CN100456335C - Visual evaluation method of urban traffic system status based on traffic flow characteristics and its application - Google Patents

Abstract

The present invention provides a method for visually evaluating the state of urban traffic system based on traffic flow characteristics, comprising the following steps: A, collecting real-time signal waveforms with a duration of not less than three months at a specific road section, and constructing traffic flow state parameters; B. Carry out signal filtering on the traffic flow state parameters to obtain the sample population of traffic flow; C. Calculate the average value and standard deviation of all samples in the sample population, and use the ratio of the standard deviation to the average value as the sample dispersion coefficient; D. According to the strong aggregation characteristics of multi-dimensional state parameters of traffic flow, construct and optimize the standard characteristic phase plane diagram for evaluation; E, put the historical data, current data and forecast data of the traffic system on the standard phase diagram, and visually distinguish the traffic system operating status and trends. The method of the invention can be used in various traffic engineering technical activities carried out by analyzing traffic real-time data, and has the advantages of intuition and clarity, easy application, high judgment accuracy and wide application range.

Description

Visual evaluation method of urban traffic system status based on traffic flow characteristics and its application

technical field

The invention relates to a state evaluation technology of an urban traffic system, in particular to a method for visually evaluating the state of an urban traffic system based on traffic flow characteristics and its application.

Background technique

Urban road congestion is an important problem affecting the economic development of our country. With the deepening of the urbanization process and the continuous improvement of people's living standards, the urban population and motor vehicles have increased dramatically, and the urban traffic jam has become increasingly serious. There are currently two main categories of measures to control traffic congestion: the first category is to expand roads to solve the contradiction that road capacity cannot meet traffic demand in terms of quantity; the second category is to coordinate the above-mentioned contradictions through advanced scientific and technological means and traffic management measures The two parties will qualitatively change the traffic management system of the existing road network, that is, the intelligent transportation system. From years of exploration and practice at home and abroad, it can be seen that intelligent transportation systems have the advantage of improving the overall operational efficiency of road network traffic at a relatively low cost.

The release of traffic information is an important function and part of the intelligent transportation system application field. Based on the theory of traffic flow movement mechanism, it provides travelers and traffic managers with real-time and effective traffic information through a variety of media, and points out the smooth driving route of the road network, so as to avoid drivers blindly traveling or road managers making wrong decisions. To achieve the purpose of smooth and efficient operation of the road network, and to maintain the entire road network in a conscious, purposeful, subjectively active, real-time adjustable, and overall traffic network dynamic balance state.

Compared with the urgent need for traffic congestion solutions and the huge market for building traffic infrastructure, the basic theoretical research of intelligent transportation systems forms a big contrast. The research on the evaluation model of road operation status based on the movement mechanism of urban traffic flow is far behind the needs of the national economic development situation. The existing traffic flow theory cannot explain and predict the generation mechanism and evolution trend of urban traffic congestion. In recent years, people questioned the method of using mathematical models to explain traffic system problems, and began to return to the research on the phenomenon of traffic flow itself. Scientists represented by Greenshield (1935), Edie (1961), Helbing (1995), and Kerner (2004) have adopted the analysis method of traffic flow external characteristics research. Compared with the model analysis method, the core of the feature analysis method is to describe the overall phenomenon based on the external characteristics of the traffic flow, rather than describing a certain type of phenomenon purely based on mathematical functions. The analysis of a large amount of traffic data shows that as the density increases, traffic flow will experience three distinct phases: free flow, congested flow and blocked flow. The state variables of different phases will show strong aggregation characteristics at an appropriate time-space scale, otherwise, they will be discretely distributed in a complex geometric area, and cannot even be expressed by functional relationships. The state of the traffic system is not only affected by the geometric conditions of the road, such as the narrowing of the lane, but also closely related to various factors inside the traffic system. There is a possibility of traffic flow transforming from free phase to congested phase, from congested phase to congested phase, and there is also the possibility of a sharp transition to congested phase when disturbed in the free phase state.

At present, many patent applications and authorizations on related topics have been published at home and abroad, and they describe the nature of traffic flow from their own perspectives and motion mechanisms. Among them, the Chinese invention patent application with the application number 200510026214.7 discloses a method for estimating the state of urban road network traffic flow, focusing on vehicle-mounted GPS satellite positioning data, combined with the traffic signal state provided by the corresponding Sydney Adaptive Traffic Control System (SCATS) Information, with the road section as the object, the traffic flow state speed of the urban road network is fitted and modeled, and the average speed of each directional road section along the road section in the urban road network at a fixed time is obtained, and the current traffic flow is calculated using the speed as the index. Analysis and estimation of congestion status. The invention patent with the patent number WO2005064565-A1 discloses a method of providing traffic state information. In the text of the traffic state identification, especially the GPS information of the vehicle positioning detection device is used to judge the average speed of the vehicle, and the average speed of the congestion detection device is preset. value to determine the traffic state. The above two technologies both use speed parameters to judge the state of signal-controlled intersections, which is not suitable for intersections whose control parameters change. The application number is 200510040621.3 Chinese invention patent application discloses a traffic signal control system operation mode adaptive conversion method, which divides real-time traffic demand into three states: light traffic, medium traffic and heavy traffic; wherein the traffic flow is lower than a certain A set value V1 is a light traffic state, when the traffic flow is higher than a certain set value V2, it is a heavy traffic state, and when the traffic flow is higher than a certain set value V1 and lower than another set value V2, it is a medium traffic state state. The patent uses the flow rate as the discriminant parameter, which is inconsistent with the actual situation, because the flow rate is a multi-valued function, and the state of the traffic flow cannot be uniquely determined, and the selected flow rate threshold contains subjective factors. Japanese Patent No. JP2006085511-A discloses a traffic information forecasting system through road traffic sensors or time series data accumulated by detecting driving in congested areas. The detection data of congested areas are evaluated by clustering method with various classes for traffic status. At the same time, time factors such as the week of the week and statutory holidays are taken into consideration. This patent provides an efficient analysis method for the steady-state structure of traffic flow.

However, more patent applications and authorized patents focus on detection methods for traffic systems and equipment, or advanced control methods based on traffic conditions. Among them, the Chinese invention patent No. 02113826.5 discloses a traffic flow detection system based on video vehicle optical feature recognition and matching. The system uses machine vision technology to collect images of vehicles driving on two or more lanes in different positions on any section of urban traffic roads or high-speed high-grade highways, and recognizes the optical characteristics of the vehicles. Based on the feature matching results, the vehicle capacity on the road section is calculated, including traffic flow, density, speed, distance, retrograde, overspeed, and technical indicators of retention, providing traffic flow information necessary for intelligent management of traffic system engineering. The Chinese invention patent with the patent number of 03116977.5 discloses a self-organized control method of urban traffic signals based on cellular automata. The urban traffic signal control system is treated as a traffic network, and each intersection is used as an intelligent system capable of autonomously collecting and processing information. The system relies on the self-organization of the network to realize the dynamic decision-making of traffic signal control at each intersection. The state information of the local intersection and its adjacent intersections is expressed by the attribute matrix, and the relationship between the connected intersections is described by the relative orientation. The traffic signal control system is established as a virtual network model with the characteristics of cellular automata.

Here, the traffic system state evaluation problem between real-time traffic data and various advanced control methods is a key technology to ensure the effectiveness of the control system, but it is rarely involved.

Contents of the invention

The purpose of the present invention is to overcome the shortcomings and deficiencies of the prior art, and provide a visual evaluation method of urban traffic system status based on traffic flow characteristics with strong practicability, high judgment accuracy and wide application range.

Another object of the present invention is to provide the application of the above-mentioned visual evaluation method for the state of urban traffic system.

The purpose of the present invention is achieved through the following technical solutions: a method for visual evaluation of urban traffic system status based on traffic flow characteristics, comprising the following steps—

A. Collect real-time signal waveforms with a duration of not less than three months in a specific road section, and construct traffic flow state parameters;

B. Carry out signal filtering on traffic flow state parameters to obtain a sample population of traffic flow;

C. Calculate the mean and standard deviation of all samples in the sample population, and use the ratio of the standard deviation to the mean as the sample dispersion coefficient;

D. Construct and optimize the standard characteristic phase diagram (referred to as the standard phase diagram) for evaluation according to the strong aggregation characteristics of multi-dimensional state parameters of traffic flow;

E. Put the historical data, current data and forecast data of the traffic system on the standard phase diagram, and intuitively judge the operation status and change trend of the traffic system.

Said step A may include the following specific steps:

A1. Select an appropriate traffic flow detection point on a specific road type to set up a traffic detector. Try to avoid the deployment of traffic detectors in the entrance ramp merge area, exit ramp diversion area, road section weaving area, road sections less than 600 meters away from the exit line of signal-controlled intersections, or road sections affected by queuing vehicles at downstream signal-controlled intersections; the traffic detection The detector can be a traffic detector based on different detection principles and detection technologies of site cross-section measurement;

A2. Collect the real-time signal waveform within the measurement range of the vehicle passing through the detection point through the traffic detector. For traffic detectors with fixed sampling period or adjustable sampling period, considering the measurement noise that may be generated in the actual process, it is advisable to select a sampling period ranging from 20 seconds to 1 minute;

A3. Guarantee continuous sampling for 24 hours, and the continuous measurement time shall not be less than three months;

A4. Construct traffic flow state parameters from real-time signal waveforms, namely flow, speed and density.

The step B may include the following specific steps:

B1. Data preprocessing. Perform data processing on the state parameters generated in step A4, remove abnormal values, and repair missing values;

B2. Select a sliding interval of 0.5 to 5 minutes, and an averaging period of 3 to 15 minutes, and perform a sliding average on the three state parameters generated in the above step A4 to obtain a traffic flow sample population.

Said step C comprises the following steps:

C1. Calculate the mean and standard deviation of the three state parameters in the sample population;

C2. Using the ratio of the standard deviation to the mean as the sample dispersion coefficient, calculate the sample dispersion coefficients of the three state parameters in the traffic flow sample population.

Said step D comprises the following detailed steps:

D1, setting standard phase diagram parameter; Select density (=time occupancy ratio) and flow as the parameter of standard phase diagram; Select density (=time occupancy ratio) and speed as the parameter of reference phase diagram;

D2. Set the area boundary; select the flow range from 0 to 3000 (v/h), the speed range from 0 to 200 (km/h) (it can also be set according to the road type and traffic environment), and the density range from 0 to 100 (%);

D3. Determine that the minimum velocity value of the corresponding reference phase diagram when the standard phase diagram satisfies the low-density function relationship and the correlation coefficient is > 0.9 is the velocity critical value of the free phase;

D4. Calculate the density average value and standard deviation at the lowest speed, and determine the density deviation value (E ± σ) at the lowest speed as the density critical region of the free phase;

D5. Calculate the flow rate and velocity sample dispersion coefficient corresponding to the density; select the transition area where the sample dispersion coefficient changes sharply as the density critical region of the plugging phase;

D6. Calculate the average value and standard deviation of the flow rate by traversing the density value range;

D7, calculate the free phase boundary fitting function of standard phase diagram; Carry out quadratic power function fitting to the flow deviation value (E+3σ) in the free phase density range with least square method, as free phase upper boundary L ₁ ; The flow deviation value (E-3σ) in the density range is fitted with a quadratic power function as the lower boundary L ₂ of the free phase;

D8. Calculate the non-free phase boundary fitting function of the standard phase diagram; use the least squares method to perform quadratic power function fitting on the flow deviation value (E+3σ) within the range of the non-free phase density, and use it as the upper boundary L of the non-free phase ₃ ;

D9. Adjust the upper boundary line of the standard phase diagram; if L ₁ and L ₃ do not intersect at one point at the critical upper value of the density of the free phase, then keep the flow rate as the upper boundary fitting function corresponding to the large value, and adjust the small value of the flow rate The coefficients of the corresponding upper boundary fitting function and keep the constant term unchanged, so that the upper boundary fitting functions of the free phase and the non-free phase intersect at the critical upper value of the density of the free phase and are equal;

D10. Determine the fuzzy boundary lines between free phase and crowded phase, crowded phase and jammed phase; connect the intersection point of the lower boundary value of the density of the free phase with L ₂ and the intersection point of the upper boundary value of the density of the free phase and L ₁ to form a straight line L ₄ , as the fuzzy boundary line between the free phase and the crowded phase; from the intersection point of the lower boundary value of the density of the jammed phase and the horizontal axis of the coordinate, and the intersection point of the upper boundary value of the density of the jammed phase and L ₃ , a straight line L _{5 is formed, which is used as the line L 5} for the crowded phase and the jammed phase Fuzzy boundary lines of phases;

D11. Structural standard characteristic phase plan; the free phase area is composed of boundary lines L ₁ , L ₄ , L ₂ and coordinate axes; the crowded phase area is composed of boundary lines L ₂ , L ₄ , L ₃ , L ₅ and coordinate axes; The boundary lines L ₅ , L ₃ and the coordinate axes constitute the plugging phase region. Points located in these areas indicate that the traffic system is in a smooth, congested or blocked state, respectively;

D12. Regularly adjust the standard feature phase plan; due to the gradual increase in the number of urban motor vehicles, the fitting function of each phase boundary line will include a low power trend factor. Every 1 to 3 months, it is necessary to use the historical data of the previous 3 months to make regular adjustments to the standard phase diagram and the reference phase diagram.

Described step E comprises following detailed steps:

E1. Put the real-time traffic data in the standard phase plan, and intuitively judge and evaluate the current state and nature of the traffic system according to its position;

E2. Put the historical data, current data, and forecast data in the standard phase diagram, connect each point sequentially, and intuitively analyze the movement law of the traffic system and the formation and evolution trend of traffic congestion.

The working principle of the present invention is: use the phase characteristics and the sample dispersion coefficient to realize the three-phase discrimination and evaluation of the traffic flow, and obtain the flow-density and speed-density (or related parameters) phase plane diagram and flow rate through the collection and analysis of a large number of samples. and the sample coefficient of dispersion for the density. The data analysis shows that the flow-density phase plane diagram presents a strongly aggregated power function relationship in the free phase region, which can be described by a low-order power function or even a linear function expression, and its correlation coefficient satisfies R ² > 0.9. Velocity-density phase plane diagram presents a strong aggregation function relationship in the non-free phase region, and its correlation coefficient satisfies R ² >0.9. Through the distribution of the sample dispersion coefficients of flow and density, it can be found that the important difference between the crowded phase area and the plugged phase area is represented by the difference of the sample dispersion coefficient. In the congested phase area, the vehicles are closely restricted by the front, rear, left, and right vehicles, and the speed is synchronized, which is manifested as a strong car-following phenomenon. In the blockage phase area, the vehicles stop and go, and the instantaneous speed varies from slow to fast, showing a slow forward creeping state. Obviously, the coefficient of dispersion of traffic flow samples in the congestion phase area is much larger than that of the congestion phase.

According to the characteristics of traffic flow phases represented by free phase, congested phase, and jammed phase at different time and space scales, we can construct a standard characteristic phase plane diagram, referred to as the standard phase diagram. The relevant areas correspond to different states of smooth, congested or blocked in the urban traffic system. If historical data of a certain time range, current data, and forecast data obtained by using various forecast models are placed in the standard phase diagram, it is possible to intuitively understand the movement law of traffic flow under different roads and traffic conditions, and evaluate the operation of the traffic system. State, analyze the formation and evolution trend of traffic congestion.

The method of the present invention is applicable to various traffic engineering technical activities carried out by using traffic real-time data analysis, such as short-time scale traffic information release, dynamic path planning and navigation, emergency dispatch, traffic signal control system and urban traffic operation management, and medium and long-term Time-scale traffic management and decision-making activities such as traffic organization, traffic planning, road maintenance and road renovation plans.

Compared with the prior art, the present invention has the following advantages and effects: (1) intuitive and clear, easy to apply; the method of the present invention is a kind of visualization method to evaluate the status of urban traffic system with reference to the standard phase diagram in a complex environment, and the judgment process It is simple and convenient, and the results are displayed in a visual form, which is very convenient for the application of traffic management departments and urban planning departments. (2) The judgment accuracy is high; the real-time signal whose duration is not less than three months is collected by the method of the present invention is used as the basic structure and optimized standard feature phase plane diagram for evaluation, and every 1 to 3 months, all need to use the previous The 3-month historical data regularly adjusts the standard characteristic phase plan, so the data collection is comprehensive and the accuracy of traffic status reflection is good. (3) wide range of application; the present invention is suitable for various traffic engineering technical activities utilizing traffic real-time data analysis, and has a wide range of applications; it can be particularly applied to control traffic jams, qualitatively improving the traffic management level of the existing road network, Improve the overall operation efficiency of road network traffic with a small cost, and provide decision support for road traffic management.

Description of drawings

Figure 1 is a flow chart of the method of the present invention.

Fig. 2 is a traffic flow rate-density phase plan view in the method of the present invention.

Fig. 3 is a traffic velocity-density phase plane diagram in the method of the present invention.

Fig. 4 is the sample dispersion coefficient distribution curve of the flow rate and the density in the method of the present invention.

Fig. 5 is a three-phase area diagram of a standard characteristic phase plane diagram in the method of the present invention.

Fig. 6 is a visual evaluation and analysis diagram of the traffic system state in the method of the present invention.

Detailed ways

The present invention will be further described in detail below in conjunction with taking urban expressway as an embodiment and accompanying drawings.

Example

As shown in Figure 1, the present invention's urban traffic system state visualization evaluation method based on traffic flow characteristics comprises the following steps:

A. Collect real-time signal waveforms with a duration of not less than three months in a specific road section, and construct traffic flow state parameters;

B. Perform signal filtering on state parameters to obtain a sample population of traffic flow;

C. Calculate the mean and standard deviation of all samples in the sample population, and use the ratio of the standard deviation to the mean as the sample dispersion coefficient;

D. Construct and optimize the standard characteristic phase diagram (referred to as the standard phase diagram) for evaluation according to the strong aggregation characteristics of multi-dimensional state parameters of traffic flow;

E. Put the historical data, current data and forecast data of the traffic system on the standard phase diagram, and intuitively judge the operation status and change trend of the traffic system.

Executing Step A specifically includes the following steps:

A1. Select the appropriate traffic flow detection point for a specific road type and install traffic detectors; try to avoid in the entrance ramp merge area, exit ramp diversion area, road section weaving area, road section less than 600 meters away from the exit line of the signal control intersection or affected areas. Arrange traffic detectors in the road section affected by queuing vehicles at downstream signal control intersections;

A2. Collect the real-time signal waveform within the measurement range of the vehicle passing through the detection point through the traffic detector;

A3. Guaranteed continuous sampling for 24 hours and continuous measurement time of three months;

A4. Construct state parameters from real-time signal waveforms, namely flow rate, speed and density.

Executing Step B specifically includes the following steps:

B1. Data preprocessing. Perform data processing on the state parameters generated in step A4, remove abnormal values, and repair missing values;

B2. Select the sliding interval as 1 minute and the averaging period as 5 minutes, and perform sliding average on the three state parameters generated in the above steps to obtain the overall traffic flow sample.

Executing step C specifically includes the following steps:

C1. Calculate the mean and standard deviation of the three state parameters in the traffic flow sample population;

C2. Using the ratio of the standard deviation to the average value as the sample dispersion coefficient, calculate the sample dispersion coefficients of the three state parameters in the traffic flow sample population.

Step D is a key part of the present invention, including the following detailed steps;

D1. Set the parameters of the standard phase diagram. Embodiment Select density (=time occupancy) and flow as the parameter of standard phase diagram, as shown in Figure 2; Select density (=time occupancy) and speed as the parameter of reference phase diagram, as shown in Figure 3;

D2. Set the area boundary. The embodiment selection flow range is 0-2500 (v/h), the speed range is 0-100 (km/h), and the density range is 0-100 (%);

D3. Determine that the minimum velocity value of the corresponding reference phase diagram when the standard phase diagram satisfies the low-density function relationship and the correlation coefficient is >0.9 is the velocity critical value of the free phase. In the embodiment, the speed critical value of the free phase is 35.0 km/h.

D4, density average value and standard deviation when calculating the lowest speed, determine that the density critical region of free phase in the embodiment is (23.34, 29.54);

D5. Calculate the flow rate and velocity sample dispersion coefficient corresponding to the density, and select the transition region where the sample dispersion coefficient changes sharply as the density critical region of the plugging phase, as shown in Figure 4. The density critical region of blocking phase is (60,65) in the embodiment;

D6. Calculate the average value and standard deviation of the flow rate by traversing the density value range;

D7. Calculate the free phase boundary fitting function of the standard phase diagram. Use the least square method to perform quadratic power function fitting on the flow deviation value (E+3σ) with a density range of (0, 29.54) as the upper boundary L ₁ of the free phase; for the flow deviation with a density range of (0, 23.34) The value (E-3σ) is fitted with a power-of-two function and used as the lower boundary L ₂ of the free phase. Its fitting function is:

The fitting function of the upper boundary _L1 of the free phase:

${\mathrm{<span\; class="notranslate">\; Q</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}^{<span\; class="notranslate">\; +</span>}<span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; a</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; *</span>{\mathrm{<span\; class="notranslate">\; o</span>}}_{\mathrm{<span\; class="notranslate">\; cc</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; b</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; *</span>{\mathrm{<span\; class="notranslate">\; o</span>}}_{\mathrm{<span\; class="notranslate">\; cc</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; c</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}$

a ₁ =-0.0548

Where: b ₁ =2.8350

c ₁ =-0.3738

The correlation coefficient R ² =0.98.

The fitting function of the lower boundary _L2 of the free phase:

${\mathrm{<span\; class="notranslate">\; Q</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}^{<span\; class="notranslate">\; -</span>}<span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; a</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; *</span>{\mathrm{<span\; class="notranslate">\; o</span>}}_{\mathrm{<span\; class="notranslate">\; cc</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; b</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; *</span>{\mathrm{<span\; class="notranslate">\; o</span>}}_{\mathrm{<span\; class="notranslate">\; cc</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; c</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}$

a ₂ =0.0357

Where: b ₂ =0.4312

c ₂ =0.2114

The correlation coefficient R ² =0.97.

D8. Calculate the non-free phase boundary fitting function of the standard phase diagram. Use the least squares method to fit the flow deviation value (E+3σ) in the density range (29.54, 100) to the second power function, and use it as the upper boundary L ₃ of the non-free phase:

The fitting function of the upper boundary L ₃ of the non-free phase:

${\mathrm{<span\; class="notranslate">\; Q</span>}}_{\mathrm{<span\; class="notranslate">\; 3</span>}}^{<span\; class="notranslate">\; +</span>}<span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; a</span>}}_{\mathrm{<span\; class="notranslate">\; 3</span>}}<span\; class="notranslate">\; *</span>{\mathrm{<span\; class="notranslate">\; o</span>}}_{\mathrm{<span\; class="notranslate">\; cc</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; b</span>}}_{\mathrm{<span\; class="notranslate">\; 3</span>}}<span\; class="notranslate">\; *</span>{\mathrm{<span\; class="notranslate">\; o</span>}}_{\mathrm{<span\; class="notranslate">\; cc</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; c</span>}}_{\mathrm{<span\; class="notranslate">\; 3</span>}}$

a ₃ =-0.0045

Where: b ₃ =0.0403

c ₃ =40.97

The correlation coefficient R ² =0.93.

D9. Adjust the upper boundary line of the standard phase diagram. If L ₁ and L ₃ are 29.54 at the critical upper density of the free phase and do not intersect at one point, then retain the upper boundary fitting function corresponding to the large value of the flow rate, and adjust the coefficient of the upper boundary fitting function corresponding to the small value of the flow rate And keep the constant term unchanged, so that the upper boundary fitting functions of the free phase and the non-free phase intersect with the critical upper value of the density of the free phase and are equal.

D10. Determine the fuzzy boundary lines between free phase and crowded phase, crowded phase and jammed phase in the standard phase diagram. The intersection of the lower boundary value of the free phase density and L ₂ and the intersection point of the upper boundary value of the free phase density and L ₁ form a straight line L ₄ , which serves as the fuzzy boundary line between the free phase and the crowded phase; the lower boundary of the density of the blocked phase The intersection point of the value and the horizontal axis of the coordinates and the intersection point of the upper boundary value of the density of the jammed phase and L ₃ form a straight line L ₅ , which is used as the fuzzy boundary line between the jammed phase and the jammed phase;

D11. Structural standard characteristic phase plan. The free phase area is formed by the boundary lines L ₁ , L ₄ , L ₂ and the coordinate axes; the crowded phase area is formed by the boundary lines L ₂ , L ₄ , L ₃ , L ₅ and the coordinate axes; the crowded phase area is formed by the boundary lines L ₅ , L ₃ and The coordinate axes constitute the plugging phase region, as shown in Fig. 5. Points located in these areas indicate that the traffic system is in a smooth, congested, or blocked state, respectively;

D12. Periodic adjustment of the standard characteristic phase plan. The embodiment is an expressway traffic environment in a central business district of a city, and the traffic flow varies greatly. Therefore, choose to adjust the standard phase diagram and reference phase diagram regularly every 1 month.

Executing Step E specifically includes the following steps:

E1. Put the real-time traffic data in the standard phase diagram, and intuitively judge and evaluate the current state and nature of the traffic system according to its position;

E2. Put the historical data, current data, and forecast data in the standard phase diagram, connect the points sequentially, and intuitively analyze the movement law of the traffic system and the formation and evolution trend of traffic congestion, as shown in Figure 6.

It can be seen from the use results that the present invention can help users quickly, conveniently and intuitively judge and evaluate the nature of the traffic flow state, greatly improves work efficiency, and is a useful tool for traffic management and decision-making.

Claims (8)

1, a kind of city traffic system state visualization evaluation method based on traffic flow phase characteristic, it is characterized in that comprising the following steps: A. Collect real-time signal waveforms with a duration of not less than three months in a specific road section, and construct traffic flow state parameters; B. Carry out signal filtering on traffic flow state parameters to obtain a sample population of traffic flow; C. Calculate the mean and standard deviation of all samples in the sample population, and use the ratio of the standard deviation to the mean as the sample dispersion coefficient; D. Construct and optimize the standard feature phase plan for evaluation according to the strong aggregation characteristics of multi-dimensional state parameters of traffic flow; E. Put the historical data, current data and forecast data of the traffic system on the standard phase diagram, and intuitively judge the operation status and change trend of the traffic system. 2. The urban traffic system state visualization evaluation method based on traffic flow characteristics according to claim 1, characterized in that: The step A includes the following specific steps— A1. Select the appropriate traffic flow detection point for a specific road type and install traffic detectors; try to avoid in the entrance ramp merge area, exit ramp diversion area, road section weaving area, road section less than 600 meters away from the exit line of the signal control intersection or affected areas. Arrange traffic detectors in the road section affected by the queuing vehicles at the downstream signal control intersection; A2. Collect the real-time signal waveform within the measurement range of the vehicle passing through the detection point through the traffic detector; A3. Guarantee continuous sampling for 24 hours, and the continuous measurement time shall not be less than three months; A4. Construct traffic flow state parameters from real-time signal waveforms, namely flow, speed and density; The step B includes the following specific steps: select a sliding interval of 0.5 to 5 minutes, and an average period of 3 to 15 minutes, and perform sliding averaging on the three state parameters generated in the step A4 to obtain a traffic flow sample population; The step C includes the following specific steps: using the ratio of the standard deviation and the mean value as the sample dispersion coefficient to calculate the sample dispersion coefficient of the three state parameters in the traffic flow sample population; The step D includes the following specific steps— D1. Set the parameters of the standard phase diagram; select density and flow as the parameters of the standard phase diagram, and select density and velocity as the parameters of the reference phase diagram; D2. Set the area boundary; select the flow range from 0 to 3000v/h, the speed range from 0 to 200km/h, and the density range from 0 to 100%; D3. Determine that the minimum velocity value of the corresponding reference phase diagram when the standard phase diagram satisfies the low-density function relationship and the correlation coefficient is > 0.9 is the velocity critical value of the free phase; D4. Calculate the density average and standard deviation at the lowest speed, and determine the density deviation value at the lowest speed as the density critical region of the free phase; D5. Calculate the flow rate and velocity sample dispersion coefficient corresponding to the density; select the transition area where the dispersion coefficient of the sample changes sharply as the density critical region of the plugging phase; D7, calculate the free phase boundary fitting function of the standard phase diagram; use the least squares method to carry out quadratic power function fitting to the flow deviation value in the free phase density range, as the free phase upper boundary L ₁ ; to the flow deviation in the density range The value is fitted with a quadratic power function as the lower boundary L ₂ of the free phase; D8, calculate the non-free phase boundary fitting function of the standard phase diagram; use the least squares method to carry out quadratic power function fitting to the flow deviation value in the non-free phase density range, as the non-free phase upper boundary L ₃ ; D9. Adjust the upper boundary line of the standard phase diagram; if L ₁ and L ₃ do not intersect at one point at the critical upper value of the density of the free phase, then keep the flow rate as the upper boundary fitting function corresponding to the large value, and adjust the small value of the flow rate The coefficients of the corresponding upper boundary fitting function and keep the constant term unchanged, so that the upper boundary fitting functions of the free phase and the non-free phase intersect at the critical upper value of the density of the free phase and are equal; D10. Determine the fuzzy boundary lines between free phase and crowded phase, crowded phase and jammed phase; connect the intersection point of the lower boundary value of the density of the free phase with L ₂ and the intersection point of the upper boundary value of the density of the free phase and L ₁ to form a straight line L ₄ , as the fuzzy boundary line between the free phase and the crowded phase; from the intersection point of the lower boundary value of the density of the jammed phase and the horizontal axis of the coordinate, and the intersection point of the upper boundary value of the density of the jammed phase and L ₃ , a straight line L _{5 is formed, which is used as the line L 5} for the crowded phase and the jammed phase Fuzzy boundary lines of phases; D11. Structural standard characteristic phase plan; the free phase area is composed of boundary lines L ₁ , L ₄ , L ₂ and coordinate axes; the crowded phase area is composed of boundary lines L ₂ , L ₄ , L ₃ , L ₅ and coordinate axes; The boundary lines L ₅ , L ₃ and the coordinate axes constitute the congestion phase area; the points located in these areas indicate that the traffic system is in a smooth, congested or blocked state, respectively; D12. Regularly adjust the standard feature phase plan; due to the gradual increase in the number of urban motor vehicles, the fitting function of each phase boundary line will include a low power trend factor; The step E includes the following specific steps— E1. Put the real-time traffic data in the standard phase diagram, and intuitively judge and evaluate the current state and nature of the traffic system according to its position; E2. Put the historical data, current data, and forecast data in the standard phase diagram, connect each point sequentially, and intuitively analyze the movement law of the traffic system and the formation and evolution trend of traffic congestion. 3. The urban traffic system state visualization evaluation method based on traffic flow characteristics according to claim 2, characterized in that: in the step A2, for the traffic detector with a fixed sampling period or an adjustable sampling period, considering the actual For the measurement noise that may be generated during the process, the sampling period is selected to range from 20 seconds to 1 minute. 4. The method for visual evaluation of urban traffic system status based on traffic flow characteristics according to claim 2, characterized in that: in the step B, the sliding interval is 1 minute, and the average period is 5 minutes. 5. The method for visual evaluation of urban traffic system status based on traffic flow phase characteristics according to claim 2, characterized in that: in the step D12, every 1 to 3 months, the historical data of the previous 3 months are used to The standard feature phase plan is adjusted periodically. 6. The method for visual evaluation of urban traffic system status based on traffic flow characteristics according to claim 2, characterized in that: said traffic detectors are traffic detectors based on different detection principles and detection technologies based on location cross-section measurement. 7. According to any one of claims 1-6, the application of the urban traffic system state visualization evaluation method based on traffic flow phase characteristics is characterized in that it is used for various traffic engineering technical activities carried out by analyzing real-time traffic data. 8. The application according to claim 7, characterized in that: the traffic engineering technical activities are short-time-scale traffic information release, dynamic route planning and navigation, emergency dispatch, traffic signal control system and urban traffic operation management, As well as medium and long-term scale traffic organization, traffic planning, road maintenance and road reconstruction plans.

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