveler in the travel demand submodel, and the attribute of a bus line in the public traffic submodel, based on the corresponding snow depth and / or the corresponding traffic management strategy of the target scenario. The target scenario is any scenario in the different scenarios.

[0082] Exemplarily, taking the public transport scheduling strategy as an example, different kinds of public transport scheduling strategies can be realized by adjusting the attributes of the bus lines in the MATSim simulation platform: for example, for the "increasing bus frequency" sub-strategy, the bus line frequency can be increased, i.e., the bus line interval can be reduced; for the "emergency bus line" sub-strategy, an additional bus line can be added to the public transport sub-model, and the corresponding bus travel demand can be generated; for the "bus priority" sub-strategy, the travel speed of the bus under congestion conditions or the delay suffered by the bus can be increased. Correspondingly, for different kinds of public transport scheduling strategies, as the attributes of the bus lines change, each traveler in the travel demand sub-model can dynamically adjust his / her travel plan.

[0083] In step 103, the snow depth corresponding to different scenarios and / or the traffic management strategies are different; the different traffic management strategies can include different types of traffic management strategies, or different sub-strategies under the same type of traffic management strategy. The number of scenarios that need to be simulated, and the snow depth and traffic management strategy corresponding to each scenario can be set as needed, so that the combination of different snow depths and different traffic management strategies can be realized in the simulation. For example, scenario 1 can be a moderate snow scenario and a "main road priority snow removal" sub-strategy, scenario 2 can be a moderate snow scenario and an "equal snow removal" sub-strategy, scenario 3 can be a heavy snow scenario and a "partial road closure" sub-strategy, scenario 4 can be a snow disaster scenario and a "main road priority snow removal" sub-strategy and a "partial road closure" sub-strategy, scenario 5 can be a moderate snow scenario and no traffic management strategy, and so on.

[0084] For any scenario, the change of the operating state of the target urban traffic system in the scenario can reflect the degree of deterioration of the performance of the target urban traffic system under the influence of the snow depth in the scenario, and the traffic recovery process of the target urban traffic system under the influence of the traffic management strategy in the scenario, so that the risk and resilience of the target urban traffic system in the scenario can be evaluated.

[0085] Exemplarily, the change of the operating state of the target urban traffic system in any scenario can include: the change of the travel plan of each traveler in the travel demand sub-model, the change of the traffic capacity of each road in the road network sub-model, and the change of the overall traffic flow of the target urban traffic system; thereby realizing the comprehensive simulation of the operating state of the target urban traffic system in the scenario at the micro (individual travel), meso (road traffic capacity), and macro (overall urban traffic flow) levels, so as to provide rich data support for the risk and resilience evaluation of the target urban traffic system in the snowfall.

[0086] Exemplarily, for any scenario, if the traffic flow of the target urban traffic system in the simulation process is basically stable, the simulation of the scenario is completed; further, the simulation data output in the simulation process can be analyzed and processed to evaluate the risk and resilience of the target urban traffic system during snowfall, especially during snow disaster.

[0087] As an example, the performance degradation of the target urban traffic system during snowfall at different snow depths can be analyzed based on the simulation data of scenarios with different snow depths and without traffic management strategies, to evaluate the risk of the target urban traffic system at different snow depths. The method in the embodiments of the present disclosure can accurately capture the dynamic changes of the running state of the target urban traffic system during snowfall, such as the decrease of road capacity, congestion conduction effect, etc., to provide more forward-looking risk assessment data for urban traffic managers; further, based on the simulation data of different scenarios, the law of deterioration of urban traffic system performance (also referred to as traffic service level) with the increase of snow depth can be found, so that the risk of the target urban traffic system during snowfall can be better evaluated.

[0088] As another example, the traffic recovery of the urban traffic system under different snow depths and different traffic management strategies can be analyzed based on the simulation data of scenarios with different snow depths and different traffic management strategies, to evaluate the resilience of the target urban traffic system under different traffic management strategies. Among them, the resilience of the urban traffic system to snow disaster generally reflects two aspects: disturbance resistance (the performance decrease amplitude of the urban traffic system during snowfall, especially when the snow disaster comes) and recovery force (the time required for the performance of the urban traffic system to recover to the normal state under the baseline condition). Exemplarily, the two aspects of the target urban traffic system can be quantified through simulation data, such as for any scenario, the disturbance resistance can be expressed as: the total traffic delay time increases by X%, the recovery force can be expressed as: it takes Y hours to recover to the normal state, etc.

[0089] In a possible implementation, the evaluation of the risk and resilience of the target urban traffic system during snowfall can include: evaluation based on overall traffic performance indicators, evaluation based on time-varying recovery curves, evaluation of regional traffic conditions, multi-strategy comparative analysis evaluation, etc.

[0090] The overall traffic performance index can include: average travel time, total travel distance, and total delay time of all travelers in the trip demand sub-model of the target urban traffic system, and the like macro indicators, which can be calculated through simulation data. The total delay time is the cumulative difference between the actual travel time of each traveler and the travel time of each traveler under the baseline condition. For example, the proportion of travel delay caused by snowfall can be quantified by comparing the average travel time under different snow depth scenarios with the average travel time under the baseline condition. For example, the average travel time of all travelers in scenario A of moderate snow without traffic management strategy is 30% higher than the average travel time of all travelers under the baseline condition, while the average travel time of all travelers in scenario B of moderate snow with a certain traffic management strategy is 20% higher than the average travel time of all travelers under the baseline condition. It is proved that the use of this traffic management strategy reduces the impact of moderate snow on the target urban traffic system. For different traffic management strategies, the less the average travel time of all travelers increases compared to the average travel time of all travelers under the baseline condition, the higher the resilience of the target urban traffic system under that traffic management strategy.

[0091] For scenarios with different traffic management strategies, based on the simulation data of these scenarios, the curve of the operating state of the target urban traffic system changing with time is extracted to describe the traffic recovery trajectory. For example, the curve of the operating state of the target urban traffic system changing with time can include the curve of the average driving speed or congestion delay index of the entire road network changing with time. Generally, in the initial stage of snow disaster, the average driving speed of the entire road network drops sharply and the congestion delay index increases, and then gradually recovers with the intervention of traffic management strategy. As an example, a time-varying recovery curve can be constructed, and the recovery time index T can be defined through the time-varying recovery curve, for example: the time T{0.9} required for the average driving speed of the entire road network to recover to 90% of the baseline condition, or the time for the congestion delay index to recover to near the baseline condition, from the start of snowfall. When comparing different traffic management strategies, the shorter T{0.9} indicates the faster recovery, and the better resilience of the urban traffic system under that traffic management strategy. For another example, the recovery rate, such as the rate of congestion delay reduction per unit time, can be calculated to measure the recovery efficiency of the urban traffic system under different traffic management strategies. The higher the efficiency, the better the resilience of the urban traffic system under that traffic management strategy.

[0092] The sub-area traffic condition evaluation includes analyzing the conditions of different sub-areas and different levels of roads in the target city traffic system during the traffic recovery process. For example, the traffic capacity recovery curves of the main roads and secondary roads, the congestion duration comparison between the central area (i.e., the core functional area) and the suburbs of the target city, and the like can be constructed. Such fine-grained evaluation helps to find the weak links in the target city traffic system: which areas in the city are inefficient for a long time after the snow? Which key roads become the bottleneck restricting the overall traffic recovery? In this way, the simulation data can be used to locate the areas or roads that need to be prioritized, thereby evaluating the risk of the city traffic system during the snowfall, and at the same time, providing a basis for further improving the snow disaster resilience of the target city traffic system.

[0093] The multi-traffic management strategy comparison and evaluation includes comparing the indicators under multiple traffic management strategies to evaluate which traffic management strategy or combination of traffic management strategies is the most effective. For example, for the same snow depth, the traffic recovery time indicators T{0.9} under two different road snow removal order strategies, i.e., "main road snow removal first" and "balanced snow removal", can be compared. The strategy with a shorter indicator is more effective. For another example, the total delay time under the "temporary speed limit" sub-strategy and the condition without the "temporary speed limit" sub-strategy can be compared. The strategy with smaller total delay time is more effective. If a certain traffic management strategy performs well in various measures, it means that the effect of this traffic management strategy on improving the resilience of the target city traffic system under this snow depth is significant, i.e., it is the recommended traffic management strategy. On the contrary, if a certain traffic management strategy intensifies local congestion or slows down traffic recovery of the target city traffic system, it should be avoided. Exemplarily, the effect of the public transportation scheduling strategy can be confirmed by checking the private vehicle travel substitution rate, the public bus full load rate, and the like, which reflects whether residents change their travel mode in response to the public transportation scheduling strategy, and is also an important aspect of evaluating the resilience of the city traffic system.

[0094] In the embodiments of the present disclosure, basic data of a target urban traffic system is acquired; the basic data represents data related to urban traffic; based on the basic data, a simulation model of the target urban traffic system is established; wherein the simulation model at least includes: a road network sub-model and a travel demand sub-model; the road network sub-model is used to represent the connection relationship between different roads and the attributes of each road; the travel demand sub-model is used to represent a plurality of travelers and the travel scheme of each traveler, and the traveler has the ability to autonomously decide the travel scheme according to the attributes of the road; the running state changes of the target urban traffic system in different scenarios are simulated by using the simulation model, so as to evaluate the risk and resilience of the target urban traffic system in the snowfall; wherein the corresponding snow depth and / or traffic management strategy of different scenarios are different. In this way, based on the dynamic simulation of multi-agent (i.e. traveler), the running state of the target urban traffic system in the scenario of different snow depth and different traffic management strategy combination is simulated at three levels of micro (individual travel), meso (road traffic capacity) and macro (urban overall traffic flow), which can fully depict the response behavior of the target urban traffic system in various scenarios; from the basic scenario of light snow without measures to the extreme scenario of heavy snow superimposed with multiple interventions, the running state covering all stages of the target urban traffic system can be obtained through simulation, so as to provide rich data support for the snow disaster risk and resilience analysis of the target urban traffic system, and then the precision of the risk and resilience evaluation of the urban traffic system during snowfall can be improved; the risk and resilience of the urban traffic network during snowfall are efficiently and intelligently evaluated.

[0095] In some scenarios, the traffic management strategies corresponding to different scenarios can also be evaluated in the embodiments of the present disclosure. As an example, for traffic control strategies, the degree of improvement of traffic congestion and safety and the influence on traffic recovery time can be evaluated by comparing the "no traffic control strategy" benchmark and different kinds of traffic control strategy scenarios. For example, for the "temporary speed limit" sub-strategy, although the driving speed of a single vehicle is reduced, the overall safety and order degree can be improved, and the influence on the average delay can be observed through simulation. Whether the "closure of part of the road" sub-strategy will increase the burden of the main road can also be quantitatively analyzed. Comprehensive comparison can help to develop a reasonable combination of control strategies to balance safety and smoothness. As another example, for public transportation scheduling strategies, based on the simulation data of different kinds of public transportation scheduling strategies, indicators such as average travel time of public transportation passengers and car travel proportion can be calculated to judge the effect of various kinds of public transportation scheduling strategies. For example, if the "increase of public transportation frequency" sub-strategy can significantly reduce the total delay time of the urban traffic system and the public transportation vehicle full load rate rises, it indicates that the "increase of public transportation frequency" sub-strategy has a positive effect in snowy weather. On the contrary, if the road is too rough to cause the public transportation vehicle to be seriously delayed, it needs to be improved by means such as snow removal. In addition, the key factors to improve the snow disaster resilience of the urban traffic system can also be revealed in the embodiments of the present disclosure. For example, it is found that the timely investment of snow removal resources contributes the most to the reduction of recovery time, or the traffic control in advance can reduce the congestion peak in the initial stage. Based on these findings, urban planners can develop more scientific snow disaster emergency response plans, such as reserving enough snow removal vehicles to clean specific roads first, issuing travel guidelines to implement control in the snow disaster warning stage, and strengthening public transportation support to reduce the traffic pressure of private cars, etc. In this way, the urban traffic system has dynamic prediction ability and intelligent cooperative scheduling mechanism to cope with snowfall, especially sudden snow disaster, and can improve the emergency response ability and traffic resilience of the urban traffic system under extreme snow disaster conditions, and optimize the allocation of snow removal resources.

[0096] Figure 2 A flowchart of another method for evaluating the risk and resilience of an urban traffic system in snowy weather is shown according to an embodiment of the present disclosure. As shown in Figure 2 , the method can include the following steps:

[0097] Step 201, obtaining the basic data of the target urban traffic system; the basic data represents data related to urban traffic.

[0098] This step is the same as step 101 in the above Figure 1 , which will not be repeated here.

[0099] In step 202, a free flow speed reduction model is established by regression analysis based on the historical meteorological and traffic operation data, to be used in the simulation process to determine the free flow speed of each road in the road network submodel under different snow depths; wherein the free flow speed reduction model represents the correspondence between different snow depths and free flow speed reduction coefficients, and the free flow speed reduction coefficient is the ratio of the free flow speed of the road to the reference free flow speed.

[0100] The historical meteorological and traffic operation data in the basic data of the target urban traffic system can reflect the influence of different snow depths on road driving speed, wherein the road driving speed includes the free flow speed of the road, so in this step, the historical meteorological and traffic operation data are used to support the establishment and calibration of the free flow speed reduction model. The free flow speed reduction model can also be called a reduction coefficient function, which is defined as the proportion of the free flow speed of the road under different snow depths, i.e. the ratio of the free flow speed of the road to the reference free flow speed.

[0101] Exemplarily, the free flow speed reduction model corresponding to each road in the road network submodel can be established respectively; the free flow speed reduction model corresponding to each road level in the road network submodel can also be established respectively; and the free flow speed reduction model corresponding to the entire road network submodel can also be established. Taking the road level of each road in the road network submodel as an example, a plurality of sample road segments can be selected on the road of the road level for different road levels, and based on the historical meteorological and traffic operation data, the reduction amplitude of the average free flow speed of each sample road segment under different snow depth scenarios is analyzed, for example, the meteorological data of multiple snowfall events and the road traffic operation data corresponding to the period can be statistically analyzed, taking the snow depth s (unit: cm) as the independent variable and the ratio of the actual free flow speed of the road to the reference free flow speed of the road (i.e. the free flow speed reduction coefficient f(s)) as the dependent variable, to fit the free flow speed reduction model; wherein under the reference condition, the snow depth s = 0, and the free flow speed reduction coefficient f(s) = 1 at this time.

[0102] Exemplarily, a segmented linear model or a nonlinear model can be selected for regression analysis to establish the free flow speed reduction model; or regression analysis can be performed by the segmented linear model and the nonlinear model respectively to fit different free flow speed reduction models, and the one with good fitting effect is selected as the final free flow speed reduction model.

[0103] As an example, taking the segmented linear model for regression analysis as an example, the established free flow speed reduction model can be expressed as:

[0104] f(s) = 1 - β * s

[0105] The range of snow depth s is divided into multiple intervals, and the reduction coefficient f(s) corresponding to different intervals has different values; β is an undetermined coefficient, which represents the proportion of free flow velocity reduction caused by unit snow depth.

[0106] As another example, when the nonlinear effect of snow depth is significant, a nonlinear model can be used for regression analysis. Taking the exponential decay model as an example, the established free flow speed reduction model can be expressed as:

[0107] f(s)=α*e -βs

[0108] Where α and β are regression coefficients, s is the snow depth, and f(s) is the free flow velocity reduction coefficient when the snow depth is s.

[0109] Step 203: Based on the basic data, a simulation model of the target city traffic system is established; wherein the simulation model includes at least: a road network sub-model and a travel demand sub-model; the road network sub-model is used to represent the connection relationship between different roads and the attributes of each road; the travel demand sub-model is used to represent multiple travelers and the travel plan of each traveler, and the travelers have the ability to independently decide on travel plans based on the attributes of the roads.

[0110] This step is the same as above Figure 1 Step 102 is the same as above and will not be described again here.

[0111] Exemplarily, the simulation model may further include: a public transportation sub-model.

[0112] Step 204: Using the simulation model, simulate the changes in the operating status of the target city traffic system in different scenarios to evaluate the risk and resilience of the target city traffic system during snowfall; wherein different scenarios correspond to different snow depths and / or traffic management strategies.

[0113] This step is the same as above Figure 1 The same as step 103 in FIG. 1 is omitted here for brevity.

[0114] In the process of simulating the operating state changes of the target city traffic system in any scenario, the free flow speed reduction model constructed in the above step 202 is used to determine the free flow speed of each road in the road network sub-model at the snow depth corresponding to the scenario; thereby, by integrating the influence mechanism of snow depth on road free flow speed, the operating state changes of the target city traffic system under different snow depth levels and multiple traffic management strategies are simulated.

[0115] Exemplarily, in any scenario, for any road in the road network submodel, a free flow speed reduction model corresponding to the road can be obtained, for example, the free flow speed reduction model corresponding to the road level of the road can be determined based on the road level of the road; then, based on the free flow speed reduction model and the snow depth corresponding to the scenario, the free flow speed reduction coefficient of the road is determined, and further, based on the free flow speed reduction coefficient of the road and the reference free flow speed of the road, the free flow speed of the road in the scenario is corrected to obtain the free flow speed of the road under the snow depth; which can be expressed by the following formula:

[0116] V snow (s)=f(s)*V0

[0117] Wherein, s is the snow depth corresponding to a certain scenario, V0 is the reference free flow speed of the road under the reference condition, V snow (s) is the free flow speed of the road when the snow depth is s.

[0118] Exemplarily, the free flow speed reduction model can be directly embedded into the simulation platform such as MATSim, and the free flow speed of each road in the road network submodel is dynamically adjusted by using the free flow speed reduction model in the simulation process, so that the simulation is closer to the real snowfall situation, and the scientificity of further simulation of traffic management strategies is improved.

[0119] In the related art, snow disaster traffic evaluation is mostly based on empirical formula or historical statistical data, and it is difficult to reflect the specific influence of snow depth on traffic running state in real time; in the embodiment of the present disclosure, it is considered that the snow depth will significantly affect the road capacity; different snow depths have different influences on the road capacity; based on the historical meteorological and traffic running data of the target city traffic system, the free flow speed reduction model is established through regression analysis; the free flow speed reduction model represents the corresponding relationship between different snow depths and free flow speed reduction coefficients, that is, it can represent the quantitative relationship between snow depth and free flow speed reduction coefficient, which can quantitatively evaluate the road capacity of different types of roads under different snow depths; further, in the process of simulating the change of the running state of the target city traffic system in different scenarios, the free flow speed of each road in the road network submodel in different scenarios is accurately adjusted through the free flow speed reduction model, which is the upper limit of the driving speed and can reflect the road capacity in the scenario. In this way, the free flow speed reduction model is established by coupling the snow depth and the traffic free flow speed, and the influence of the snow depth corresponding to different scenarios on the road capacity is quantitatively integrated into the simulation of the change of the running state of the target city traffic system in the scenario, so as to realize more accurate quantitative evaluation of the snow depth on the city traffic system.

[0120] The above Figure 1The process of simulating the change of the running state of the target urban traffic system in different scenarios in step 103 is exemplarily described.

[0121] Figure 3 A flowchart of simulating the change of the running state of the target urban traffic system in different scenarios is shown according to an embodiment of the present disclosure, as shown in Figure 3 The process can include the following steps:

[0122] In step 301, initial values of the properties of each road in the road network submodel in the target scenario are determined based on the snow depth corresponding to the target scenario.

[0123] Exemplarily, the initial values of the properties of each road can include initial values of the free flow speed of each road; the reference free flow speed of each road in the road network submodel in the target scenario can be reduced by using the free flow speed reduction model established by regression analysis in the above Figure 2 For example, the free flow speed reduction model corresponding to each road and the reference free flow speed of each road in the road network submodel can be obtained; if the snow depth corresponding to the target scenario is s, the reference free flow speed V0 of each road can be replaced by the corrected free flow speed V snow , where V snow = f(s) * V0, V snow is the initial value of the free flow speed of each road in the target scenario.

[0124] In this way, for scenarios with different snow depths, the initial values of the free flow speed of each road in the road network submodel can be configured based on the snow depth corresponding to each scenario and in combination with the established free flow speed reduction model corresponding to each road, so as to reflect the consistent influence of the snow depth on the traffic capacity of each road in the entire urban road network. Exemplarily, the initial values of the free flow speed of each road in the road network submodel can also be configured before simulating the target scenario, for example, the network file of the target scenario can be directly generated in the MATSim simulation platform, so that the upper limit of the reduced driving speed of each road under the snow depth corresponding to the target scenario can be reflected at the beginning of the simulation of the target scenario.

[0125] Exemplarily, for the simulation of the target scene, a sudden snowfall can be assumed before the daily morning rush hour, and the snow depth is s, and the time of the sudden snowfall is recorded as simulation time t=0. At t=0, the reference free flow speed of each road in the entire road network submodel can be uniformly reduced according to the corresponding free flow speed reduction model, and the reduced free flow speed is the initial value of the free flow speed of each road, which represents the initial damaged state of the traffic operation of each road in the road network submodel under the target scene with a snow depth of s when the snow is not cleared, so as to simulate the impact of the initial snow disaster on the traffic operation of the target urban traffic system; at this time, the driving speed in each road is limited, and congestion will be formed quickly.

[0126] Step 302, based on the traffic management strategy corresponding to the target scene, setting at least one event and the occurrence time point of each event in the target scene.

[0127] Exemplarily, the traffic management strategy includes a road snow removal sequence strategy and a traffic control strategy.

[0128] As an example, the traffic management strategy corresponding to the target scene includes a road snow removal sequence strategy, a plurality of events and the occurrence time points of each event can be set in the MATSim simulation platform according to the type of the pre-set road snow removal sequence strategy, so as to gradually restore the traffic capacity of each road at different time points. For example, the type of the road snow removal sequence strategy is the “main road priority cleaning” sub-strategy: three events (event 1, event 2, event 3) and the occurrence time points of the three events (t1, t2, t3) can be set, wherein t1 is less than t2 and t3, and t1 can be the middle of the simulated morning rush hour; event 1 is to restore the free flow speed and traffic capacity of all main roads in the road network submodel to the original value, so as to represent that the snow on these roads is cleared and the normal traffic can be restored; event 2 is to restore the free flow speed and traffic capacity of all secondary roads in the road network submodel to the original value; event 3 is to restore the free flow speed and traffic capacity of all branch roads in the road network submodel to the original value, so as to represent that the secondary roads and branch roads are restored slightly later than the main roads.

[0129] As another example, taking the traffic management strategy corresponding to the target scenario as an example, the traffic control strategy, according to the pre-set category of the traffic control strategy, a plurality of events and the occurrence time points of the events can be set in the MATSim simulation platform. For example, the category of the traffic control strategy is the “temporary speed limit” sub-strategy: an event and the occurrence time point of the event (such as t=0) can be set, and the event is to additionally reduce the speed limit of a specific congestion serious road segment; for another example, the category of the traffic control strategy is the “close part of the road” sub-strategy: two events and the occurrence time points of the two events (such as t=0, t=Treopen) can be set, and the two events are to close some dangerous road segments and reopen the closed road segments respectively, so as to represent closing some dangerous road segments until a certain time and reopening.

[0130] Step 303, triggering the corresponding event at the occurrence time point of each event, and adjusting the properties of each road in the road network sub-model.

[0131] As an example, taking the category of the road snow removal sequence strategy in step 302 above as the “main road priority cleaning” sub-strategy: at the simulation time t=t1, event 1 is triggered, and the free flow speed and the traffic capacity of all main roads in the road network sub-model are restored to the original values; at the simulation time t=t2, event 2 is triggered, and the free flow speed and the traffic capacity of all secondary roads in the road network sub-model are restored to the original values; at the simulation time t=t3, event 3 is triggered, and the free flow speed and the traffic capacity of all branch roads in the road network sub-model are restored to the original values. Similarly, if other categories of road snow removal sequence strategies are used, the restoration time sequence of different levels of roads can be adjusted accordingly; in this way, through this series of events, the simulation process can dynamically represent the road network traffic recovery situation as the simulation time advances.

[0132] As another example, taking the category of the traffic control strategy in step 302 above as the “temporary speed limit” sub-strategy: at the simulation time t=0, the speed limit of a specific congestion serious road segment can be additionally reduced.

[0133] As another example, taking the category of the traffic control strategy in step 302 above as the “close part of the road” sub-strategy: at the simulation time t=0, closing some dangerous road segments can be triggered, and at the simulation time t=Treopen, reopening the closed road segments can be triggered.

[0134] Step 304, based on the properties of each road, each traveler in the travel demand sub-model adjusts his / her travel plan.

[0135] In the simulation process, the attribute changes of each road in the road network sub-model of the target city traffic system are perceivable to each traveler: for example, during the simulation process, the traffic recovery of a road will immediately affect the vehicles that are driving on the road or are about to enter the road, so as to make the driving speed faster; and the closed road will force the driver to detour; such dynamic perception capability makes the simulation surpass the existing static allocation mode, and can simulate the time sequence effect of the traffic management strategy in the emergency state.

[0136] In a possible implementation, in the step 304, the adjusting, by each traveler in the travel demand sub-model, of the travel scheme of the traveler based on the attributes of the roads comprises: adopting a multi-round iteration manner, so that each traveler in the travel demand sub-model learns the optimal travel scheme in the target scenario; and in each round of iteration, each traveler in the travel demand sub-model adjusts the travel scheme in the current round of iteration based on the result of the previous round of iteration. In this way, for any scenario, each traveler in the travel demand sub-model can adjust the travel scheme of the traveler through iterative simulation, so as to learn the optimal travel scheme in different scenarios.

[0137] For example, taking the MATSim multi-agent traffic simulation platform as an example, each agent is a traveler; in the first iteration, all agents travel according to the generated initial travel plan; due to the reduction in driving speed caused by the snow depth, some originally smooth routes may appear congestion and delay. After the first iteration simulation, each agent gets the actual experience of this trip (travel time, congestion waiting, etc.). In subsequent iterations, each agent adjusts its strategy based on the last travel experience and tries to improve the travel plan; among them, a part of the agents may choose to bypass the heavily congested section or adjust the travel time (such as avoiding the morning and evening peak); the built-in strategy module of the MATSim simulation platform will allow agents to try new travel plans according to a certain probability (such as new travel routes suggested by the shortest path algorithm), and keep multiple travel plans, and select the better travel plan through a scoring mechanism. Iterative simulation is constantly looped, and all agents travel simultaneously in each iteration, and the running state of the target city traffic system also changes. The agents gradually "learn" the more optimal travel plan under the current scene corresponding to the snow depth and traffic management strategy. After several iterations, the travel plan of most agents tends to be stable (i.e., there is no significant benefit in further changing the travel route or travel time), and the simulation reaches a state of approximate user equilibrium; in this equilibrium state, the traffic flow of the target city traffic system is basically stable, so this traffic flow can be used as a convergent solution to evaluate the performance of the target city traffic system in the scene. In this way, MATSim uses an iterative simulation solution, that is, by repeatedly simulating the travel process of multiple days (i.e., multiple iterations), the adaptation of experience by each traveler and the evolution of the traffic flow of the target city traffic system are simulated.

[0138] For example, the number of iterations of the simulation platform can be set according to the convergence, for example, it can be 50 times, 100 times, etc., to ensure that the main indicators (average travel time, total delay, etc.) converge within a negligible range; the converged traffic flow not only contains the direct physical impact of different snow depths and traffic management strategies, but also reflects the equilibrium effect of the travel plan adjustment of the travelers.

[0139] Through the above steps 301-304, the properties of each road in the road network sub-model and the travel plan of each traveler in the travel demand sub-model are adjusted based on the snow depth corresponding to the target scene and / or the traffic management strategy corresponding to the target scene in the process of simulating the change of the running state of the target city traffic system in the target scene.

[0140] In the embodiments of the present disclosure, the influence mechanism of snow depth on road free-flow speed is fused to simulate the changes of the running state of the target urban traffic system under different snow depth levels and various traffic management strategies. In the simulation process, a plurality of events and time points of each event are preset in the time dimension to represent the time points of implementing different traffic management strategies, and these events trigger changes in the properties of specified roads. Meanwhile, with the changes in the properties of each road, each traveler in the trip demand sub-model adjusts his / her trip plan. Through the above mechanism, the influences of spatial heterogeneity (different characteristics of different roads, snow removal order) and time evolution (snow removal step by step, traffic condition improvement) are considered to realistically reproduce the change process of the running state of the target urban traffic system under the impact of snow disaster. As an example, the overall simulation adopts an iterative simulation method, and through iterative simulation, the autonomous adaptive behavior of travelers in response to snow disturbance can be captured, which is particularly important for evaluating the risk and resilience of the target urban traffic system. For example, when the snow on the main road is not cleaned in time and causes congestion, drivers may change to the secondary road, and this effect will be reflected in the iteration, causing the traffic volume of the secondary road to rise. Thus, the deterioration process of the running state of the target urban traffic system when the snow disaster occurs and the influence of the traffic management strategy on the traffic recovery efficiency of the target urban traffic system can be simulated more accurately.

[0141] Figure 4 A flowchart for simulating the changes of the running state of the target urban traffic system in different scenarios is shown according to an embodiment of the present disclosure, as shown in Figure 4 The simulation process can include the following steps:

[0142] Step 401: Based on the snow depth corresponding to the target scenario, the initial values of the properties of each road in the road network sub-model in the target scenario are determined.

[0143] This step is the same as step 301 in the above Figure 3 , and will not be described here.

[0144] Step 402: Based on the traffic management strategy corresponding to the target scenario, at least one event in the target scenario and the occurrence time point of each event are set.

[0145] This step can refer to the related description in step 302 in the above Figure 3 .

[0146] Exemplarily, the traffic management strategy includes a public transportation scheduling strategy, and can also include a road snow removal order strategy and a traffic control strategy.

[0147] As an example, taking the traffic management strategy corresponding to the target scene as a public transport scheduling strategy as an example, a plurality of events and time points of occurrence of each event can be set in the MATSim simulation platform according to a pre-set type of the public transport scheduling strategy, so as to gradually restore the traffic capacity of each road at different time points. For example, the type of the public transport scheduling strategy is an "emergency public transport line" sub-strategy: an event and a time point of occurrence of the event (such as t = 0) can be set, and the event is to add a new public transport line. For another example, the type of the public transport scheduling strategy is a "public transport priority" sub-strategy: an event and a time point of occurrence of the event (such as t = 0) can be set, and the event is to modify the driving speed of the public transport vehicle, for example, a smaller free flow speed reduction coefficient can be used to calculate the modified free flow speed of the public transport lane, so as to give special treatment to the public transport vehicle.

[0148] Step 403: triggering the corresponding event at the time point of occurrence of each event, and adjusting the properties of each road in the road network sub-model and / or the properties of the public transport line in the public transport sub-model.

[0149] This step can refer to the related description in step 303 in the above Figure 3 .

[0150] As an example, taking the type of the public transport scheduling strategy in step 402 above as the "emergency public transport line" sub-strategy: a new public transport line can be added in the public transport sub-model at the simulation time t = 0.

[0151] As another example, taking the type of the public transport scheduling strategy in step 402 above as the "public transport priority" sub-strategy: the driving speed of the public transport vehicle can be modified in the public transport sub-model at the simulation time t = 0 to improve the driving speed of the public transport vehicle under congestion conditions.

[0152] Step 404: based on the properties of each road and / or the properties of the public transport line, each traveler in the travel demand sub-model adjusts his / her own travel plan.

[0153] This step can refer to the related description in step 304 in the above Figure 3 .

[0154] In a possible implementation, each traveler in the travel demand sub-model can adjust his / her own travel plan based on the properties of each road and / or the properties of the public transport line through iterative simulation; and the specific iterative process can refer to the related description in step 304 above.

[0155] Through the steps 401-404, one or more of the attributes of the roads in the road network submodel, the travel schemes of the travelers in the travel demand submodel, and the attributes of the public transport lines in the public transport submodel are adjusted based on the corresponding snow depth of the target scene and / or the corresponding traffic management strategy in the process of simulating the change in the operating state of the target urban traffic system in the target scene.

[0156] In the embodiments of the present disclosure, the traffic management strategy includes a public transport scheduling strategy, and can also include a road snow removal sequence strategy and a traffic control strategy, so that the traffic characteristics of the city can be more realistically simulated to better evaluate the effect of the public transport scheduling strategy in snowfall.

[0157] Compared with other ways of evaluating traffic risk and resilience in snowfall, the method provided in the above embodiments has at least the following outstanding effects:

[0158] In the related art, the influence of snow on a single road section is mainly focused on, and the impact of snow on the operating efficiency and performance of the entire urban traffic system cannot be quantified systematically. However, snow not only affects the traffic capacity of a single road section, but also causes a cascading effect on the entire urban traffic system, resulting in traffic congestion and transportation delay. In the embodiments of the present disclosure, precise snow depth-entire urban traffic flow modeling is achieved, and the quantitative evaluation of the impact of snow depth on the urban traffic system is optimized, so that fine-grained resilience evaluation and coping strategy analysis can be provided, so that city managers can make scientific decisions based on objective data, thereby more reasonably allocating snow removal resources and improving traffic recovery efficiency.

[0159] In the related art, modeling and evaluation are mainly focused on highways or regional roads, and there is a lack of a global risk evaluation and resilience evaluation framework for complex urban road networks. However, the urban traffic system is a highly complex dynamic network, and a decrease in the traffic capacity of a key road section can trigger a chain reaction and even cause the entire city to be paralyzed. In the embodiments of the present disclosure, urban-level traffic system modeling and simulation in snowfall are focused on, and a set of snow disaster risk and resilience simulation analysis methods for urban traffic systems are proposed; the traffic risk transmission mechanism at the urban level is simulated, and in the snow disaster emergency response process, traffic bottlenecks can be quickly identified, congestion development trends can be predicted, and snow removal resources can be reasonably allocated for intervention.

[0160] In the related art, it is difficult to capture the dynamic evolution of the traffic flow of the urban traffic system in the snow disaster process based on static data or historical statistical analysis, and it is difficult to comprehensively simulate the complex behavior mode of the urban traffic. The urban traffic management department lacks intelligent decision support tools, and mainly relies on artificial experience decision-making when the snow disaster occurs, which leads to a lag in response strategies, inefficient snow removal resource scheduling, and even may exacerbate the impact of the snow disaster on traffic. In the embodiment of the present disclosure, the intelligent and dynamic snow disaster traffic risk assessment is realized based on multi-agent dynamic simulation.

[0161] In the related art, the snow disaster response strategy often relies on artificial experience decision-making, which leads to a lack of systematization in snow removal scheduling, road closure, public transportation adjustment and other response measures. The embodiment of the present disclosure provides a dynamic assessment method based on intelligent simulation, which can predict key congestion sections and areas prone to paralysis before the snow disaster occurs, and optimize the allocation of snow removal resources. For example, according to the risk and resilience assessment of the urban traffic system during snowfall, snow removal equipment can be preferentially arranged to high-impact areas or sections, and public transportation frequency can be dynamically adjusted to ensure that the core functional areas of the city are restored to traffic priority, thereby improving the efficiency of emergency response and achieving scientific scheduling and resource optimization.

[0162] In the related art, it is difficult to comprehensively evaluate the cascading impact of snowfall, especially snow disaster, on the overall urban traffic, which may lead to unreasonable traffic management strategies and further exacerbate traffic congestion. In the embodiment of the present disclosure, multi-agent simulation can simulate the traffic recovery process of the target urban traffic system under different traffic management strategies, help the urban traffic manager to select the optimal traffic management strategy, and achieve rapid traffic recovery, thereby improving the resilience of the urban traffic system and reducing the impact of snow disaster on the social economy. The method in the embodiment of the present disclosure can be widely applied in the fields of snowfall urban traffic management, smart city construction, emergency plan optimization, etc., which helps to reduce economic losses during the snow disaster and ensure the continuity of the safety of residents and urban functions.

[0163] Based on the same inventive concept as the above method embodiment, the embodiments of the present disclosure also provide a risk and resilience assessment device for an urban traffic system during snowfall, which can be used to execute the technical solutions described in the above method embodiments.

[0164] Figure 5 The structure diagram of a risk and resilience assessment device for an urban traffic system during snowfall according to an embodiment of the present disclosure is shown in FIG. 1. Figure 5As shown, the apparatus comprises: an acquisition module 501 configured to acquire basic data of a target urban traffic system; the basic data represents data related to urban traffic; a modeling module 502 configured to establish a simulation model of the target urban traffic system based on the basic data; wherein the simulation model at least comprises: a road network sub-model, a travel demand sub-model; the road network sub-model is used to represent the connection relationship between different roads and the attributes of each road; the travel demand sub-model is used to represent a plurality of travelers and the travel scheme of each traveler, and the traveler has the ability to autonomously decide the travel scheme according to the attributes of the road; a simulation module 503 configured to simulate the change of the running state of the target urban traffic system in different scenarios by using the simulation model, so as to evaluate the risk and resilience of the target urban traffic system in the snowfall; wherein the snow depth and / or traffic management strategy corresponding to different scenarios are different.

[0165] In the embodiments of the present disclosure, the basic data of a target urban traffic system is acquired; the basic data represents data related to urban traffic; a simulation model of the target urban traffic system is established based on the basic data; wherein the simulation model at least comprises: a road network sub-model, a travel demand sub-model; the road network sub-model is used to represent the connection relationship between different roads and the attributes of each road; the travel demand sub-model is used to represent a plurality of travelers and the travel scheme of each traveler, and the traveler has the ability to autonomously decide the travel scheme according to the attributes of the road; the change of the running state of the target urban traffic system in different scenarios is simulated by using the simulation model, so as to evaluate the risk and resilience of the target urban traffic system in the snowfall; wherein the snow depth and / or traffic management strategy corresponding to different scenarios are different. In this way, based on the dynamic simulation of multiple agents (i.e. travelers), the running state of the target urban traffic system in the scenario of different snow depths and different traffic management strategies is comprehensively simulated at three levels of micro (individual travel), meso (road traffic capacity), and macro (overall traffic flow of the city), which can comprehensively depict the response behavior of the target urban traffic system in various scenarios; from the basic scenario of light snow without measures to the extreme scenario of heavy snow superimposed with multiple interventions, the running state covering all stages of the target urban traffic system can be obtained through simulation, thereby providing rich data support for the snow disaster risk and resilience analysis of the target urban traffic system, and further improving the accuracy of the risk and resilience evaluation of the urban traffic system during snowfall; the risk and resilience of the urban traffic network during snowfall are efficiently and intelligently evaluated, so that the urban traffic system has dynamic prediction ability and intelligent collaborative scheduling mechanism when responding to snow, especially sudden snow disasters, thereby improving the emergency response ability and traffic resilience of the urban traffic system under extreme snow disaster conditions, and optimizing the snow removal resource allocation ability.

[0166] In a possible implementation, the basic data includes: road network data and historical meteorological and traffic operation data; the attribute of each road at least includes: free flow speed of each road; the modeling module 502 is further configured to: based on the historical meteorological and traffic operation data, establish a free flow speed reduction model by regression analysis, to be used in the simulation process to determine the free flow speed of each road in the road network submodel under different snow depths; wherein the free flow speed reduction model represents the corresponding relationship between different snow depths and free flow speed reduction coefficients, and the free flow speed reduction coefficient is the ratio of the free flow speed of the road to the reference free flow speed.

[0167] In a possible implementation, the basic data includes: population and land use data, resident travel survey data; the modeling module 502 is further configured to: based on the population and land use data, estimate the trip generation and trip attraction of each region in the target city traffic system; based on the resident travel survey data, adjust the trip generation and trip attraction of each region to obtain the trip volume between different regions; generate the plurality of travelers and the trip scheme of each traveler according to the trip volume between different regions.

[0168] In a possible implementation, the simulation module 503 is further configured to: in the process of simulating the change of the running state of the target city traffic system in the target scenario, based on the snow depth corresponding to the target scenario and / or the traffic management strategy corresponding to the target scenario, adjust the attribute of each road in the road network submodel and the trip scheme of each traveler in the trip demand submodel; the target scenario is any one of the different scenarios.

[0169] In a possible implementation, the simulation module 503 is further configured to: based on the snow depth corresponding to the target scenario, determine the initial value of the attribute of each road in the road network submodel in the target scenario; based on the traffic management strategy corresponding to the target scenario, set at least one event and the occurrence time point of each event in the target scenario; trigger the corresponding event at the occurrence time point of each event and adjust the attribute of each road in the road network submodel; based on the attribute of each road, each traveler in the trip demand submodel adjusts the trip scheme of each traveler.

[0170] In a possible implementation, the simulation module 503 is further configured to: in a multi-round iteration manner, so that each traveler in the trip demand submodel learns the optimal trip scheme in the target scenario; wherein in each round of iteration, each traveler in the trip demand submodel adjusts the trip scheme in the current round of iteration based on the result of the previous round of iteration.

[0171] In a possible implementation, the simulation model further includes a public transportation sub-model configured to represent attributes of bus lines; and the simulation module 503 is further configured to: in the process of simulating the change of the running state of the target urban traffic system in the target scene, adjust one or more of the attributes of the roads in the road network sub-model, the travel schemes of the travelers in the travel demand sub-model, and the attributes of the bus lines in the public transportation sub-model based on the corresponding snow depth and / or the corresponding traffic management strategy of the target scene.

[0172] In some embodiments, the apparatus provided by the embodiments of the present disclosure has functions or includes modules that can be used to perform the methods described in the above method embodiments, and the specific implementation can refer to the description of the above method embodiments. For brevity, it will not be repeated here.

[0173] The embodiments of the present disclosure also provide an electronic device, including a memory, a processor and a computer program stored in the memory, and the processor executes the computer program to implement the steps of the above method.

[0174] The embodiments of the present disclosure also provide a non-volatile computer readable storage medium having a computer program stored thereon, and the computer program is executed by a processor to implement the steps of the above method.

[0175] The embodiments of the present disclosure also provide a computer program product, including a computer program or a non-volatile computer readable storage medium carrying a computer program, and the computer program is executed by a processor to implement the steps of the above method.

[0176] Figure 6 A block diagram of an electronic device 1900 according to an embodiment of the present disclosure is shown. For example, the electronic device 1900 can be provided as a server or a terminal device. Referring to Figure 6 , the electronic device 1900 includes a processing component 1922, which further includes one or more processors, and a memory resource represented by a memory 1932, for storing instructions executable by the processing component 1922, such as an application program. The application program stored in the memory 1932 can include one or more than one module each corresponding to a set of instructions. In addition, the processing component 1922 is configured to execute the instructions to perform the above method.

[0177] The electronic device 1900 can further include a power supply component 1926 configured to perform power management of the electronic device 1900, a wired or wireless network interface 1950 configured to connect the electronic device 1900 to a network, and an input / output interface 1958 (I / O interface). The electronic device 1900 can operate based on an operating system stored in the memory 1932, such as Windows Server TM , Mac OS X TM , Unix TM , Linux TM , FreeBSD TM , or the like.

[0178] In an exemplary embodiment, there is also provided a non-transitory computer readable storage medium, such as the memory 1932 including computer program instructions, which can be executed by the processing component 1922 of the electronic device 1900 to perform the above method.

[0179] The computer readable storage medium can be a tangible device that can retain and store instructions for execution by a processor. The computer readable storage medium can be, for example, but is not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium include the following: a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanically encoded device such as punch-cards or punched tape, a

[0180] The computer program (or computer readable program instructions) described herein can be downloaded from a computer readable storage medium to various computing / processing devices by way of a network, e.g., the Internet, a local area network, a wide area network, and / or a wireless network. The network can include copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers and / or edge servers. A network adapter card or network interface in each computing / processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium within the respective computing / processing device.

[0181] Computer readable program instructions for carrying out operations of the present disclosure can be assembler instructions, instruction-set-architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state-setting data, or either source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C++ or the like, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The computer readable program instructions can execute entirely on the user's computing / processing device, partly on the user's computing / processing device, as a stand-alone software package, partly on the user's computing / processing device and partly on a remote computing / processing device or entirely on the remote computing / processing device or server. In the latter scenario, the remote computing / processing device can be connected to the user's computing / processing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection can be made to an external computing / processing device, for example, through the Internet using an Internet Service Provider. In some embodiments, electronic circuitry including, for example, programmable logic circuitry, field-programmable gate arrays (FPGA), or programmable logic arrays (PLA) can execute the computer readable program instructions by utilizing state information of the computer readable program instructions to personalize the electronic circuitry, in order to perform aspects of the present disclosure.

[0182] The computer readable program instructions can also be loaded onto a computing / processing device, other programmable data processing apparatus, or other device to cause a series of operations to be performed on the computing / processing device, other programmable apparatus or other device to produce a computer implemented process, such that the instructions which execute on the computing / processing device, other programmable apparatus, or other device implement the functions / acts specified in the flowchart and / or block diagram block or blocks.

[0183] These computer readable program instructions can be provided to a processor of a general purpose computer, special purpose computer, 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, create means for implementing the functions / acts specified in the flowchart and / or block diagram block or blocks. These computer readable program instructions can also be stored in a computer readable storage medium that can include a non-transitory computer readable storage medium that can be a computer- readable storage medium having no data storage cycles that change state. The instructions can be executed by one or more processors of a computer, to cause a series of operational steps to be performed on the computer to produce a computer-implemented process. The instructions can also cause one or more processors of a computer or other programmable data processing apparatus to

[0184] The computer readable program instructions can also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable data processing apparatus, or other device to produce a computer implemented process, such that the instructions which execute on the computer, other programmable data processing apparatus, or other device implement the functions / acts specified in the flowchart and / or block diagram block or blocks.

[0185] The computer readable program instructions can also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable data processing apparatus, or other device to produce a computer implemented process, such that the instructions which execute on the computer, other programmable data processing apparatus, or other device implement the functions / acts specified in the flowchart and / or block diagram block or blocks.

[0186] Embodiments of the present disclosure have been described above, and the description is intended to be illustrative of the embodiments and not restrictive of the disclosure. Many modifications and variations of the described embodiments are possible in light of this disclosure without departing from the scope and spirit of the described embodiments. The choice of words in this document is intended to best explain the principles of the embodiments, the practical application, or technical improvement over prior art, or to enable others skilled in the art to understand the embodiments disclosed herein.

Claims

1. A method for assessing the risk and resilience of urban transportation systems during snowfall, characterized in that: The method comprises: Acquiring basic data of a target city's transportation system; the basic data represents data related to urban transportation; Based on the basic data, a simulation model of the target city's transportation system is established; wherein the simulation model includes at least: a road network sub-model and a travel demand sub-model; the road network sub-model is used to represent the connection relationship between different roads and the attributes of each road; the travel demand sub-model is used to represent multiple travelers and the travel plans of each traveler, and the travelers have the ability to independently decide on their travel plans based on the attributes of the roads; The simulation model is used to simulate the changes in the operating status of the target city's transportation system in different scenarios to evaluate the risks and resilience of the target city's transportation system during snowfall; wherein different scenarios correspond to different snow depths and / or traffic management strategies.

2. The method according to claim 1, characterized in that The basic data includes: road network data and historical meteorological and traffic operation data; the attributes of each road include at least: the free flow speed of each road; The method further comprises: Based on the historical meteorological and traffic operation data, a free-flow speed reduction model is established through regression analysis to be used in a simulation process to determine the free-flow speed of each road in the road network submodel at different snow depths. The free-flow speed reduction model represents the corresponding relationship between different snow depths and a free-flow speed reduction coefficient, where the free-flow speed reduction coefficient is the ratio of the free-flow speed of the road to a baseline free-flow speed.

3. The method according to claim 1 or 2, characterized in that The basic data include: population and land use data, and residents' travel survey data; The step of establishing a simulation model of the target city's traffic system based on the basic data includes: estimating the trip generation and trip attraction of each area in the target city's transportation system based on the population and land use data; Based on the resident travel survey data, the travel generation volume and travel attraction volume of each area are adjusted to obtain the travel volume between different areas; According to the travel volume between the different areas, a travel plan for the multiple travelers and each of the travelers is generated.

4. The method according to claim 1, wherein The simulation model is used to simulate the changes in the operating state of the target city traffic system in different scenarios, including: In the process of simulating the changes in the operating status of the target city traffic system in the target scenario, the attributes of each road in the road network submodel and the travel plans of each traveler in the travel demand submodel are adjusted based on the snow depth and / or the corresponding traffic management strategy corresponding to the target scenario; the target scenario is any one of the different scenarios.

5. The method according to claim 4, characterized in that The adjusting of the attributes of each road in the road network sub-model and the travel plans of each traveler in the travel demand sub-model based on the snow depth corresponding to the target scenario and / or the corresponding traffic management strategy includes: Determining, based on the snow depth corresponding to the target scene, an initial value of an attribute of each road in the road network submodel in the target scene; Based on the traffic management strategy corresponding to the target scenario, set at least one event in the target scenario and the time point of occurrence of each event; triggering corresponding events at the time of occurrence of each event, and adjusting the attributes of each road in the road network sub-model; Based on the attributes of the roads, each traveler in the travel demand sub-model adjusts his or her travel plan.

6. The method according to claim 5, characterized in that Based on the attributes of the roads, each traveler in the travel demand sub-model adjusts his or her travel plan, including: A multi-round iteration approach is adopted so that each traveler in the travel demand sub-model learns the optimal travel plan in the target scenario; wherein, in each round of iteration, each traveler in the travel demand sub-model adjusts the travel plan in the current round of iteration based on the results of the previous round of iteration.

7. The method according to claim 1, characterized in that The simulation model further includes: a public transportation sub-model, the public transportation sub-model being used to represent attributes of a bus route; The use of the simulation model to simulate the changes in the operating status of the target city traffic system in different scenarios includes: in the process of simulating the changes in the operating status of the target city traffic system in the target scenario, based on the snow depth and / or the corresponding traffic management strategy corresponding to the target scenario, adjusting one or more of the attributes of each road in the road network submodel, the travel plan of each traveler in the travel demand submodel, and the attributes of the bus line in the public transportation submodel.

8. A risk and resilience assessment device for urban transportation systems during snowfall, characterized in that: The device comprises: An acquisition module, configured to acquire basic data of a target city's transportation system; the basic data represents data related to urban transportation; a modeling module for establishing a simulation model of the target city's transportation system based on the basic data; wherein the simulation model includes at least: a road network sub-model and a travel demand sub-model; the road network sub-model is used to represent the connection relationship between different roads and the attributes of each road; the travel demand sub-model is used to represent multiple travelers and the travel plans of each traveler, wherein the travelers have the ability to independently decide on their travel plans based on the attributes of the roads; A simulation module is used to use the simulation model to simulate the changes in the operating status of the target city's traffic system in different scenarios to evaluate the risks and resilience of the target city's traffic system during snowfall; wherein different scenarios correspond to different snow depths and / or traffic management strategies.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory, characterized in that: The processor executes the computer program to implement the steps of the method according to any one of claims 1 to 7.

10. A non-volatile computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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