A basic road section traffic simulation method based on multi-lane cellular transmission model

Abstract

The present invention discloses a basic road section traffic simulation method based on a multi-lane cellular transmission model, comprising the following steps: according to the road topology in reality, a road cell node table and a road cell connection table of a simulated road network are established; the transfer flow between road cells is calculated; the transfer flow between road cells is distributed among lane cells according to a multi-lane flow distribution algorithm, the transfer flow between lane cells is obtained, and the input and output flows of lane cells are determined; according to the input flow and output flow of each lane cell, the density, flow and speed of lane cells and road cells are calculated in combination with the basic graph model of the lane; according to the density, flow and speed of road cells at different times, a time-space change heat map is drawn. The present invention has simple steps and efficient calculation, has important practical significance for realizing traffic simulation of basic road sections, and provides method support for traffic simulation of traffic management systems.

Description

Technical Field

[0001] The present invention relates to a basic road section traffic simulation method based on a multi-lane cellular transmission model, and belongs to the field of traffic simulation. Background Art

[0002] In the field of traffic engineering, traffic simulation technology has gradually become one of the commonly used technical means in active traffic control. Traffic simulation is a mathematical abstraction of complex traffic flow behavior and is an indispensable basic function of the advanced traffic monitoring and control system of smart roads. Traffic simulation can be divided into two categories: macro traffic simulation and micro traffic simulation. Macro traffic simulation does not consider the specificity of individual vehicle behavior, focuses on the aggregate characteristics of traffic flow, regards traffic flow as an inviscid fluid, and analyzes traffic flow dynamics with the help of fluid mechanics methods. Micro traffic simulation is based on individual driving characteristics and focuses on the differences in lateral and longitudinal driving behaviors of individual drivers to the surrounding environment. Micro traffic simulation is difficult to use directly in active traffic control due to its defects such as many input parameters, difficult parameter calibration, and the need for preheating. Macro traffic simulation is widely used in traffic control due to its simple structure, fast running speed, and the ability to depict complex traffic phenomena. The most classic macro traffic simulation model is the cellular transmission model. The cellular transmission model uses each section of the road as a cell, simulates traffic operation based on transmission rules, and can depict the traffic conditions of free flow and dense flow.

[0003] An analysis of existing research shows that the cellular transmission model currently used in road traffic simulation usually takes a section of road as a cell, and does not consider the differences between lanes within the cell. This type of model assumes that the traffic flow in different lanes is homogeneous, and does not consider the impact of vehicle lane changes. Therefore, it is difficult to apply to refined active traffic control, such as lane-by-lane speed limits. Seeking a new type of traffic simulation technology that has the advantages of simple macro-traffic simulation structure and rapid operation, while taking into account the characteristics of vehicle lane changes has become a topic that technical personnel in this field need to continue to study. To this end, the present invention provides a basic road section traffic simulation method based on a multi-lane cellular transmission model. Summary of the invention

[0004] In order to solve the above problems, the present invention provides a basic road section traffic simulation method based on a multi-lane cellular transmission model. The purpose of the present invention is to use extensive radar and other sensor data on the road as the current status of traffic flow, and through an extended cellular transmission model, to deduce future traffic flow changes under different traffic control, and provide support for refined traffic management. To achieve this purpose, the present invention provides a high-speed kilometer traffic simulation method based on a multi-lane cellular transmission model, comprising the following steps:

[0005] (1) According to the topological structure of the actual roads, the road cell node table and road cell connection table of the simulated road network are established;

[0006] (2) Calculate the transfer flow between road cells;

[0007] (3) Allocate the transfer flow between road cells among lane cells according to the multi-lane flow allocation algorithm, obtain the transfer flow between lanes, and determine the input and output flows of lane cells;

[0008] (4) According to the input flow and output flow of each lane cell in the current simulation step, combined with the basic graph model of the lane, the density, flow and speed of each lane cell in the current simulation step are calculated, and the average density, flow and speed of the road cells in the current simulation step are calculated and summarized;

[0009] (5) Draw a spatiotemporal heat map based on the density, flow, and speed of road cells at different simulation steps.

[0010] As a further improvement of the present invention, the step (1) includes the following sub-steps:

[0011] (1.1) According to the layout of road detectors and speed limit information boards, the road is divided into multiple continuous sections, and each section of the road is regarded as a cell;

[0012] (1.2) According to the number of lanes on the road, each road cell is divided into multiple lane cells, and the initial vehicle density and basic graph model are set for each lane cell. All road cells containing lane cells are saved in the database as a road cell node table;

[0013] (1.3) According to the actual road topology, the adjacency table method is used to establish the connection relationship of road cells. Then, based on the connection relationship of road cells, the connection between lane cells is established. The connection relationship between road cells and lane cells is saved in the database as a road cell connection table.

[0014] As a further improvement of the present invention, the step (2) includes the following sub-steps:

[0015] (2.1) Calculate the sending flow and receiving flow of lane cells according to the density of lane cells and the basic graph model;

[0016] (2.2) The sending flow and receiving flow of all lane cells in the road cell are summed up respectively as the sending flow and receiving flow of the road cell;

[0017] (2.3) Calculate the transfer flow Q between road cells. The calculation formula is:

[0018] Q = min{upstream road cells send traffic, downstream road cells receive traffic}.

[0019] As a further improvement of the present invention, the step (3) includes the following sub-steps:

[0020] (3.1) According to lane L i With Lane L j The number of lane intervals sets the impedance weight w for cross-lane traffic distribution i,j ,In principle, the more lanes are separated, the greater the impedance weight;

[0021] (3.2) Calculate the lane flow distribution of the basic road section area, where the basic road section area is the road cell C k Only connected to the road cell C k+1 The specific form is as follows Figure 2 As shown, C k Contains lane cell L i (i=1…n), where n is C k The number of lanes; C k+1 Contains lane cell L j (j=1…m), where m is C k+1 The number of lanes.

[0022] The impedance weight is used to construct the optimization equation for multi-lane flow distribution on the basic road section, and the flow between lanes is obtained by solving the optimization equation. The specific optimization equation is as follows:

[0023] Optimization goal:

[0024] constraint:

[0025]

[0026]

[0027] q i,j ≥0,i=1...n,j=1...m

[0028] In the formula, w i,j is the lane cell L i To lane cell L j The weight, q i,j is the lane cell L i To lane cell L j The transfer flow, s i is the lane cell L i The sending traffic, r j is the lane cell L j The received flow, Q k is the road cell C k to C k+1The optimization equation is a linear programming problem, which is solved by the simplex method to obtain the transfer flow between cells in each lane. Transfer flow q i,i is the lane cell L i Output flow and lane cell L j of input flow.

[0029] As a further improvement of the present invention, the calculation formula for the lane cell density in the current simulation step in step (4) is:

[0030] Lane cell density = [number of lane cell vehicles in the current simulation step + (input flow - output flow) * simulation step length] / lane cell length.

[0031] As a further improvement of the present invention, the step (5) draws a temporal and spatial variation heat map with time as the horizontal axis and the road cell position as the vertical axis.

[0032] Compared with the prior art, the basic road section traffic simulation method based on the multi-lane cellular transmission model of the present invention has the following technical effects:

[0033] (1) The present invention converts the distribution problem of multi-lane cellular transmission flow into a linear programming problem, which greatly reduces the complexity of multi-vehicle macroscopic traffic simulation and solves the problem of multi-lane macroscopic traffic simulation in engineering applications. It belongs to the application of operations research in the field of intelligent traffic simulation.

[0034] (2) The present invention can provide back-end data for the evolution of traffic status trends by outputting road density, flow and speed, and provide method and data support for traffic management systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 It is the overall flow chart of the method of the present invention;

[0036] Figure 2 Schematic diagram of road cells and lane cells;

[0037] Figure 3 It is the spatiotemporal map of road density. DETAILED DESCRIPTION

[0038] The present invention is further described in detail below with reference to the accompanying drawings and specific embodiments:

[0039] The present invention provides a basic road section traffic simulation method based on a multi-lane cellular transmission model. The purpose of the present invention is to use sensor data such as extensive radars on the road as the current traffic flow status, and through a multi-lane cellular transmission model, to deduce future traffic flow changes under different traffic control, so as to provide support for refined traffic management.

[0040] It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as generally understood by those skilled in the art in the art to which the present invention belongs. It should also be understood that terms such as those defined in common dictionaries should be understood to have meanings consistent with the meanings in the context of the prior art, and will not be interpreted with idealized or overly formal meanings unless defined as herein.

[0041] As an embodiment, a basic section on a road is selected for modeling. The road is 5 kilometers long with 4 lanes. The speed limits of the lanes are 120km / h, 120km / h, 100km / h, and 100km / h from inside to outside.

[0042] like Figure 1 As shown, a basic road section traffic simulation method based on a multi-lane cellular transmission model includes the following steps:

[0043] (1) According to the actual road topology structure, a road cell node table and a road cell connection table of the simulated road network are established, which specifically includes the following sub-steps:

[0044] (1.1) According to the layout of road detectors and speed limit information boards, every 100 meters is divided into a section as a road cell;

[0045] (1.2) Each road cell is divided into four lane cells. The initial vehicle density is set for each lane cell according to the road detector data. A triangular basic graph model is set for each road. The resulting road cell node table is shown in Table 1.

[0046] Table 1 Road cell node table

[0047]

[0048] (1.3) According to the road topological relationship, the adjacency list method is used to establish the connection relationship of road cells. Then, based on the connection relationship of road cells, the connection between lane cells is established. The connection relationship between road cells and lane cells is saved as a road cell connection table, as shown in Table 2.

[0049] Table 2 Road cell connection table

[0050] side Starting cell number End cell numbering 1 U1 U2 2 U2 U3 … … …

[0051] (2) Calculate the transfer flow Q between road cells, which specifically includes the following steps:

[0052] (2.1) According to the density of lane cells and the basic graph model, the sending flow and receiving flow of lane cells are calculated;

[0053] (2.2) The sending flow and receiving flow of all lane cells in the road cell are summed up respectively as the sending flow and receiving flow of the road cell;

[0054] (2.3) Calculate the total transfer flow Q between road cells, the calculation formula is:

[0055] Q = min{upstream road cells send traffic, downstream road cells receive traffic}.

[0056] (3) According to the multi-lane flow distribution algorithm, the transfer flow between road cells is distributed among the lane cells, the transfer flow between lane cells is obtained, and the input and output flows of the lane cells are determined.

[0057] (4) According to the input and output flow of lane cells in the current simulation step and combined with the basic graph model of the lane, the density, flow and speed of each lane cell in the current simulation step are calculated, and the average density, flow and speed of the road cells in the current simulation step are calculated and summarized.

[0058] (5) Based on the density, flow and speed of road cells at different simulation steps, a spatiotemporal heat map is drawn with time as the horizontal axis and the position of the road cells as the vertical axis, such as Figure 3 shown.

[0059] It should be pointed out that the description of the above embodiments is only used to help understand the method of the present application and its core idea. For ordinary technicians in this technical field, several improvements and modifications can be made to the present application without departing from the principles of the present application. These improvements and modifications are also within the scope of protection of the claims of the present application.

Claims

1. A basic road section traffic simulation method based on a multi-lane cellular transmission model, characterized in that: The specific steps are as follows: (1) According to the topological structure of the road, establish the road cell node table and road cell connection table of the simulation road network; (2) Calculate the transfer flow between road cells; (3) Allocate the transfer flow between road cells among lane cells according to the multi-lane flow allocation algorithm, obtain the transfer flow between lane cells, and determine the input and output flows of lane cells; (4) According to the input and output flow of lane cells in the current simulation step, combined with the basic graph model of the lane, the density, flow and speed of each lane cell in the current simulation step are calculated, and the average density, flow and speed of the road cells in the current simulation step are calculated and summarized; (5) Draw a temporal and spatial variation heat map based on the density, flow, and speed of road cells at different simulation steps; The step (1) comprises the following sub-steps: (1.1) Divide the road into multiple continuous segments, each segment being a road cell; (1.2) According to the number of lanes on the road, each road cell is divided into multiple lane cells, and the initial vehicle density and basic graph model are set for each lane cell. All road cells containing lane cells are saved in the database as a road cell node table; (1.3) According to the topological structure of the road, the adjacency table method is used to establish the connection relationship of the road cells. Then, based on the connection relationship of the road cells, the connection between the lane cells is established. The connection relationship between the road cells and the lane cells is saved in the database as a road cell connection table. The step (2) comprises the following sub-steps: (2.1) According to the density of lane cells and the basic graph model, the sending flow and receiving flow of lane cells are calculated; (2.2) The sending flow and receiving flow of all lane cells in the road cell are summed up respectively as the sending flow and receiving flow of the road cell; (2.3) Calculate the flow Q between road cells. The calculation formula is: Q = min{upstream road cells send flow, downstream road cells receive flow}; The step (3) comprises the following sub-steps: (3.1) The impedance weight of cross-lane flow distribution is set according to the number of lanes between two lanes. In principle, the more lanes there are between two lanes, the greater the impedance weight. (3.2) Using impedance weights, we construct an optimization equation for multi-lane flow distribution on the basic road section. By solving the optimization equation, we can obtain the transfer flow between lanes. The specific optimization equation is as follows: Optimization goal: constraint: q i,j ≥0,i=1...n,j=1...m In the formula, q i,j is the lane cell L i To lane cell L j The transfer flow, w i,j is the lane cell L i To lane cell L j The impedance weight, s i is the lane cell L i The sending traffic, r j is the lane cell L j The received flow, Q k is the road cell C k to C k+1 The total flow of road cell C k Only connected to the road cell C k+1 , C k Contains lane cell L i , i=1…n, n is C k The number of lanes, C k+1 Contains lane cell L j , j = 1…m, m is C k+1 The number of lanes; According to the input and output flow of the lane cell and the basic graph model of the lane, the density, flow and speed of each lane are calculated, and the average density, flow and speed of the road cell are summarized and calculated.

2. The basic road section traffic simulation method based on a multi-lane cellular transmission model according to claim 1 is characterized by: The calculation formula for the lane cell density in the current simulation step in step (4) is: Lane cell density = [number of lane cell vehicles in the current simulation step + (input flow - output flow) * simulation step length] / lane cell length.

3. The basic road section traffic simulation method based on a multi-lane cellular transmission model according to claim 1 is characterized by: In the step (4), the lane speed and flow are calculated after the lane density is input according to the basic graph model of the lane.

4. The basic road section traffic simulation method based on a multi-lane cellular transmission model according to claim 1 is characterized by: In the step (5), a temporal and spatial variation heat map is drawn with time as the horizontal axis and the road cell position as the vertical axis.

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