Multi-source data fused road network traffic flow macroscopic fundamental diagram construction and evaluation method

Abstract

The invention discloses a road network traffic flow macroscopic fundamental diagram construction and evaluation method fusing multi-source data. The method comprises the following steps: dividing a whole road network into two sub-road networks according to the data reliability degree; calculating the traffic flow density and speed of the sub-road network with high data reliability and the traffic flow speed of the sub-road network with low reliability by adopting a simple averaging method; estimating a distribution function of the traffic flow density of the whole road network by adopting the traffic flow density data of the sub-road network with high data reliability, and calculating the traffic flow density of the sub-road network with low reliability by adopting a probability weighted average method; and calculating the average traffic flow density and speed of the whole road network by combining the traffic flow densities and speeds of the two sub road networks, and constructing a road network traffic flow macroscopic basic diagram. According to the method, the error of the road network traffic flow macroscopic fundamental diagram parameter estimation value is effectively reduced, optimization of urban road network traffic design and reconstruction schemes is facilitated, reasonable and effective real-time control and induction strategies are formulated, and the method has application value for relieving urban road traffic congestion.

Description

Technical Field

[0001] The present invention relates to a method for constructing and evaluating a macro basic graph of road network traffic flow that integrates multi-source data, and belongs to the technical field of the intersection of urban road traffic control technology and data information processing technology. Background Art

[0002] Urban road traffic flow data (traffic flow speed, traffic density and traffic flow) are usually obtained by fixed detectors installed on the roadside, such as video detectors and coil sensors. Fixed detectors are usually placed on both sides of the road to obtain real-time traffic information. The accuracy and reliability of traffic flow data obtained by fixed detectors are closely related to their placement. In order to obtain accurate road section traffic flow data, detectors must be deployed at appropriate locations on the roadside. By spatially weighted averaging accurate road section traffic flow data, traffic flow data of the entire road network can be obtained, namely, road network traffic flow speed, road network traffic density and road network flow. However, due to the high installation and maintenance costs of fixed detectors, they are usually only installed on limited sections. Unlike fixed detectors, floating vehicles equipped with positioning systems can provide trajectory data when driving in the road network, and are a mobile data source. By combining the data obtained by floating vehicles and fixed detectors, the penetration rate of floating vehicles on the road can be estimated. If floating vehicles are evenly distributed in the road network and the penetration rate is known, the traffic data of the entire road network can be obtained by proportionally expanding the floating vehicle data.

[0003] Although the fusion of floating vehicle and fixed detector data can improve the accuracy of road network traffic data, when the traffic status of sections in the urban road network is very different, the floating vehicle data has large errors. Floating vehicles are usually taxis, buses or freight trucks, which have significant differences in travel patterns from cars in terms of temporal and spatial distribution, resulting in large differences in the penetration rate of floating vehicles in different sections, and the data obtained has large errors. In this case, compared with fixed detectors, floating vehicles are a less reliable data source in terms of data accuracy and reliability. Using low-reliability section traffic data to estimate road network traffic data results in large deviations in the estimated values ​​of road network traffic data, and low accuracy and reliability of the average flow-speed-density relationship of road network traffic flow. Summary of the invention

[0004] Purpose of the invention: The purpose of the present invention is to propose a method for constructing and evaluating a road network macro basic map that integrates multi-source data (including high-reliability and low-reliability data), uses road section traffic data with different measurement errors to predict the parameters of the road network macro basic map, and reduces the error of the estimated value of the road network macro basic map parameters. The proposed method can effectively reduce the error of the estimated value of the road network macro basic map parameters, reduce the discreteness of the relationship between urban road network traffic flow-speed-density, and help traffic design and management departments optimize urban road network traffic design and transformation plans, formulate reasonable and effective real-time control and induction strategies, and has application value in alleviating urban road traffic congestion.

[0005] Technical solution: The above purpose is achieved through the following technical solution:

[0006] In a first aspect, the present invention provides a method for constructing a macro basic graph of road network traffic flow by integrating multi-source data, comprising the following steps:

[0007] According to the reliability of traffic flow density data acquisition of road sections, the entire road network is divided into a first sub-road network consisting of core road sections and a second sub-road network consisting of non-core road sections; the reliability of traffic density flow data acquisition of core road sections is higher than that of non-core road sections;

[0008] For the first sub-network, according to the traffic flow speed u of the core section in time period t v.t and traffic flow density k v.t , the traffic flow speed of the first sub-network is calculated using the simple average method and traffic flow density

[0009] Calculate the overall distribution of traffic flow density on the road section in the t time period using the traffic flow density of the core road section in and are the expectation and variance of the normal distribution;

[0010] For the second sub-network, calculate the traffic flow density k of the non-core road segment w.t The probability density function of

[0011] The probability weighted average method is used to calculate the traffic flow density of the second sub-network. Among them l w is the length of the non-core section, n coverage is the number of non-core road sections; the traffic flow speed is calculated using a simple average method

[0012] Combine the traffic flow parameters of the first sub-network and the second sub-network to calculate the traffic flow speed of the entire network and road network traffic flow density

[0013] Traffic flow speed of road network and road network traffic flow density Construct a basic diagram of macroscopic traffic flow in the road network. in are the S parameter estimates of the basic graph, ε t is the measurement error.

[0014] Furthermore, the traffic flow speed of the first sub-network Traffic flow density Among them l v is the length of the core section, n core The number of core sections.

[0015] Furthermore, the expectation of the overall distribution of traffic flow density is variance where n core The number of core sections.

[0016] Furthermore, the traffic flow speed of the entire road network Traffic flow density of the entire road network l v is the length of the core section, n core The number of core sections.

[0017] Furthermore, the traffic flow speed of the road network and road network traffic flow density The basic diagram of the macroscopic traffic flow of the road network is constructed as follows: in and are the free flow speed, the congestion density, and the estimated value of the motion wave speed at the congestion density, ε t is the measurement error.

[0018] In a second aspect, the present invention provides a method for evaluating a macro basic graph of a road network by integrating multi-source data, comprising the following steps:

[0019] According to the method for constructing a macro basic graph of road network traffic flow by integrating multi-source data, the estimated parameter values ​​of the macro basic graph of road network traffic flow are obtained.

[0020] The parameter estimates Substitute the basic graph F of the macroscopic traffic flow of the road network to obtain the traffic flow density based on the road network and parameter estimates Basic diagram of

[0021] Using Taylor expansion At the true value of the parameter Expand at, and get the first-order expansion

[0022] The mean absolute error is used as the evaluation index to evaluate the macro basic map of the road network. The calculation formula is: Where N is the number of samples.

[0023] Furthermore, the free flow speed estimate Congestion density estimate and an estimate of the wave velocity at the congestion density Substitute the basic graph F of the macroscopic traffic flow of the road network to obtain the traffic flow density based on the road network and parameter estimates Basic diagram of Using Taylor expansion At the true value of the parameter The first-order expansion obtained by expanding at is:

[0024] The calculation formula of the mean absolute error of the evaluation index of the road network macro basic diagram is:

[0025] In a third aspect, the present invention provides a computer system comprising a memory, a processor, and a computer program / instructions stored in the memory and executable on the processor, wherein the computer program / instructions implement the steps of any of the aforementioned methods when executed by the processor.

[0026] In a fourth aspect, the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of any of the aforementioned methods are implemented.

[0027] In a fifth aspect, the present invention provides a computer program product, comprising a computer program / instruction, which implements the steps of any of the aforementioned methods when executed by a processor.

[0028] Beneficial effects: The present invention proposes a method for constructing and evaluating a macro basic graph of road network traffic flow that integrates multi-source data. The method uses road section traffic data with different measurement errors to predict road network traffic flow parameters and reduce the error of the estimated value of the road network traffic flow basic graph parameter. The core idea of ​​the method is to increase the weight of high-reliability road section traffic flow data and reduce the weight of low-reliability road section traffic flow data. Specifically, the entire road network is divided into two sub-road networks, the first sub-road network is composed of road sections with high-reliability data sources, and the second sub-road network is composed of road sections with low-reliability data sources. The data with high reliability is used to estimate the traffic flow parameters of the first sub-road network and the overall distribution of the traffic flow parameters of the entire road network. In order to reduce the influence of the traffic flow density of the second sub-road network with low reliability on the estimated value of the road network traffic flow basic graph parameter, the probability weighted average method is used to estimate the traffic flow density of the second sub-road network. The estimated values ​​of the traffic flow parameters of the two sub-road networks can be combined to estimate the traffic flow speed and density of the entire road network. The road network traffic flow speed and density are used to construct the road network macro basic graph, and the average absolute error is used as an evaluation index to evaluate the accuracy of the road network macro basic graph parameters. The proposed method can effectively reduce the error of the estimated parameters of the macro basic map of the road network and the discreteness of the relationship between traffic flow, speed and density in the urban road network. It is helpful for traffic design and management departments to optimize the traffic design and transformation plan of the urban road network and formulate reasonable and effective real-time control and induction strategies, which has application value in alleviating urban road traffic congestion. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 It is a framework diagram of the probability weighted average method used in the embodiment of the present invention.

[0030] Figure 2 It is a schematic diagram of the framework of the method for constructing a macro basic graph of road network traffic flow according to an embodiment of the present invention.

[0031] Figure 3 It is a schematic diagram of the framework of the road network traffic flow macro basic graph evaluation method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0032] The present invention is further explained below in conjunction with the accompanying drawings and specific embodiments. It should be understood that these embodiments are only used to illustrate the present invention and are not used to limit the scope of the present invention. After reading the present invention, various equivalent forms of modifications to the present invention by those skilled in the art all fall within the scope defined by the claims attached to this application.

[0033] Suppose a road network contains n totalroad sections and two data sources S1 and S2 with different reliabilities. Data source S1 is a data source with high reliability and small error, and data source S2 is a data source with low reliability and large error. The road section where data source S1 is located is called the core section of the road network, and the road section where data source S2 is located is the non-core section of the road network. The road section traffic flow parameters include the road section traffic flow speed and the road section traffic flow density. The traffic flow speed and density of the core section are obtained from data source S1, and both are high-reliability data. The traffic flow speed and density of the non-core section are obtained from data source S2. Generally, the reliability of the traffic flow density observation value of the non-core section is low, and the reliability of the traffic flow speed observation value is relatively high. Therefore, the traffic flow speed of the non-core section is high-reliability data, and the traffic flow density is low-reliability data.

[0034] The embodiment of the present invention discloses a method for constructing a macro basic graph of road network traffic flow by integrating multi-source data, comprising the following steps:

[0035] The first step is to divide the road network into sub-networks according to the reliability of the traffic flow density data obtained on the road sections.

[0036] In this embodiment, the entire road network is divided into a first sub-network and a second sub-network, which are denoted as sub-networks SN1 and SN2. Sub-network SN1 is composed of core segments, including core segment n core Sub-network SN2 consists of non-core road segments, including non-core road segments n coverage strip.

[0037] In the second step, the simple average method is used to estimate the traffic flow parameters of sub-network SN1.

[0038] For sub-network SN1, time period n core The traffic flow speed and traffic flow density of the core road sections are u v.t and k v.t ,v=1,2,…,n core The length of each road segment is l v The traffic flow speed expression of sub-network SN1 is The traffic flow density expression of sub-network SN1 is:

[0039] The third step is to estimate the overall distribution of traffic flow density in the entire road network.

[0040] Calculate the overall distribution of traffic flow density on the road section in the t time period using the traffic flow density of the core road section in and is the expectation and variance of the normal distribution, expressed as and

[0041] The fourth step is to calculate the probability density function of the traffic flow density of non-core sections obtained by the low reliability data source.

[0042] For sub-network SN2, time period n coverage The traffic flow density of non-core road sections is k w.t ,w=1,2,…,n coverage The length of each road segment is l w . Calculate the traffic flow density k of non-core road sections w.t The probability density function f k (k w.t ), whose expression is

[0043] Step 5: Estimate the traffic flow parameters of sub-network SN2.

[0044] For sub-network SN2, time period n coverage The traffic flow speed and traffic flow density of the non-core road sections are u w.t and k w.t ,w=1,2,…,n coverage The length of each road segment is l w The simple average method is used to estimate the traffic flow density of sub-network SN2, and the expression is: The probability weighted average method is used to estimate the traffic flow density of sub-network SN2, which is expressed as

[0045] Step 6: Combine the estimated values ​​of traffic flow parameters of sub-networks SN1 and SN2 to estimate the traffic flow parameters of the entire network.

[0046] Combine the traffic flow parameters of sub-networks SN1 and SN2 to calculate the traffic flow parameters of the entire network. Traffic flow speed of the entire network The expression is Traffic flow density of the entire road network The expression

[0047] Step 7: Use the road network traffic flow speed and road network traffic flow density Construct a macro basic diagram of road network traffic flow:

[0048] in are the S parameter estimates of the basic graph, ε t is the measurement error.

[0049] Exemplarily, in this embodiment, the traffic flow model for constructing the basic diagram of macroscopic traffic flow of the road network is defined as:

[0050]

[0051] in and are the free flow speed, the congestion density, and the estimated value of the motion wave speed at the congestion density, ε t is the measurement error.

[0052] The method for evaluating a macro basic diagram of a road network traffic flow by integrating multi-source data disclosed in an embodiment of the present invention further includes, based on the above seven steps:

[0053] In the eighth step, the estimated values ​​of the basic traffic flow diagram parameters are substituted into the model and the first-order expansion is performed using Taylor expansion.

[0054] Specifically, the parameter estimates Substitute the basic graph F of the macroscopic traffic flow of the road network to obtain the traffic flow density based on the road network and parameter estimates Basic diagram of Using Taylor expansion At the true value of the parameter Expand at, and get the first-order expansion For the traffic flow model in the above example, the free flow speed estimate Congestion density estimate and an estimate of the wave velocity at the congestion density Substitute the basic graph F of the macroscopic traffic flow of the road network to obtain the traffic flow density based on the road network and parameter estimates Basic diagram of Using Taylor expansion At the true value of the parameter The first-order expansion obtained by expanding at is:

[0055] The ninth step is to calculate the mean absolute error of the evaluation indicators of the road network macro basic map.

[0056] The calculation formula is Where N is the number of samples. For the traffic flow model in the above example, the calculation formula for the mean absolute error is:

[0057]

[0058] Calculation example

[0059] In order to prove the accuracy of the present invention, a case analysis is performed using simulated road network traffic flow data.

[0060] Simulation data generation: There is a city road network with 1500 road section traffic flow density samples Its value is {0.1, 0.2, …, 150}. Road network traffic flow speed sample Obtained from the following traffic flow model,

[0061]

[0062] where u f =80, k j =150, C j =20.

[0063] The road network consists of 2,000 road sections, each with an accurate sample of traffic flow density. From the lognormal distribution Generate accurate traffic flow speed samples for each road segment From the lognormal distribution produce.

[0064] The entire road network is divided into two sub-networks, sub-network SN1 has 100 core sections and sub-network SN2 has 1900 non-core sections. and speed is the accurate traffic flow density and traffic flow speed. The traffic flow density k of non-core road sections w.t ,w=1,2,…,1900From the truncated normal distribution Generate, traffic flow speed u w.t ,w=1,2,…,1900, which is the accurate traffic flow speed.

[0065] Calculation results: According to the proposed method for constructing a macro basic diagram of road network traffic flow that integrates high-reliability and low-reliability data, the traffic flow density of the core section is used to estimate the overall distribution of the traffic flow density of the entire road network, and the probability density function of the traffic flow density of the non-core section is calculated. The probability weighted average method is used to estimate the traffic flow density of sub-road network SN2, and the simple average method is used to estimate the traffic flow speed and density of sub-road network SN1, as well as the traffic flow speed of sub-road network SN2. Combined with the estimated values ​​of the traffic flow parameters of sub-road networks SN1 and SN2, the traffic flow speed and density of the entire road network are estimated. Use the estimated traffic flow speed of the road network and road network traffic flow density The macro basic graph of road network traffic flow is constructed, and the results are shown in Table 1. In order to compare with the proposed method for constructing a macro basic graph of road network traffic flow that integrates high reliability and low reliability data, the results of the method for constructing a macro basic graph of road network traffic flow based on the traditional simple average method are also presented in Table 1. The comparison results show that the proposed method for constructing a macro basic graph of road network traffic flow that integrates high reliability and low reliability data can effectively reduce the errors of the model parameters of the macro basic graph of road network traffic flow.

[0066] Table 1 Parameter estimation results of road network traffic flow model

[0067]

[0068] A computer system disclosed in an embodiment of the present invention includes a memory, a processor, and a computer program / instruction stored in the memory and executable on the processor. When the computer program / instruction is executed by the processor, the steps of a method for constructing and evaluating a macro basic diagram of road network traffic flow that integrates multi-source data in the aforementioned embodiment are implemented.

[0069] A computer-readable storage medium is disclosed in an embodiment of the present invention. The computer-readable storage medium stores a computer program. When the computer program is executed by a processor, the steps of a method for constructing and evaluating a macro basic diagram of road network traffic flow that integrates multi-source data in the aforementioned embodiment are implemented.

[0070] A computer program product disclosed in an embodiment of the present invention includes a computer program / instruction, which, when executed by a processor, implements the steps of a method for constructing and evaluating a macro basic diagram of road network traffic flow that integrates multi-source data in the aforementioned embodiment.

[0071] The program / instruction code for implementing the method of the present invention can be written in any combination of one or more programming languages. These programs / instruction codes can be provided to a processor or controller of a general-purpose computer, a special-purpose computer or other programmable data processing device, so that the program / instruction code, when executed by the processor or controller, enables the steps of the method of the present invention to be implemented. The program / instruction code can be executed completely on the machine, partially on the machine, partially on the machine as an independent software package and partially on a remote machine or completely on a remote machine or server. The parts not described in detail in the present invention are all known technologies of those skilled in the art.

Claims

1. A method for constructing a macro basic graph of road network traffic flow by integrating multi-source data, characterized by: The following steps are involved: According to the reliability of traffic flow density data acquisition of road sections, the entire road network is divided into a first sub-road network consisting of core road sections and a second sub-road network consisting of non-core road sections; the reliability of traffic density flow data acquisition of core road sections is higher than that of non-core road sections; For the first sub-network, according to the traffic flow speed u of the core section in time period t v.t and traffic flow density k v.t , the traffic flow speed of the first sub-network is calculated using the simple average method and traffic flow density Calculate the overall distribution of traffic flow density on the road section in the t time period using the traffic flow density on the core road section in and are the expectation and variance of the normal distribution; For the second sub-road network, calculate the traffic flow density k of the non-core road segment w.t The probability density function of The probability weighted average method is used to calculate the traffic flow density of the second sub-network. Among them l w is the length of the non-core section, n coverage is the number of non-core road sections; the traffic flow speed is calculated using a simple average method Combine the traffic flow parameters of the first sub-network and the second sub-network to calculate the traffic flow speed of the entire network and road network traffic flow density Traffic flow speed of road network and road network traffic flow density Construct a basic diagram of macroscopic traffic flow in the road network. in are the S parameter estimates of the basic graph, ε t is the measurement error.

2. The method for constructing a macro basic graph of road network traffic flow by integrating multi-source data according to claim 1 is characterized in that: Traffic flow speed of the first sub-network Traffic flow density Among them l v is the length of the core section, n core The number of core sections.

3. The method for constructing a macro basic graph of road network traffic flow by integrating multi-source data according to claim 1, characterized in that: Expected overall distribution of traffic flow density variance where n core The number of core sections.

4. The method for constructing a macro basic graph of road network traffic flow by integrating multi-source data according to claim 1, characterized in that: Traffic flow speed of the entire road network Traffic flow density of the entire road network l v is the length of the core section, n core The number of core sections.

5. The method for constructing a macro basic graph of road network traffic flow by integrating multi-source data according to claim 1, characterized in that: Traffic flow speed of road network and road network traffic flow density The basic diagram of the macroscopic traffic flow of the road network is constructed as follows: in and are the free flow speed, the congestion density, and the estimated value of the motion wave speed at the congestion density, ε t is the measurement error.

6. A road network macro basic graph evaluation method integrating multi-source data, characterized by: The following steps are involved: The method for constructing a macro basic graph of road network traffic flow by integrating multi-source data according to any one of claims 1 to 5 obtains the estimated parameter value of the macro basic graph of road network traffic flow The parameter estimates Substitute the basic graph F of the macroscopic traffic flow of the road network to obtain the traffic flow density based on the road network and parameter estimates Basic diagram Using Taylor expansion At the true value of the parameter Expand at, and get the first-order expansion The mean absolute error is used as the evaluation index to evaluate the macro basic map of the road network. The calculation formula is: Where N is the number of samples.

7. The method for evaluating a road network macroscopic basic graph by integrating multi-source data according to claim 6 is characterized by: The free flow speed estimate Congestion density estimate and an estimate of the wave velocity at the congestion density Substitute the basic graph F of the macroscopic traffic flow of the road network to obtain the traffic flow density based on the road network and parameter estimates Basic diagram Using Taylor expansion At the true value of the parameter The first-order expansion obtained by expanding at is: The calculation formula of the mean absolute error of the evaluation index of the road network macro basic diagram is:

8. A computer system comprising a memory, a processor, and a computer program / instruction stored in the memory and executable on the processor, characterized in that: When the computer program / instructions are executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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

10. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instructions are executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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