Two-phase intersection straight-left conflict influence factor analysis method and system

Abstract

The invention discloses a two-phase intersection direct-left conflict influence factor analysis method and system, and the method comprises the steps: collecting the traffic conflict number, traffic flow, intersection signal control, traffic design and other data in a certain time period, carrying out the analysis of the direct-left conflict frequency distribution, and carrying out the analysis of the direct-left conflict influence factors based on the distribution characteristics. A relation model between the straight-left traffic conflict frequency and influence factors such as traffic volume, traffic design and traffic signal control is established, a cumulative residual error method is adopted to analyze the model test precision, and a sensitivity analysis method is adopted to quantitatively analyze the influence degree of each influence factor on the traffic conflict. And the safety performance of the intersection is accurately improved by quantitatively adjusting influence factors. According to the invention, actual intersection parameters are substituted into the model, the number of straight-left conflicts can be obtained, the number of conflicts can be reduced by adjusting influence parameters, and the purpose of improving traffic safety is achieved.

Description

Technical Field

[0001] The invention relates to a method and system technology for analyzing factors affecting straight-left conflicts at a two-phase intersection, and belongs to the technical field of urban road traffic safety management. Background Art

[0002] Traffic conflicts can be collected and observed as a pre-event safety evaluation indicator before an accident occurs, and the number of conflicts collected can be used to evaluate the safety level of intersections. In urban roads, two-phase intersections do not have a dedicated left-turn phase, and straight traffic and left-turn traffic are prone to conflict, which poses a major threat to traffic safety and is the focus of road management. Current research mainly collects the number of conflicts per unit time and uses the number of conflicts as an indicator to evaluate the safety performance of facilities, ignoring the mechanism of traffic conflicts and failing to analyze the factors affecting traffic conflicts for specific conflict types, making it difficult to find the key factors of road safety management and to achieve the goal of reducing traffic conflicts and improving traffic safety performance by adjusting road facility parameters. Summary of the invention

[0003] The purpose of the present invention is to provide a method and system for analyzing factors affecting straight-left conflicts at two-phase intersections, so as to solve the problem that the prior art lacks analysis of factors affecting straight-left conflicts and cannot reduce conflicts and improve traffic safety performance through quantitative parameter adjustment.

[0004] To achieve the above object, the first aspect of the present invention proposes a method for analyzing factors affecting left-right conflicts at two-phase intersections, wherein a traffic conflict affecting factor model is constructed through the following steps S1 to S5, the influence of each factor is analyzed, and the safety level is improved by adjusting parameters:

[0005] Step S1, select a two-phase intersection within the scope of urban roads, collect information such as intersection flow, traffic design and traffic signal control through video, extract traffic conflict data at different time intervals through video recognition technology, and establish corresponding set groups of conflict number and flow, as well as traffic design and traffic signal control according to different intersection characteristics.

[0006] Step S2: Analyze the distribution characteristics of straight-left conflicts under different traffic characteristics according to the corresponding relationship between the number of conflicts and the traffic set groups at different time intervals, and use the goodness of fit method to determine the type with the best fit as the straight-left conflict frequency distribution type.

[0007] Step S3: Based on the data collected from each two-phase intersection, the influencing factors are divided into continuous variables and discrete variables, and the modeling form of the continuous variables and the value range of the discrete variables are determined. Based on the distribution characteristics of straight-left conflicts and the distribution type of straight-left conflicts, a relationship model is established between the frequency of straight-left conflicts and influencing factors such as intersection traffic flow, traffic design, and traffic signal control.

[0008] Step S4, perform model fitting verification on the relationship model between the influencing factors obtained in S3, use the cumulative residual analysis method to determine the residual limit, draw the residual curve, observe the fluctuation of the residual curve and the satisfaction of the limit, so as to determine the inspection accuracy of the model.

[0009] Step S5: Use a sensitivity analysis method to analyze the impact of various factors on the generation of conflicts, and reduce the frequency of traffic conflicts and improve the safety performance of two-phase intersections by optimizing traffic signs and markings, safety facilities, and signal schemes.

[0010] Preferably, the different time intervals described in step S1 include 5 minutes, 15 minutes, and 30 minutes.

[0011] Preferably, the goodness of fit method in step S2 includes Poisson distribution, negative binomial distribution and normal distribution test, and the mean value and variance of the actual conflict are used to fit the three distributions and select the best fitting case.

[0012] Preferably, the continuous variables and discrete variables described in step S3 include the straight traffic volume T, the left-turn traffic volume L, the left-turn radius r, and the continuous variables. L , the effective green light duration G in the left turn phase. The straight traffic volume T and the left turn traffic volume L are modeled in logarithmic form lnT and lnL. Discrete variables include lane type R, central dividing strip type S, and left turn guide line type M.

[0013] Based on the distribution characteristics of the straight-left conflict frequency, a relationship model between the straight-left conflict frequency CF and the above-mentioned influencing factor variables is established according to the following formula.

[0014] CF=a0×exp(a1 lnT+a2lnL+a3×R+a4×S+a5×M+a6×r L +a7G)

[0015] Among them, a i (i=0,…,n, n represents the number of variables) represents the fitting coefficient of each influencing factor.

[0016] Preferably, the cumulative residual analysis method in step S4 calculates the residual value σ(n) and the residual limit value σ * (n), calculated according to the following formula:

[0017]

[0018]

[0019] Among them, σ 2 (n) represents 1 to n conflicting predicted values ​​y and observed values ​​in the sample set The residual sum of squares; N represents the number of sets in step S1.

[0020] Preferably, the sensitivity analysis method in step S5 includes trend impact analysis and impact degree analysis. The positive or negative value of the coefficient of the influencing factor variable can be used to determine whether the relevant influencing factor is positively correlated or negatively correlated. Under the condition that other influencing factors remain unchanged, the absolute value of the coefficient can be used to determine the impact degree.

[0021] The second aspect disclosed in the present invention further provides a two-phase intersection straight-left conflict influencing factor analysis system, including a module for collecting basic information, a module for establishing an influencing factor analysis model, and a module for analyzing the degree of influence of influencing factors;

[0022] The modules used to collect basic information include:

[0023] It is used to select two-phase intersections within the scope of urban roads, collect information such as intersection flow, traffic design and traffic signal control through video, extract traffic conflict data at different time intervals through video recognition technology, and establish corresponding set groups of conflict numbers and flow, as well as modules of traffic design and traffic signal control set groups at different intersections.

[0024] It is used to classify traffic into high, medium and low at different time intervals, analyze the distribution characteristics of traffic conflicts under different traffic characteristics according to the correspondence between the number of conflicts and traffic set groups, and use the goodness of fit method to determine the module of straight-left conflict frequency distribution type.

[0025] The modules used to establish the influencing factor analysis model include:

[0026] It is used to collect data at each two-phase intersection, divide the influencing factors into continuous variables and discrete variables, and determine the modeling form of continuous variables and the value range of discrete variables. Based on the distribution characteristics of straight-left conflicts, a module is used to establish a relationship model between straight-left conflicts and parameters such as traffic flow, traffic design, and traffic control.

[0027] The module is used to verify the model fitting of the influencing factor analysis model, use the cumulative residual analysis method to determine the residual limit, draw the residual curve, observe the fluctuation of the residual curve and the satisfaction of the limit, so as to determine the test accuracy of the model.

[0028] The modules used to analyze the impact of influencing factors include:

[0029] A module used to analyze the influence of various factors on conflicts by using sensitivity analysis methods, reduce the frequency of traffic conflicts and improve the safety performance of two-phase intersections by optimizing traffic signs and markings, safety facilities settings, and signal schemes.

[0030] Beneficial effects of the present invention:

[0031] 1. The present invention studies the influencing factors of two-phase straight-left conflicts, collects influencing factors such as intersection flow, traffic design and traffic signal control, and establishes an influencing factor analysis model, which overcomes the shortcomings of previous studies that lack modeling and analysis for specific conflict types and insufficient exploration of influencing factors of conflicts. At the same time, a sensitivity analysis method is used to quantitatively analyze the influence of each influencing factor, which overcomes the shortcomings of existing studies that lack quantitative analysis methods for influencing factors and cannot reduce traffic conflicts and improve safety by adjusting relevant influencing factors.

[0032] 2. The present invention studies the influencing factors of two-phase straight-left conflicts, collects influencing factors such as intersection flow, traffic design and traffic signal control, and establishes an influencing factor analysis model, which overcomes the shortcomings of previous studies that lack modeling and analysis for specific conflict types and insufficient exploration of influencing factors of conflicts. At the same time, a sensitivity analysis method is used to quantitatively analyze the degree of influence of each influencing factor, which overcomes the shortcomings of existing studies that lack quantitative analysis methods for influencing factors and cannot reduce traffic conflicts and improve safety by adjusting relevant influencing factors. The present invention, through the influencing factor analysis method and system, can bring actual intersection parameters into the influencing factor analysis model, obtain the degree of influence of each influencing factor on the conflict, and can reduce the number of traffic conflicts by quantitatively adjusting the influencing parameters, thereby achieving the goal of improving traffic safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 A flow chart of a method for analyzing factors affecting straight-left conflicts at a two-phase intersection provided by an exemplary embodiment of the present invention;

[0034] Figure 2 It is a schematic diagram of a system for analyzing factors affecting straight-left conflicts at two-phase intersections according to an exemplary embodiment of the present invention;

[0035] Figure 3 is the residual curve plotted. DETAILED DESCRIPTION

[0036] Various aspects of the present invention are described in this disclosure with reference to the accompanying drawings, in which many illustrative embodiments are shown. The embodiments of the present disclosure are not necessarily defined to include all aspects of the present invention. It should be understood that the various concepts and embodiments introduced above, as well as those described in more detail below, can be implemented in any of many ways, because the concepts and embodiments disclosed by the present invention are not limited to any implementation. In addition, some aspects disclosed by the present invention can be used alone, or in any appropriate combination with other aspects disclosed by the present invention.

[0037] The present invention will be further described below in conjunction with the accompanying drawings.

[0038] Combination Figure 1 The process of the analysis method of the factors affecting left-right conflicts at two-phase intersections in the exemplary embodiment of the present invention shown in the figure is based on the existing method of conducting safety evaluation based on the number of conflicts, lacks modeling analysis for specific conflict types, and does not sufficiently explore the factors affecting traffic conflicts. There is a lack of quantitative analysis methods for the degree of influence of related factors, and it is impossible to determine and quantitatively adjust related factors according to different intersection characteristics. The present invention establishes a model of factors affecting left-right conflicts, quantifies the degree of influence of each factor through sensitivity analysis, and takes measures to improve traffic safety according to the degree of influence. Figure 1 and 2 The present invention is further described.

[0039] Embodiment 1:

[0040] like Figure 1 The exemplary process shown in the figure represents a method for analyzing factors affecting left-right conflicts at two-phase intersections, which collects and analyzes variables of influencing factors, constructs an analysis model of influencing factors, and uses a sensitivity method to evaluate the degree of influence through the following steps S1 to S5:

[0041] Step S1: Select two-phase intersections within the scope of urban roads, collect information such as traffic flow, traffic design and traffic signal control of 25 two-phase intersections through video, extract traffic conflict data at 5min, 15min and 30min time intervals through video recognition technology, and establish corresponding collection groups of conflict number and traffic flow, as well as traffic design and traffic signal control collection groups according to different intersection characteristics. A total of 125 hours of video data are collected, which are divided into 1500, 500 and 250 collection groups at 5min, 15min and 30min time intervals respectively.

[0042] Step S2: According to the time intervals of 5min, 15min, and 30min, the traffic is classified into high, medium, and low. According to the corresponding relationship between the number of conflicts and the traffic set group, the goodness of fit method is used to calculate the mean and variance of the conflicts at different time intervals, and the existing traffic conflict frequency is compared with the Poisson distribution, negative binomial distribution, and normal distribution under the same mean and variance. The 15min conflict frequency under high, medium, and low traffic conforms to the negative binomial distribution, with the smallest difference and the best fit. The mean and variance of the actual conflict are used to fit the three distributions under high, medium, and low traffic. At the 5min time interval, the conflict frequency under low and medium traffic conforms to the negative binomial distribution, and the conflict frequency under high traffic conforms to the Poisson distribution; at the 30min time interval, the conflict frequency under low traffic conforms to the negative binomial distribution, and the conflict frequency under medium and high traffic conforms to the normal distribution. At the 15min time interval, the conflict frequency under high, medium, and low traffic conforms to the negative binomial distribution, which is the type with the best fit, and the model is established.

[0043] Step S3: Based on the data collected from each two-phase intersection, the influencing factors are divided into continuous variables and discrete variables, and the modeling form of the continuous variables and the value range of the discrete variables are determined. Through the t-test, it is determined that there is no correlation between the influencing factors. Based on the characteristics of the straight-left conflict distribution, the influencing factor variables are included one by one to achieve the best fitting effect, and finally the relationship model between the frequency of straight-left conflicts and the influencing factors such as intersection traffic flow, traffic design, and traffic control is determined.

[0044] Continuous variables include straight traffic volume T, left-turn traffic volume L, left-turn radius r L , G is the effective green light duration of the left turn phase. The straight traffic volume T and the left turn traffic volume L are modeled in the logarithmic form lnT and lnL.

[0045] The range of discrete variables includes: Lane type R = {r i＝1,2,3}(If the opposite entrance direction has three lanes and the intersection road has six lanes in both directions, then r1=1, otherwise r1=0; if the opposite entrance direction has three lanes and the intersection road has four lanes in both directions, then r2=1, otherwise r2=0; if the opposite entrance direction has two lanes and the intersection road has four lanes in both directions, then r3=1, otherwise r3=0); Central median type S={S i＝1,2}(S1 represents a curb-type central median, S2 represents a separation fence); left-turn guide line type M (if there is a left-turn guide line, M=1, otherwise M=0).

[0046] According to the following formula, an analytical model between the straight-left conflict frequency CF and the above-mentioned influencing factor variables is established.

[0047] CF=0.068×exp(0.4482lnT+0.4681lnL+0.6270r1

[0048] +0.4767r2+0.3884r3-0.4939S1-0.2718S2

[0049] -0.1852M+0.0083r L -0.0060G)

[0050] Step S4: perform model fitting verification on the influencing factor model, use the cumulative residual analysis method to determine the residual limit, draw the residual curve, observe the fluctuation of the residual curve and the limit satisfaction, so as to determine the test accuracy of the model. Calculate the residual value σ(n) and the residual limit σ of the influencing factor analysis model according to the following formula: * (n), based on the actual data, the residual curve of the model does not exceed the residual limit, the model test accuracy meets the requirements, and the influencing factor variables in the model can be used to analyze the causes of the conflict.

[0051]

[0052]

[0053] Where: 2 (n) represents 1 to n conflicting predicted values ​​y and observed values ​​in the sample set The residual sum of squares; N represents the number of sets in step S1 (in this embodiment, N = 500); σ 2 (N) is the sum of all residual sums of squares.

[0054] Step S5: Use sensitivity analysis to analyze the influence of various factors on the conflict, and reduce the frequency of traffic conflicts and improve the safety performance of two-phase intersections by optimizing traffic signs and markings, safety facilities, and signal schemes. Based on the influencing factor analysis model in step S3, in the trend impact analysis, straight and left turn traffic flow (T, L), left turn radius (r L ), the number of lanes (r1, r2, r3) is positively correlated with the number of conflicts. As the related influencing factors increase, the frequency of straight-left conflicts increases; the other influencing factors are negatively correlated. In the influence degree analysis, under the condition that other influencing factors remain unchanged, the influence degree can be known from the absolute value of the coefficient. The through and left-turn traffic T and L modeled in logarithmic form, the index is the elasticity coefficient. For every 1% increase in the through traffic T and left-turn traffic L, the frequency of straight-left conflicts increases by 0.4482% and 0.4681%. The left-turn traffic has a greater impact on the conflict frequency than the through traffic. It can be seen that the main cause of straight-left conflicts is that the left-turn traffic does not give way to the through traffic.

[0055] Among the negatively correlated factors, the absolute value of the coefficient shows that the establishment of a curb-type central divider S1, the establishment of a separation fence S2, the establishment of a left-turn guide line M, and the effective green light duration G for the left-turn phase can reduce the frequency of traffic conflicts, and the degree of influence decreases in turn. The establishment of a curb-type central divider reduces conflicts 1.87 times more than the separation fence Traffic managers can combine specific safety improvement requirements with the perspective of reducing the frequency of conflicts, and quantitatively adjust traffic flow, optimize traffic facilities, traffic design, traffic control parameters and other influencing factors based on the straight-left conflict influencing factor analysis method provided by the present invention.

[0056] Embodiment 2:

[0057] According to the embodiments disclosed in the present invention, a signalized intersection straight-left conflict influencing factor analysis system is also proposed, comprising a module for collecting basic information, a module for establishing an influencing factor analysis model, and a module for analyzing the degree of influence of influencing factors;

[0058] The modules used to collect basic information include:

[0059] It is used to select two-phase intersections within the scope of urban roads, collect information such as intersection flow, traffic design and traffic signal control through video, extract traffic conflict data at different time intervals through video recognition technology, and establish corresponding set groups of conflict numbers and flow, as well as modules of traffic design and traffic signal control set groups at different intersections.

[0060] It is used to classify traffic into high, medium and low at different time intervals, analyze the distribution characteristics of traffic conflicts under different traffic characteristics according to the correspondence between the number of conflicts and traffic set groups, and use the goodness of fit method to determine the module of straight-left conflict frequency distribution type.

[0061] The modules used to establish the influencing factor analysis model include:

[0062] It is used to collect data at each two-phase intersection, divide the influencing factors into continuous variables and discrete variables, and determine the modeling form of continuous variables and the value range of discrete variables. Based on the distribution characteristics of straight-left conflicts, a module is used to establish a relationship model between the frequency of straight-left conflicts and parameters such as traffic flow, traffic design, and traffic control.

[0063] A module used to verify the model fitting of the influencing factor model, use the cumulative residual analysis method to determine the residual limit, draw the residual curve, observe the fluctuation of the residual curve and the satisfaction of the limit, so as to determine the test accuracy of the model.

[0064] The modules used to analyze the impact of influencing factors include:

[0065] A module used to analyze the influence of influencing factors on conflicts by using sensitivity analysis methods, reduce the frequency of traffic conflicts and improve the safety performance of two-phase intersections by optimizing traffic signs and markings, safety facilities settings, and signal schemes.

[0066] The series of detailed descriptions listed above are only specific descriptions of feasible implementation methods of the present invention. They are not intended to limit the scope of protection of the present invention. All equivalent methods or changes that do not deviate from the technical creation of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for analyzing factors affecting left-turn conflicts at two-phase intersections, characterized in that: include: Step S1, selecting a two-phase intersection within the scope of an urban road, collecting intersection flow, traffic design and traffic signal control information through video, extracting traffic conflict data at different time intervals through video recognition technology, and establishing a corresponding set group of conflict number and flow, as well as a set group of traffic design and traffic signal control; Step S2, according to the corresponding relationship between the number of conflicts and the flow set group at different time intervals, the straight-left conflict distribution characteristics under different flow characteristics are analyzed, and the type with the best fitting degree is determined as the straight-left conflict frequency distribution type by using the goodness of fit method; Step S3: Based on the data collected at the two-phase intersection, the influencing factors are divided into continuous variables and discrete variables, and based on the characteristics and types of the straight-left conflict frequency distribution, a relationship model between the straight-left conflict frequency and the intersection traffic flow, traffic design, and traffic control influencing factors is established; Step S4, fitting and verifying the relationship model between the frequency of straight-left conflicts and traffic influencing factors in S3, using the cumulative residual analysis method to determine the residual limit, drawing the residual curve, observing the fluctuation of the residual curve and the satisfaction of the limit, so as to determine the inspection accuracy of the model; Step S5: Use sensitivity analysis method to analyze the influence of various factors on the conflict.

2. The method for analyzing factors affecting left-turn conflicts at two-phase intersections according to claim 1 is characterized in that: The different time intervals in S1 include 5 minutes, 15 minutes, and 30 minutes.

3. The method for analyzing factors affecting left-turn conflicts at two-phase intersections according to claim 1 is characterized in that: The goodness of fit method in S2 uses the mean and variance of actual conflicts to fit three distributions under high, medium and low traffic. At a 5-minute time interval, the conflict frequency under low and medium traffic conforms to the negative binomial distribution, and the conflict frequency under high traffic conforms to the Poisson distribution; at a 30-minute time interval, the conflict frequency under low traffic conforms to the negative binomial distribution, and the conflict frequency under medium and high traffic conforms to the normal distribution. At the 15-min time interval, the conflict frequencies under high, medium, and low traffic all conform to the negative binomial distribution, which is the type with the best fit.

4. The method for analyzing factors affecting left-turn conflicts at two-phase intersections according to claim 1 is characterized in that: In S3, the continuous variables include straight traffic flow T, left turn traffic flow L, left turn radius r L , the effective green light duration G of the left turn phase, wherein the straight traffic volume T and the left turn traffic volume L are modeled in logarithmic form lnT and lnL; the discrete variables include lane type R, central dividing strip type S, and left turn guide line type M; Establish a relationship model between the frequency of straight-left conflicts CF and the above-mentioned influencing factor variables. <h2 style=";text-align:left;direction:ltr">CF = a0×exp(a1 lnT+a2 lnL+a3×R+a4×S+a5×M+a6×r<h2 style=";text-align:left;direction:ltr"> L <h2 style=";text-align:left;direction:ltr"> +a7G) Among them, a i (i=0,…,n, n represents the number of variables) represents the fitting coefficient of each influencing factor.

5. The method for analyzing factors affecting left-turn conflicts at two-phase intersections according to claim 4 is characterized in that: The value range of the discrete variable is specifically designed as follows: R = {r i＝1,2,3 }, if the opposite import direction has three lanes and the intersection road has six lanes in both directions, then r1 = 1, otherwise r1 = 0; if the opposite import direction has three lanes and the intersection road has four lanes in both directions, then r2 = 1, otherwise r2 = 0; if the opposite import direction has two lanes and the intersection road has four lanes in both directions, then r3 = 1, otherwise r3 = 0); Median strip type S = {S i＝1,2 }, S1 represents the curb-type central median, S2 represents the isolation fence; Left turn guide line type M, if there is a left turn guide line, M = 1, otherwise M = 0.

6. The method for analyzing factors affecting left-turn conflicts at two-phase intersections according to claim 5 is characterized in that: According to the optimal type of fitting degree determined in step 2, the relationship model between the frequency of straight-left conflict and the influencing factor variables is specifically designed as follows: The 15-minute straight-left conflict conforms to the negative binomial distribution characteristics, and the modeling formula is as follows: CF=0.068×exp(0.4482lnT+0.4681lnL+0.6270r1+0.4767r2+0.3884r3-0.4939S1-0.2718S2-0.1852M+0.0083r L -0.0060G)。 7. The method for analyzing factors affecting left-turn conflicts at two-phase intersections according to claim 1, characterized in that: The residual value σ(n) and the residual limit value σ of the cumulative residual analysis method in step S4 are * (n), calculated according to the following formula: Among them, σ 2 (n) represents 1 to n conflicting predicted values ​​y and observed values ​​in the sample set The residual sum of squares; N represents the number of sets.

8. The method for analyzing factors affecting left-turn conflicts at two-phase intersections according to claim 1, characterized in that: The sensitivity analysis method in S5 includes trend impact analysis and impact degree analysis. The positive or negative value of the coefficient of the influencing factor variable can tell whether the relevant influencing factor is positively correlated or negatively correlated; under the condition that other influencing factors remain unchanged, the absolute value of the coefficient can tell the degree of impact.

9. The method for analyzing factors affecting straight-left conflicts at two-phase intersections according to claim 8 is characterized in that: The specific analysis of S5 is as follows: In the trend impact analysis, through traffic flow, left-turn traffic flow, left-turn radius, and number of lanes are positively correlated with the number of conflicts. As the related influencing factor variables increase, the frequency of straight-left conflicts increases, and the other influencing factor variables are negatively correlated. In the impact degree analysis, the absolute value of the coefficient is used to obtain the impact degree. The through traffic T and left-turn traffic L are modeled in logarithmic form, and their exponents are the elasticity coefficients. For every 1% increase in through traffic T and left-turn traffic L, the frequency of straight-left conflicts increases by 0.4482% and 0.4681%, respectively. The impact of left-turn traffic on the conflict frequency is greater than that of through traffic. Therefore, it can be seen that the main cause of straight-left conflicts is that left-turn traffic does not give way to through traffic. Among the negatively correlated factors, the absolute value of the coefficient shows that the establishment of a curb-type central divider S1, the establishment of an isolation fence S2, the establishment of a left-turn guide line M, and the effective green light duration G of the left-turn phase can reduce the frequency of traffic conflicts, and the degree of influence decreases in turn; the degree of conflict reduction caused by the establishment of a curb-type central divider is 1.87 times that of the isolation fence 10. A two-phase intersection straight-left conflict influencing factor analysis system, characterized by comprising: a module for collecting basic information, a module for establishing an influencing factor analysis model, and a module for analyzing the degree of influence of influencing factors; The module for collecting basic information can implement the contents of S1 and S2 in claim 1; The module for establishing the influencing factor analysis model can implement the contents of S3 and S4 in claim 1; The module for analyzing the influence degree of influencing factors can realize the content of S5 in claim 1.

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