Large-scale urban network multi-agent traffic simulation prediction method, system and equipment

Abstract

The invention discloses a large-scale urban network multi-agent traffic simulation prediction method, and relates to the technical field of traffic simulation prediction. The method comprises the following steps: constructing a multi-agent city network model; processing and analyzing traffic demand data; urban network traffic flow distribution; constructing a vehicle driving model; urban network traffic monitoring and feedback; and iterating the simulation scheme. The travel demand data is processed by receiving the travel demand, the traffic path is allocated to the vehicle agent by using the classic user balance model, the vehicle agent simulates the driving behavior according to the car-following model and the lane changing model, and data support is provided for the vehicle agent to select the path according to the traffic flow macroscopic parameters fed back by the road section agent. And the simulation precision and the prediction reliability of the traffic flow are improved by utilizing the simulation of the multi-agent.

Description

Technical Field

[0001] The present invention relates to the technical field of traffic simulation prediction, and in particular to a method, system and equipment for large-scale urban network multi-agent traffic simulation prediction. Background Art

[0002] The development of urban road traffic is closely linked to the process of urbanization. With the growth of population and economic development, the urban road network continues to expand and traffic facilities are becoming more and more complete. However, the traffic problems that follow are becoming increasingly prominent and have become a bottleneck restricting urban development. The traffic problems of urban roads are mainly reflected in the following aspects: First, traffic congestion is becoming increasingly serious, especially during peak hours in the morning and evening, when vehicles often queue up and drive slowly on major roads and transportation hubs; second, frequent traffic accidents not only threaten people's lives and safety, but also seriously affect the smoothness of traffic; in addition, with the increase in motor vehicles, environmental pollution problems are becoming increasingly prominent, and exhaust emissions have become an important source of urban air pollution.

[0003] In order to solve these problems, reasonable traffic control methods need to be used. Traditional traffic control mainly relies on manual scheduling and experience judgment, which is inefficient and difficult to adapt to the complex urban traffic environment. Modern traffic control methods, such as intelligent transportation systems (ITS), integrate advanced information technology, communication technology and control technology to achieve real-time collection, processing and release of traffic information, and improve the intelligence level of traffic control.

[0004] Traffic simulation technology plays an important role in modern traffic control. By building a virtual model of urban roads, traffic simulation can simulate various traffic scenarios, predict traffic flow and traffic conditions, and provide decision support for traffic control. To this end, we propose a large-scale urban network multi-agent traffic simulation prediction method, system and equipment. Summary of the invention

[0005] The purpose of the present invention is to provide a large-scale urban network multi-agent traffic simulation prediction method, system and equipment, which receives travel demand and processes travel demand data, uses the classic user equilibrium model to allocate traffic paths for vehicle agents, simulates driving behavior according to the following model and the lane changing model, and provides data support for the vehicle agent to select a path based on the traffic flow macro parameters fed back by the road section agent; uses multi-agent simulation to improve the simulation accuracy and prediction reliability of traffic flow.

[0006] According to a first aspect of the present invention, in order to achieve the above-mentioned purpose, the present invention provides the following technical solution: a large-scale urban network multi-agent traffic simulation prediction method, comprising the following steps:

[0007] S10. Construct a multi-agent urban network model: receive urban road network data, establish a road network topology map based on the road network data, and delineate the simulation area based on the road network topology map, pre-set traffic scene agents, road section agents, and vehicle agents, establish the connection relationship and interaction logic of each agent, and generate the attributes of the large-scale urban network simulation area;

[0008] S20. Traffic demand data processing and analysis: Receive multi-source traffic demand data and use it as input to extract traffic spatiotemporal characteristics. Use the GCN-LSTM-Attention hierarchical coupling network, with GCN capturing spatial features and LSTM processing temporal features. The attention mechanism is introduced to enhance the model's ability to identify travel characteristics. By building a neural network model that integrates multiple data sources and deep learning technology, accurate prediction of traffic demand and generation of OD matrix are achieved.

[0009] S30. Traffic flow distribution in urban networks: Based on traffic demand data, a distribution model combining multi-objective user equilibrium and system optimization is constructed to distribute traffic flow. The objective function is composed of the improved road section resistance function integral, the system time sum and the target weight. At the same time, the impedance value added by the user due to obeying the induced information is introduced into the constraint condition to simulate the effect of traffic induction on traffic flow distribution.

[0010] S40. Constructing a vehicle driving model: combining the physical properties of the vehicle, the distance between vehicles, and the road conditions to establish a vehicle following model and a lane changing model with real-time dynamic update parameters, so as to realize dynamic adjustment of the vehicle;

[0011] S50. Urban network traffic monitoring and feedback: Based on the traffic conditions, the road segment agent calculates the traffic parameters of the road and evaluates the road traffic conditions, and feeds the road conditions back to the path selection model of the vehicle agent. At the same time, when the road segment traffic exceeds the capacity threshold, a Bayesian game theory path selection model is constructed to determine the path set of travelers, the type of travelers, and the beliefs about other types of travelers. The cost of path selection is calculated based on factors such as travel time and congestion, and the benefits are repeatedly calculated and the path is adjusted until the system reaches a Bayesian Nash equilibrium.

[0012] S60. Iterate the simulation scheme, repeat steps S30 to S50, perform traffic simulation of the urban road network, and obtain simulation results.

[0013] Furthermore, the urban road network data includes map data, traffic flow data, and traffic signal configuration data.

[0014] Furthermore, the multi-agent city network model includes the following steps:

[0015] (11) The scope of the large-scale urban road network is consistent with the scope of the simulated urban network. During the simulation process, the urban road network data can be modified. The traffic scene, road section and vehicle data are saved as individual attributes, and the data of different vehicles do not interfere with each other;

[0016] (12) At the beginning of the simulation, if the vehicle is already within the road network, it will be removed from the simulation until it reaches its destination; if the vehicle is not within the road network at the beginning of the simulation, the simulation will start from the time it enters the road network and will be removed after it reaches its destination.

[0017] Furthermore, the traffic demand data includes GPS tracks, mobile phone signaling data, city weather information, and date and time data.

[0018] Furthermore, the traffic demand data processing and analysis specifically includes the following steps:

[0019] (21) receiving traffic demand data, preprocessing the traffic demand data to eliminate the influence of noise and standardizing the data to eliminate the influence of different dimensions, and extracting multi-source data features, including speed, route, location, POI stay time, and timestamp;

[0020] (22) Construct a GCN-LSTM-Attention model and construct the traffic network into a graph structure. Nodes represent intersections in the network, and edges represent the connections between nodes in time, i.e., road sections. The graph representation uses the adjacency matrix A to represent the connection relationship between nodes and the feature matrix X to represent the time series features of each point. The dimensions are (N, T, F), where N is the number of nodes, T is the number of time steps, and F is the feature dimension. The formula for constructing the GCN layer is as follows:

[0021]

[0022] Where H l is the node representation of the lth layer, represents the normalized adjacency matrix, the initial H (0) =X,W l is the weight matrix of the lth layer, σ is the activation function;

[0023] Constructing LSTM layer to process time series data {h t , c t}=LSTM(X t ,{h t-1 , C t-1}), where X t is the input feature at time step t, h t and c t decibels are the hidden state and cell state with step size t;

[0024] Build the model's Attention mechanism and extract time step features where H is the hidden state at all time steps, α t is the attention weight at time step t;

[0025] (23) The feature data is converted into a sequence form, and the historical data is used to train the model and optimize the model parameters. Based on the prediction results, the traffic OD matrix is ​​generated to represent the traffic flow between different starting points and end points.

[0026] Furthermore, the urban network traffic flow distribution specifically includes the following steps:

[0027] (31) Incorporating the elastic demand model into the traffic flow allocation model reflects travelers’ responses to changes in travel costs, including travel time, travel expenses, and travel distance factors;

[0028] Define the demand function D = f(T, C, L), where D is the demand, T is the travel time, C is the travel cost, and L is the travel distance. Use the elasticity coefficient to relate changes in demand to changes in travel time and cost: Where ΔD, ΔT, ΔC, and ΔL represent the changes in demand, travel time, travel cost, and travel distance, respectively. T , η C , η L They represent the elasticity coefficients of travel time, travel cost, and travel distance, respectively, and describe the changes in travel demand under different travel conditions;

[0029] (32) The traffic flow allocation method combining UE and SO is used for initial allocation. The model objective function is the accumulation of the integral of the road resistance function of each road section and the sum of the travel time of all vehicles on the network, which can be written as follows:

[0030]

[0031] Where w UE and w SO Respectively represent the weights of user equilibrium and system optimization objectives, x a represents the traffic flow of road section a, t a (w) is the road resistance function of road section a;

[0032] Among them, the road resistance function reflects the situation of traffic congestion and has nonlinear properties. The improved BPR model is adopted:

[0033]

[0034] In the formula, represents the travel time of road section a in free flow state, C ais the capacity of road section a, x a represents the flow rate of section a, k a represents the density of road section a, k a，jam represents the congestion density of road section a, 5, β, γ, δ are all parameters to be calibrated;

[0035] (33) There is an identity constraint on the flow between the path and the OD, that is, on an OD pair, the sum of the flow of all paths is equal to the OD flow. rs represents the flow of OD pair (r, s), and the flow of the kth path on OD pair (r, s) is recorded as

[0036]

[0037] (34) There is an identity constraint on the flow between the road section and the path, that is, on a road section, the sum of the flow of all the paths passing through the road section is equal to the road section flow, expressed as It means that the OD pair (r, s) passes through the section a in the kth path. If the value is 0, it does not pass through the section a. The identity constraint of the section path is expressed as follows:

[0038]

[0039] In the formula, x a represents the flow rate of road section a, is the flow of the kth path on the OD pair;

[0040] (35) The flow of the road section and the path flow are non-negative constraints. At the same time, due to the elastic change of travel demand, the flow between OD needs to be restricted by travel demand, that is:

[0041] x a ≥0, a∈A

[0042]

[0043] 0≤q rs ≤D rs

[0044] Where D rs represents the travel demand for OD pair (r, s), which is calculated by the travel demand elasticity formula in step (31).

[0045] Furthermore, constructing a vehicle driving model specifically includes the following steps:

[0046] (41) The improved intelligent driver following model is expressed as follows:

[0047]

[0048] In the formula, an (t) represents the acceleration of vehicle n at time t, v n (t) represents the speed of vehicle n at time t, Δv n (t) represents the speed difference between vehicle n and the preceding vehicle at time t, v0 represents the driver’s expected driving speed, represents the distance the driver expects to maintain from the vehicle in front, S0 represents the safe distance when the vehicle is stationary, T is the safe time interval, a represents the starting acceleration, and b represents the comfortable deceleration ∈ a ,∈ s They represent the random terms between acceleration and vehicle spacing, respectively, and their distributions are calibrated according to measured data;

[0049] S42: Using the MOBIL lane-changing model, the current vehicle acceleration gain after changing lanes plus the acceleration gain of the vehicle behind the target lane plus the acceleration gain of the vehicle behind the current lane are multiplied by the politness coefficient. When the value is greater than a certain threshold, lane change is allowed. The model is expressed as follows:

[0050]

[0051] Among them, the acceleration is improved by calculating the intelligent driver following model. Indicates the acceleration gain of the current vehicle after changing lanes, represents the acceleration gain of the vehicle behind the target lane, It represents the acceleration gain of the vehicle behind in this lane, and p is the politeness coefficient.

[0052] Furthermore, the urban network traffic monitoring and feedback specifically includes the following steps:

[0053] (51) Define a total of n participants in a traffic trip, and each traveler’s path selection set S i , the type of each trip θ i represents the departure and destination of the traveler. The traveler type space Θ contains all possible combinations of traveler types. Each traveler’s belief about other travel types is expressed as P(θ -i |θ i ), that is, traveler i believes that other travelers are of type θ -i probability;

[0054] (52) The benefit function u of traveler i i (s i ,s -i ,θ i ) represents its selection strategy s i and other traveler choice strategies -i The benefit at that time is defined as the benefit function u of traveler i i as follows:

[0055] u i (s i ,s -i ,θ i )=-(α·travel(s i )+β·congestion(s i ,s -i ))

[0056] Among them, α and β are weight parameters used to balance the relative importance of travel time and congestion in the benefit function; travel(s i ) is the path s i The sum of travel time of all sections on the road, congestion(s i , si) is the path s i The sum of the congestion levels of all road segments; the travel time function and congestion function are expressed as follows:

[0057]

[0058] where time e is the passing time of section e, V e is the flow rate of section e, C e It refers to the traffic capacity of road section e. γ and ε are unknown parameters, which can be calibrated according to historical traffic data. is the congestion level of road segment e, which depends on the paths si chosen by all travelers.

[0059] (53) In the Bayesian game, the Bayesian Nash equilibrium is the optimal strategy combination of a traveler given his travel type and his beliefs about the travel types of other travelers. Formally, the strategy combination is a Bayesian Nash equilibrium if for each traveler i:

[0060]

[0061] That is, considering that other travelers are also using the optimal strategy during the period, the optimal strategy for traveler i is to maximize his expected benefit.

[0062] According to a second aspect of the present invention, the present invention provides a multi-agent traffic simulation prediction system for implementing the above-mentioned large-scale urban network multi-agent traffic simulation prediction method, comprising:

[0063] The first construction module is used to receive urban road network data, establish a road network topology map based on the road network data, and delineate the simulation area based on the road network topology map, pre-set traffic scene agents, road section agents, and vehicle agents, establish the connection relationship and interaction logic of each agent, and generate the attributes of the large-scale urban network simulation area;

[0064] The processing and analysis module is used to receive multi-source traffic demand data and use it as input to extract traffic spatiotemporal features. n Hierarchical coupling network, GCN captures spatial features, LSTM processes temporal features, and introduces attention mechanism to enhance the model's ability to identify travel characteristics. By building a neural network model that integrates multiple data sources and deep learning technology, accurate prediction of traffic demand and generation of OD matrix are achieved;

[0065] The allocation module is used to construct an allocation model combining multi-objective user equilibrium and system optimization to allocate traffic flow according to traffic demand data. The objective function is composed of the improved section resistance function integral, the system time sum and the target weight. At the same time, the impedance value added by the user due to obeying the induced information is introduced into the constraint condition to simulate the effect of traffic induction on traffic flow distribution.

[0066] The second building module is used to establish a vehicle following model and a lane changing model with real-time dynamic update parameters based on vehicle physical properties, vehicle spacing, and road conditions, so as to achieve dynamic vehicle adjustment;

[0067] Traffic monitoring and feedback module, which is used to calculate the traffic parameters of the road and evaluate the road traffic conditions according to the traffic conditions. It also feeds the road conditions back to the path selection model of the vehicle agent. At the same time, when the road traffic exceeds the capacity threshold, it builds a Bayesian game theory path selection model to determine the path set of travelers, the type of travelers, and the beliefs about other types of travelers. It calculates the cost of path selection based on factors such as travel time and congestion, repeatedly calculates the benefits, and adjusts the path until the system reaches a Bayesian Nash equilibrium.

[0068] The iteration module is used to iterate the simulation scheme, repeat the above modules, conduct traffic simulation of the urban road network, and obtain simulation results.

[0069] According to the third aspect of the present invention, the present invention provides a terminal device, including a memory, a processor, and a computer program stored in the memory and capable of running on the processor, wherein the memory stores a computer program capable of running on the processor, and when the processor loads and executes the computer program, the above-mentioned large-scale urban network multi-agent traffic simulation prediction method is adopted.

[0070] The present invention has at least the following beneficial effects:

[0071] The present invention processes travel demand data by receiving travel demand, allocates traffic paths to vehicle agents using the classic user equilibrium model, simulates driving behavior based on the following model and the lane-changing model, provides data support for vehicle agents to select paths based on traffic flow macro parameters fed back by road section agents, and utilizes multi-agent simulation to improve the simulation accuracy and prediction reliability of traffic flow.

[0072] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0073] Figure 1 It is a schematic diagram of the process of the method of the present invention;

[0074] Figure 2 It is a schematic diagram of the visualization of the city OD in the present invention;

[0075] Figure 3 This is a schematic diagram of the simulation demonstration of the vehicle intelligent body in the urban road network of the present invention;

[0076] Figure 4 It is a visualized schematic diagram of the traffic conditions of the urban road network in the present invention. DETAILED DESCRIPTION

[0077] The following will be combined with the drawings in the embodiments of the present disclosure to clearly and completely describe the technical solutions in the embodiments of the present disclosure. Obviously, the described embodiments are only part of the embodiments of the present disclosure, rather than all the embodiments. Based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present disclosure.

[0078] See also Figure 1-Figure 4 The present invention provides a technical solution: a large-scale urban network multi-agent traffic simulation prediction method, comprising the following steps:

[0079] S10. Construct a multi-agent urban network model: Receive urban road network data, establish a road network topology map based on the road network data, and delineate the simulation area based on the road network topology map, pre-set traffic scene agents, road section agents, and vehicle agents, establish the connection relationship and interaction logic of each agent, and generate the attributes of the large-scale urban network simulation area, as follows:

[0080] S11. First, conduct a detailed analysis of the urban road network, including collecting map data, traffic flow data, traffic signal configuration, and other factors that may affect traffic flow. Using these data, a detailed road network topology map can be created to clarify the nodes, road sections, and connection relationships in the road network;

[0081] S12. Based on the road network topology, design the division of simulation areas. Each simulation area should represent a part of the urban road network with similar traffic characteristics, such as commercial areas, residential areas, or industrial areas. In each simulation area, different types of agents are pre-set according to the actual traffic scenario, including road segment agents and vehicle agents.

[0082] S13. In order to establish the connection relationship and interaction logic between agents, it is necessary to define the behavior rules and decision-making process of the agents, including the driving rules and lane-changing behavior of vehicles. The multi-agent system (MAS) framework can be used to simulate the autonomous behavior and interaction between these agents. The MATsim multi-agent simulation platform is selected to realize the multi-agent traffic flow simulation of the urban road network.

[0083] S20. Traffic demand data processing and analysis: Receive multi-source traffic demand data and use it as input to extract traffic spatiotemporal features. Use the GCN-LSTM-Attention hierarchical coupling network, with GCN capturing spatial features and LSTM processing temporal features. The attention mechanism is introduced to enhance the model's ability to identify travel features. By building a neural network model that integrates multiple data sources and deep learning technology, accurate prediction of traffic demand and generation of OD matrix are achieved. The details are as follows:

[0084] (21) receiving traffic demand data, including map data, traffic flow data, traffic signal configuration, and other factors that may affect traffic flow, preprocessing the traffic demand data to eliminate noise influences and standardizing the data to eliminate influences of different dimensions, and extracting multi-source data features, including speed, route, location, POI stay time, timestamp, and other data;

[0085] (22) Construct a GCN-LSTM-Attention model and construct the traffic network into a graph structure. Nodes represent intersections in the network, and edges represent the connections between nodes in time, i.e., road sections. The graph representation uses the adjacency matrix A to represent the connection relationship between nodes and the feature matrix X to represent the time series features of each point. The dimensions are (N, T, F), where N is the number of nodes, T is the number of time steps, and F is the feature dimension. The formula for constructing the GCN layer is as follows:

[0086]

[0087] Where H l is the node representation of layer 1, represents the normalized adjacency matrix, the initial H (0) =X,W l is the weight matrix of the first layer, σ is the activation function;

[0088] Constructing LSTM layer to process time series data {h t, c t}=LSTM(X t ,{h t-1 , C t-1}), where X t is the input feature at time step t, h t and c t decibels are the hidden state and cell state with step size t;

[0089] Build the model's Attention mechanism and extract time step features where H is the hidden state at all time steps, α t is the attention weight at time step t;

[0090] (23) Convert the feature data into a sequence form, use the historical data to train the model and optimize the model parameters, and generate the traffic OD matrix to represent the traffic flow between different starting points and end points according to the prediction results;

[0091] S30. Traffic flow distribution in urban network: Based on traffic demand data, a distribution model combining multi-objective user equilibrium and system optimization is constructed to distribute traffic flow. The objective function is composed of the improved road section resistance function integral, the system time sum and the target weight. At the same time, the impedance value added by the user due to obeying the induced information is introduced into the constraint condition to simulate the effect of traffic induction on traffic flow distribution. The details are as follows:

[0092] S31. Incorporating the elastic demand model into the traffic flow allocation model reflects travelers’ responses to changes in travel costs, including travel time, travel expenses, and travel distance factors;

[0093] Define the demand function D = f(T, C, L), where D is the demand, T is the travel time, C is the travel cost, and L is the travel distance. Use the elasticity coefficient to relate changes in demand to changes in travel time and cost: Where ΔD, ΔT, ΔC, and ΔL represent the changes in demand, travel time, travel cost, and travel distance, respectively. T , η C , η L They represent the elasticity coefficients of travel time, travel cost, and travel distance, respectively, and describe the changes in travel demand under different travel conditions;

[0094] S32. The traffic flow allocation method combining UE and SO is used for initial allocation. The model objective function is the accumulation of the integral of the road resistance function of each road section and the sum of the travel time of all vehicles on the network, which can be written as follows:

[0095]

[0096] Where w UE and w SO Respectively represent the weights of user equilibrium and system optimization objectives, x a represents the traffic flow of road section a, t a (w) is the road resistance function of road section a;

[0097] Among them, the road resistance function reflects the situation of traffic congestion and has nonlinear properties. The improved BPR model is adopted:

[0098]

[0099] In the formula, represents the travel time of road section a in free flow state, C a is the capacity of road section a, x a represents the flow rate of section a, k a represents the density of road section a, k a，jam represents the congestion density of road section a, 5, β, γ, δ are all parameters to be calibrated;

[0100] S33. There is an identity constraint on the flow between the path and the OD, that is, on an OD pair, the sum of the flow of all paths is equal to the OD flow, using q rs represents the flow of OD pair (r, s), and the flow of the kth path on OD pair (r, s) is recorded as

[0101]

[0102] S34. There is an identity constraint on the flow between the road section and the path, that is, on the road section, the sum of the flow of all the paths passing through the road section is equal to the road section flow, It means that the OD pair (r, s) passes through the section a in the kth path. If the value is 0, it does not pass through the section a. The identity constraint of the section path is expressed as follows:

[0103]

[0104] In the formula, x a represents the flow rate of road section a, is the flow of the kth path on the OD pair;

[0105] S35. The flow of the road segment and the flow of the path are non-negative. At the same time, due to the elastic changes in travel demand, the flow between OD needs to be limited by travel demand, that is:

[0106] x a ≥0, a∈A

[0107]

[0108] 0≤q rs ≤D rs

[0109] Where D rs represents the travel demand of OD for (r, s), which is calculated by the travel demand elasticity formula in step S31;

[0110] S40. Constructing a vehicle driving model: combining the physical properties of the vehicle, the distance between vehicles, and the road conditions to establish a vehicle following model and a lane changing model with real-time dynamic parameter updates, which are used to achieve dynamic vehicle adjustment, as follows:

[0111] S41. The improved intelligent driver following model is expressed as follows:

[0112]

[0113] In the formula, a n (t) represents the acceleration of vehicle n at time t, v n (t) represents the speed of vehicle n at time t, Δv n (t) represents the speed difference between vehicle n and the preceding vehicle at time t, v0 represents the driver’s expected driving speed, represents the distance the driver expects to maintain from the vehicle in front, S0 represents the safe distance when the vehicle is stationary, T is the safe time interval, a represents the starting acceleration, and b represents the comfortable deceleration ∈ a ,∈ s They represent the random terms between acceleration and vehicle spacing, respectively, and their distributions are calibrated according to measured data;

[0114] S42. Using the MOBIL lane-changing model, the current vehicle acceleration gain after lane change plus the target lane rear vehicle acceleration gain plus the current lane rear vehicle acceleration gain are multiplied by the politness coefficient. Lane change is allowed when the gain is greater than a certain threshold. The model is expressed as follows:

[0115]

[0116] Among them, the acceleration is improved by calculating the intelligent driver following model. Indicates the acceleration gain of the current vehicle after changing lanes, represents the acceleration gain of the vehicle behind the target lane, represents the acceleration gain of the vehicle behind in this lane, and p is the politeness coefficient;

[0117] The calibration of model parameters requires the collection of actual vehicle behavior data in different traffic scenarios, including speed, acceleration, vehicle distance, lane-changing behavior, etc. These data are obtained through on-board sensors, traffic monitoring systems, etc.

[0118] Based on the collected data, the parameters in the IDM model, such as safety distance, acceleration capability, deceleration capability, etc., are preliminarily set. Through SUMO, the preliminarily set parameters are run to simulate traffic flow, and the simulation results are compared with the actual data to evaluate the prediction accuracy of the model.

[0119] The MOBIL lane-changing model needs to focus on the behavioral parameters of the driver's lane-changing decision, including lane-changing willingness, lane-changing timing, and safe distance during the lane-changing process. Similarly, lane-changing behavior is simulated through SUMO, and the results are compared with the actual lane-changing behavior data to evaluate the effectiveness of the model;

[0120] After the model parameters are calibrated, the simulation analysis results are generated as follows: Figure 2 ;

[0121] S50. Urban network traffic monitoring and feedback: According to the traffic conditions, the road segment agent calculates the traffic parameters of the road and evaluates the road traffic conditions, and feeds the road conditions back to the path selection model of the vehicle agent. At the same time, for the situation where the road segment traffic exceeds the capacity threshold, a Bayesian game theory path selection model is constructed to determine the path set of travelers, the type of travelers, and the beliefs about other types of travelers. The cost of path selection is calculated based on factors such as travel time and congestion, and the benefits are repeatedly calculated and the path is adjusted until the system reaches a Bayesian Nash equilibrium, as follows:

[0122] (51) Define a total of n participants in a traffic trip, and each traveler’s path selection set S i , the type of each trip θ i represents the departure and destination of the traveler. The traveler type space Θ contains all possible combinations of traveler types. Each traveler’s belief about other travel types is expressed as P(θ -i |θ i ), that is, traveler i believes that other travelers are of type θ -i probability;

[0123] (52) The benefit function u of traveler i i (s i ,s -i ,θ i ) represents its selection strategy s i and other traveler choice strategies -i The benefit at that time is defined as the benefit function u of traveler i i as follows:

[0124] u i (s i ,s -i ,θ i )=-(α·travel(s i)+β·congestion(s i ,s -i ))

[0125] Among them, α and β are weight parameters used to balance the relative importance of travel time and congestion in the benefit function; travel(s i ) is the path s i The sum of travel time of all sections on the road, congestion(s i ,s -i ) is the path s i The sum of the congestion levels of all road segments; the travel time function and congestion function are expressed as follows:

[0126]

[0127] where time e is the passing time of section e, V e is the flow rate of section e, C e It refers to the traffic capacity of road section e. γ and ε are unknown parameters, which can be calibrated according to historical traffic data. is the congestion level of road segment e, which depends on the paths si chosen by all travelers.

[0128] (53) In the Bayesian game, the Bayesian Nash equilibrium is the optimal strategy combination of a traveler given his travel type and his beliefs about the travel types of other travelers. Formally, the strategy combination is a Bayesian Nash equilibrium if for each traveler i:

[0129]

[0130] That is, considering that other travelers are also using the optimal strategy during the period, the optimal strategy for traveler i is to maximize his expected benefit;

[0131] The road condition is fed back to the vehicle agent, and the vehicle agent reselects the travel path. The process returns to step S30 and iterates the simulation results again. The road section agent detects the road traffic condition. Figure 3 ;

[0132] S60. Iterate the simulation scheme, repeat steps S30 to S50, perform traffic simulation of the urban road network, and obtain simulation results.

[0133] In summary, this embodiment establishes a multi-agent urban road network simulation model, which includes road section agents and vehicle agents; then, the city's traffic demand information is input, and a path is selected on the road network according to the traffic behavior assigned by the urban road network, and the driving behavior of the vehicle agent on the urban road is simulated according to the vehicle following, lane changing, and lane selection models; by monitoring indicators such as the number and speed of road agents, the macro traffic status indicators of the road section are statistically analyzed, such as the average speed of the road section, the road section density, the vehicle flow rate, etc.; finally, the path selection behavior is affected according to the traffic status of the road section, and the iteration of the simulation of the urban road network traffic behavior is realized; through the above-mentioned multi-agent-based traffic simulation prediction method, the prediction ability of the urban road network traffic status can be effectively improved, and technical support can be provided for intelligent transportation systems and urban traffic management.

[0134] Embodiment 2:

[0135] According to a second aspect of the present invention, the present invention provides a multi-agent traffic simulation prediction system for implementing the above-mentioned large-scale urban network multi-agent traffic simulation prediction method, comprising:

[0136] The first construction module is used to receive urban road network data, establish a road network topology map based on the road network data, and delineate the simulation area based on the road network topology map, pre-set traffic scene agents, road section agents, and vehicle agents, establish the connection relationship and interaction logic of each agent, and generate the attributes of the large-scale urban network simulation area;

[0137] The processing and analysis module is used to receive multi-source traffic demand data and use it as input to extract traffic spatiotemporal characteristics. The GCN-LSTM-Attention hierarchical coupling network is used, with GCN capturing spatial features and LSTM processing temporal features. The attention mechanism is introduced to enhance the model's ability to identify travel characteristics. By building a neural network model that integrates multiple data sources and deep learning technology, accurate prediction of traffic demand and generation of OD matrix are achieved.

[0138] The allocation module is used to construct an allocation model combining multi-objective user equilibrium and system optimization to allocate traffic flow according to traffic demand data. The objective function is composed of the improved section resistance function integral, the system time sum and the target weight. At the same time, the impedance value added by the user due to obeying the induced information is introduced into the constraint condition to simulate the effect of traffic induction on traffic flow distribution.

[0139] The second building module is used to establish a vehicle following model and a lane changing model with real-time dynamic update parameters based on vehicle physical properties, vehicle spacing, and road conditions, so as to achieve dynamic vehicle adjustment;

[0140] Traffic monitoring and feedback module, which is used to calculate the traffic parameters of the road and evaluate the road traffic conditions according to the traffic conditions. It also feeds the road conditions back to the path selection model of the vehicle agent. At the same time, when the road traffic exceeds the capacity threshold, it builds a Bayesian game theory path selection model to determine the path set of travelers, the type of travelers, and the beliefs about other types of travelers. It calculates the cost of path selection based on factors such as travel time and congestion, repeatedly calculates the benefits, and adjusts the path until the system reaches a Bayesian Nash equilibrium.

[0141] The iteration module is used to iterate the simulation scheme, repeat the above modules, conduct traffic simulation of the urban road network, and obtain simulation results.

[0142] Specifically, the above-mentioned first building module, processing and analysis module, allocation module, second building module, traffic monitoring and feedback module and iteration module can be embedded in a computer processing system. The computer calls the above-mentioned modules to complete the task of traffic simulation prediction based on the above-mentioned large-scale urban network multi-agent traffic simulation prediction method; the above-mentioned first building module, processing and analysis module, allocation module, second building module, traffic monitoring and feedback module and iteration module can perform operations according to the specific steps given in the large-scale urban network multi-agent traffic simulation prediction method.

[0143] It should be noted that it should be understood that the division of the various modules of the above system is only the division of logical functions. In actual implementation, they can be fully or partially integrated into one physical entity, or they can be physically separated, and these modules can all be implemented in the form of software calling through processing elements; they can also be all implemented in the form of hardware; some modules can also be implemented in the form of software calling through processing elements, and some modules can be implemented in the form of hardware. For example, the allocation module can be a separately established processing element, or it can be integrated in a chip of the above device. In addition, it can also be stored in the memory of the above device in the form of program code, and called and executed by a processing element of the above device. The functions of the above signal processing module, and the implementation of other modules is similar. In addition, these modules can be fully or partially integrated together, or they can be implemented independently. The processing element described here can be an integrated circuit with signal processing capabilities. In the implementation process, each step of the above method or each module above can be completed by the hardware integrated logic circuit in the processor element or the instructions in the form of software.

[0144] For example, the above modules may be one or more integrated circuits configured to implement the above methods, such as one or more application specific integrated circuits (ASIC), or one or more digital singnal processors (DSP), or one or more field programmable gate arrays (FPGA). For another example, when a module above is implemented in the form of a processing element scheduling program code, the processing element may be a general-purpose processor, such as a central processing unit (CPU) or other processor that can call program code. For another example, these modules may be integrated together and implemented in the form of a system-on-a-chip (SOC).

[0145] Embodiment three:

[0146] The present invention provides a terminal device, including a memory, a processor, and a computer program stored in the memory and capable of running on the processor. The memory stores a computer program capable of running on the processor. When the processor loads and executes the computer program, the large-scale urban network multi-agent traffic simulation prediction method in Example 1 is adopted.

[0147] It should be noted that the terminal device can be a computer device such as a desktop computer, a laptop computer or a cloud server, and the terminal device includes but is not limited to a processor and a memory. For example, the terminal device can also include input and output devices, network access devices and buses.

[0148] Furthermore, the processor may adopt a central processing unit (CPU). Of course, according to actual usage, other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGAs) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. may also be adopted. The general-purpose processor may adopt a microprocessor or any conventional processor, etc., and the present application does not impose any restrictions on this.

[0149] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0150] For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to the specific circumstances. When an element is referred to as being "assembled on", "installed on", "fixed on" or "set on" another element, it can be directly on the other element or there can also be a centered element. When an element is considered to be "connected" to another element, it can be directly connected to the other element or there may be a centered element at the same time. The terms "vertical", "horizontal", "up", "down", "left", "right" and similar expressions used herein are only for illustrative purposes and are not intended to be the only implementation method.

[0151] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

[0152] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present disclosure. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

Claims

1. A large-scale urban network multi-agent traffic simulation prediction method, characterized in that: The following steps are involved: S10. Construct a multi-agent urban network model: receive urban road network data, establish a road network topology map based on the road network data, and delineate the simulation area based on the road network topology map, pre-set traffic scene agents, road section agents, and vehicle agents, establish the connection relationship and interaction logic of each agent, and generate the attributes of the large-scale urban network simulation area; S20. Traffic demand data processing and analysis: Receive multi-source traffic demand data and use it as input to extract traffic spatiotemporal characteristics. Use the GCN-LSTM-Attention hierarchical coupling network, with GCN capturing spatial features and LSTM processing temporal features. The attention mechanism is introduced to enhance the model's ability to identify travel characteristics. By building a neural network model that integrates multiple data sources and deep learning technology, accurate prediction of traffic demand and generation of OD matrix are achieved. S30. Traffic flow distribution in urban networks: Based on traffic demand data, a distribution model combining multi-objective user equilibrium and system optimization is constructed to distribute traffic flow. The objective function is composed of the improved road section resistance function integral, the system time sum and the target weight. At the same time, the impedance value added by the user due to obeying the induced information is introduced into the constraint condition to simulate the effect of traffic induction on traffic flow distribution. S40. Constructing a vehicle driving model: combining the physical properties of the vehicle, the distance between vehicles, and the road conditions to establish a vehicle following model and a lane changing model with real-time dynamic update parameters, so as to realize dynamic adjustment of the vehicle; S50. Urban network traffic monitoring and feedback: Based on the traffic conditions, the road segment agent calculates the traffic parameters of the road and evaluates the road traffic conditions, and feeds the road conditions back to the path selection model of the vehicle agent. At the same time, when the road segment traffic exceeds the capacity threshold, a Bayesian game theory path selection model is constructed to determine the path set of travelers, the type of travelers, and the beliefs about other types of travelers. The cost of path selection is calculated based on factors such as travel time and congestion, and the benefits are repeatedly calculated and the path is adjusted until the system reaches a Bayesian Nash equilibrium. S60. Iterate the simulation scheme, repeat steps S30 to S50, perform traffic simulation of the urban road network, and obtain simulation results.

2. The large-scale urban network multi-agent traffic simulation prediction method according to claim 1 is characterized by: The urban road network data includes map data, traffic flow data, and traffic signal configuration data.

3. The large-scale urban network multi-agent traffic simulation prediction method according to claim 2 is characterized by: The multi-agent city network model comprises the following steps: (11) The scope of the large-scale urban road network is consistent with the scope of the simulated urban network. During the simulation process, the urban road network data can be modified. The traffic scene, road section and vehicle data are saved as individual attributes, and the data of different vehicles do not interfere with each other; (12) At the beginning of the simulation, if the vehicle is already within the road network, it will be removed from the simulation until it reaches its destination; if the vehicle is not within the road network at the beginning of the simulation, the simulation will start from the time it enters the road network and will be removed after it reaches its destination.

4. The large-scale urban network multi-agent traffic simulation prediction method according to claim 3 is characterized by: The traffic demand data includes GPS tracks, mobile phone signaling data, city weather information, and date and time data.

5. The large-scale urban network multi-agent traffic simulation prediction method according to claim 4 is characterized by: Traffic demand data processing and analysis specifically includes the following steps: (21) receiving traffic demand data, preprocessing the traffic demand data to eliminate the influence of noise and standardizing the data to eliminate the influence of different dimensions, and extracting multi-source data features, including speed, route, location, POI stay time, and timestamp; (22) Construct a GCN-LSTM-Attention model and construct the traffic network into a graph structure. Nodes represent intersections in the network, and edges represent the connections between nodes in time, i.e., road sections. The graph representation uses the adjacency matrix A to represent the connection relationship between nodes and the feature matrix X to represent the time series features of each point. The dimension is (N, T, F), where N is the number of nodes, T is the number of time steps, and F is the feature dimension. The formula for constructing the GCN layer is as follows: Where H l is the node representation of the lth layer, represents the normalized adjacency matrix, the initial H (0) =X,W l is the weight matrix of the lth layer, σ is the activation function; Constructing LSTM layer to process time series data {h t ,c t }=LSTM(X t ,{h t-1 ,c t-1 }), where X t is the input feature at time step t, h t and c t decibels are the hidden state and cell state with step size t; Build the model's Attention mechanism and extract time step features where H is the hidden state at all time steps, α t is the attention weight at time step t; (23) The feature data is converted into a sequence form, and the historical data is used to train the model and optimize the model parameters. Based on the prediction results, the traffic OD matrix is ​​generated to represent the traffic flow between different starting points and end points.

6. The large-scale urban network multi-agent traffic simulation prediction method according to claim 1 is characterized by: Urban network traffic flow distribution includes the following steps: (31) Incorporating the elastic demand model into the traffic flow allocation model reflects travelers’ responses to changes in travel costs, including travel time, travel expenses, and travel distance factors; Define the demand function D = f(T,C,L), where D is the demand, T is the travel time, C is the travel cost, and L is the travel distance. Use the elasticity coefficient to relate changes in demand to changes in travel time and cost: Where ΔD, ΔT, ΔC, and ΔL represent the changes in demand, travel time, travel cost, and travel distance, respectively. T , η C , η L They represent the elasticity coefficients of travel time, travel cost, and travel distance, respectively, and describe the changes in travel demand under different travel conditions; (32) The traffic flow allocation method combining UE and SO is used for initial allocation. The model objective function is the accumulation of the integral of the road resistance function of each road section and the sum of the travel time of all vehicles on the network, which can be written as follows: Where w UE and w SO Respectively represent the weights of user equilibrium and system optimization objectives, x a represents the traffic flow of road section a, t a (w) is the road resistance function of road section a; Among them, the road resistance function reflects the situation of traffic congestion and has nonlinear properties. The improved BPR model is adopted: In the formula, represents the travel time of section a in free flow state, C a is the capacity of road section a, x a represents the flow rate of section a, k a represents the density of road section a, k a,jam represents the congestion density of road section a, α, β, γ, δ are all parameters to be calibrated; (33) There is an identity constraint on the flow between the path and the OD, that is, on an OD pair, the sum of the flow of all paths is equal to the OD flow. rs represents the flow of OD pair (r, s), and the flow of the kth path on OD pair (r, s) is recorded as (34) There is an identity constraint on the flow between the road section and the path, that is, on a road section, the sum of the flow of all the paths passing through the road section is equal to the road section flow, expressed as It means that the OD pair (r, s) passes through section a in the kth path. If the value is 0, it does not pass through section a. The identity constraint of the section path is expressed as follows: In the formula, x a represents the flow rate of road section a, is the flow of the kth path on the OD pair; (35) The flow of the road section and the path flow are non-negative constraints. At the same time, due to the elastic change of travel demand, the flow between OD needs to be restricted by travel demand, that is: x a ≥0,a∈A 0≤q rs ≤D rs Where D rs represents the OD travel demand for (r, s), which is calculated by the travel demand elasticity formula in step (31).

7. The large-scale urban network multi-agent traffic simulation prediction method according to claim 6 is characterized by: Building a vehicle driving model includes the following steps: (41) The improved intelligent driver following model is expressed as follows: In the formula, a n (t) represents the acceleration of vehicle n at time t, v n (t) represents the speed of vehicle n at time t, Δv n (t) represents the speed difference between vehicle n and the preceding vehicle at time t, v0 represents the driver’s expected driving speed, represents the distance the driver expects to maintain from the vehicle in front, S0 represents the safe distance when the vehicle is stationary, T is the safe time interval, a represents the starting acceleration, and b represents the comfortable deceleration ∈ a ,∈ s They represent the random terms between acceleration and vehicle spacing, respectively, and their distributions are calibrated according to measured data; (42) The MOBIL lane-changing model is used. The acceleration gain of the current vehicle after changing lanes plus the acceleration gain of the vehicle behind the target lane plus the acceleration gain of the vehicle behind the current lane are multiplied by the politeness coefficient. When the value is greater than a certain threshold, lane change is allowed. The model is expressed as follows: Among them, the acceleration is improved by calculating the intelligent driver following model. Indicates the acceleration gain of the current vehicle after changing lanes, represents the acceleration gain of the vehicle behind the target lane, It represents the acceleration gain of the vehicle behind in this lane, and p is the politeness coefficient.

8. The large-scale urban network multi-agent traffic simulation prediction method according to claim 1 is characterized by: Urban network traffic monitoring and feedback includes the following steps: (51) Define a total of n participants in a traffic trip, and each traveler’s path selection set S i , the type of each trip θ i represents the departure and destination of the traveler. The traveler type space Θ contains all possible combinations of traveler types. Each traveler’s belief about other travel types is expressed as P(θ -i |θ i ), that is, traveler i believes that other travelers are of type θ -i probability; (52) The benefit function u of traveler i i (s i ,s -i ,θ i ) represents its selection strategy s i and other traveler choice strategies -i The benefit at that time is defined as the benefit function u of traveler i i as follows: you i (s i ,s -i ,i i )=-(a·travel(s i )+β·congestion(s i ,s -i )) Among them, α and β are weight parameters used to balance the relative importance of travel time and congestion in the benefit function; travel(s i ) is the path s i The sum of travel time of all sections on the road, congesion(s i ,s -i ) is the path s i The sum of the congestion levels of all road segments; the travel time function and congestion function are expressed as follows: where time e is the passing time of section e, V e is the flow rate of section e, C e It refers to the traffic capacity of road section e. γ and ε are unknown parameters, which can be calibrated according to historical traffic data. is the congestion level of road segment e, which depends on the paths s chosen by all travelers. -i . (53) In the Bayesian game, the Bayesian Nash equilibrium is the optimal strategy combination of a traveler given his travel type and his beliefs about the travel types of other travelers. Formally, the strategy combination is a Bayesian Nash equilibrium if for each traveler i: That is, considering that other travelers are also using the optimal strategy during the period, the optimal strategy for traveler i is to maximize his expected benefit.

9. A multi-agent traffic simulation prediction system, used to implement the large-scale urban network multi-agent traffic simulation prediction method according to any one of claims 1 to 8, characterized in that: include: The first construction module is used to receive urban road network data, establish a road network topology map based on the road network data, and delineate the simulation area based on the road network topology map, pre-set traffic scene agents, road section agents, and vehicle agents, establish the connection relationship and interaction logic of each agent, and generate the attributes of the large-scale urban network simulation area; The processing and analysis module is used to receive multi-source traffic demand data and use it as input to extract traffic spatiotemporal characteristics. The GCN-LSTM-Attention hierarchical coupling network is used, with GCN capturing spatial features and LSTM processing temporal features. The attention mechanism is introduced to enhance the model's ability to identify travel characteristics. By building a neural network model that integrates multiple data sources and deep learning technology, accurate prediction of traffic demand and generation of OD matrix are achieved. The allocation module is used to construct an allocation model combining multi-objective user equilibrium and system optimization to allocate traffic flow according to traffic demand data. The objective function is composed of the improved section resistance function integral, the system time sum and the target weight. At the same time, the impedance value added by the user due to obeying the induced information is introduced into the constraint condition to simulate the effect of traffic induction on traffic flow distribution. The second building module is used to establish a vehicle following model and a lane changing model with real-time dynamic update parameters based on vehicle physical properties, vehicle spacing, and road conditions, so as to achieve dynamic vehicle adjustment; Traffic monitoring and feedback module, which is used to calculate the traffic parameters of the road and evaluate the road traffic conditions according to the traffic conditions. It also feeds the road conditions back to the path selection model of the vehicle agent. At the same time, when the road traffic exceeds the capacity threshold, it builds a Bayesian game theory path selection model to determine the path set of travelers, the type of travelers, and the beliefs about other types of travelers. It calculates the cost of path selection based on factors such as travel time and congestion, repeatedly calculates the benefits, and adjusts the path until the system reaches a Bayesian Nash equilibrium. The iteration module is used to iterate the simulation scheme, repeat the above modules, conduct traffic simulation of the urban road network, and obtain simulation results.

10. A terminal device, comprising a memory, a processor, and a computer program stored in the memory and capable of running on the processor, characterized in that: The memory stores a computer program that can be run on the processor. When the processor loads and executes the computer program, the large-scale urban network multi-agent traffic simulation prediction method described in any one of claims 1 to 8 is adopted.

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