Mountain area expressway section traffic risk assessment method considering discrete risk source

Abstract

The invention belongs to the technical field of expressway operation safety management, and discloses a mountain area expressway section traffic risk assessment method considering discrete risk sources, comprising the following steps: S1, identifying all discrete risk structure points included in a to-be-assessed section, and obtaining traffic risk values of the structure points; s2, identifying whether a road section risk source exists in the target road section, and calculating a traffic risk value of the road section risk source; s3, aggregating the structural points and the risk value of the road section risk source into a basic risk of the road section; s4, acquiring traffic flow and meteorological early warning data of the target road section, and calculating a dynamic risk adjustment coefficient of the traffic flow and meteorological early warning data; s5, acquiring historical traffic accident data and road attributes, and determining an additional risk correction coefficient; and S6, calculating the traffic risk. According to the method, the traffic risk condition of the highway in the mountainous area can be reflected from the local to the whole comprehensively, the interaction effect among the factors is described accurately, and accurate support is provided for operation safety management and control of the highway in the mountainous area.

Description

Technical Field

[0001] This invention belongs to the field of highway operation safety management technology, specifically involving a method for assessing traffic risk on mountain highway sections that considers discrete risk sources. It is particularly applicable to mountain highway sections affected by complex geological conditions and extreme weather. It addresses the correlation of multiple factors such as geological disasters, special structures, meteorological changes, and traffic flow in complex mountain highway scenarios, and realizes risk assessment at the highway section level to support comprehensive judgment of operation safety. Background Technology

[0002] Due to complex geological conditions and variable weather, mountain highways face multiple risks, including geological disasters, floods, deterioration of road infrastructure, and unforeseen events, posing serious challenges to driving safety and traffic resilience. Existing risk assessments for mountain highways often focus on only a few elements, lacking comprehensive systemic analysis and failing to achieve a complete risk assessment from local structural points to the entire road segment. For example, traditional methods may independently assess the safety of bridges and tunnels, failing to focus on road segment levels more closely related to driving experience. Furthermore, some assessment methods often overlook the coupling effect between inherent road segment attributes (such as alignment and geological structure) and dynamic traffic flow, real-time weather warnings, and historical accident data, resulting in lower accuracy in risk quantification. With the rapid development of transportation infrastructure and advancements in multi-source traffic data acquisition technology, it is necessary to explore a comprehensive risk assessment method for mountain highways that considers multiple risks such as geological disasters, extreme weather, and traffic congestion.

[0003] A review of relevant patents and research reveals significant limitations in current safety technologies for mountainous highways: they often focus on assessing the construction phase of a single structure or have a limited range of operational assessment dimensions, generally lacking a holistic integration of construction and operational risks. Patent CN121094658A designs a method and system for assessing highway slope construction safety. By dynamically adjusting the limits and weights of monitoring indicators based on real-time monitoring data, construction operation type, and environmental factors, it improves the accuracy of slope construction safety assessments. However, this method only addresses safety assessments during the slope construction phase, failing to extend to traffic risk scenarios during the operational phase and unable to cover comprehensive risk assessments of multiple structures. Patent CN120911981A proposes an IoT-based highway bridge construction safety monitoring system and method, utilizing IoT technology to construct a construction risk monitoring system, achieving graded assessment and visualized early warning of bridge construction risks. However, this method is only applicable to the bridge construction phase, neither quantifying traffic risks during the operational phase nor considering the impact of local structures on the overall structure. Patent CN119479289A proposes a method and system for assessing the traffic safety status of highways. By matching historical accident data with road alignment data, combining the analytic hierarchy process (AHP) and entropy weight method to determine indicator weights and construct a spatiotemporal risk index, and then using K-means clustering to classify safety levels, it can identify high-incidence periods and sections of accidents and formulate targeted control measures. However, this method only focuses on the single-dimensional risk coupling between accidents and road alignment, without integrating multi-source dynamic risk factors unique to mountainous areas such as geology and meteorology, and without establishing a multi-scale assessment framework. It is difficult to adapt to the full-chain traffic risk assessment in the complex environment of mountainous highways. Summary of the Invention

[0004] In view of this, the purpose of this invention is to integrate multi-dimensional data such as geology, meteorology, and traffic flow, and based on the framework of "basic risk-dynamic risk-additional risk", to address the problems of single-scale limitations, insufficient multi-factor coordination, and lack of quantitative accuracy in the existing risk assessment of mountain expressways. This invention provides a method for assessing the traffic risk of mountain expressway sections that considers discrete risk sources, realizing a risk assessment scheme from risk source to road section, and providing precise support for the safe operation and management of mountain expressways.

[0005] To achieve the above objectives, the present invention adopts the following technical solution:

[0006] A method for assessing traffic risk on mountainous highway sections considering discrete risk sources includes the following steps:

[0007] S1. Identify all discrete risk structure points within the road segment to be evaluated and obtain the traffic risk value for each structure point;

[0008] S2. Identify whether there are road risk sources in the target road segment, and calculate the traffic risk value of the road risk sources;

[0009] S3. Aggregate the risk values ​​of structural points and road segment risk sources into the basic risk of the road segment;

[0010] S4. Obtain traffic flow and meteorological early warning data for the target road segment, and calculate its dynamic risk adjustment coefficient;

[0011] S5. Obtain historical traffic accident data and road attributes to determine additional risk correction coefficients;

[0012] S6. Based on the calculation results of steps S3-S5, calculate the passage risk, classify the risk level, and output the risk level and corresponding control recommendations.

[0013] Furthermore, in step S1, the discrete risk structural points include bridges, tunnels, slopes, and roadbed pavements.

[0014] Furthermore, the road segment risk source in step S2 refers to the traffic risk in a continuous space caused by a certain factor;

[0015] The contributing factors to road segment risk sources include road segment units with poor alignment combinations and road segment units affected by adverse weather conditions;

[0016] Unfavorable road alignment combinations include the following three types of combinations: "long straight line + steep slope", "long straight line + small radius + short vertical curve", and "continuous downhill + small radius".

[0017] Adverse weather conditions affect road sections including those affected by fog, crosswinds, water accumulation, snow accumulation, and icy conditions.

[0018] Furthermore, in step S3, the calculation expression for the basic risk of the road segment is:

[0019]

[0020] In the formula, The base risk value after aggregation for the i-th risk unit, with the final result within the interval [0, 100]; the first base risk. Take directly The following Allocation is based on the remaining risk capacity and is carried out proportionally. Values ​​for each basic risk factor to be considered.

[0021] Furthermore, in step S4, the dynamic risk adjustment coefficient of the target road segment is a quantitative representation of the impact of dynamic traffic elements on the road segment's traffic risk under the road segment scenario; the dynamic risk adjustment coefficient of the target road segment includes the longitudinal stability coefficient of traffic flow, the lateral stability coefficient of traffic flow, congestion, the proportion of large vehicles, and the frequency of various adverse weather warnings.

[0022] I. Traffic flow longitudinal stability coefficient and traffic flow lateral stability coefficient;

[0023]

[0024]

[0025] In the formula, This represents the longitudinal stability coefficient of traffic flow; This indicates the traffic volume on the downstream road segment; This indicates the average speed of the downstream section; This indicates the traffic volume of the upstream section of the road; This indicates the average speed of the travel segment; Indicates the distance between upstream and downstream data detection points; This represents the lateral stability coefficient of traffic flow; This represents the traffic volume of the j-th road segment; represents the average speed of the j-th road segment; n represents the total number of sub-segments within the evaluated road segment; NL represents the total number of road segments in both directions of the evaluated road segment;

[0026] 0≤ When the value is less than 5, it is classified as low risk, and the adjustment coefficient is 1.0;

[0027] 5≤ When the risk level is less than 20, it is classified as medium risk, with an adjustment factor of 1.05.

[0028] When the value is ≥5, it is classified as high risk, and the adjustment coefficient is 1.1;

[0029] 0≤ When the value is less than 0.2, it is classified as low risk, and the adjustment coefficient is 1.0;

[0030] 0.2≤ When the value is less than 0.5, it is classified as low risk, and the adjustment coefficient is 1.0;

[0031] When the value is ≥0.5, it is classified as high-risk, and the adjustment coefficient is 1.1;

[0032] II. Crowding level and proportion of large vehicles;

[0033] Congestion level represents the ratio of actual traffic flow to designed capacity, and can intuitively reflect traffic flow trends;

[0034] The proportion of large vehicles refers to the proportion of large passenger vehicles, freight vehicles, and special vehicles in the total traffic flow passing through the checkpoint per unit time.

[0035] The congestion level and the proportion of large vehicles are characterized by the maximum and minimum values ​​at each detection point to evaluate the road segment. The value at the congestion level detection point is the average value of the peak hours within a two-month period, and the value at the large vehicle proportion detection point is the total value of the large vehicle proportion within a two-month period.

[0036] When the congestion level is ≥0.95, the adjustment coefficient is 1.10;

[0037] When the congestion level is 0.85 ≤ congestion degree < 0.95, the adjustment coefficient is 1.08;

[0038] When the congestion level is 0.60 ≤ congestion degree < 0.85, the adjustment coefficient is 1.05;

[0039] When the congestion level is <0.60, the adjustment coefficient is 1.00;

[0040] When the proportion of large vehicles is 40% < 60%, the adjustment coefficient is 1.10;

[0041] When the proportion of large vehicles is 30% < 40% or 60% < 70% and the proportion of large vehicles is 40% or 40% or 60% or 70% respectively, the adjustment coefficient is 1.08.

[0042] When the proportion of large vehicles is 20% < 30% or 70% < 80% and the proportion of large vehicles is 1.05;

[0043] When the proportion of large vehicles is ≤20% or >80%, the adjustment coefficient is 1.00;

[0044] III. Frequency of various adverse weather warnings;

[0045] Adjustment coefficients are determined based on the number of various meteorological warnings issued within the administrative region along the route of the road section to be evaluated;

[0046] When the frequency of severe weather warnings is ≥110% of the sample mean, the adjustment coefficient is 1.05;

[0047] When the frequency of adverse weather warnings is less than or equal to 90% of the sample mean and less than 110% of the sample mean, the adjustment coefficient is 1.02.

[0048] When the frequency of severe weather warnings is less than 90% of the sample mean, the adjustment coefficient is 1.0.

[0049] Furthermore, step S5 includes the following sub-steps:

[0050] S5.1 Classifying traffic accident risk levels based on accident attribute indices;

[0051] The accident attribute index is obtained by multiplying the accident frequency per kilometer, which represents the probability of an accident, and the calibrated score of economic loss per person per incident, which represents the severity of the consequences of the accident; then, traffic accident risk levels are classified according to the accident attribute index.

[0052] S5.2 Determine the additional risk correction coefficient based on road attributes;

[0053] When a road is a tourist highway and it is during the peak tourist season, the highway attribute correction factor is 1.1.

[0054] The road is a tourist highway, but during the off-season, the highway attribute correction factor is 1.0;

[0055] When the road is a freight highway, the highway attribute correction factor is 1.1;

[0056] When a road has no specific segment attributes, i.e., it is not a tourist road or a freight road, the road attribute correction factor is 1.0.

[0057] S5.3 The accident risk amplification coefficient for additional risk factors in traffic incidents is calculated using the following formula:

[0058]

[0059] In the formula, Z represents the final risk amplification coefficient, which is 1 when there are no serious accidents in the history of the road section; m usually represents the accident loss score; and n represents the average accident frequency score per kilometer of the road section.

[0060] Furthermore, step S6 includes the following sub-steps:

[0061] S6.1 Calculate traffic risk;

[0062]

[0063] In the formula, This represents the traffic risk value of the assessed road segment; This represents the base risk value of the i-th risk unit; n represents the total number of risk units included in the assessment. represents the adjustment coefficient for the j-th traffic volume dynamic risk factor; m represents the number of dynamic risk adjustment coefficients; represents the adjustment coefficient for the k-th additional risk factor; s represents the number of additional risk adjustment coefficients;

[0064] S6.2 Risk Level Classification;

[0065] When P > 100, the risk level is Level 1, which is a major risk and the acceptable level is unacceptable.

[0066] When 80 < P ≤ 100, the risk level is level two, which is relatively high risk, and the acceptable level is not expected.

[0067] When 60 < P ≤ 80, the risk level is level three, which is general risk and the acceptable level is acceptable.

[0068] When P≤60, the risk level is level four, which is low risk, and the acceptable level is acceptable.

[0069] Beneficial effects:

[0070] 1. It achieves multi-scale assessment, taking into account the impact of discrete risk points and road segment risk sources on the overall traffic risk level of the road segment, and constructs a comprehensive assessment system from point to line, breaking the limitations of a single scale, and can comprehensively reflect the traffic risk status of mountain expressways from the local to the overall.

[0071] 2. Multi-dimensional risk synergistic quantification: Establish a coupled model of three types of risks: "basic-dynamic-additional", scientifically integrate multi-dimensional factors such as geology, topography, meteorology, traffic flow, and historical accidents, and accurately depict the interaction effects between various factors.

[0072] 3. High quantification accuracy and strong operability: Risk values ​​are compressed and aggregated through a competitive allocation model. The design of dynamic adjustment coefficients and additional correction coefficients enables the standardized quantification of risk indicators. Moreover, the indicator data can be obtained through existing monitoring equipment and historical records, making it easy to apply in engineering.

[0073] Other advantages, objectives, and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination, or may be learned from practice of the invention. The objectives and other advantages of the invention can be realized and obtained through the following description. Attached Figure Description

[0074] Figure 1 A logical flowchart illustrating the factors influencing traffic risks on a road segment;

[0075] Figure 2 A flowchart for quantitative assessment of traffic risks on road sections. Detailed Implementation

[0076] To make the technical solutions, advantages, and objectives of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the described embodiments of the present invention without creative effort are within the protection scope of this application.

[0077] like Figure 1 and Figure 2 As shown, this invention provides a method for assessing traffic risk on mountainous highway sections considering discrete risk sources, comprising the following steps:

[0078] Step 1: Identify all discrete risk structure points within the road segment to be evaluated and obtain the traffic risk value for each structure point;

[0079] Discrete risk sources specifically include risky structural points such as bridges and tunnels, as well as continuous road sections affected by adverse linear combinations, fog, icing, and other adverse weather conditions. These discrete risk structural points mainly include four forms: bridges, tunnels, slopes, roadbeds, and pavements. The identification criteria are locations prone to natural disasters or those with poor technical condition assessment results. For identified structural points, an appropriate structural point traffic risk assessment method is used to evaluate their traffic risk value, taking into account their disaster frequency, technical condition, and other possible indicators.

[0080] Step 2: Identify whether there are road risk sources in the target road segment and calculate the traffic risk value of the road risk sources;

[0081] The aforementioned road segment risk source refers to the traffic risk in a continuous space caused by a certain factor. Specific factors include road segment units with poor alignment combinations and road segment units affected by adverse weather. First, it is necessary to refer to relevant specifications to determine the identification criteria of the road segment unit and assign an appropriate risk value.

[0082] For traffic risks caused by poor road alignment features, the risk value of poor linear combinations is considered, mainly including three typical combinations: "long straight line + steep slope", "long straight line + small radius + short vertical curve", and "continuous downhill + small radius". For example, long straight lines can easily cause driver visual fatigue and speed illusion, while steep slopes cause drastic changes in vehicle speed. The combination of the two will increase the risk of accidents due to the interaction of speed difference and driver fatigue. The basic risk score is assigned based on whether there are poor alignment combinations in the section of road to be evaluated, as shown in Table 1 below.

[0083] Table 1 Scoring Table for Poor Linear Combination Road Sections

[0084]

[0085] Similar to unfavorable road alignments, certain sections within the assessed road segment may be identified as areas prone to adverse weather conditions such as patchy fog, posing significant risks. These sections are assigned a basic risk score. Specific types include patchy fog sections, crosswind sections, waterlogged sections, snow-covered sections, and icy sections. For example, patchy fog sections are primarily identified based on visibility classification and the extent of fog coverage. As shown in Table 2, the basic risk scoring standard is based on whether any identified adverse weather-affected sections exist within the assessed road segment area. Multiple affected sections are calculated separately for each.

[0086] Table 2 Scoring Table for Road Sections Affected by Adverse Weather

[0087]

[0088] Step 3: Aggregate the risk values ​​of structural points and road segment risk sources into the basic risk of the road segment.

[0089] The risk values ​​of risk units on a road segment, including structural points and continuous space, are aggregated to form the basic risk value of the road segment. That is, the basic risk level of a road segment is related to several risk units on it. The specific formula is as follows:

[0090] Based on the above indicators, regarding the basic risks of the road section, The calculation formula is as follows:

[0091]

[0092] in, The basic risk factors to be considered include the traffic risk value of structural points, the poor alignment characteristics of road sections, and the adverse weather characteristics of road sections. To comprehensively consider the risk values ​​of each factor, an overall basic risk value within the range [0, 100] is obtained, avoiding excessive numerical inflation when multiple factors are superimposed. Therefore, the design logic of this formula is: the first basic risk... Take directly The following The remaining risk capacity (100 - allocated risk value) is allocated proportionally to reflect the competitive relationship and mutual influence among various structural points, highlighting the structural point with the highest risk.

[0093] Step 4: Obtain traffic flow and meteorological early warning data for the target road segment, and calculate its dynamic risk adjustment coefficient.

[0094] The dynamic risk adjustment coefficient is a quantitative representation of the impact of dynamic traffic factors on the traffic risk of a road segment. Dynamic factors often include changes in traffic flow and weather changes in the area through which the road passes. Specific indicators are as follows:

[0095] The dynamic risk indicators for road segments include the longitudinal stability coefficient of traffic flow, the lateral stability coefficient of traffic flow, congestion, the proportion of large vehicles, and the frequency of various adverse weather warnings. The longitudinal and lateral stability coefficients of traffic flow are calculated using traffic flow, average speed, and road segment distance. They respectively characterize the density difference between upstream and downstream traffic flows or traffic flows in adjacent lanes, describing the degree of difference and spatial unevenness, and reflecting the overall stability of traffic flow at the mesoscopic road segment level. The default value for their corresponding adjustment coefficients is set at 1.0. The specific calculation method is as follows:

[0096]

[0097]

[0098] In the formula, This represents the longitudinal stability coefficient of traffic flow; This indicates the traffic volume on the downstream road segment; This indicates the average speed of the downstream section; This indicates the traffic volume of the upstream section of the road; This indicates the average speed of the travel segment; Indicates the distance between upstream and downstream data detection points; This represents the lateral stability coefficient of traffic flow; This represents the traffic volume of the j-th road segment; represents the average speed of the j-th road segment; n represents the total number of sub-segments within the evaluated road segment; NL represents the total number of road segments in both directions of the evaluated road segment; , This is the corresponding value for the (j-1)th road segment.

[0099] In order to reasonably classify the above indicators in high-speed scenarios and determine different risk levels and corresponding adjustment coefficients, referring to the "Highway Engineering Technical Standards" and the "Interim Technical Requirements for Highway Network Operation Monitoring and Service", it can be clarified that under the highway level, traffic flow speed and flow level can be classified from the perspective of service level. Among them, the V / C value refers to the ratio of traffic flow (V) to capacity (C), which is the core indicator for measuring the road load degree, also known as "saturation".

[0100] Table 3. Speed ​​and Traffic Volume [pcu / (h・ln)] at Different Service Levels (Refer to Table)

[0101]

[0102] As shown in Table 3, combining the physical meanings of the longitudinal and lateral stability coefficients of traffic flow, and relying on the current highway engineering specifications for defining traffic flow states, the longitudinal stability coefficient classification logic focuses on the effect of sudden changes in upstream and downstream density on the longitudinal stability of traffic flow. The greater the density difference, the more likely the traffic flow is to experience congestion or disturbance due to the "spatial bottleneck / evacuation effect." The specifications are mainly based on the provisions of the "Highway Service Level Classification" in the "Highway Engineering Technical Standards." The specific threshold standards are shown in the table above, and with reference to the typical interval of sensors on road sections (typically 2-10km) in engineering practice, the range of values ​​for "density difference per unit length" at different lengths is calculated, and the classification is shown in Table 4 below.

[0103] Table 4. Traffic Flow Longitudinal Stability Classification Table

[0104]

[0105] The lateral stability coefficient of traffic flow characterizes the "relative difference in traffic density between adjacent lanes within the same section," addressing the risks of lane-changing conflicts and lateral disturbances caused by uneven lane density—the more significant the density difference, the more frequent the lane-changing behavior, and the higher the risk of accidents or congestion chain reactions. Referring to the requirements for lane function adaptability in the *Highway Engineering Technical Standards* and the research conclusions on "the impact of lane balance on traffic flow operation" in the *Highway Capacity Manual*, and combining the correlation between lane-changing frequency and lane density difference in engineering practice, a relative proportional division is used to reflect the conflict risk level under different degrees of density difference, as shown in Table 5.

[0106] Table 5. Traffic Flow Lateral Stability Classification Table

[0107]

[0108] Congestion level represents the ratio of actual traffic flow to designed capacity, providing a direct reflection of traffic flow trends. Large vehicle proportion refers to the percentage of large passenger vehicles, freight vehicles, and special vehicles in the overall traffic flow passing through the monitoring point per unit time. Both congestion level and large vehicle proportion are represented by the maximum and minimum values ​​at each monitoring point for the evaluated road segment. The former value at each monitoring point is the average of peak hours over a two-month period, while the latter is the total large vehicle proportion over the two-month period. See Table 6.

[0109] Table 6 Risk Table for Basic Traffic Flow Indicators

[0110]

[0111] The meteorological early warning assessment of road segment dynamic risks is mainly based on the adjustment coefficient of the number of various meteorological early warnings within the administrative region through which the road segment to be assessed passes, as shown in Table 7.

[0112] Table 7. Adjustment Coefficients for the Frequency of Short-Term Adverse Weather Warnings

[0113]

[0114] Step 5: Obtain historical traffic accident data and road attributes to determine the additional risk correction coefficient.

[0115] The additional risk correction coefficient, while not dynamically changing in real time under the road segment scenario, can still influence the comprehensive evaluation of the road segment's traffic risk level by reflecting certain historical characteristics of the road segment. Specific indicators and calculation methods are as follows:

[0116] Additional risk assessment indicators for road sections mainly include the accident risk index and road section functional attribute labels. The accident attribute index is obtained by multiplying the accident frequency per kilometer, which characterizes the probability of an accident, and the score for the economic loss of personnel in a single incident, which characterizes the severity of the accident consequences. Referring to the "Basic Norms for Safety Production Risk Identification, Assessment and Control in the Highway and Waterway Industry", the quantitative standards for assessing the probability of an accident based on the accident frequency per kilometer are shown in Table 8.

[0117] Table 8. Traffic Accident Probability Values

[0118]

[0119] The severity of risk is determined as follows, with the specific values ​​based on the losses in personnel and economic aspects caused by production accidents as specified in relevant regulations. In the current scenario, the average value of all traffic accident losses on a certain road section is taken. The specific standards refer to the "Regulations on Reporting and Handling Production Safety Accidents". The quantitative standards for assessing the severity of accident consequences based on the average personnel and economic losses of the incident are shown in Table 9.

[0120] Table 9. Severity Values ​​for Traffic Accidents

[0121]

[0122] Combining the above two aspects, the frequency of accidents and the most serious accident hazards of the road objects to be evaluated within the period are multiplied together to obtain the results, as shown in Table 10.

[0123] Table 10 Correspondence Table of Traffic Accident Risk Levels

[0124]

[0125] Regarding the amplification factor for additional risk factors in traffic incidents, assuming the risk level is n, the formula for calculating Z is as follows:

[0126]

[0127] In the formula, Z represents the final risk amplification coefficient. When there are no serious accidents in the history of the road section, Z=1; m represents the accident loss score; n represents the average accident frequency score per kilometer of the road section.

[0128] Regarding road attributes, tourist highways and freight highways are considered special types of highways. Considering their uniqueness and importance in the road network, risk assessments require expanding the risk value. Seasonal peak passenger flows on tourist highways and heavy loads and hazardous materials transport on freight highways lower the risk tolerance threshold, resulting in stronger risk spillover effects. Conventional assessments are insufficient to cover these risks; expanding the risk value more accurately reflects the nature of the risk and allows for safety redundancy. Specific coefficient selections correspond to Table 11 below. If no specific road segment attribute is specified, the expansion coefficient for this part takes the default value of 1.

[0129] Table 11 Highway Attribute Correction Coefficient Table

[0130]

[0131] Step 6: Calculate the risk of passage.

[0132] The traffic risk of the aforementioned road segment needs to be comprehensively considered, taking into account the basic risks of the aforementioned risk points and continuous risk units, as well as the dynamic risk adjustment coefficient considering dynamic factors and the additional risk correction coefficient considering historical characteristics. The specific calculation formula is as follows:

[0133] Based on the indicator system corresponding to each factor shown in the table above, the indicators of each component are calculated according to the following formula to obtain the road segment traffic risk value, which characterizes the degree of traffic risk of the road segment, and further classified into levels according to specific thresholds. The formula for calculating the road segment traffic risk value is as follows:

[0134]

[0135] Where P represents the traffic risk value of the assessed road segment; n is the number of structural points and basic risk factors included in the assessed road segment; This refers to the assessed risk value or basic risk factor of the i-th structural point on the road segment; This is the dynamic adjustment coefficient for traffic volume dynamic risk factors; This is a correction factor for additional risk factors.

[0136] The process for quantifying traffic risks on road sections is as follows: Figure 2As shown, the comprehensive assessment process for road segment traffic risk first integrates the traffic risk at structural points, the adverse alignment characteristics of the road segment, and the adverse weather characteristics of the road segment, and obtains a basic risk value through weighting; then, a dynamic adjustment coefficient is generated from the longitudinal stability coefficient, lateral stability coefficient, congestion degree, and large vehicle ratio of traffic flow, while a correction coefficient is calculated from traffic events and road segment attributes; finally, the road segment traffic risk value is comprehensively quantified by multiplying the basic risk value, dynamic adjustment coefficient, and correction coefficient.

[0137] After the road segment traffic risk value is calculated, the risk level needs to be classified according to the traffic risk value. The road segment traffic risk level is divided into four levels: Level 1, Level 2, Level 3, and Level 4, with Level 4 being the lowest and Level 1 being the highest. The risk level classification and corresponding risk degree are shown in Table 12 below.

[0138] Table 12 Classification of Road Section Traffic Risk Levels

[0139]

[0140] It is hereby declared that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for assessing traffic risk on mountainous expressway sections considering discrete risk points, characterized in that, Includes the following steps: S1. Identify all discrete risk structure points within the road segment to be evaluated and obtain the traffic risk value for each structure point; S2. Identify whether there are road risk sources in the target road segment, and calculate the traffic risk value of the road risk sources; S3. Aggregate the risk values ​​of structural points and road segment risk sources into the basic risk of the road segment; S4. Obtain traffic flow and meteorological early warning data for the target road segment, and calculate its dynamic risk adjustment coefficient; S5. Obtain historical traffic accident data and road attributes to determine additional risk correction coefficients; S6. Based on the calculation results of steps S3-S5, calculate the passage risk, classify the risk level, and output the risk level and corresponding control recommendations.

2. The method for assessing traffic risk on mountainous expressway sections considering discrete risk sources according to claim 1, characterized in that, In step S1, the discrete risk structural points include bridges, tunnels, slopes, and roadbed pavements.

3. The method for assessing traffic risk on mountainous expressway sections considering discrete risk sources according to claim 2, characterized in that, The road segment risk source in step S2 refers to the traffic risk in a continuous space caused by a certain factor. The contributing factors to road segment risk sources include road segment units with poor alignment combinations and road segment units affected by adverse weather conditions; Unfavorable road alignment combinations include the following three types of combinations: "long straight line + steep slope", "long straight line + small radius + short vertical curve", and "continuous downhill + small radius". Adverse weather conditions affect road sections including those affected by fog, crosswinds, water accumulation, snow accumulation, and icy conditions.

4. The method for assessing traffic risk on mountainous expressway sections considering discrete risk sources according to claim 3, characterized in that, In step S3, the calculation expression for the basic risk of the road segment is: In the formula, The base risk value after aggregation for the i-th risk unit, with the final result within the range [0, 100]. First basic risk Take directly The following Allocation is based on the remaining risk capacity and is carried out proportionally. Values ​​for each basic risk factor to be considered.

5. The method for assessing traffic risk on mountainous expressway sections considering discrete risk sources according to claim 4, characterized in that: In step S4, the dynamic risk adjustment coefficient of the target road segment is a quantitative representation of the impact of dynamic traffic elements on the road segment's traffic risk under the road segment scenario. The dynamic risk adjustment coefficients for the target road segment include the longitudinal stability coefficient of traffic flow, the lateral stability coefficient of traffic flow, congestion, the proportion of large vehicles, and the frequency of various adverse weather warnings; I. Traffic flow longitudinal stability coefficient and traffic flow lateral stability coefficient; In the formula, This represents the longitudinal stability coefficient of traffic flow; This indicates the traffic volume on the downstream road segment; This indicates the average speed of the downstream section; This indicates the traffic volume of the upstream section of the road; This indicates the average speed of the travel segment; Indicates the distance between upstream and downstream data detection points; This represents the lateral stability coefficient of traffic flow; This represents the traffic volume of the j-th road segment; The average speed of the j-th road segment is represented by n; n represents the total number of sub-segments within the evaluated road segment. NL indicates the total number of road segments in both directions of the assessment. 0≤ When the value is less than 5, it is classified as low risk, and the adjustment coefficient is 1.0; 5≤ When the risk level is less than 20, it is classified as medium risk, with an adjustment factor of 1.

05. When the value is ≥5, it is classified as high risk, and the adjustment coefficient is 1.1; 0≤ When the value is less than 0.2, it is classified as low risk, and the adjustment coefficient is 1.0; 0.2≤ When the value is less than 0.5, it is classified as low risk, and the adjustment coefficient is 1.0; When the value is ≥0.5, it is classified as high-risk, and the adjustment coefficient is 1.1; II. Crowding level and proportion of large vehicles; Congestion level represents the ratio of actual traffic flow to designed capacity, and can intuitively reflect traffic flow trends; The proportion of large vehicles refers to the proportion of large passenger vehicles, freight vehicles, and special vehicles in the total traffic flow passing through the checkpoint per unit time. The congestion level and the proportion of large vehicles are characterized by the maximum and minimum values ​​at each detection point to evaluate the road segment. The value at the congestion level detection point is the average value of the peak hours within a two-month period, and the value at the large vehicle proportion detection point is the total value of the large vehicle proportion within a two-month period. When the congestion level is ≥0.95, the adjustment coefficient is 1.10; When the congestion level is 0.85 ≤ congestion degree < 0.95, the adjustment coefficient is 1.08; When the congestion level is 0.60 ≤ congestion degree < 0.85, the adjustment coefficient is 1.05; When the congestion level is <0.60, the adjustment coefficient is 1.00; When the proportion of large vehicles is 40% < 60%, the adjustment coefficient is 1.10; When the proportion of large vehicles is 30% < 40% or 60% < 70% and the proportion of large vehicles is 40% or 40% or 60% or 70% respectively, the adjustment coefficient is 1.

08. When the proportion of large vehicles is 20% < 30% or 70% < 80% and the proportion of large vehicles is 1.05; When the proportion of large vehicles is ≤20% or >80%, the adjustment coefficient is 1.00; III. Frequency of various adverse weather warnings; Adjustment coefficients are determined based on the number of various meteorological warnings issued within the administrative region along the route of the road section to be evaluated; When the frequency of severe weather warnings is ≥110% of the sample mean, the adjustment coefficient is 1.05; When the frequency of adverse weather warnings is less than or equal to 90% of the sample mean and less than 110% of the sample mean, the adjustment coefficient is 1.

02. When the frequency of severe weather warnings is less than 90% of the sample mean, the adjustment coefficient is 1.

0.

6. The method for assessing traffic risk on mountainous expressway sections considering discrete risk sources according to claim 5, characterized in that, Step S5 includes the following sub-steps: S5.1 Classifying traffic accident risk levels based on accident attribute indices; The accident attribute index is obtained by multiplying the accident frequency per kilometer, which represents the probability of an accident, and the calibrated score of economic loss per person per incident, which represents the severity of the consequences of the accident; then, traffic accident risk levels are classified according to the accident attribute index. S5.2 Determine the additional risk correction coefficient based on road attributes; When a road is a tourist highway and it is during the peak tourist season, the highway attribute correction factor is 1.

1. The road is a tourist highway, but during the off-season, the highway attribute correction factor is 1.0; When the road is a freight highway, the highway attribute correction factor is 1.1; When a road has no specific segment attributes, i.e., it is not a tourist road or a freight road, the road attribute correction factor is 1.

0. S5.3 The accident risk amplification coefficient for additional risk factors in traffic incidents is calculated using the following formula: In the formula, Z represents the final risk amplification coefficient, which is 1 when there are no serious accidents in the history of the road section; m usually represents the accident loss score; and n represents the average accident frequency score per kilometer of the road section.

7. The method for assessing traffic risk on mountainous expressway sections considering discrete risk sources according to claim 6, characterized in that, Step S6 includes the following sub-steps: S6.1 Calculate traffic risk; In the formula, This represents the traffic risk value of the assessed road segment; This represents the base risk value of the i-th risk unit; n represents the total number of risk units included in the assessment. represents the adjustment coefficient for the j-th traffic volume dynamic risk factor; m represents the number of dynamic risk adjustment coefficients; represents the adjustment coefficient for the k-th additional risk factor; s represents the number of additional risk adjustment coefficients; S6.2 Risk Level Classification; when When the value is greater than 100, the risk level is Level 1, which is a major risk and the acceptable level is unacceptable. When 80 < When the value is ≤100, the risk level is Level 2, which is relatively high risk, and the acceptable level is not expected. When 60 < When the risk level is ≤80, the risk level is Level 3, which is considered moderate risk, and the acceptable level is considered acceptable. when When the value is ≤60, the risk level is level four, which is low risk and the acceptable level is acceptable.

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