A method and system for determining spatiotemporal scales of IVCPS traffic state detection

Abstract

The present invention relates to a method and system for determining the spatiotemporal scale of IVCPS traffic state detection, and belongs to the technical field of traffic state detection. The method includes: obtaining the spatiotemporal distribution data and traffic parameters of vehicles on the road, and preliminarily judging whether the traffic is in a normal operating state; based on the acquired data, determining the minimum number of sample collections under the current traffic state according to the confidence interval and accuracy requirements; determining the time scale and spatial scale of sampling under the minimum number of sample collections; judging whether the degree of change of the traffic state on the road exceeds the preset fluctuation range, and if so, adjusting the sampling spatiotemporal scale; adjusting the minimum number of sample collections according to the adjusted sampling spatiotemporal scale, and re-determining the sampling spatiotemporal scale. The present invention can adjust the sampling strategy in real time, optimize the data collection accuracy and frequency, and accurately capture the changes in traffic state on the road, thereby providing accurate data support for urban traffic scheduling and management.

Description

Technical Field

[0001] The present invention belongs to the technical field of traffic state detection and relates to a method and system for determining spatiotemporal scales of IVCPS traffic state detection. Background Art

[0002] With the development of electric vehicles, connected cars are increasingly entering our lives. Vehicle speed, acceleration, operation, location and other information can be provided to traffic management systems through networking, forming intelligent connected vehicle cyber-physical systems (IVCPS), which facilitates refined traffic scheduling.

[0003] Traffic conditions are affected by changes in the environment and weather, and often switch between free flow, unimpeded flow, and congestion. According to the principles of information theory, the frequency of data collection by the system is related to the degree of change in the system state. The higher the frequency of system changes, the higher the sampling frequency required, and the more data required to be collected.

[0004] Traffic flow changes are random, and the optimal amount of data collection is determined by the traffic density, the length of the road section (spatial parameter), and the sampling time (temporal parameter). Determining the appropriate spatiotemporal scale method for IVCPS status detection is of great significance to optimizing the information collection granularity of IVCPS. Summary of the Invention

[0005] In view of this, the purpose of the present invention is to provide a method and system for determining the spatiotemporal scale of IVCPS traffic status detection, with the goal of ensuring information credibility and optimizing data scale, to determine the spatiotemporal scale of traffic data acquisition from the perspective of IVCPS, and to facilitate refined traffic scheduling.

[0006] In order to achieve the above object, the present invention provides the following technical solutions:

[0007] A method for determining the spatiotemporal scale of IVCPS traffic state detection is proposed. This method uses confidence intervals and accuracy requirements to determine the minimum sampling requirements for the current traffic state. Based on the relationship between traffic flow density, speed, and flow rate, the required sampling space and time scale are determined under the premise of minimum sampling.

[0008] Specifically, the method comprises the following steps:

[0009] S1. Obtain the spatiotemporal distribution data and traffic parameters of vehicles on the road, and preliminarily determine whether the traffic is in normal operation;

[0010] S2. Based on the acquired data, determine the minimum number of samples to be collected under the current traffic conditions according to the confidence interval and accuracy requirements;

[0011] S3. Determine the time and space scales of sampling under the minimum number of samples collected;

[0012] S4. Determine whether the degree of change in the traffic state on the road exceeds a preset fluctuation range. If so, adjust the sampling time and space scale;

[0013] S5. Adjust the minimum number of samples collected according to the adjusted sampling time and space scale, and re-determine the sampling time and space scale.

[0014] On the other hand, the present invention proposes an IVCPS traffic state detection spatiotemporal scale determination system, which includes:

[0015] The data acquisition module is used to obtain the spatiotemporal distribution data and traffic parameters of vehicles on the road and preliminarily determine whether the traffic operation status is normal;

[0016] A data extraction module extracts parameters for determining a minimum number of sample collections and parameters for determining a sampling spatiotemporal scale based on the data acquired by the data acquisition module;

[0017] A sample quantity determination module is used to determine the minimum number of samples required for collection;

[0018] The sampling scale determination module determines the sampling spatiotemporal scale of traffic state detection based on the minimum number of samples collected;

[0019] a sampling scale adjustment module, which determines whether the degree of change in the traffic state on the road exceeds a preset fluctuation range based on the data of the data acquisition module, and adjusts the sampling scale based on the judgment result;

[0020] The sample quantity optimization module redefines the minimum sample collection quantity based on the adjusted sampling scale.

[0021] Furthermore, under normal traffic operation conditions, the sample quantity determination module determines that the minimum number of sample collections required is:

[0022]

[0023] In the formula, n represents the minimum number of vehicle samples to be collected, represents the critical value of the standard normal distribution corresponding to the confidence level 1-α, α is the credibility, and γ represents the average speed of traffic flow. The relative error, σ v Indicates the standard deviation of vehicle speed.

[0024] Under abnormal traffic conditions, the sample quantity determination module determines the minimum number of sample collections required as follows:

[0025]

[0026] Where n j represents the minimum number of vehicle samples required to be collected under abnormal state j, γ j The average speed of traffic flow is The relative error of q 95,j -q 5,j is the quantile difference, which indicates the range of data variation under the jth abnormal state.

[0027] The abnormal traffic operation state includes a light congestion state, a moderate congestion state and a severe congestion state.

[0028] Furthermore, under normal traffic operation conditions, the sampling time scale determined by the sampling scale determination module is:

[0029]

[0030] Where Q represents traffic flow;

[0031] The determined sampling space scale is:

[0032]

[0033] Where K represents the traffic density;

[0034] Under abnormal traffic conditions, the sampling time scale determined by the sampling scale determination module is:

[0035]

[0036] The determined sampling space scale is:

[0037]

[0038] Furthermore, in the sample quantity optimization module, the minimum number of sample collection required is re-determined based on the adjusted sampling spatiotemporal scale;

[0039] Establish the minimum number of samples collected n and the sampling time scale T S and the sampling space scale L S The mathematical relationship is expressed as:

[0040]

[0041] Under normal traffic conditions, the minimum number of samples collected is redefined as:

[0042]

[0043] Under abnormal traffic conditions, the minimum number of samples collected is redefined as:

[0044]

[0045] The beneficial effects of the present invention are as follows: the present invention adjusts the sampling spatiotemporal scale by detecting the degree of change in traffic conditions, and then adjusts the minimum number of sample collections. Through a closed-loop adjustment process, the cloud server can adjust the sampling strategy in real time, optimize the accuracy and frequency of data collection, and ensure that the traffic management system can accurately capture changes in traffic conditions on the road, thereby providing accurate data support for urban traffic scheduling and management.

[0046] Other advantages, objects, and features of the present invention will be described in part in the following description and, in part, will be apparent to those skilled in the art upon examination of the following description or may be learned from practice of the present invention. The objects and other advantages of the present invention may be realized and obtained through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to make the purpose, technical solutions and advantages of the present invention more clear, the present invention will be described in detail below with reference to the accompanying drawings, in which:

[0048] Figure 1 A schematic flow chart of the method proposed in the present invention;

[0049] Figure 2 The input and output structure diagram of the sampling scale determination module;

[0050] Figure 3 The figure is a schematic diagram of an application scenario of the method of the present invention. DETAILED DESCRIPTION

[0051] The following describes the embodiments of the present invention by means of specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present invention, and the following embodiments and features in the embodiments can be combined with each other without conflict.

[0052] In IVCPS, roadside equipment and vehicle-mounted equipment work together to detect traffic conditions on the road in real time, such as Figure 3As shown in the figure, as vehicles travel through urban roads, roadside equipment (such as cameras and radars installed on both sides of the road) monitors their dynamic behavior and captures various traffic parameters, including but not limited to vehicle speed, traffic density, and inter-vehicle spacing. This collected monitoring data is transmitted to a cloud server via a communication network. The cloud server analyzes the data from the roadside equipment and calculates optimal spatiotemporal scale parameters, which determine the interval and spatial resolution for subsequent data collection. These calculated spatiotemporal scale parameters are fed back to the roadside equipment via the communication network, enabling the equipment to dynamically adjust its monitoring strategy, such as sampling frequency and monitoring range, based on current traffic conditions. As vehicles travel on the road, onboard equipment can also send real-time data directly to the cloud server via the network. This data, combined with data collected by the roadside equipment, provides the cloud server with more comprehensive traffic status information. After receiving real-time data from vehicles and roadside equipment, the cloud server conducts a more detailed analysis of traffic flow and promptly provides feedback on the adjusted spatiotemporal scale parameters, ensuring the system can respond promptly to changing traffic conditions.

[0053] Through the above-mentioned closed-loop control process, the cloud server can adjust the sampling strategy in real time, optimize the accuracy and frequency of data collection, and ensure that the traffic management system can accurately capture changes in traffic conditions on the road, thereby providing accurate data support for urban traffic scheduling and management.

[0054] One embodiment of the present invention proposes a method for determining the spatiotemporal scale of IVCPS traffic status detection, with the goal of ensuring information credibility and optimizing data scale. The spatiotemporal scale of traffic data acquisition is determined from the perspective of IVCPS to achieve the above-mentioned closed-loop control process.

[0055] Specifically, if Figure 1 As shown, the method is as follows:

[0056] 1. Obtain vehicle traffic data in urban road environments and extract the data required to determine the spatiotemporal scale from the acquired data

[0057] By obtaining the spatiotemporal distribution data and traffic parameters of vehicles on urban roads (including vehicle speed, acceleration, position and other parameters), a preliminary judgment can be made as to whether the traffic is in normal operation.

[0058] When traffic is operating normally, based on experience and research in the field, vehicle speeds generally follow a normal distribution, as shown below:

[0059]

[0060] Through standardization, we can get:

[0061]

[0062] For a confidence interval with a confidence level of 1-α, the interval is expressed as:

[0063]

[0064] in, is the sample mean speed, which represents the average of all sample vehicle speeds; The critical value of the standard normal distribution corresponding to the confidence level 1-α is used to estimate the reliability of the results; σ v is the standard deviation of vehicle speed, which indicates the degree of dispersion of sample data.

[0065] Under the same confidence level, the smaller the confidence interval, the higher the accuracy of the estimate. Half the length of the confidence interval is the acceptable sampling error Δv, so the confidence interval can also be expressed as 2Δv, then:

[0066]

[0067] The requirement for the number of samples can be obtained as follows:

[0068]

[0069] Generally, the error requirement is related to the speed. If the speed of the stable traffic flow is The required relative error is γ, that is, the proportional error between the estimated value and the actual value is allowed to be:

[0070]

[0071] Then we have:

[0072]

[0073] After the confidence level and relative error are determined, the size of the sample is Obviously, the larger the speed variation range, the larger the number of samples required.

[0074] When traffic data is under abnormal operating conditions, it may not necessarily follow a normal distribution and may exhibit asymmetric or skewed distributions. This embodiment uses the quantile method to determine the sampling scale, replacing the standard deviation with the data's range of variation, namely the quantile difference. The quantile method does not rely on the assumption of a normal distribution. Its main idea is to ensure sampling accuracy by observing the extreme values ​​of the data and utilizing the actual distribution of the sample data (i.e., the quantiles).

[0075]

[0076] Among them, q 95,j -q 5,jis the quantile difference, which indicates the range of data variation under the jth abnormal state; The coefficient γ for adjusting the confidence level j The allowable relative error determines the tolerance of the error; is the average speed under abnormal conditions, used for normalization.

[0077] 2. If Figure 2 As shown, the sampling time and space scale is determined

[0078] (1) Determination of spatial scale

[0079] Assume the length of the sampling section is L S , the number of collected vehicles is n, and the traffic density is K, where:

[0080] n=KL S (9)

[0081] Combining equations (7) and (8), the spatial scale under normal operating conditions is obtained as follows:

[0082]

[0083] The spatial scale under abnormal operation state is obtained as follows:

[0084]

[0085] (2) Determination of time scale

[0086] Assume that the sampling time interval is T S , the number of collected vehicles is n, and the traffic flow is Q, where:

[0087] n=QT S (12)

[0088] The time scale under normal operating conditions is:

[0089]

[0090] The time scale in abnormal state is:

[0091]

[0092] 3. Decisions on Sampling Scale Adjustment

[0093] Under normal conditions, traffic flow and vehicle speed usually remain within a relatively stable range with small fluctuations. Therefore, in order to reduce unnecessary adjustments, a relatively loose adjustment decision threshold can be set.

[0094] For example, under normal conditions, vehicle speed fluctuations typically remain within 5%, so the speed fluctuation threshold can be set to ±5%. That is, when the real-time detected speed changes fall within this range, the system will assume that traffic conditions remain normal and no sampling scale adjustment is required. For traffic flow fluctuations, a threshold of ±10% can be set. For example, if a road's normal traffic flow is 1,000 vehicles per hour, the system will maintain the current sampling scale when the real-time detected traffic flow is between 900 and 1,100 vehicles.

[0095] When traffic enters an abnormal state, the system needs to capture changes in traffic conditions more finely to provide more accurate data support. At this time, the threshold needs to be set more strictly. Under abnormal conditions, such as minor congestion or accidents, the fluctuation range of vehicle speed will usually increase, but in order to capture abnormal situations in a timely manner, the threshold can be set to a fluctuation of 1-2%. For example, in a state of minor congestion, if the vehicle speed drops by more than 2% (such as from 60km / h to 58.8km / h), the system's sampling scale adjustment mechanism will be triggered. When traffic suddenly increases or decreases by more than a set threshold (for example, ±5%), this usually indicates that the traffic conditions have changed significantly, and the system needs to re-evaluate the sampling frequency and spatial scale to adapt to the new traffic conditions.

[0096] In order to deal with different degrees of traffic anomalies, the abnormal state can be divided into multiple levels, each level corresponding to different thresholds and response strategies. For example, the abnormal state can be divided into "mild congestion", "moderate congestion" and "severe congestion", and the threshold settings for each level are shown in Table 1:

[0097] Table 1

[0098]

[0099] When it is detected that the traffic state changes from a normal state to a certain level of abnormal state, the sampling strategy of the spatiotemporal scale of traffic state detection is immediately adjusted.

[0100] The adjustment has the following characteristics:

[0101] 1) Rapid adjustment: When the threshold is triggered, the sampling parameters are adjusted immediately to ensure timely data collection;

[0102] 2) Feedback: By analyzing the adjusted data, we can further optimize the threshold settings to ensure more accurate responses to changes in traffic conditions in the future;

[0103] 3) Multi-level response: Provides different levels of response for different levels of abnormal conditions. For example, in the case of mild congestion, only the sampling frequency is adjusted, while in the case of severe congestion, both the frequency and spatial resolution are adjusted.

[0104] 4. Based on the extracted data, starting from a statistical point of view, the number of samples is determined by optimizing the spatiotemporal scale parameters.

[0105] When calculating the minimum sample size, the complexity and accuracy requirements of the traffic detection system are taken into account, the time scale and spatial scale are included in the constraints, and the sampling number is iteratively optimized to establish a direct mathematical relationship between the sample size and these two parameters. This can enhance the close connection between the sample size and the time and spatial scales, and help optimize the detection accuracy and resource utilization efficiency of the entire system.

[0106] Among them, the time scale constraint condition is: define the time scale T S and traffic flow Q

[0107] Assume that the time scale is T S The vehicles are sampled for a period, and the number of sampling samples is n. The relationship between the number of samples and the time scale can be described by the following inequality:

[0108]

[0109] Spatial scale constraint: define the spatial scale L S and traffic density K

[0110] Assume that the spatial scale is L S The vehicles are sampled for a period, and the number of sampling samples is n. The relationship between the number of samples and the spatial scale can be described by the following inequality:

[0111]

[0112] After introducing the above constraints of time scale and space scale, the minimum number of samples n can be redefined as the function g(T S ,L S ), when traffic is in normal operation:

[0113]

[0114] When traffic is in an abnormal state:

[0115]

[0116] In another embodiment of the present invention, a system for determining spatiotemporal scales of traffic state detection is provided, the system comprising:

[0117] The data acquisition module is used to obtain the spatiotemporal distribution data and traffic parameters of vehicles on urban roads and preliminarily determine whether the traffic is in a normal operating state;

[0118] A data extraction module is used to screen and determine key data and traffic parameters for determining the spatiotemporal scale of traffic status detection, as well as to screen and determine parameters for determining the number of sample collections;

[0119] A sample quantity determination module determines the number of samples that need to be collected;

[0120] A sampling scale determination module determines the sampling time scale (sampling frequency) and spatial scale (sampling resolution) based on the determined number of samples;

[0121] The sampling scale adjustment module is used to describe the severity of changes in traffic conditions on urban roads, and determine whether the sampling time and space scale needs to be re-determined based on a pre-set reasonable fluctuation range, and generate results;

[0122] The sample quantity optimization module re-determines the number of samples based on the adjusted sampling scale.

[0123] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not limiting. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention can be modified or replaced by equivalents without departing from the purpose and scope of the technical solutions, which should all be included in the scope of the claims of the present invention.

Claims

1. An IVCPS traffic state detection spatiotemporal scale determination system, characterized by: The system includes: The data acquisition module is used to obtain the spatiotemporal distribution data and traffic parameters of vehicles on the road and preliminarily determine whether the traffic operation status is normal; A data extraction module extracts parameters for determining a minimum number of sample collections and parameters for determining a sampling spatiotemporal scale based on the data acquired by the data acquisition module; A sample quantity determination module is used to determine the minimum number of samples required for collection; The sampling scale determination module determines the sampling time and space scale for traffic state detection based on the minimum number of sample collections. Under normal traffic operation conditions, the sampling time scale determined by the sampling scale determination module is: Where Q represents traffic flow; The determined sampling space scale is: Where K represents the traffic density; Under abnormal traffic conditions, the sampling time scale determined by the sampling scale determination module is: The determined sampling space scale is: a sampling scale adjustment module, which determines whether the degree of change in traffic conditions on the road exceeds a preset fluctuation range based on the data from the data acquisition module, and adjusts the sampling scale based on the determination result; and The sample quantity optimization module re-determines the minimum number of samples required based on the adjusted sampling time and space scale; The sample quantity optimization module redefines the minimum sample collection quantity, including: establishing the minimum sample collection quantity n and the sampling time scale T S and the sampling space scale L S The mathematical relationship is expressed as: Under normal traffic conditions, the minimum number of samples collected is redefined as: Under abnormal traffic conditions, the minimum number of samples collected is redefined as: Where, represents the critical value of the standard normal distribution corresponding to the confidence level 1-α, α is the credibility, and γ represents the average speed of traffic flow. The relative error, σ v represents the standard deviation of vehicle speed; n j represents the minimum number of vehicle samples required to be collected under abnormal state j, γ j The average speed of traffic flow is The relative error of q 95,j -q 5,j is the quantile difference, which indicates the range of data variation under the jth abnormal state.

2. The system according to claim 1, wherein: Under normal traffic operation conditions, the sample quantity determination module determines the minimum number of sample collections required as: In the formula, n represents the minimum number of vehicle samples to be collected, represents the critical value of the standard normal distribution corresponding to the confidence level 1-α, α is the credibility, and γ represents the average speed of traffic flow. The relative error, σ v represents the standard deviation of vehicle speed; Under abnormal traffic conditions, the sample quantity determination module determines the minimum number of sample collections required as follows: Where n j represents the minimum number of vehicle samples required to be collected under abnormal state j, γ j The average speed of traffic flow is The relative error of q 95,j -q 5,j is the quantile difference, which indicates the range of data variation under the jth abnormal state.

3. The system according to claim 2, characterized in that: The abnormal traffic operation state includes a light congestion state, a moderate congestion state and a severe congestion state.

4. The method for determining spatiotemporal scales of IVCPS traffic state detection for the system according to any one of claims 1 to 3, characterized in that: Obtain the spatiotemporal distribution data and traffic parameters of vehicles on the road, and make a preliminary judgment on whether the traffic is in normal operation; Based on the acquired data, determine the minimum number of samples collected under the current traffic conditions according to the confidence interval and accuracy requirements; Determine the temporal and spatial scales of sampling with the minimum number of samples collected; Determine whether the degree of change in traffic conditions on the road exceeds a preset fluctuation range, and adjust the sampling time and space scale based on the judgment result; The minimum number of samples collected is adjusted according to the adjusted sampling time and space scale, and the sampling time and space scale is re-determined.

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