Tidal lane traffic determination method and apparatus

Abstract

The present disclosure provides a tidal lane traffic flow determination method and device thereof, the method comprising: obtaining real traffic flow data of a target area within a preset time period, wherein the real traffic flow data comprises vehicle driving-in road data and vehicle driving-out road data; constructing a target directed topology graph according to a traffic network of the target area and a tidal lane setting strategy of the target area, wherein the traffic network comprises a plurality of road segments, and a road segment is composed of a road and an intersection; inputting the vehicle driving-in road data and the target directed topology graph into a target urban traffic network model to output predicted traffic flow data, wherein the target urban traffic network model is obtained by adjusting parameters of an initial urban traffic network model according to the real traffic flow data; the initial urban traffic network model is constructed based on driving behavior of vehicles on the road and vehicle flow conversion rules at the intersection; and determining congestion of vehicles on the tidal lane based on the predicted traffic flow data.

Description

TECHNICAL FIELD

[0001] The present disclosure relates to the technical field of computational mathematics and traffic travel, and particularly relates to a tidal lane traffic flow determination method and device thereof. BACKGROUND

[0002] With the advancement of urbanization and the development of the automobile industry, the number of motor vehicles in cities continues to rise, and the contradiction between vehicles and roads intensifies, and urban road congestion is common. City managers have proposed a series of traffic congestion management measures to alleviate urban traffic pressure and improve people's travel experience. Tidal lane is one of the effective measures to manage traffic congestion. Due to the allocation of urban land resources and the travel habits of citizens, most motor vehicles enter and exit the urban area along a certain route during the morning and evening peak hours, resulting in a sharp increase in motor vehicle flow in the direction of entering or exiting the city, and a smaller flow in the opposite direction, exhibiting a clear tidal phenomenon, which makes the urban road resources unable to fully function.

[0003] In the implementation of the present disclosure, it is found that the existing traffic flow prediction method requires a large data set and a long training time based on the training model of the neural network, has high requirements for hardware, and the model has high dependence on data. In a new scenario, the model needs to be retrained, which is costly. The prediction method based on time series has a single research dimension and requires high-quality original data, and the prediction accuracy is poor. SUMMARY

[0004] In view of the above problems, the present disclosure provides a tidal lane traffic flow determination method, device, equipment, medium and program product.

[0005] According to a first aspect of the present disclosure, a tidal lane traffic flow determination method is provided, comprising:

[0006] obtaining real traffic flow data of a target area in a preset time period, wherein the real traffic flow data includes vehicle entry road data and vehicle exit road data;

[0007] constructing a target directed topology graph according to the traffic network of the target area and the tidal lane setting strategy of the target area, wherein the traffic network includes a plurality of road segments, and the road segment is composed of a road and an intersection;

[0008] inputting the vehicle entry road data and the target directed topology graph into a target urban traffic network model to output predicted traffic flow data, wherein the target urban traffic network model is obtained by adjusting parameters of an initial urban traffic network model according to the real traffic flow data; the initial urban traffic network model is constructed based on the driving behavior of vehicles on the road and the vehicle flow conversion rule at the intersection; and

[0009] determining the congestion status of the vehicle on the tidal lane based on the predicted traffic flow data.

[0010] According to an embodiment of the present disclosure, the initial urban traffic network model comprises a vehicle driving sub-model on a road and a traffic flow conversion sub-model at an intersection;

[0011] The method further comprises:

[0012] The vehicle driving sub-model on the road is constructed according to the traffic network of the target region and the driving behavior of the vehicle on the road;

[0013] The traffic flow conversion sub-model at the intersection is constructed according to the traffic network of the target region and the traffic flow conversion rule of the vehicle at the intersection.

[0014] According to an embodiment of the present disclosure, the vehicle driving sub-model on the road is constructed according to the traffic network of the target region and the driving behavior of the vehicle on the road, comprising:

[0015] An initial directed topological graph is constructed according to the traffic network of the target region;

[0016] For each road segment in the initial directed topological graph:

[0017] A traffic flow conservation model at a preset time is determined according to the density and traffic flow of the vehicle;

[0018] The driving direction of the vehicle is determined according to the traffic time of the vehicle driving from a preset position to the intersection at the preset time and the driving speed of the vehicle;

[0019] The vehicle driving sub-model on the road is obtained according to the traffic flow conservation model and the driving direction of the vehicle.

[0020] According to an embodiment of the present disclosure, the road comprises an inflow road and an outflow road; and the intersection is arranged between the inflow road and the outflow road;

[0021] The traffic flow conversion sub-model at the intersection is constructed according to the traffic flow conversion rule of the vehicle at the intersection, comprising:

[0022] The outflow of the vehicle at a preset time is determined according to the outflow of the intersection corresponding to any position on the inflow road;

[0023] The inflow of the vehicle at a preset time is determined according to the inflow of the intersection corresponding to any position on the outflow road;

[0024] The traffic flow conversion sub-model at the intersection is determined according to the outflow, the inflow and the traffic flow conversion rule.

[0025] According to an embodiment of the present disclosure, the traffic flow conversion rule comprises a first rule, a second rule and a third rule;

[0026] The first rule is used to represent that the first number of vehicles flowing out of the inflow road is consistent with the second number of vehicles flowing into the outflow road.

[0027] The second rule is used to represent that the third number of vehicles flowing out of the inflow road meets a first preset threshold when the vehicles pass through the intersection.

[0028] The third rule is used to represent that a preset number of vehicles flowing into the outflow road flow out of the inflow road according to a preset proportion.

[0029] According to an embodiment of the present disclosure, the intersection flow conversion sub-model is determined according to the outflow, the inflow, and the vehicle flow conversion rule, and includes:

[0030] In a case where it is determined that the outflow is less than or equal to the inflow, the vehicle flow of the inflow road is determined according to the first rule and the second rule.

[0031] In a case where it is determined that the outflow is greater than the inflow, the vehicle flow of the outflow road is determined according to the third rule.

[0032] The intersection flow conversion sub-model is obtained according to the vehicle flow of the inflow road and the vehicle flow of the outflow road.

[0033] According to an embodiment of the present disclosure, the tidal lane vehicle flow determination method further includes:

[0034] The vehicle driving into road data is input into an initial urban traffic network model, and initial predicted vehicle flow data is output.

[0035] Parameters are adjusted based on the initial predicted vehicle flow data and the vehicle driving out road data.

[0036] In a case where it is determined that the initial predicted vehicle flow data and the vehicle driving out road data are consistent, a target urban traffic network model is obtained.

[0037] According to an embodiment of the present disclosure, the congestion condition of the vehicle on the tidal lane is determined based on the predicted vehicle flow data, and includes:

[0038] In a case where it is determined that the predicted vehicle flow data meets a second preset threshold, it is determined that the vehicle on the tidal lane is in an uncongested condition.

[0039] A second aspect of the present disclosure provides a tidal lane vehicle flow determination device, including:

[0040] The acquisition module is configured to acquire real vehicle flow data of a target region in a preset time period, wherein the real vehicle flow data includes vehicle driving into road data and vehicle driving out road data.

[0041] The construction module is configured to construct a target directed topology graph according to a traffic network of a target region and a tidal lane setting strategy of the target region, wherein the traffic network comprises a plurality of road segments, and each road segment is composed of a road and an intersection;

[0042] The prediction module is configured to input the vehicle entering road data and the target directed topology graph into a target urban traffic network model to output predicted traffic flow data, wherein the target urban traffic network model is obtained by adjusting parameters of an initial urban traffic network model according to real traffic flow data, and the initial urban traffic network model is constructed based on driving behaviors of vehicles on roads and vehicle flow conversion rules at intersections.

[0043] The determination module is configured to determine congestion of the vehicles on the tidal lane based on the predicted traffic flow data.

[0044] A third aspect of the present disclosure provides an electronic device, comprising: one or more processors; a memory for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors execute the tidal lane traffic flow determination method described above.

[0045] A fourth aspect of the present disclosure further provides a computer-readable storage medium having stored executable instructions, which, when executed by a processor, cause the processor to execute the tidal lane traffic flow determination method described above.

[0046] A fifth aspect of the present disclosure further provides a computer program product comprising a computer program, which, when executed by a processor, implements the tidal lane traffic flow determination method described above.

[0047] According to the embodiments of the present disclosure, the target directed topology graph is constructed according to the traffic network of the target region and the tidal lane setting strategy of the target region, and the target urban traffic network model is used to predict the traffic flow data in combination with the vehicle entering road data. The macroscopic model, i.e., the target urban traffic network model, is used to study the vehicle flow in the target urban traffic network, which requires less data, has high calculation efficiency, and has high prediction accuracy. The rationality of setting the tidal lane is evaluated through the congestion of the vehicles on the tidal lane, which is conducive to determining the appropriate tidal lane setting strategy and effectively alleviating the urban traffic congestion problem. BRIEF DESCRIPTION OF DRAWINGS

[0048] The above and other objects, features and advantages of the present disclosure will become more apparent from the following description of embodiments of the present disclosure, taken in conjunction with the accompanying drawings, in which:

[0049] Figure 1 An application scenario diagram of the tidal lane traffic flow determination method, device, equipment, medium and program product according to the embodiments of the present disclosure is schematically shown;

[0050] Figure 2 FIG. 1 schematically illustrates a flowchart of a method for tidal lane traffic determination according to an embodiment of the present disclosure;

[0051] FIG. 3(a) schematically illustrates a schematic diagram of an initial directed topology graph according to an embodiment of the present disclosure;

[0052] FIG. 3(b) schematically illustrates a schematic diagram of a three-lane road according to an embodiment of the present disclosure;

[0053] FIG. 3(c) schematically illustrates a schematic diagram of an intersection according to an embodiment of the present disclosure;

[0054] Figure 4 FIG. 4 schematically illustrates a simplified network model schematic diagram of an intersection according to an embodiment of the present disclosure;

[0055] Figure 5 FIG. 5 schematically illustrates a flowchart of a method for tidal lane traffic determination according to another embodiment of the present disclosure;

[0056] Figure 6 FIG. 6 schematically illustrates a block diagram of a tidal lane traffic determination apparatus according to an embodiment of the present disclosure; and

[0057] Figure 7 FIG. 7 schematically illustrates a block diagram of an electronic device suitable for implementing a tidal lane traffic determination method according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0058] Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. However, it is to be understood that these descriptions are merely exemplary and are not intended to limit the scope of the present disclosure. In the following detailed description of the embodiments of the present disclosure, numerous specific details are set forth in order to provide a thorough understanding of the embodiments of the present disclosure. However, it will be apparent to one skilled in the art that the embodiments of the present disclosure can be practiced without these specific details. In other instances, well-known structures and techniques have been omitted in order to avoid obscuring the concepts of the present disclosure.

[0059] The terms used herein are merely used to describe specific embodiments and are not intended to limit the present disclosure. The terms "include", "comprise", and the like used herein indicate the presence of the features, steps, operations, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, or components.

[0060] All terms used herein, including technical and scientific terms, have the same meanings as those generally understood by those skilled in the art unless otherwise defined. It should be noted that the terms used herein should be interpreted as having meanings consistent with the context of the present specification, and should not be interpreted in an idealized or excessively formal manner.

[0061] In the case of using expressions such as "at least one of A, B, and C", it generally means all of the following: "A alone, B alone, C alone, A and B together, A and C together, B and C together, and A and B and C together", unless otherwise indicated (e.g., "a system that has at least one of A, B, and C" shall include, but not be limited to, a system that has A alone, a system that has B alone, a system that has C alone, a system that has both A and B, a system that has both A and C, a system that has both B and C, and a system that has all of A, B, and C).

[0062] In the technical solutions of the present disclosure, the collection, storage, use, processing, transmission, provision, disclosure and application of the data (such as including but not limited to user personal information) involved comply with the relevant legal regulations, necessary security measures are taken, and the public order and good customs are not violated.

[0063] In the technical solutions of the embodiments of the present disclosure, the authorization or consent of the user is obtained before the user's personal information is acquired or collected.

[0064] In the process of implementing the present disclosure, it is found that the existing traffic flow prediction method is mostly started from the neural network algorithm, and the multilayer perceptron and long short-term memory network are mainly studied to train the traffic flow prediction model. Specifically, a large amount of traffic flow data is first collected, and then the data set is divided to construct training, testing and validation data sets. The model is trained in combination with the constructed traffic flow prediction model, and the model adjustment or parameter adjustment will be experienced during the training to achieve the target expectation. In addition, the traffic flow prediction method based on time series, such as autoregressive model and autoregressive-moving average model, is widely used. This kind of method predicts the traffic flow data by constructing the relationship function between time and traffic flow.

[0065] The existing traffic flow prediction method requires a large data set and a long training time based on the neural network training model, has high requirements for hardware, and has high dependence on data. The model needs to be retrained in a new scenario, which is costly. The prediction method based on time series has a single research dimension and high requirements for original data, and has poor prediction accuracy.

[0066] Embodiments of the present disclosure provide a tidal lane traffic flow determination method, comprising: obtaining real traffic flow data of a target area in a preset time period, wherein the real traffic flow data comprises vehicle driving-in road data and vehicle driving-out road data; constructing a target directed topology graph according to a traffic network of the target area and a tidal lane setting strategy of the target area, wherein the traffic network comprises a plurality of road segments, and a road segment is composed of a road and an intersection; inputting the vehicle driving-in road data and the target directed topology graph into a target urban traffic network model to output predicted traffic flow data, wherein the target urban traffic network model is obtained by adjusting parameters of an initial urban traffic network model according to the real traffic flow data; the initial urban traffic network model is constructed based on driving behaviors of vehicles on the roads and vehicle flow conversion rules at the intersections; and determining congestion conditions of vehicles on the tidal lane based on the predicted traffic flow data.

[0067] Figure 1 An application scenario diagram of the tidal lane traffic flow determination method, apparatus, device, medium and program product according to embodiments of the present disclosure is schematically shown.

[0068] As shown in Figure 1 , an application scenario 100 according to the embodiments can include a first terminal device 101, a second terminal device 102, a third terminal device 103, a network 104, and a server 105. The network 104 is a medium for providing communication links between the first terminal device 101, the second terminal device 102, the third terminal device 103, and the server 105. The network 104 can include various connection types, such as wired, wireless communication links, or optical fiber cables, etc.

[0069] A user can use at least one of the first terminal device 101, the second terminal device 102, and the third terminal device 103 to interact with the server 105 through the network 104 to receive or send messages, etc. Various communication client applications can be installed on the first terminal device 101, the second terminal device 102, and the third terminal device 103, such as shopping applications, web browser applications, search applications, instant messaging tools, email clients, social platform software, etc. (only as examples).

[0070] The first terminal device 101, the second terminal device 102, and the third terminal device 103 can be various electronic devices with display screens and supporting web browsing, including but not limited to smartphones, tablet computers, laptop computers, desktop computers, etc.

[0071] The server 105 can be a server that provides various services, such as a background management server that provides support for a website browsed by a user using the first terminal device 101, the second terminal device 102, and the third terminal device 103 (only as an example). The background management server can perform analysis and the like on received user requests and the like, and feed back a processing result (such as a web page, information, or data, or the like, obtained or generated according to a user request) to a terminal device.

[0072] It should be noted that the tidal lane traffic flow determination method provided by the embodiments of the present disclosure can generally be executed by the server 105. Accordingly, the tidal lane traffic flow determination apparatus provided by the embodiments of the present disclosure can generally be arranged in the server 105. The tidal lane traffic flow determination method provided by the embodiments of the present disclosure can also be executed by a server or a server cluster different from the server 105 and capable of communicating with the first terminal device 101, the second terminal device 102, the third terminal device 103, and / or the server 105. Accordingly, the tidal lane traffic flow determination apparatus provided by the embodiments of the present disclosure can also be arranged in a server or a server cluster different from the server 105 and capable of communicating with the first terminal device 101, the second terminal device 102, the third terminal device 103, and / or the server 105.

[0073] It should be understood that Figure 1 The number of terminal devices, networks, and servers in the above-described scenario is only illustrative. Any number of terminal devices, networks, and servers can be provided according to implementation needs.

[0074] The tidal lane traffic flow determination method according to the embodiments of the present disclosure will be described in detail below based on the scenario described above. Figure 1 Figures 2-5 The tidal lane traffic flow determination method according to the embodiments of the present disclosure will be described in detail below based on the scenario described above.

[0075] Figure 2 A flowchart of a tidal lane traffic flow determination method according to an embodiment of the present disclosure is schematically shown.

[0076] As shown in FIG. 2, Figure 2 The tidal lane traffic flow determination method 200 of this embodiment includes operations S210-S240.

[0077] In operation S210, real traffic flow data of a target region in a preset time period is obtained, wherein the real traffic flow data includes vehicle driving-in road data and vehicle driving-out road data.

[0078] According to an embodiment of the present disclosure, the preset time period can be one day, two days, three days, one month, two months, etc., which is not specifically limited herein. The target region can be a target road traffic area of a target city.

[0079] ​In operation S220, a target directed topology graph is constructed according to a traffic network of a target region and a tidal lane setting strategy of the target region, where the traffic network includes a plurality of road segments, and each road segment is composed of a road and an intersection.

[0080] According to an embodiment of the present disclosure, the traffic network of the target region can be obtained from a road network database. The tidal lane setting strategy of the target region can include adjusting the change rule of the traffic light of one or more intersections or setting one or more roads with time-varying driving directions. The target directed topology graph can be obtained by abstract modeling in combination with the traffic network of the target region and the tidal lane setting strategy of the target region. In the target directed topology graph, a node represents an intersection in the traffic network, and a directed line segment represents a road in the traffic network.

[0081] In operation S230, the vehicle driving into the road data and the target directed topology graph are input into a target city traffic network model, and predicted traffic flow data are output, where the target city traffic network model is obtained by adjusting parameters of an initial city traffic network model according to real traffic flow data, and the initial city traffic network model is constructed based on the driving behavior of the vehicle on the road and the traffic flow conversion rule of the vehicle at the intersection.

[0082] According to an embodiment of the present disclosure, the driving behavior of the vehicle on the road can include the traffic flow density of the vehicle on the road and the driving direction of the vehicle on the road. The traffic flow conversion rule of the vehicle at the intersection can include the road outflow vehicle and road inflow vehicle quantity conservation rule and the road vehicle passing efficiency maximum rule.

[0083] According to an embodiment of the present disclosure, the initial city traffic network model can be constructed by first constructing a vehicle driving on road sub-model according to the driving behavior of the vehicle on the road and the traffic network of the target region, and then constructing an intersection flow conversion sub-model according to the traffic flow conversion rule of the vehicle at the intersection and the traffic network of the target region. The initial city traffic network model is composed of the vehicle driving on road sub-model and the intersection flow conversion sub-model. The target city traffic network model is obtained by adjusting the model parameters of the vehicle driving on road sub-model and the intersection flow conversion sub-model of the initial city traffic network model according to the real traffic flow data.

[0084] In operation S240, the congestion condition of the vehicle on the tidal lane is determined based on the predicted traffic flow data.

[0085] According to an embodiment of the present disclosure, the congestion degree of the vehicle in the predicted traffic network is determined according to the predicted traffic flow data. The congestion condition is determined according to the congestion degree of the vehicle.

[0086] According to the embodiment of the present disclosure, the target directed topology graph is constructed through the traffic network of the target region and the tidal lane setting strategy of the target region, and the vehicle flow data is predicted by using the target city traffic network model in combination with the vehicle driving-in road data. The macro model, i.e., the target city traffic network model, is used to study the vehicle flow in the target city traffic network, which requires less data, has high calculation efficiency, and has high prediction accuracy; the congestion condition of the vehicle on the tidal lane is used to evaluate the rationality of setting the tidal lane, thereby facilitating the determination of the appropriate tidal lane setting strategy and effectively relieving the urban traffic congestion problem.

[0087] According to the embodiment of the present disclosure, the initial city traffic network model can include a vehicle driving-on-road sub-model and an intersection flow conversion sub-model.

[0088] In addition to the operations S210-S240, the tidal lane vehicle flow determination method can further include:

[0089] According to the traffic network of the target region and the driving behavior of the vehicle on the road, the vehicle driving-on-road sub-model is constructed; and according to the traffic network of the target region and the vehicle flow conversion rule of the vehicle at the intersection, the intersection flow conversion sub-model is constructed.

[0090] According to the embodiment of the present disclosure, the traffic network of the target region can be obtained through the road network database. The driving behavior of the vehicle on the road can include the density of the vehicle, the vehicle flow, the driving speed of the vehicle, and the driving direction of the vehicle, etc. The vehicle flow conversion rule of the vehicle at the intersection can be determined according to the road passing efficiency by considering the case that the vehicle changes the driving direction after entering the road and passing through the intersection.

[0091] According to the embodiment of the present disclosure, by constructing the vehicle driving-on-road sub-model, the running condition of the vehicle flow on the road can be simulated, and by constructing the intersection flow conversion sub-model, the motion law of the vehicle at the intersection can be analyzed, and the running condition of the vehicle flow between intersections can be simulated.

[0092] FIG. 3(a) schematically shows a schematic diagram of an initial directed topology graph according to an embodiment of the present disclosure; FIG. 3(b) schematically shows a schematic diagram of a three-lane road according to an embodiment of the present disclosure; and FIG. 3(c) schematically shows a schematic diagram of a crossroad according to an embodiment of the present disclosure.

[0093] According to the embodiment of the present disclosure, constructing the vehicle driving-on-road sub-model according to the traffic network of the target region and the driving behavior of the vehicle on the road can include:

[0094] Based on the traffic network of the target area, an initial directed topology graph is constructed. For each road segment in the initial directed topology graph: based on the vehicle density and traffic flow, a traffic flow conservation model is determined at a preset time. Based on the traffic time and speed of the vehicle traveling from a preset location to the intersection at a preset time, the vehicle's direction of travel is determined. Based on the traffic flow conservation model and the vehicle's direction of travel, a vehicle driving sub-model on the road is obtained.

[0095] For example, an abstract model of the traffic network of the target area can be performed to obtain an initial directed topology graph, as shown in Figure 3(a). This initial directed topology graph can be denoted as... ,in It can represent a set of roads. A set of nodes can be represented, and each node can represent an intersection in a traffic network. In Figure 3(a), a directed line segment can represent a road, and the circle in the middle of the road can represent a node. The figure schematically shows nine nodes and the directed line segments associated with the nodes. This is merely an example and is not intended to limit the scope of this disclosure.

[0096] As shown in Figure 3(b), on each road In the middle, there can be three types of vehicles: those turning left, those going straight, and those turning right. The road entrance can be denoted as... All three types of vehicles can enter the lane from the entrance, and the edges of the road on both sides are designated as... Record the exits for right-turning, straight-going, and left-turning vehicles on the road as follows: A dashed dividing line in the middle of the road indicates that all three types of vehicles can freely change lanes; conversely, a solid dividing line indicates that vehicles cannot change lanes. After entering the road, all three types of vehicles can freely change lanes, but near the exit, vehicles must not change lanes and should enter the corresponding waiting lane.

[0097] At every intersection In a crossroads, the movement of vehicles in each direction is controlled by traffic lights. For a crossroads, as shown in Figure 3(c).

[0098] The traffic flow conservation model can be represented by the following equation (1):

[0099] (1)

[0100] in, , can be expressed as the density of the l-th type of vehicle on the l-th road at time t; This can be represented as the traffic flow of the l-th type of vehicle on the i-th road at time i; This can be expressed as the speed of the l-th type of vehicle on the n-th road; the vehicle speed can depend on the vehicle density; and when the traffic density is zero or at its maximum, ,when Traffic flow Take the maximum value, here This is called the critical density.

[0101] Based on the direction of travel of the vehicle within the two-dimensional road, we can record the l-th type of vehicle on the m-th road. The travel time per unit distance at the location is Then, on the m-th road, the l-th type of vehicle departs from at time t. Travel time from location to exit It satisfies the following Eikonal equation (2):

[0102] (2)

[0103] According to the Reactive Dynamic User Optimization (RDUO) principle, the vehicle's driving direction satisfies the following equation (3):

[0104] (3)

[0105] in, It can be represented as the speed of the l-th type of vehicle on the m-th road.

[0106] According to embodiments of this disclosure, a vehicle driving sub-model is constructed by using the traffic network of the target area and the driving behavior of vehicles on the road. This model can realistically simulate the traffic flow on the road and is beneficial for accurately predicting the traffic flow on the road.

[0107] Figure 4 A simplified network model of an intersection according to an embodiment of the present disclosure is illustrated schematically.

[0108] According to embodiments of this disclosure, a road may include an inflow road and an outflow road; an intersection is provided between the inflow road and the outflow road.

[0109] Specifically, based on the traffic flow conversion rules at the intersection, an intersection traffic flow conversion sub-model is constructed, which may include: determining the outflow of vehicles at a preset time based on the outflow at any location on the corresponding inflow road of the intersection; determining the inflow of vehicles at a preset time based on the inflow at any location on the corresponding outflow road of the intersection; and determining the intersection traffic flow conversion sub-model based on the outflow, inflow, and traffic flow conversion rules.

[0110] According to embodiments of this disclosure, traffic flow conversion rules may include: a first rule, a second rule, and a third rule.

[0111] The first rule is used to represent that the first number of vehicles flowing out of the inflow road is consistent with the second number of vehicles flowing into the outflow road; the second rule is used to represent that the third number of vehicles flowing out of the inflow road satisfies a first preset threshold when the vehicles pass through the intersection; and the third rule is used to represent that a preset number of vehicles flowing into the outflow road flow out of the inflow road according to a preset proportion. The first preset threshold is determined according to the number of vehicles actually passing through the intersection. The preset number of vehicles can be Q vehicles. The preset proportion can be determined according to the preset number of vehicles and the road.

[0112] For example, when considering the vehicle flow conversion of each intersection, the crossroads can be simplified into a network model as shown in FIG. 1, which includes three inflow roads (1-straight, 2-right turn, and 3-left turn) and one outflow road 4. Figure 4

[0113] The number of vehicles flowing out of the roads 1, 2, and 3 is equal to the number of vehicles flowing into the road 4. When the vehicles pass through the intersection, the number of vehicles flowing out of the roads 1, 2, and 3 is maximized. Among the Q vehicles flowing into the road 4, the number of vehicles flowing out of the roads 1, 2, and 3 is q1Q, (1-q1-q2)Q, and q2Q, respectively, and the proportion is q1:(1-q1-q2):q2. Wherein q1 can represent the proportion of vehicles flowing out of the road 1 to the preset number of vehicles flowing into the outflow road; and q2 can represent the proportion of vehicles flowing out of the road 3 to the preset number of vehicles flowing into the outflow road.

[0114] According to the embodiments of the present disclosure, the intersection flow conversion sub-model is determined according to the outflow, the inflow, and the vehicle flow conversion rule, which can analyze the vehicle motion law of the intersection, simulate the vehicle flow running condition between intersections, and be beneficial to accurately predict the vehicle flow running condition between intersections.

[0115] According to the embodiments of the present disclosure, the intersection flow conversion sub-model is determined according to the outflow, the inflow, and the vehicle flow conversion rule, which can include:

[0116] In a case where the outflow is determined to be less than or equal to the inflow, the vehicle flow of the inflow road is determined according to the first rule and the second rule; in a case where the outflow is determined to be greater than the inflow, the vehicle flow of the outflow road is determined according to the third rule; and the intersection flow conversion sub-model is obtained according to the vehicle flow of the inflow road and the vehicle flow of the outflow road.

[0117] For example, the flow transmission between the roads connected with the intersection can be determined by solving the Riemann problem. The maximum outflow of an arbitrary position of the i-th road outlet can be defined as shown in the following formula (4):

[0118] (4)

[0119] ​​The total maximum outflow of the ith vehicle at the intersection at time t can be shown as equation (5) below:

[0120] (5)

[0121] where i = {1, 2, 3}.

[0122] Similarly, at the entrance of road 4 , the corresponding maximum inflow and total maximum inflow can be defined as shown in equation (6) and equation (7) below:

[0123] (6)

[0124] (7)

[0125] In the case where the total outflow of all vehicles passing through the intersection (i.e., the traffic flow of right turns, straight ahead, and left turns) is less than or equal to the maximum inflow of road 4, i.e., as shown in equation (8) below, the vehicles on roads 1, 2, and 3 can always pass through the entrance at the maximum outflow, and the actual traffic flow can be shown as equation (9) below:

[0126] (8)

[0127] (9)

[0128] Correspondingly, the actual inflow of road 4 can be shown as equation (10) and (11) below:

[0129] (10)

[0130] (11)

[0131] In the case where the total outflow of all vehicles passing through the intersection (i.e., the traffic flow of right turns, straight ahead, and left turns) is greater than the maximum inflow of road 4, i.e., as shown in equation (12) below, at least one road cannot flow out at the corresponding maximum traffic flow, and the actual inflow of road 4 can be shown as equation (13) below:

[0132] (12)

[0133] (13)

[0134] The outflow of vehicles on roads 1, 2, and 3 can be divided into the following three cases, according to the proportion of vehicles flowing out of roads 1, 2, and 3 among the Q vehicles flowing into road 4, which is q1: (1-q1-q2):q2, respectively:

[0135] The actual outflow of the roads 1, 2, and 3 is given by the following equations (16) to (18), respectively, when the following equations (14) and (15) are satisfied.

[0136] (14)

[0137] (15)

[0138] The actual outflow of the roads 1, 2, and 3 is given by the following equations (16) to (18), respectively, when the following equations (14) and (15) are satisfied.

[0139] (16)

[0140] (17)

[0141] (18)

[0142] The actual outflow of the roads 1, 2, and 3 is given by the following equations (21) to (23), respectively, when the following equations (19) and (20) are satisfied.

[0143] (19)

[0144] (20)

[0145] The actual outflow of the roads 1, 2, and 3 is given by the following equations (21) to (23), respectively, when the following equations (19) and (20) are satisfied.

[0146] (21)

[0147] (22)

[0148] (23)

[0149] The actual outflow of the roads 1, 2, and 3 is given by the following equations (26) to (28), respectively, when the following equations (24) and (25) are satisfied.

[0150] (24)

[0151] (25)

[0152] The actual outflow of the roads 1, 2, and 3 is given by the following equations (26) to (28), respectively, when the following equations (24) and (25) are satisfied.

[0153] (26)

[0154] (27)

[0155] (28)

[0156] According to the embodiment of the present disclosure, the traffic flow conversion of the vehicle at the intersection is solved by solving the Riemann problem, and the value of the traffic flow at the intersection can be accurately and efficiently solved.

[0157] According to the embodiment of the present disclosure, according to the mass conservation rule of the number of outflow vehicles and the number of inflow vehicles, the maximum traffic efficiency rule when the vehicle passes through the intersection, and the preset ratio outflow rule of the inflow vehicles and the outflow vehicles, the vehicle motion law at the intersection can be analyzed, the traffic flow running condition between intersections can be simulated, and the traffic flow running condition between intersections can be accurately predicted.

[0158] According to the embodiment of the present disclosure, the traffic flow determination method of the tidal lane can further include:

[0159] The vehicle driving into the road data is input into an initial urban traffic network model, and initial predicted traffic flow data is output; the initial predicted traffic flow data and the vehicle driving out of the road data are used for parameter adjustment; and in a case where the initial predicted traffic flow data and the vehicle driving out of the road data are consistent, a target urban traffic network model is obtained.

[0160] According to the embodiment of the present disclosure, the initial urban traffic network model is adjusted according to the real traffic flow data of the target area. Wherein, it mainly includes adjusting the vehicle driving sub-model on the road, adjusting the intersection flow conversion sub-model, and adjusting the initial value condition setting.

[0161] According to the embodiment of the present disclosure, the initial urban traffic network model is adjusted according to the real traffic flow data, and the target urban traffic network model obtained has high prediction accuracy, which is beneficial to determine the congestion condition of the vehicle on the tidal lane.

[0162] According to the embodiment of the present disclosure, based on the predicted traffic flow data, the congestion condition of the vehicle on the tidal lane can be determined, which can include:

[0163] In a case where the predicted traffic flow data meets the second preset threshold, it is determined that the vehicle on the tidal lane is in a non-congestion condition.

[0164] According to the embodiment of the present disclosure, the second preset threshold can be determined according to the actual vehicle traffic flow data on the road. In a case where the predicted traffic flow data is less than or equal to the second preset threshold, the second preset threshold can be met.

[0165] According to the embodiment of the present disclosure, based on the predicted traffic flow data, the congestion condition of the vehicle on the tidal lane can be determined, and if congestion occurs, suitable tidal lane setting strategies can be obtained through repeated numerical simulation, so as to alleviate the traffic congestion problem of the traffic network or the city.

[0166] Figure 5A flow chart of a tidal lane traffic flow determination method according to another embodiment of the present disclosure is schematically shown.

[0167] As shown in Figure 5 , the tidal lane traffic flow determination method of this embodiment can include selecting a traffic network for study, then establishing a directed topological graph, constructing a vehicle driving model on the road and a traffic flow conversion model at the intersection according to the directed topological graph, and combining the vehicle driving model on the road and the traffic flow conversion model at the intersection to form a city traffic network model. The entire traffic model, through the mutual influence of different directional traffic flows in the road, collectively forms a strongly coupled equation set. Finally, the model parameters are further corrected in combination with actual data for numerical simulation. After the model is adjusted, the traffic flow movement of the tidal lane can be predicted by changing the parameters in the city road grid model. By continuously changing the design strategy of the tidal lane and the predicted traffic flow movement, the tidal lane setting method is determined. The actual data can be the real traffic flow data of the selected traffic network area within a preset time period.

[0168] According to the embodiments of the present disclosure, the vehicle driving model on the road can be constructed according to the directed topological graph and the driving behavior of the vehicle on the road, and the traffic flow conversion model at the intersection can be constructed according to the directed topological graph and the traffic flow conversion rule of the vehicle at the intersection. The construction of the vehicle driving model on the road can be similar to the construction of the vehicle driving sub-model method, and the construction of the traffic flow conversion model at the intersection can be similar to the construction of the traffic flow conversion sub-model method.

[0169] For example, taking a road section with four bidirectional roads and one intersection as an example, each road contains three types of vehicles, i.e., left turn, straight and right turn. First, the road section can be abstractly represented as a directed topological graph, where the road set is , and the node set is . Based on the real traffic flow data of the road section for a period of time, the corresponding inflow data for the period of time is determined. According to the established vehicle driving model on the road and the traffic flow conversion model at the intersection, the traffic flow variation of the vehicles in each direction at the intersection of the road section within the period of time is calculated. Then, the parameters in the model are adjusted so that the simulated traffic flow data is consistent with the actual traffic flow data. Finally, the traffic efficiency of the road section is maximized by changing the traffic light change rule, thereby achieving the purpose of setting the tidal lane.

[0170] Based on the above tidal lane traffic flow determination method, the present disclosure further provides a tidal lane traffic flow determination device. The device will be described in detail below. Figure 6

[0171] Figure 6 A structural block diagram of a tidal lane traffic flow determination device according to an embodiment of the present disclosure is schematically shown. ​

[0172] As Figure 6 shown, the tidal lane traffic flow determination apparatus 600 of this embodiment comprises an obtaining module 610, a constructing module 620, a predicting module 630 and a determining module 640.

[0173] The obtaining module 610 is configured to obtain real traffic flow data of a target region within a preset time period, wherein the real traffic flow data comprises vehicle entering road data and vehicle exiting road data. In an embodiment, the obtaining module 610 can be configured to perform the operation S210 described above, and details are not repeated here.

[0174] The constructing module 620 is configured to construct a target directed topology graph according to a traffic network of the target region and a tidal lane setting strategy of the target region, wherein the traffic network comprises a plurality of road segments, and each road segment is composed of a road and an intersection. In an embodiment, the constructing module 620 can be configured to perform the operation S220 described above, and details are not repeated here.

[0175] The predicting module 630 is configured to input the vehicle entering road data and the target directed topology graph into a target urban traffic network model to output predicted traffic flow data, wherein the target urban traffic network model is obtained by adjusting parameters of an initial urban traffic network model according to the real traffic flow data; and the initial urban traffic network model is constructed based on driving behaviors of vehicles on roads and vehicle flow conversion rules at intersections. In an embodiment, the predicting module 630 can be configured to perform the operation S230 described above, and details are not repeated here.

[0176] The determining module 640 is configured to determine congestion conditions of vehicles on the tidal lane based on the predicted traffic flow data. In an embodiment, the determining module 640 can be configured to perform the operation S240 described above, and details are not repeated here.

[0177] According to an embodiment of the present disclosure, any of the modules of the acquiring module 610, the constructing module 620, the predicting module 630 and the determining module 640 can be combined in one module, or any of the modules can be split into multiple modules. Alternatively, at least part of the functions of one or more of the modules can be combined with at least part of the functions of the other modules, and implemented in one module. According to an embodiment of the present disclosure, at least one of the acquiring module 610, the constructing module 620, the predicting module 630 and the determining module 640 can be at least partially implemented as a hardware circuit, such as a field programmable gate array (FPGA), a programmable logic array (PLA), a system on chip, a system on board, a system in package, an application specific integrated circuit (ASIC), or any other reasonable manner of integrating or packaging a circuit, etc. or implemented by hardware or firmware, or implemented in any one of software, hardware and firmware or in a proper combination of any of them. Alternatively, at least one of the acquiring module 610, the constructing module 620, the predicting module 630 and the determining module 640 can be at least partially implemented as a computer program module which can perform the corresponding functions when the computer program module is run.

[0178] Figure 7 A block diagram of an electronic device suitable for implementing the tidal lane traffic flow determination method according to an embodiment of the present disclosure is schematically shown.

[0179] As shown in Figure 7 The electronic device 700 according to an embodiment of the present disclosure includes a processor 701 which can perform various appropriate actions and processes according to programs stored in a read only memory (ROM) 702 or loaded from a storage portion 708 into a random access memory (RAM) 703. The processor 701 can include, for example, a general purpose microprocessor (such as a CPU), an instruction set processor and / or a related chipset and / or a special purpose microprocessor (such as an application specific integrated circuit (ASIC)), etc. The processor 701 can also include an on-board memory for cache use. The processor 701 can include a single processing unit or multiple processing units for performing different actions of the method processes according to embodiments of the present disclosure.

[0180] In the RAM 703, various programs and data required for the operation of the electronic device 700 are stored. The processor 701, the ROM 702, and the RAM 703 are connected to each other via the bus 704. The processor 701 performs various operations of the method flow according to the embodiments of the present disclosure by executing the programs in the ROM 702 and / or the RAM 703. It should be noted that the programs can also be stored in one or more memories other than the ROM 702 and the RAM 703. The processor 701 can also perform various operations of the method flow according to the embodiments of the present disclosure by executing the programs stored in the one or more memories.

[0181] According to the embodiments of the present disclosure, the electronic device 700 can further include an input / output (I / O) interface 705, which is also connected to the bus 704. The electronic device 700 can further include one or more of the following components connected to the I / O interface 705: an input part 706 including a keyboard, a mouse, and the like; an output part 707 including a cathode ray tube (CRT), a liquid crystal display (LCD), and the like, and a speaker, and the like; a storage part 708 including a hard disk, and the like; and a communication part 709 including a network interface card such as a LAN card, a modem, and the like. The communication part 709 performs communication processing via a network such as the Internet. A drive 710 is also connected to the I / O interface 705 as necessary. A removable medium 711 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, and the like is mounted on the drive 710 as necessary, so that a computer program read therefrom is installed in the storage part 708 as necessary.

[0182] The present disclosure also provides a computer readable storage medium, which can be included in the device / apparatus / system described in the above embodiments; or can exist separately without being assembled into the device / apparatus / system. The above computer readable storage medium carries one or more programs, when the one or more programs are executed, the method according to the embodiments of the present disclosure is implemented.

[0183] According to an embodiment of the present disclosure, the computer readable storage medium can be a nonvolatile computer readable storage medium, for example, can include, but is not limited to, 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 portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any appropriate combination thereof. In this disclosure, the computer readable storage medium can be any tangible medium that contains or stores a program that can be used by or in connection with an instruction execution system, apparatus, or device. For example, according to an embodiment of the present disclosure, the computer readable storage medium can include the ROM 702 and / or the RAM 703 described above and / or one or more memory other than the ROM 702 and the RAM 703.

[0184] Embodiments of the present disclosure also include a computer program product, which includes a computer program containing program codes for executing the methods shown in the flowcharts. When the computer program product is run in a computer system, the program codes are used to make the computer system implement the methods provided by the embodiments of the present disclosure.

[0185] The above-described functions defined in the system / device / apparatus of the embodiments of the present disclosure are performed when the computer program is executed by the processor 701. According to an embodiment of the present disclosure, the system, apparatus, module, unit, etc. described above can be implemented by computer program modules.

[0186] In one embodiment, the computer program can rely on a tangible storage medium such as an optical storage device, a magnetic storage device, etc. In another embodiment, the computer program can also be transmitted, distributed, and downloaded in the form of a signal on a network medium and installed and downloaded through the communication part 709 and / or installed from the detachable medium 711. The program codes contained in the computer program can be transmitted by any appropriate network medium, including but not limited to wireless, wired, etc., or any appropriate combination thereof.

[0187] In such an embodiment, the computer program can be downloaded and installed from the network through the communication part 709 and / or installed from the detachable medium 711. When the computer program is executed by the processor 701, the above-described functions defined in the system of the embodiments of the present disclosure are performed. According to an embodiment of the present disclosure, the system, device, apparatus, module, unit, etc. described above can be implemented by computer program modules.

[0188] According to embodiments of the present disclosure, program code of the computer program for performing the methods provided by the embodiments of the present disclosure can be written in any combination of one or more programming languages, and can be implemented in a computer program product. Specifically, the computer program can be implemented in a high-level procedural and / or object-oriented programming language, and / or in assembly / machine language. The programming language includes, but is not limited to, Java, C++, python, “C” language, or similar programming languages. The program code can execute entirely on the user's computing device, partly on the user's device, and partly on a remote computing device, or entirely on the remote computing device or server. In the latter scenario, the remote computing device can be connected to the user's computing device through any kind of network, including a local area network (LAN) or a wide area network (WAN), or the connection can be made to an external computing device, such as through the Internet using an Internet Service Provider (ISP).

[0189] The computer program product of the present disclosure can be a computer program product, which is a machine-readable medium (or computer readable medium) having stored therein a sequence of instructions readable by a machine (e.g., a computer). The instructions, in combination with the machine, cause the machine to effectuate the methods provided by the embodiments of the present disclosure. The instructions can reside in the memory of a computer during execution thereof by a computer, and the instructions executed by the computer can cause the computer to operate as described. Alternatively, the instructions stored in the memory of a computer can implement one or more virtual machines that operate on the computer.

[0190] Those skilled in the art will understand that features of the various embodiments and / or claims of the present disclosure can be combined or / and integrated with one another, even though such combinations or integrations are not expressly disclosed in the present disclosure. In particular, the features of the various embodiments and / or claims of the present disclosure can be combined and / or integrated with one another in any manner, without departing from the spirit and scope of the present disclosure. All such combinations and / or integrations are within the scope of the present disclosure.

[0191] The above describes embodiments of the present disclosure. However, these embodiments are merely for illustrative purposes, and are not intended to limit the scope of the present disclosure. Although each embodiment is described above separately, this does not mean that the measures in each embodiment cannot be used advantageously in combination. The scope of the present disclosure is defined by the appended claims and their equivalents. Those skilled in the art can make various substitutions and modifications without departing from the scope of the present disclosure, and these substitutions and modifications should all fall within the scope of the present disclosure.

Claims

1. A method for determining traffic flow of a tidal lane, comprising: obtaining real traffic flow data of a target area in a preset time period, the real traffic flow data comprising vehicle entering road data and vehicle exiting road data; constructing a target directed topological graph according to a traffic network of the target area and a tidal lane setting strategy of the target area, the traffic network comprising a plurality of road segments, the road segments being composed of roads and intersections, the roads comprising inflow roads and outflow roads, and the intersections being provided between the inflow roads and the outflow roads; inputting the vehicle entering road data and the target directed topological graph into a target urban traffic network model to output predicted traffic flow data, the target urban traffic network model being obtained by adjusting parameters of an initial urban traffic network model according to the real traffic flow data, and the initial urban traffic network model being constructed based on driving behaviors of vehicles on the roads and traffic flow conversion rules of the vehicles at the intersections; determining congestion conditions of the vehicles on the tidal lane based on the predicted traffic flow data; and the initial urban traffic network model comprising a vehicle driving sub-model on the roads and an intersection flow conversion sub-model. The method further comprises: constructing the vehicle driving sub-model on the roads according to the traffic network of the target area and the driving behaviors of the vehicles on the roads; constructing the intersection flow conversion sub-model according to the traffic network of the target area and the traffic flow conversion rules of the vehicles at the intersections, the traffic flow conversion rules comprising a mass conservation rule of the number of vehicles flowing out of the inflow roads and the number of vehicles flowing into the outflow roads, a rule of maximum traffic efficiency when the vehicles pass through the intersections, and a preset proportion flow-out rule of the number of vehicles flowing into the outflow roads and the number of vehicles flowing out of the inflow roads, the preset proportion flow-out rule representing that a preset number of vehicles flowing into the outflow roads flow out of the inflow roads according to a preset proportion; inputting the vehicle entering road data into the initial urban traffic network model to output initial predicted traffic flow data; adjusting parameters based on the initial predicted traffic flow data and the vehicle exiting road data; and obtaining the target urban traffic network model when the initial predicted traffic flow data and the vehicle exiting road data are consistent.

2. The method of claim 1, wherein, The method further comprises: constructing an initial directed topological graph according to the traffic network of the target area; for each of the road segments in the initial directed topological graph: determining a traffic flow conservation model at a preset time according to the density and traffic flow of the vehicles; determining a driving direction of the vehicles according to a traffic time of the vehicles driving from a preset position to the intersection at the preset time and a driving speed of the vehicles; and obtaining the vehicle driving sub-model on the roads according to the traffic flow conservation model and the driving direction of the vehicles.

3. The method of claim 1, wherein, The method further comprises: determining a traffic flow conservation model at a preset time according to the density and traffic flow of the vehicles; determining a driving direction of the vehicles according to a traffic time of the vehicles driving from a preset position to the intersection at the preset time and a driving speed of the vehicles; and obtaining the vehicle driving sub-model on the roads according to the traffic flow conservation model and the driving direction of the vehicles. determine the outflow of the vehicle at the preset time according to the inflow of the intersection corresponding to any position on the outflow road; determine the inflow of the vehicle at the preset time according to the outflow of the intersection corresponding to any position on the inflow road; determine the intersection flow conversion sub-model according to the outflow, the inflow and the vehicle flow conversion rule.

4. The method of claim 3, wherein, The vehicle flow conversion rule includes a first rule, a second rule and a third rule. The first rule is used to represent that the first number of vehicles flowing out of the inflow road is consistent with the second number of vehicles flowing into the outflow road. The second rule is used to represent that when the vehicle passes through the intersection, the third number of vehicles flowing out of the inflow road satisfies a first preset threshold. The third rule is used to represent that a preset number of vehicles flowing into the outflow road flow out of the inflow road according to a preset proportion.

5. The method of claim 4, wherein, The determination of the intersection flow conversion sub-model according to the outflow, the inflow and the vehicle flow conversion rule includes: In a case where it is determined that the outflow is less than or equal to the inflow, the vehicle flow of the inflow road is determined according to the first rule and the second rule. In a case where it is determined that the outflow is greater than the inflow, the vehicle flow of the outflow road is determined according to the third rule. The intersection flow conversion sub-model is obtained according to the vehicle flow of the inflow road and the vehicle flow of the outflow road.

6. The method according to any one of claims 1 to 5, wherein, In a case where it is determined that the predicted traffic flow data satisfies a second preset threshold, it is determined that the vehicle on the tidal lane is in an uncongested state.

7. A tidal lane traffic flow determination apparatus, comprising: an acquisition module configured to acquire real traffic flow data of a target region within a preset time period, the real traffic flow data including vehicle entry road data and vehicle exit road data; a construction module configured to construct a target directed topology graph according to a traffic network of the target region and a tidal lane setting strategy of the target region, the traffic network including a plurality of road segments, the road segments being composed of roads and intersections, the roads including inflow roads and outflow roads, and the intersections being provided between the inflow roads and the outflow roads; a prediction module configured to input the vehicle entry road data and the target directed topology graph into a target urban traffic network model to output predicted traffic flow data, the target urban traffic network model being obtained by adjusting parameters of an initial urban traffic network model according to the real traffic flow data, and the initial urban traffic network model being constructed based on vehicle driving behavior on the roads and a vehicle flow conversion rule at the intersections; a determination module configured to determine congestion of the vehicle on the tidal lane based on the predicted traffic flow data. The initial urban traffic network model includes a vehicle driving sub-model on the roads and an intersection flow conversion sub-model. The tidal lane traffic flow determination apparatus is further configured to: ​ constructing a vehicle driving sub-model on the road according to a traffic network of the target area and driving behavior of the vehicle on the road; constructing a traffic flow conversion sub-model at the intersection according to a traffic network of the target area and a vehicle flow conversion rule at the intersection, the vehicle flow conversion rule including a mass conservation rule of a number of outflow vehicles of the inflow road and a number of inflow vehicles of the outflow road, a rule of maximum traffic efficiency when a vehicle passes through the intersection, and a preset proportion outflow rule of inflow vehicles and outflow vehicles, the preset proportion outflow rule representing a preset number of vehicles inflowing into the outflow road and outflowing from the inflow road according to a preset proportion; inputting the vehicle driving-in road data into the initial urban traffic network model to output initial predicted vehicle flow data; adjusting parameters based on the initial predicted vehicle flow data and the vehicle driving-out road data; obtaining the target urban traffic network model when it is determined that the initial predicted vehicle flow data and the vehicle driving-out road data are consistent. 8.An electronic device, comprising: one or more processors; a storage device for storing one or more programs, wherein the one or more programs, when executed by the one or more processors, enable the one or more processors to perform the method of any one of claims 1-6.

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