Reversible lane traffic flow determination method and device

Abstract

The invention provides a reversible lane traffic flow determination method and device, and the method comprises the steps: obtaining the real traffic flow data of a target region in a preset time period, and enabling the real traffic flow data to comprise vehicle entry road data and vehicle exit road data; a target directed topological graph is constructed according to a traffic road network of the target area and a reversible lane setting strategy of the target area, the traffic road network comprises a plurality of road sections, and the road sections are composed of roads and intersections; the vehicle driving-in road data and the target directed topological graph are input into a target urban traffic network model, predicted traffic flow data are output, and the target urban traffic network model is obtained by performing parameter adjustment on an initial urban traffic network model according to real traffic flow data; the initial urban 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; and determining the congestion condition of the vehicle on the reversible lane based on the predicted traffic flow data.

Description

Technical Field

[0001] The present disclosure relates to technical fields such as computational mathematics and traffic travel, and in particular to a method and device for determining traffic flow in a tidal lane. Background Art

[0002] With the advancement of urbanization and the development of the automobile industry, the number of urban motor vehicles continues to rise, the contradiction between cars and roads continues to intensify, and urban road congestion is common. Urban managers have proposed a series of traffic congestion management measures to ease urban traffic pressure and improve people's travel experience. Tidal lanes are 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 city along certain routes during peak hours in the morning and evening, resulting in a surge in motor vehicle traffic in or out of the city, while the traffic in the opposite direction is relatively small, with obvious tidal phenomena, which makes it impossible for urban road resources to fully play their role.

[0003] In the process of implementing the present disclosure, it is found that the existing traffic flow prediction methods, based on the training model of the neural network, require a huge data set and a long training time, have high hardware requirements, and the model is highly dependent on data. In new scenarios, the model needs to be retrained, which is costly. The prediction method based on time series has a single research dimension, high requirements for original data, and poor prediction accuracy. Summary of the invention

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

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

[0006] Acquire real traffic flow data of a target area within a preset time period, wherein the real traffic flow data includes data of vehicles entering a road and data of vehicles leaving a road;

[0007] According to the traffic road network of the target area and the tidal lane setting strategy of the target area, a target directed topological graph is constructed, wherein the traffic road network includes multiple road sections, and the road sections are composed of roads and intersections;

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

[0009] Determine the congestion of vehicles on tidal lanes based on predicted traffic flow data.

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

[0011] The method for determining the traffic flow of a tidal lane further includes:

[0012] According to the traffic road network of the target area and the driving behavior of vehicles on the road, a vehicle driving sub-model on the road is constructed;

[0013] According to the traffic road network of the target area and the traffic flow conversion rules of vehicles at the intersection, a traffic flow conversion sub-model at the intersection is constructed.

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

[0015] Construct an initial directed topology graph based on the traffic network of the target area;

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

[0017] Determine the traffic flow conservation model at a preset time according to the density and traffic flow of vehicles;

[0018] Determine the direction of travel of the vehicle based on the traffic time for the vehicle to travel from the preset position to the intersection at the preset time and the travel speed of the vehicle;

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

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

[0021] Among them, according to the traffic flow conversion rules of vehicles at the intersection, the intersection flow conversion sub-model is constructed, including:

[0022] Determine the outflow volume of vehicles at a preset time according to the outflow volume at any position on the inflow road corresponding to the intersection;

[0023] Determine the inflow volume of vehicles at a preset time according to the inflow volume at any position on the outflow road corresponding to the intersection;

[0024] According to the outflow, inflow and traffic flow conversion rules, the intersection traffic flow conversion sub-model is determined.

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

[0026] The first rule is used to characterize that the first number of vehicles flowing out of the incoming road is consistent with the second number of vehicles flowing into the outgoing road;

[0027] The second rule is used to indicate that when the vehicle passes through the intersection, the number of third vehicles flowing out of the incoming road meets the first preset threshold;

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

[0029] According to an embodiment of the present disclosure, a traffic flow conversion sub-model at an intersection is determined according to outgoing traffic, incoming traffic and traffic flow conversion rules, including:

[0030] When it is determined that the outflow volume is less than or equal to the inflow volume, the vehicle volume of the inflow road is determined according to the first rule and the second rule;

[0031] When it is determined that the outflow volume is greater than the inflow volume, the vehicle volume of the outflow road is determined according to the third rule;

[0032] According to the traffic flow of the incoming road and the traffic flow of the outgoing road, the intersection traffic conversion sub-model is obtained.

[0033] According to an embodiment of the present disclosure, the method for determining traffic flow in a tidal lane further includes:

[0034] Input the vehicle entry road data into the initial urban traffic network model and output the initial predicted traffic flow data;

[0035] Adjust parameters based on the initial predicted traffic flow data and vehicle exit road data;

[0036] When the initial predicted traffic flow data and the vehicle exit road data are determined to be consistent, the target city traffic network model is obtained.

[0037] According to an embodiment of the present disclosure, based on the predicted traffic flow data, determining the congestion status of vehicles on a tidal lane includes:

[0038] When it is determined that the predicted traffic flow data meets the second preset threshold, it is determined that the vehicle is not in congestion on the tidal lane.

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

[0040] An acquisition module, used to acquire real traffic flow data of a target area within a preset time period, wherein the real traffic flow data includes data of vehicles entering a road and data of vehicles leaving a road;

[0041] A construction module is used to construct a target directed topology graph according to a traffic road network of a target area and a tidal lane setting strategy of the target area, wherein the traffic road network includes a plurality of road sections, and the road sections are composed of roads and intersections;

[0042] A prediction module is used to input vehicle entry road data and a target directed topology graph into a target urban traffic network model, and 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; the initial urban traffic network model is constructed based on the driving behavior of vehicles on the road and the traffic flow conversion rules of vehicles at intersections; and

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

[0044] The third aspect of the present disclosure provides an electronic device, comprising: one or more processors; and 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 above-mentioned tidal lane traffic flow determination method.

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

[0046] The fifth aspect of the present disclosure also provides a computer program product, including a computer program, which implements the above-mentioned tidal lane traffic flow determination method when executed by a processor.

[0047] According to the embodiments of the present disclosure, a target directed topology graph is constructed through the traffic road network of the target area and the tidal lane setting strategy of the target area, and then combined with the vehicle entry road data, the target city traffic network model is used to predict the traffic flow data. The macro model, that is, the target city traffic network model, is used to study the vehicle flow in the target city traffic road network, which requires less data, has high calculation efficiency, and high prediction accuracy; the rationality of setting up tidal lanes is evaluated through the congestion of vehicles on tidal lanes, which is conducive to determining the appropriate tidal lane setting strategy and effectively alleviating the city's traffic congestion problem. BRIEF DESCRIPTION OF THE DRAWINGS

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

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

[0050] Figure 2 A flowchart of a method for determining traffic flow in a tidal lane according to an embodiment of the present disclosure is schematically shown;

[0051] FIG3( a) schematically shows a schematic diagram of an initial directed topology graph according to an embodiment of the present disclosure;

[0052] FIG3( b ) schematically shows a three-lane road diagram according to an embodiment of the present disclosure;

[0053] FIG3( c ) schematically shows a schematic diagram of an intersection according to an embodiment of the present disclosure;

[0054] Figure 4 A simplified network model diagram of a crossroad according to an embodiment of the present disclosure is schematically shown;

[0055] Figure 5 A flowchart of a method for determining traffic flow in a tidal lane according to another embodiment of the present disclosure is schematically shown;

[0056] 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; and

[0057] Figure 7 A block diagram of an electronic device suitable for implementing a method for determining traffic flow in a tidal lane according to an embodiment of the present disclosure is schematically shown. DETAILED DESCRIPTION

[0058] Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of the present disclosure. In the following detailed description, for ease of explanation, many specific details are set forth to provide a comprehensive understanding of the embodiments of the present disclosure. However, it is apparent that one or more embodiments may also be implemented without these specific details. In addition, in the following description, descriptions of known structures and technologies are omitted to avoid unnecessary confusion of the concepts of the present disclosure.

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

[0060] All terms (including technical and scientific terms) used herein have the meanings commonly understood by those skilled in the art, unless otherwise defined. It should be noted that the terms used herein should be interpreted as having a meaning consistent with the context of this specification, and should not be interpreted in an idealized or overly rigid manner.

[0061] When using expressions such as "at least one of A, B, and C, etc.", they should generally be interpreted according to the meaning of the expression commonly understood by those skilled in the art (for example, "a system having at least one of A, B, and C" should include but is not limited to a system having A alone, B alone, C alone, A and B, A and C, B and C, and / or A, B, C, etc.).

[0062] In the technical solution of the present disclosure, the collection, storage, use, processing, transmission, provision, disclosure and application of the data involved (including but not limited to user personal information) comply with the provisions of relevant laws and regulations, take necessary confidentiality measures, and do not violate public order and good morals.

[0063] In the technical solution of the embodiment of the present disclosure, the user's authorization or consent is obtained before obtaining or collecting the user's personal information.

[0064] In the process of implementing the present disclosure, it is found that the existing traffic flow prediction methods are mostly based on neural network algorithms, with multi-layer perceptrons and long short-term memory networks as the key research objects to train traffic flow prediction models; specifically, firstly, a large amount of traffic flow data is collected, and then the data set is divided, and training, testing, and verification data sets are constructed. Combined with the constructed traffic flow prediction model, model training is carried out, and multiple model adjustments or parameter adjustments will be made during the training period to achieve the target expectations. In addition, the traffic flow prediction methods based on time series, such as autoregression, autoregression-moving average and other models are widely used. This type of method predicts traffic flow data by constructing a relationship function between time and traffic flow.

[0065] The existing traffic flow prediction methods, such as neural network-based training models, require huge data sets and long training time, have high hardware requirements, and the models are highly dependent on data. In new scenarios, the models need to be retrained, which is costly. The prediction methods based on time series have a single research dimension, high requirements for raw data, and poor prediction accuracy.

[0066] An embodiment of the present disclosure provides a method for determining traffic flow in a tidal lane, comprising: obtaining real traffic flow data of a target area within a preset time period, wherein the real traffic flow data includes vehicle entry road data and vehicle exit road data; constructing a target directed topology graph according to the traffic road network of the target area and the tidal lane setting strategy of the target area, wherein the traffic road network includes multiple road sections, and the road sections are composed of roads and intersections; inputting the vehicle entry road data and the target directed topology graph into a target city traffic network model, and outputting predicted traffic flow data, wherein the target city traffic network model is obtained by adjusting parameters of an initial city traffic network model according to the real traffic flow data; the initial city traffic network model is constructed based on the driving behavior of vehicles on the road and the traffic flow conversion rules of vehicles at intersections; and determining the congestion condition of vehicles on the tidal lane based on the predicted traffic flow data.

[0067] Figure 1 The 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.

[0068] like Figure 1 As shown, the application scenario 100 according to this embodiment may 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 used to provide a medium for a communication link between the first terminal device 101, the second terminal device 102, the third terminal device 103, and the server 105. The network 104 may include various connection types, such as wired, wireless communication links, or optical fiber cables, etc.

[0069] The user may 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 may 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 may be various electronic devices having display screens and supporting web browsing, including but not limited to smart phones, tablet computers, laptop computers, desktop computers, and the like.

[0071] The server 105 may be a server that provides various services, such as a background management server (only as an example) that provides support for websites browsed by users using the first terminal device 101, the second terminal device 102, and the third terminal device 103. The background management server may analyze and process the received data such as user requests, and feed back the processing results (such as web pages, information, or data obtained or generated according to user requests) to the terminal device.

[0072] It should be noted that the tidal lane traffic flow determination method provided in the embodiment of the present disclosure can generally be executed by the server 105. Accordingly, the tidal lane traffic flow determination device provided in the embodiment of the present disclosure can generally be set in the server 105. The tidal lane traffic flow determination method provided in the embodiment of the present disclosure can also be executed by a server or server cluster that is different from the server 105 and can communicate with the first terminal device 101, the second terminal device 102, the third terminal device 103 and / or the server 105. Correspondingly, the tidal lane traffic flow determination device provided in the embodiment of the present disclosure can also be set in a server or server cluster that is different from the server 105 and can communicate 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 embodiment is only for illustration. Any number of terminal devices, networks and servers may be provided according to implementation requirements.

[0074] The following will be based on Figure 1 The scene described by Figure 2 to Figure 5 The method for determining the traffic flow in a tidal lane of the disclosed embodiment is described in detail.

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

[0076] like Figure 2 As shown, the tidal lane traffic flow determination method 200 of this embodiment includes operations S210 to S240.

[0077] In operation S210, real traffic flow data of a target area within a preset time period is acquired, wherein the real traffic flow data includes data of vehicles entering a road and data of vehicles leaving a road.

[0078] According to an embodiment of the present disclosure, the preset time period may be one day, two days, three days, one month, two months, etc., which is not specifically limited here. The target area may 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 road network of the target area and a tidal lane setting strategy of the target area, wherein the traffic road network includes a plurality of road sections, and the road sections are composed of roads and intersections.

[0080] According to an embodiment of the present disclosure, the traffic road network of the target area can be obtained through a road network database. The tidal lane setting strategy of the target area may include adjusting the traffic light change rules of one or some intersections, or setting one or more roads whose driving directions change over time. Abstract modeling can be performed by combining the traffic road network of the target area and the tidal lane setting strategy of the target area, where the nodes in the target directed topology graph represent the intersections in the traffic road network, and the directed line segments in the target directed topology graph represent the roads in the traffic road network.

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

[0082] According to the disclosed embodiment, the driving behavior of a vehicle on a road may include the traffic density of the vehicle on the road and the driving direction of the vehicle on the road. The traffic flow conversion rules of vehicles at intersections may include the conservation rules of the number of vehicles flowing out of the road and the number of vehicles flowing into the road and the maximum traffic efficiency rule of the road vehicles.

[0083] According to the disclosed embodiment, the initial urban traffic network model can be a vehicle driving sub-model on the road first constructed according to the driving behavior of vehicles on the road and the traffic network of the target area; and then a traffic flow conversion sub-model at the intersection is constructed according to the traffic flow conversion rules of vehicles at the intersection and the traffic network of the target area. The initial urban traffic network model is composed of the vehicle driving sub-model on the road and the traffic flow conversion sub-model at the intersection. The model parameters of the vehicle driving sub-model on the road and the traffic flow conversion sub-model at the intersection of the initial urban traffic network model are adjusted according to the real traffic data to obtain the target urban traffic network model.

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

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

[0086] According to the disclosed embodiment, a target directed topology graph is constructed through the traffic road network of the target area and the tidal lane setting strategy of the target area, and then combined with the vehicle entry road data, the target city traffic network model is used to predict the traffic flow data. The macro model, that is, the target city traffic network model, is used to study the vehicle flow in the target city traffic road network, which requires less data, has high calculation efficiency, and high prediction accuracy; the rationality of setting up tidal lanes is evaluated through the congestion of vehicles on tidal lanes, which is conducive to determining the appropriate tidal lane setting strategy and effectively alleviating the city's traffic congestion problem.

[0087] According to an embodiment of the present disclosure, the initial urban traffic network model may include: a vehicle travel sub-model on the road and a traffic flow conversion sub-model at the intersection.

[0088] The method for determining the traffic flow in a tidal lane may further include, in addition to the above operations S210 to S240:

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

[0090] According to the disclosed embodiment, the traffic road network of the target area can be obtained through the road network database. The driving behavior of vehicles on the road may include vehicle density, traffic volume, vehicle speed, and vehicle driving direction. The traffic flow conversion rules of vehicles at intersections can be determined according to the road traffic efficiency by considering the situation that the vehicle changes its driving direction after entering the road and passing through the intersection.

[0091] According to the embodiments of the present disclosure, by constructing a sub-model of vehicle travel on the road, the traffic flow on the road can be simulated, and by constructing a sub-model of intersection flow conversion, the movement patterns of vehicles at the intersection can be analyzed and the traffic flow between intersections can be simulated.

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

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

[0094] According to the traffic road network of the target area, an initial directed topology map is constructed; for each road section in the initial directed topology map: according to the vehicle density and traffic volume, the traffic flow conservation model at the preset time is determined; according to the traffic time for the vehicle to travel from the preset position to the intersection at the preset time and the vehicle's driving speed, the vehicle's driving direction is determined; according to the traffic flow conservation model and the vehicle's driving direction, a vehicle driving sub-model on the road is obtained.

[0095] For example, the traffic network of the target area can be abstractly modeled to obtain an initial directed topology graph, as shown in Figure 3(a). The initial directed topology graph can be recorded as in It can represent a set of roads. It can represent a set of nodes, and each node can represent an intersection in a traffic road network. In FIG3(a), a directed line segment can represent a road, and a circle in the middle of the road can represent a node. Nine nodes and directed line segments associated with the nodes are schematically shown, which is only exemplary and not intended to limit the scope of the present disclosure.

[0096] As shown in Figure 3(b), on each road In the example, there are three types of vehicles: left-turning, straight-going, and right-turning. The road entrance can be denoted as Γ o , all three types of vehicles can enter the lane from the entrance, and the edges of both sides of the road are Γ c , the exits for right-turn, straight-going and left-turn vehicles on the road are The dashed dividing line in the middle of the road can indicate that the three types of vehicles can change lanes at will, and conversely, the solid dividing line can indicate that the vehicles cannot change lanes. After the vehicle enters the road, the three types of vehicles can change lanes at will, but near the exit, the vehicle is not allowed to change lanes, but should enter the corresponding waiting lane.

[0097] At every intersection In the traffic control system, the vehicles in each direction are controlled by traffic lights. For an intersection, as shown in Figure 3(c).

[0098] The traffic flow conservation model can be expressed as follows:

[0099]

[0100] Among them, ρ m,l (x,y,t)∈[0,ρ max ], which can be expressed as the density of the lth type of vehicle on the mth road at time t; F m ,l (ρ)=F m,l (x,y,t)=ρ m,l (x,y,t)v m,l(x, y, t) can be expressed as the traffic flow of the lth vehicle on the mth road at time t; v m,l (x, y, t) can be expressed as the speed of the lth vehicle on the mth road; the speed of the vehicle can depend on the vehicle density; and when the traffic density is zero or the maximum traffic density, F m,l (0)|=|F m,l (ρ max )|=0, when ρ=σ, the traffic flow|F m,l (ρ)| takes the maximum value, where σ is called the critical density.

[0101] According to the direction of the vehicle in the two-dimensional road, the travel time of the lth vehicle on the mth road at the position (x, y) per unit distance can be recorded as c m,l (x, y, t), then the traffic time φ for the lth vehicle on the mth road to travel from the position (x, y) to the exit at time t is m,l (x, y, t) satisfies the following Eikonal equation (2):

[0102]

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

[0104]

[0105] Among them, U m,l (x,y,t)=‖v m,l (x,y,t)‖ can be expressed as the speed of the lth vehicle on the mth road.

[0106] According to the embodiment of the present disclosure, a vehicle driving sub-model on the road is constructed through the traffic road network of the target area and the driving behavior of vehicles on the road, which can truly simulate the traffic flow operation status on the road and is conducive to accurately predicting the traffic flow operation status on the road.

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

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

[0109] Among them, according to the traffic flow conversion rules of vehicles at the intersection, a traffic flow conversion sub-model of the intersection is constructed, which can include: determining the outflow flow of vehicles at a preset time according to the outflow flow at any position on the inflow road corresponding to the intersection; determining the inflow flow of vehicles at a preset time according to the inflow flow at any position on the outflow road corresponding to the intersection; determining the intersection traffic flow conversion sub-model according to the outflow flow, inflow flow and traffic flow conversion rules.

[0110] According to an embodiment of the present disclosure, the traffic flow conversion rule may include: a first rule, a second rule and a third rule.

[0111] The first rule is used to characterize 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 characterize that when the vehicles pass through the intersection, the third number of vehicles flowing out of the inflow road meets the first preset threshold; the third rule is used to characterize that the preset number of vehicles flowing into the outflow road flows out of the inflow road according to the preset ratio. The first preset threshold is determined according to the number of vehicles when the actual vehicles pass through the intersection. The preset number of vehicles can be Q vehicles. The preset ratio can be determined according to the preset number of vehicles and the road.

[0112] For example, when considering the traffic flow conversion at each intersection, the intersection can be simplified as follows Figure 4 The network model shown includes three incoming roads (1—straight ahead, 2—right turn, 3—left turn) and one outgoing road 4.

[0113] The number of vehicles flowing out of roads 1, 2, and 3 is equal to the number of vehicles flowing into road 4. When vehicles pass through the intersection, the number of vehicles flowing out of roads 1, 2, and 3 is always maximized. Among the Q vehicles flowing into road 4, the vehicles flowing out of roads 1, 2, and 3 are q1Q, (1-q1-q2)Q, and q2Q, respectively, and the ratios are q1: (1-q1-q2): q2. Among them, q1 can be expressed as the ratio of the vehicles flowing out of road 1 to the preset number of vehicles flowing into and out of the flow road; q2 can be expressed as the ratio of the vehicles flowing out of road 3 to the preset number of vehicles flowing into and out of the flow road.

[0114] According to the embodiments of the present disclosure, a traffic flow conversion sub-model at an intersection is determined based on outbound traffic, inbound traffic and traffic flow conversion rules, which can analyze the movement patterns of vehicles at intersections and simulate the traffic flow conditions between intersections, thereby facilitating accurate prediction of the traffic flow conditions between intersections.

[0115] According to an embodiment of the present disclosure, determining the intersection traffic conversion sub-model according to the outflow, inflow and vehicle flow conversion rules may include:

[0116] When it is determined that the outflow volume is less than or equal to the inflow volume, the traffic volume of the inflow road is determined according to the first rule and the second rule; when it is determined that the outflow volume is greater than the inflow volume, the traffic volume of the outflow road is determined according to the third rule; according to the traffic volume of the inflow road and the traffic volume of the outflow road, the intersection traffic conversion sub-model is obtained.

[0117] For example, the flow transfer between roads connected to the intersection can be determined by solving the Riemann problem. The maximum outflow rate can be defined as follows:

[0118]

[0119] The total maximum outflow rate of the i-th type of vehicle at the intersection at time t can be expressed as follows:

[0120]

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

[0122] Similarly, at the entrance of road 4 (x, y)∈Γ o At , the corresponding maximum inflow and total maximum inflow can be defined as shown in the following equations (6) and (7), respectively:

[0123]

[0124]

[0125] When the total outflow of all vehicles passing through the intersection (i.e., the traffic volume of right-turning, straight-going, and left-turning vehicles) is less than or equal to the maximum inflow of road 4, as shown in the following formula (8), the vehicles on roads 1, 2, and 3 can always pass through the entrance at the maximum outflow, and the actual traffic volume can be shown in the following formula (9):

[0126]

[0127]

[0128] Accordingly, the actual inflow of road 4 can be expressed as follows (10) to (11):

[0129]

[0130]

[0131] When the total outflow of all vehicles passing through the intersection (i.e., the traffic volume of right-turning, straight-going, and left-turning vehicles) is greater than the maximum inflow of road 4, as shown in the following formula (12), there is at least one road where vehicles cannot flow out according to the corresponding maximum traffic volume. The actual inflow of road 4 can be shown in the following formula (13):

[0132]

[0133]

[0134] The outflow of vehicles on roads 1, 2, and 3 can be divided into the following three situations:

[0135] When the following equations (14) to (15) are satisfied,

[0136]

[0137]

[0138] The actual outflow of roads 1, 2, and 3 are shown in the following equations (16) to (18):

[0139]

[0140]

[0141]

[0142] When the following equations (19) to (20) are satisfied,

[0143]

[0144]

[0145] The actual outflow of roads 1, 2, and 3 are shown in the following equations (21) to (23):

[0146]

[0147]

[0148]

[0149] When the following equations (24) to (25) are satisfied,

[0150]

[0151]

[0152] The actual outflow of roads 1, 2, and 3 are shown in the following equations (26) to (28):

[0153]

[0154]

[0155]

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

[0157] According to the embodiments of the present disclosure, based on the mass conservation rule of the number of vehicles flowing out and flowing into the road, the rule of maximum traffic efficiency when vehicles pass through an intersection, and the rule of equal proportion of incoming and outgoing vehicles, it is possible to analyze the movement patterns of vehicles at intersections and simulate the traffic flow conditions between intersections, which is conducive to accurately predicting the traffic flow conditions between intersections.

[0158] According to an embodiment of the present disclosure, the method for determining traffic flow in a tidal lane may further include:

[0159] The vehicle entry road data is input into the initial urban traffic network model, and the initial predicted traffic flow data is output; the parameters are adjusted based on the initial predicted traffic flow data and the vehicle exit road data; when it is determined that the initial predicted traffic flow data and the vehicle exit road data are consistent, the target urban traffic network model is obtained.

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

[0161] According to the embodiment of the present disclosure, the parameters of the initial urban traffic network model are adjusted according to the real traffic data, and the obtained target urban traffic network model has high prediction accuracy, which is conducive to determining the congestion status of vehicles on tidal lanes.

[0162] According to an embodiment of the present disclosure, determining the congestion status of vehicles on a tidal lane based on predicted traffic flow data may include:

[0163] When it is determined that the predicted traffic flow data meets the second preset threshold, it is determined that the vehicle is not in congestion on the tidal lane.

[0164] According to an embodiment of the present disclosure, the second preset threshold value can be determined based on the traffic flow data of actual vehicles on the road. When the predicted traffic flow data is less than or equal to the second preset threshold value, the second preset threshold value can be satisfied.

[0165] According to the embodiments of the present disclosure, based on the predicted traffic data, the congestion condition of vehicles on the tidal lane can be determined. If congestion occurs, a suitable tidal lane setting strategy can be obtained through repeated numerical simulations, thereby alleviating the traffic congestion problem in the traffic network or the city.

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

[0167] like Figure 5 As shown, the method for determining the traffic flow in the tidal lane of this embodiment may include selecting a traffic network for study, then establishing a directed topology map, constructing a vehicle driving model on the road and a traffic flow conversion model at the intersection according to the directed topology map, and the vehicle driving model on the road and the traffic flow conversion model at the intersection together form an urban traffic network model. The entire traffic model, through the mutual influence of traffic flows with different orientations on the road, together constitutes a strongly coupled set of equations; finally, the model parameters are further corrected in combination with actual data, and numerical simulation is performed. After the model adjustment is completed, the traffic flow movement of the tidal lane can be predicted by changing the parameters in the urban road grid model. By constantly changing the design strategy of the tidal lane and the predicted traffic flow movement, the setting method of the "tidal lane" is determined. Among them, the actual data can be the real traffic flow data of the selected traffic network area for study within a preset time period.

[0168] According to the embodiments of the present disclosure, a vehicle driving model on the road can be constructed according to the directed topology graph and the driving behavior of the vehicle on the road; and an intersection traffic conversion model can be constructed according to the directed topology graph and the traffic conversion rules of the vehicle at the intersection. Among them, constructing the vehicle driving model on the road can be similar to the method of constructing the vehicle driving sub-model on the road, and constructing the intersection traffic conversion model can be similar to the method of constructing the intersection traffic conversion sub-model.

[0169] For example, take a road section with four two-way roads and a crossroads. Each road has three types of vehicles: left-turning, straight-going, and right-turning. First, the road section can be abstractly represented as a directed topological graph, where the road set is The node set is Based on the real traffic flow data of the road section for a period of time, the corresponding inflow data during this period is determined. According to the established vehicle driving model on the road and the intersection flow conversion model, the traffic flow changes of vehicles in each direction at the intersection of the road section during this period are calculated. Then, by adjusting the parameters in the model, the simulated traffic flow data is consistent with the actual traffic flow data. Finally, by changing the changing rules of traffic lights, the vehicle traffic efficiency of the road section is maximized, thereby achieving the purpose of setting up a "tidal lane".

[0170] Based on the above-mentioned tidal lane traffic flow determination method, the present disclosure also provides a tidal lane traffic flow determination device. Figure 6 The device is described in detail.

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

[0172] like Figure 6 As shown, the tidal lane traffic flow determination device 600 of this embodiment includes an acquisition module 610, a construction module 620, a prediction module 630 and a determination module 640.

[0173] The acquisition module 610 is used to acquire the real traffic flow data of the target area within a preset time period, wherein the real traffic flow data includes vehicle entry road data and vehicle exit road data. In one embodiment, the acquisition module 610 can be used to perform the operation S210 described above, which will not be repeated here.

[0174] The construction module 620 is used to construct a target directed topology graph according to the traffic road network of the target area and the tidal lane setting strategy of the target area, wherein the traffic road network includes multiple road sections, and the road sections are composed of roads and intersections. In one embodiment, the construction module 620 can be used to perform the operation S220 described above, which will not be repeated here.

[0175] The prediction module 630 is used to input the vehicle entry road data and the target directed topology graph into the target urban traffic network model, and output the predicted traffic flow data, wherein the target urban traffic network model is obtained by adjusting the parameters of the 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 the vehicle on the road and the traffic flow conversion rules of the vehicle at the intersection. In one embodiment, the prediction module 630 can be used to perform the operation S230 described above, which will not be repeated here.

[0176] The determination module 640 is used to determine the congestion status of vehicles on the tidal lane based on the predicted traffic flow data. In one embodiment, the determination module 640 can be used to perform the operation S240 described above, which will not be repeated here.

[0177] According to an embodiment of the present disclosure, any multiple modules in the acquisition module 610, the construction module 620, the prediction module 630 and the determination module 640 can be combined in one module for implementation, or any one of the modules can be split into multiple modules. Alternatively, at least part of the functions of one or more of these modules can be combined with at least part of the functions of other modules and implemented in one module. According to an embodiment of the present disclosure, at least one of the acquisition module 610, the construction module 620, the prediction module 630 and the determination 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 a chip, a system on a substrate, a system on a package, an application-specific integrated circuit (ASIC), or can be implemented by hardware or firmware such as any other reasonable way of integrating or packaging the circuit, or implemented in any one of the three implementation methods of software, hardware and firmware or in any appropriate combination of any of them. Alternatively, at least one of the acquisition module 610, the construction module 620, the prediction module 630 and the determination module 640 can be at least partially implemented as a computer program module, and when the computer program module is run, the corresponding function can be performed.

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

[0179] like Figure 7 As shown, 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 a program stored in a read-only memory (ROM) 702 or a program loaded from a storage part 708 into a random access memory (RAM) 703. The processor 701 may include, for example, a general-purpose microprocessor (e.g., a CPU), an instruction set processor and / or a related chipset and / or a special-purpose microprocessor (e.g., an application-specific integrated circuit (ASIC)), etc. The processor 701 may also include an onboard memory for caching purposes. The processor 701 may include a single processing unit or multiple processing units for performing different actions of the method flow according to an embodiment of the present disclosure.

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

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

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

[0183] According to an embodiment of the present disclosure, the computer-readable storage medium may be a non-volatile computer-readable storage medium, for example, may include but is not limited to: a portable computer disk, 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 disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, an apparatus or a device. For example, according to an embodiment of the present disclosure, a computer-readable storage medium may include the ROM 702 and / or RAM 703 described above and / or one or more memories other than ROM 702 and RAM 703.

[0184] The embodiment of the present disclosure also includes a computer program product, which includes a computer program, and the computer program contains program code for executing the method shown in the flowchart. When the computer program product is run in a computer system, the program code is used to enable the computer system to implement the method provided by the embodiment of the present disclosure.

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

[0186] In one embodiment, the computer program may rely on tangible storage media such as optical storage devices, magnetic storage devices, etc. In another embodiment, the computer program may also be transmitted and distributed in the form of signals on a network medium, and downloaded and installed through the communication part 709, and / or installed from the removable medium 711. The program code contained in the computer program may be transmitted using any appropriate network medium, including but not limited to: wireless, wired, etc., or any suitable combination of the above.

[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 removable medium 711. When the computer program is executed by the processor 701, the above functions defined in the system of the embodiment of the present disclosure are performed. According to the embodiment of the present disclosure, the system, device, means, module, unit, etc. described above can be implemented by a computer program module.

[0188] According to an embodiment of the present disclosure, the program code for executing the computer program provided by the embodiment of the present disclosure can be written in any combination of one or more programming languages. Specifically, these computing programs can be implemented using high-level process and / or object-oriented programming languages, and / or assembly / machine languages. Programming languages ​​include, but are not limited to, Java, C++, python, "C" language or similar programming languages. The program code can be executed entirely on the user computing device, partially on the user device, partially on the remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device can be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computing device (for example, using an Internet service provider to connect through the Internet).

[0189] The flow charts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each box in the flow chart or block diagram can represent a module, a program segment, or a part of a code, and the above-mentioned module, program segment, or a part of a code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flow chart, and the combination of the boxes in the block diagram or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0190] It will be appreciated by those skilled in the art that the features described in the various embodiments and / or claims of the present disclosure may be combined and / or combined in a variety of ways, even if such combinations and / or combinations are not explicitly described in the present disclosure. In particular, the features described in the various embodiments and / or claims of the present disclosure may be combined and / or combined in a variety of ways without departing from the spirit and teachings of the present disclosure. All of these combinations and / or combinations fall within the scope of the present disclosure.

[0191] The embodiments of the present disclosure are described above. However, these embodiments are only for illustrative purposes and are not intended to limit the scope of the present disclosure. Although the embodiments are described above separately, this does not mean that the measures in the various embodiments cannot be used in combination to advantage. The scope of the present disclosure is defined by the attached claims and their equivalents. Without departing from the scope of the present disclosure, those skilled in the art may make a variety of substitutions and modifications, which should all fall within the scope of the present disclosure.

Claims

1. A method for determining traffic flow in a tidal lane, comprising: Acquire real traffic flow data of a target area within a preset time period, wherein the real traffic flow data includes data of vehicles entering a road and data of vehicles leaving a road; According to the traffic road network of the target area and the tidal lane setting strategy of the target area, a target directed topological graph is constructed, wherein the traffic road network includes a plurality of road sections, and the road sections are composed of roads and intersections; Input the vehicle entry road data and the target directed topological graph into a target urban traffic network model, and 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 the vehicle on the road and the traffic flow conversion rule of the vehicle at the intersection; and Based on the predicted traffic flow data, the congestion condition of the vehicles on the tidal lane is determined.

2. The method according to claim 1, wherein: The initial urban traffic network model includes: a vehicle travel sub-model on the road and a traffic flow conversion sub-model at the intersection; The method further comprises: Constructing a vehicle driving sub-model on the road according to the traffic road network of the target area and the driving behavior of the vehicle on the road; The intersection traffic conversion sub-model is constructed according to the traffic road network of the target area and the traffic flow conversion rules of the vehicles at the intersection.

3. The method according to claim 2, wherein: The constructing a vehicle driving sub-model on the road according to the traffic road network of the target area and the driving behavior of the vehicle on the road comprises: Constructing an initial directed topology graph according to the traffic road network of the target area; For each of the road segments in the initial directed topological graph: Determining a vehicle flow conservation model at a preset time according to the vehicle density and vehicle flow; Determining the driving direction of the vehicle according to the traffic time for the vehicle to travel from the preset position to the intersection at the preset time and the driving speed of the vehicle; A vehicle travel sub-model on the road is obtained according to the vehicle flow conservation model and the travel direction of the vehicle.

4. The method according to claim 2, wherein: The road includes an inflow road and an outflow road; the intersection is provided between the inflow road and the outflow road; Wherein, constructing the intersection traffic conversion sub-model according to the traffic flow conversion rule of the vehicle at the intersection includes: Determining the outflow volume of the vehicle at a preset time according to the outflow volume at any position on the inflow road corresponding to the intersection; Determine the inflow volume of the vehicle at a preset time according to the inflow volume at any position on the outflow road corresponding to the intersection; The intersection traffic conversion sub-model is determined according to the outgoing traffic, the incoming traffic and the vehicle traffic conversion rule.

5. The method according to claim 4, wherein: The vehicle flow conversion rules include: a first rule, a second rule and a third rule; The first rule is used to characterize that a first number of vehicles flowing out of the inflow road is consistent with a second number of vehicles flowing into the outflow road; The second rule is used to indicate that when the vehicle passes through the intersection, the number of third vehicles flowing out of the incoming road meets a first preset threshold; The third rule is used to characterize that a preset number of vehicles flowing into the outflow road flow out from the inflow road according to a preset ratio.

6. The method according to claim 5, wherein: The determining the intersection traffic conversion sub-model according to the outgoing traffic, the incoming traffic and the traffic conversion rule comprises: When it is determined that the outflow volume is less than or equal to the inflow volume, determining the vehicle volume of the inflow road according to the first rule and the second rule; When it is determined that the outflow volume is greater than the inflow volume, determining the vehicle volume of the outflow road according to the third rule; The intersection traffic conversion sub-model is obtained according to the traffic volume of the incoming road and the traffic volume of the outgoing road.

7. The method according to claim 1, further comprising: Inputting the vehicle entry road data into the initial urban traffic network model, and outputting initial predicted traffic flow data; Adjusting parameters based on the initial predicted traffic flow data and the vehicle exit road data; When it is determined that the initial predicted traffic flow data is consistent with the vehicle exit road data, the target city traffic network model is obtained.

8. The method according to any one of claims 1 to 7, wherein: The determining, based on the predicted traffic flow data, a congestion condition of the vehicle on the tidal lane comprises: When it is determined that the predicted traffic flow data meets a second preset threshold, it is determined that the vehicle is in a non-congested condition on the tidal lane.

9. A tidal lane traffic flow determination device, comprising: An acquisition module, used to acquire real traffic flow data of a target area within a preset time period, wherein the real traffic flow data includes data of vehicles entering a road and data of vehicles leaving a road; A construction module, configured to construct a target directed topology graph according to a traffic road network of the target area and a tidal lane setting strategy of the target area, wherein the traffic road network includes a plurality of road sections, and the road sections are composed of roads and intersections; A prediction module, used for inputting the vehicle entry road data and the target directed topology graph into a target urban traffic network model, and outputting 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 the vehicle on the road and the traffic flow conversion rule of the vehicle at the intersection; and A determination module is used to determine the congestion condition of the vehicles on the tidal lane based on the predicted traffic flow data.

10. An electronic device, comprising: one or more processors; a storage device for storing one or more programs, When the one or more programs are executed by the one or more processors, the one or more processors are enabled to execute the method according to any one of claims 1 to 8.

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