Construction and evaluation method of macro basic graph of road network traffic flow by integrating multi-source data

Abstract

The present invention discloses a method for constructing and evaluating a macro-basic diagram of road network traffic flow that integrates multi-source data. The method comprises the following steps: dividing the entire road network into two sub-networks based on data reliability; calculating the traffic flow density and speed of the sub-network with high data reliability, and the traffic flow speed of the sub-network with low data reliability using a simple averaging method; estimating the distribution function of the traffic flow density of the entire road network using the traffic flow density data of the sub-network with high data reliability, and calculating the traffic flow density of the sub-network with low data reliability using a probability-weighted averaging method; and combining the traffic flow density and speed of the two sub-networks to calculate the average traffic flow density and speed of the entire road network, thereby constructing a macro-basic diagram of road network traffic flow. The method effectively reduces the error in the estimated parameters of the macro-basic diagram of road network traffic flow, helps optimize urban road network traffic design and renovation plans, and formulates reasonable and effective real-time control and guidance strategies, with application value in alleviating 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 by integrating 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 speed, traffic density, and traffic volume) is typically collected by fixed roadside detectors, such as video detectors and loop sensors. Fixed detectors are typically placed on both sides of the road to obtain real-time traffic information. The accuracy and reliability of traffic flow data collected by fixed detectors are closely related to their placement. To obtain accurate road segment traffic flow data, detectors must be deployed at appropriate roadside locations. By performing spatially weighted averaging on accurate road segment traffic flow data, traffic flow data for the entire road network can be obtained, namely, network traffic speed, network traffic density, and network volume. However, due to the high installation and maintenance costs of fixed detectors, they are typically only installed on limited road segments. Unlike fixed detectors, floating vehicles equipped with positioning systems provide trajectory data as they travel through the road network, representing a mobile data source. By combining data from floating vehicles and fixed detectors, the penetration rate of floating vehicles on the road can be estimated. If floating vehicles are evenly distributed throughout the road network and the penetration rate is known, traffic data for the entire road network can be obtained by proportionally scaling up the floating vehicle data.

[0003] Although fusing data from floating vehicles and fixed detectors can improve the accuracy of road network traffic data, floating vehicle data can suffer from significant errors when traffic conditions vary significantly across road sections within an urban road network. Floating vehicles, typically taxis, buses, or trucks, exhibit significant temporal and spatial distribution differences from automobile travel patterns. This leads to significant variations in the penetration of floating vehicles across different road sections, and the data they acquire is subject to significant errors. In this case, floating vehicles represent a less reliable data source than fixed detectors in terms of data accuracy and reliability. Using unreliable road segment traffic data to estimate road network traffic data results in significant bias in the estimated values, and the accuracy and reliability of the average flow-speed-density relationship of the road network traffic flow is low. Summary of the Invention

[0004] Purpose of the Invention: The purpose of this invention is to propose a method for constructing and evaluating a road network macro-basic graph that integrates multi-source data (including both high-reliability and low-reliability data). This method uses traffic data from road sections with varying measurement errors to predict parameters of the road network macro-basic graph, thereby reducing the error in the estimated parameters of the road network macro-basic graph. The proposed method can effectively reduce the error in the estimated parameters of the road network macro-basic graph and the dispersion of the traffic flow-speed-density relationship in urban road networks. This method can assist traffic design and management departments in optimizing urban road network traffic design and renovation plans and developing reasonable and effective real-time control and guidance strategies, and has practical application value in alleviating urban road traffic congestion.

[0005] Technical solution: The above objectives are achieved through the following technical solutions:

[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] Based on the reliability of traffic flow density data acquisition on road sections, the entire road network is divided into a first sub-network consisting of core road sections and a second sub-network consisting of non-core road sections; the reliability of traffic density flow data acquisition on core road sections is higher than that on 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 of the section in time period t using the traffic flow density of the core section in and are the expectation and variance of the normal distribution;

[0010] 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

[0011] The traffic flow density of the second sub-network is calculated using the probability weighted average method. where l w is the length of non-core road 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 and second sub-networks 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 where l v is the length of the core section, n core The number of core road sections.

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

[0017] Furthermore, the traffic flow speed of the road network is used 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 road network macroscopic basic graph by integrating multi-source data, comprising the following steps:

[0019] The parameter estimation values ​​of the road network macro traffic flow basic graph are obtained according to the method for constructing the road network traffic flow macro basic graph by fusing multi-source data.

[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

[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 motion 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:

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

[0025] In a third aspect, the present invention provides a computer system comprising a memory, a processor, and a computer program / instruction stored in the memory and executable on the processor, wherein the computer program / instruction implements 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: This invention proposes a method for constructing and evaluating a macro-basic graph of road network traffic flow that integrates multi-source data. This method uses traffic data from road sections with varying measurement errors to predict road network traffic flow parameters, thereby reducing the error in the estimated parameters of the macro-basic graph. The core concept of this method is to increase the weight of traffic flow data from highly reliable sections and decrease the weight of traffic flow data from less reliable sections. Specifically, the entire road network is divided into two sub-networks: the first sub-network consists of sections from highly reliable data sources, and the second sub-network consists of sections from less reliable data sources. The highly reliable data is used to estimate the traffic flow parameters of the first sub-network and the overall distribution of traffic flow parameters for the entire road network. To reduce the impact of the traffic flow density of the less reliable second sub-network on the estimated parameters of the macro-basic graph, a probability-weighted average method is used to estimate the traffic flow density of the second sub-network. Combining the estimated traffic flow parameters of the two sub-networks allows the estimation of the traffic flow speed and density of the entire road network. The traffic flow speed and density of the road network are used to construct a macro-basic graph of road network traffic flow, and the mean absolute error is used as an evaluation metric to assess the accuracy of the parameters of the macro-basic graph. The proposed method can effectively reduce the error of the estimated parameters of the macro-basic graph of the road network and the discreteness of the relationship between traffic flow, speed and density in the urban road network. It helps traffic design and management departments to optimize the design and transformation plans of urban road network traffic and formulate reasonable and effective real-time control and guidance 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 in 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 illustrated below with reference to 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, modifications of various equivalent forms of the present invention made 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, while 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 traffic flow parameters of the road section include the traffic flow speed and traffic flow density of the road section. 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, while 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 of 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 consists of core road segments, including core road segments 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 of the section in time period t using the traffic flow density of the core 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 road 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 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 traffic flow density of sub-network SN2 is estimated using the simple average method, which is expressed as 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. The expression is Traffic flow density of the entire road network Expression

[0047] Step 7: Use the traffic flow speed of the road network 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] For example, in this embodiment, the traffic flow model for constructing the road network macro traffic flow basic graph 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 embodiment of the present invention discloses a method for evaluating a macroscopic basic graph of road network traffic flow by integrating multi-source data, which, based on the above seven steps, further includes:

[0053] In the eighth step, the estimated values ​​of the traffic flow basic graph 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 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 example above, the free flow speed estimate Congestion density estimate and an estimate of the motion 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:

[0055] The ninth step is to calculate the mean absolute error of the evaluation index 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 formula for calculating the mean absolute error is:

[0057]

[0058] Calculation Case

[0059] In order to prove the accuracy of the present invention, a case analysis is conducted 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 2000 road sections, and each section has an accurate traffic flow density sample. 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 road sections and sub-network SN2 has 1900 non-core road sections. and speed is the accurate traffic flow density and traffic flow speed. The traffic flow density k of non-core road section w.t ,w=1,2,…,1900 is derived from the truncated normal distribution Generated, 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 the sub-road network SN2, and the simple average method is used to estimate the traffic flow speed and density of the sub-road network SN1, as well as the traffic flow speed of the sub-road network SN2. Combining the estimated values ​​of the traffic flow parameters of the sub-road networks SN1 and SN2, the traffic flow speed and density of the entire road network are estimated. The estimated traffic flow speed of the road network is used and road network traffic flow density A macro-basic graph of road network traffic flow was constructed, and the results are shown in Table 1. For comparison 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 traditional simple averaging method are also presented in Table 1. These comparisons demonstrate 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 in the model parameters of the macro-basic graph.

[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 by integrating multi-source data in the aforementioned embodiment are implemented.

[0069] An embodiment of the present invention discloses a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it 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.

[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 inventive method 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, causes the steps of the inventive method to be implemented. The program / instruction code can be executed entirely 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 macroscopic basic graph of road network traffic flow by integrating multi-source data, characterized by: The following steps are involved: Based on the reliability of traffic flow density data acquisition on road sections, the entire road network is divided into a first sub-network consisting of core road sections and a second sub-network consisting of non-core road sections; the reliability of traffic density flow data acquisition on core road sections is higher than that on 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 of the section in time period t using the traffic flow density of the core 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 traffic flow density of the second sub-network is calculated using the probability weighted average method. where l w is the length of non-core road 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 and second sub-networks 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 macroscopic basic graph of road network traffic flow by integrating multi-source data according to claim 1 is characterized by: Traffic flow speed of the first sub-network Traffic flow density where l v is the length of the core section, n core The number of core road sections.

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

4. The method for constructing a macro basic graph of road network traffic flow by integrating multi-source data according to claim 1 is characterized by: 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 road sections.

5. The method for constructing a macro basic graph of road network traffic flow by integrating multi-source data according to claim 1 is characterized by: 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 parameter estimation 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 motion 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 for the mean absolute error of the road network macro basic map evaluation index is:

8. A computer system comprising a memory, a processor, and a computer program / instruction stored in the memory and executable on the processor, wherein: 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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