A safety evaluation method for expressway weaving areas based on traffic conflicts

Abstract

The present invention discloses a method for evaluating the safety of an expressway interweaving area based on traffic conflicts, comprising the following steps: S1, dividing the expressway interweaving area into several sections according to the traffic operation characteristics; S2, collecting the traffic conflict data of each section, and dividing the traffic conflict into: flanking conflict, rear-end collision, general conflict and severe conflict; S3, selecting the traffic conflict risk index of each section as a safety evaluation index; S4, according to the safety evaluation index value of each section obtained in step S3, using the grey clustering analysis method, according to the evaluation matrix result, determining the safety level of each section of the interweaving area. The present invention can realize the safety evaluation of the interweaving area of ​​the expressway in sections, effectively identify the safety weaknesses of the interweaving area of ​​the expressway, and then improve the traffic safety of the interweaving area of ​​the expressway in a targeted manner, and has certain application value in traffic safety management.

Description

Technical Field

[0001] The invention relates to a method for evaluating the safety of an expressway weaving zone, and in particular to a method for evaluating the safety of an expressway weaving zone based on traffic conflicts. Background Art

[0002] As the main skeleton of urban roads, urban expressways play an important role in alleviating urban traffic pressure and improving the travel efficiency of citizens. They often bear a considerable proportion of motor vehicle travel with limited road resources. As an important part of urban expressways, the weaving area has changed traffic capacity, and vehicles frequently change lanes and accelerate and decelerate, which leads to an increase in the number of traffic conflicts and traffic accidents and a decrease in safety. Therefore, it is necessary to consider the safety of weaving areas. On the other hand, traffic accident data has limitations such as non-disclosure, insufficient breadth and depth, and long collection cycle. On the contrary, traffic conflict data, which is the previous level and initial form of traffic accidents, is simple to collect and has a short collection cycle. In addition, domestic and foreign studies have shown that there is a strong correlation between traffic conflicts and traffic accidents, and traffic conflicts can be understood as potential traffic accidents. Therefore, traffic conflict data can be applied to the safety evaluation of urban expressway weaving areas. Summary of the invention

[0003] Purpose of the invention: The purpose of the present invention is to provide a method for evaluating the safety of expressway weaving areas. By evaluating the safety level of expressway weaving areas, expressway weaving areas with low safety levels can be effectively identified, and then targeted traffic safety improvements can be made thereon.

[0004] Technical solution: The method for evaluating the safety of an expressway weaving area of ​​the present invention comprises the following steps:

[0005] S1, divide the expressway interweaving area into several sections according to traffic operation characteristics;

[0006] S2, collect traffic conflict data in each section and classify traffic conflicts into: flanking conflict, rear-end conflict, general conflict and severe conflict;

[0007] S3, select the traffic conflict risk index of each section as the safety evaluation index;

[0008] S4, according to the safety evaluation index value of each section obtained in step S3, using the grey cluster analysis method and according to the evaluation matrix result, determine the safety level of each section in the weaving area.

[0009] Furthermore, in the step S1, the interweaving area is divided into a merging area, an inner area of ​​the interweaving area and a diverging area.

[0010] Furthermore, in step S2, a drone equipped with a camera is used to record a video of the interweaving area of ​​the expressway; traffic conflicts in the interweaving area are collected and the sections where traffic conflicts occur are recorded; a trajectory map of the conflicting vehicles is drawn using video tracking software, and the traffic conflicts are divided into flanking conflicts and rear-end conflicts; and the traffic conflicts are divided into general conflicts and severe conflicts based on the collision time TTC of the two vehicles.

[0011] Further, in step S3, the safety evaluation index includes different types of traffic conflict risk indexes and traffic conflict risk indexes of different severity levels;

[0012] The calculation of the different types of traffic conflict risk index K1 is as follows:

[0013] K1=C1×S1+C2×S2

[0014] Wherein, C1 is the weight of the side-on collision, C2 is the weight of the rear-end collision, S1 is the number of side-on collisions per hour, and S2 is the number of rear-end collisions per hour;

[0015] The calculation of the traffic conflict risk index K2 of different severity levels is as follows:

[0016] K2=C3×S3+C4×S4

[0017] C3 is the weight of general conflicts, C4 is the weight of severe conflicts, S3 is the number of general conflicts per hour, and S4 is the number of severe conflicts per hour.

[0018] Further, the implementation process of step S4 is as follows:

[0019] S41, selecting different types of traffic conflict risk indexes K1 and traffic conflict risk indexes K2 of different severity levels in each section to establish an evaluation matrix;

[0020] S42, dividing the safety status of the interlaced area segment into four levels, namely four gray classes, and determining whitening values ​​and whitening weight functions under different gray classes respectively;

[0021] S43, determining clustering weight values ​​of different safety evaluation indicators at different safety levels;

[0022] S44, according to the evaluation matrix result, select the level with the largest evaluation value as the safety evaluation level.

[0023] Compared with the prior art, the present invention has the following significant effects:

[0024] 1. Taking into account the differences of traffic conflicts of different categories and severity, the traffic safety evaluation indicators based on traffic conflict data are more comprehensive;

[0025] 2. The grey cluster analysis method is adopted, the calculation process is simple and can fully mine data information.

[0026] 3. We conducted a section-by-section safety assessment of the weaving area, which can effectively identify safety weaknesses within the weaving area of ​​the expressway and make targeted improvements to traffic safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 It is the overall flow chart of the present invention. DETAILED DESCRIPTION

[0028] The present invention is further described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0029] like Figure 1 As shown, the method for evaluating the safety of the expressway weaving area of ​​the present invention is implemented in the following steps:

[0030] Step 1: Divide the interleaving area into segments.

[0031] A weaving area with a total length of 260m on Longpan Middle Road in Nanjing City, Jiangsu Province was selected as the research object. According to the actual traffic operation characteristics of the weaving area, it was divided into a 70m merging area, a 110m internal area of ​​the weaving area, and an 80m diverging area, which were recorded as Section 1, Section 2, and Section 3 respectively.

[0032] Step 2: Collect traffic conflict data.

[0033] Use a drone equipped with a camera to record the traffic conditions in the interweaving area of ​​the expressway.

[0034] Using video tracking software, we draw the trajectory map of the conflicting vehicles, and divide the traffic conflicts into two categories: flanking conflicts and rear-end conflicts according to the types of traffic conflicts. We calculate the collision time TTC of the two vehicles, and divide the traffic conflicts into general conflicts and severe conflicts according to the severity of the traffic conflicts.

[0035] The collected traffic conflict data of each section are shown in Table 1.

[0036] Table 1 Number of traffic conflicts in weaving areas

[0037]

[0038] Step 3: Calculate the safety evaluation index.

[0039] First, the traffic conflict risk index of the traffic zone is selected as the safety evaluation index, and the calculation formula is as follows:

[0040] K1=C1×S1+C2×S2 (1)

[0041] K2=C3×S3+C4×S4 (2)

[0042] In formula (1), K1 is the risk index of different types of traffic conflicts, S1 is the number of side-on conflicts per hour, and S2 is the number of rear-end conflicts per hour.

[0043] C1 and C2 are the weights of side-on collision and rear-end collision, respectively. They are calculated based on the direct property losses caused by different forms of road traffic accidents corresponding to side-on collision and rear-end collision in the whole country or in the region where the weaving area is located.

[0044] In this example, the average direct property losses corresponding to side collisions and rear-end collisions recorded in the National Road Traffic Accident Statistics Yearbook in 2016, 2017 and 2018 are selected as the basis, and the proportion of the average direct property losses corresponding to different accident forms to the total average direct property losses of the two accident forms is used as the weight. The calculation results are shown in Table 2.

[0045] Table 2 Calculation of weights for side-on collision and rear-end collision

[0046]

[0047] In formula (2), K2 is the traffic conflict risk index of different severity, S3 is the number of general conflicts per unit hour, and S4 is the number of severe conflicts per unit hour.

[0048] C3 and C4 are the weights of general conflict and severe conflict respectively.

[0049] The TTC value of a traffic conflict reflects the possibility of a traffic accident. The smaller the TTC value, the higher the probability of a traffic accident. Conversely, the larger the TTC value, the lower the probability of a traffic accident. The number of traffic conflicts in the weaving area is counted, and the average TTC value of the traffic conflicts is calculated. The reciprocal of the average TTC value is used as the average probability of a traffic accident. The weights of traffic conflicts of different severity are calculated according to different proportions of the average probability of a traffic accident. The calculation results are shown in Table 3.

[0050] Table 3 Calculation of weights of general conflicts and serious conflicts

[0051]

[0052] Finally, the calculation results of the two types of safety evaluation indicators in different sections of the weaving area are shown in Table 4.

[0053] Table 4 Sectional safety evaluation indicators

[0054]

[0055] Step 4: Determine the safety level of the interweaving area by segmentation using the grey cluster analysis method.

[0056] 41) Create an evaluation matrix

[0057] The different types of traffic conflict risk indexes and traffic conflict risk indexes of different severity levels in each section are selected and standardized using the maximum value standardization method to obtain the evaluation matrix K as follows:

[0058]

[0059] 42) Determine the security level whitening value and whitening weight function

[0060] Referring to the commonly used method in traffic conflict TTC research, similarly, the cumulative frequency curve is drawn according to the values ​​of the two indicators, and the values ​​corresponding to 20%, 40%, 60% and 80% in the curve are determined as the first to fourth levels of the safety status (i.e., safety level) of the interweaving section, i.e., four gray classes. After calculation, the whitening values ​​under different gray classes are shown in Table 5.

[0061] Table 5 Whitening values ​​of different gray categories of each index

[0062]

[0063] According to the whitening values ​​at different security levels, the whitening weight functions at different security levels are determined as shown in Table 6.

[0064] Table 6 Whitening weight functions at different security levels

[0065]

[0066]

[0067] 43) Determine cluster weights

[0068] The clustering weights of different indicators at different security levels are obtained by calculation, as shown in Table 7.

[0069] Table 7 Clustering weights

[0070]

[0071] 44) Determine the security level

[0072] The clustering evaluation matrix of interweaving segment obtained by calculation is:

[0073]

[0074] The level with the largest evaluation value is taken as the safety evaluation level of each section in the weaving area. The evaluation results of the three sections are shown in Table 8.

[0075] Table 8 Safety evaluation level of interweaving sections

[0076] Safety rating Segment 1 Level 4 Segment 2 Level 4 Segment 3 Level 3

[0077] Finally, according to the results, the traffic safety situation in the diverging area is slightly better than that in the merging area and the weaving area.

Claims

1. A method for evaluating the safety of an expressway weaving area based on traffic conflicts, characterized in that: Includes steps: S1, divide the expressway interweaving area into several sections according to traffic operation characteristics; S2, collect traffic conflict data in each section and classify traffic conflicts into: flanking conflict, rear-end conflict, general conflict and severe conflict; S3, selecting the traffic conflict risk index of each section as a safety evaluation index; the safety evaluation index includes different types of traffic conflict risk indexes and traffic conflict risk indexes of different severity levels; The calculation of the different types of traffic conflict risk index K1 is as follows: K1=C1×S1+C2×S2 Wherein, C1 is the weight of the side-on collision, C2 is the weight of the rear-end collision, S1 is the number of side-on collisions per hour, and S2 is the number of rear-end collisions per hour; The calculation of the traffic conflict risk index K2 of different severity levels is as follows: K2=C3×S3+C4×S4 C3 is the weight of general conflicts, C4 is the weight of severe conflicts, S3 is the number of general conflicts per hour, and S4 is the number of severe conflicts per hour; S4, according to the safety evaluation index value of each section obtained in step S3, using the grey cluster analysis method and according to the evaluation matrix result, determine the safety level of each section in the weaving area.

2. The method for evaluating the safety of an expressway weaving area based on traffic conflicts according to claim 1 is characterized in that: In the step S1, the interweaving area is divided into a merging area, an inner area of ​​the interweaving area and a diverging area.

3. The method for evaluating the safety of an expressway weaving area based on traffic conflicts according to claim 1 is characterized in that: In the step S2, a drone equipped with a camera is used to record a video of the interweaving area of ​​the expressway; traffic conflicts in the interweaving area are collected and the sections where the traffic conflicts occur are recorded; a trajectory map of the conflicting vehicles is drawn using video tracking software, and the traffic conflicts are divided into flanking conflicts and rear-end conflicts; According to the collision time TTC between two vehicles, traffic conflicts are divided into general conflicts and serious conflicts.

4. The method for evaluating the safety of an expressway weaving area based on traffic conflicts according to claim 1 is characterized in that: The implementation process of step S4 is as follows: S41, selecting different types of traffic conflict risk indexes K1 and traffic conflict risk indexes K2 of different severity levels in each section to establish an evaluation matrix; S42, dividing the security level of the interleaved area segment into four levels, which are four gray classes, and determining whitening values ​​and whitening weight functions under different gray classes respectively; S43, determining clustering weight values ​​of different safety evaluation indicators at different safety levels; S44, according to the evaluation matrix result, select the level with the largest evaluation value as the safety evaluation level.

Images