A method for assessing the resilience of mountainous expressway networks during peak travel periods

Abstract

This invention relates to the field of traffic safety management technology and discloses a method for assessing the resilience of mountainous expressway networks during peak travel periods. The method includes the following steps: Step 1: Acquiring management data for mountainous road sections, traffic flow data at mountainous entrances and exits, and environmental monitoring data for mountainous road sections; Step 2: Analyzing the slope, curvature, and flatness coefficient of each mountainous road section; Step 3: Setting target points, acquiring all driving paths between target points, and analyzing the risk coefficient of each driving path; Step 4: Setting up different types of test vehicles, predicting the travel time of different types of test vehicles under a unified path, achieving high accuracy in multi-dimensional assessment; Step 5: Assessing the resilience level of the mountainous expressway network, the resilience level of each driving path, and the adaptability of a single driving path for different types of vehicles according to road surface conditions, traffic conditions, and vehicle type dimensions, outputting assessment results and management suggestions, with strong practicality through dynamic adjustment.

Description

Technical Field

[0001] This invention relates to the field of traffic safety management technology, specifically to a method for assessing the resilience of mountainous expressway networks during peak travel periods. Background Technology

[0002] A mountain expressway network refers to an expressway system built in complex terrains such as mountains and hills, particularly in mountainous areas. Due to the unique topography of mountainous regions, expressway construction faces greater technical challenges and engineering difficulties. Mountain expressways are not only an important component of regional transportation development but also a crucial guarantee for promoting economic, social, and cultural development and driving regional integration. A mountain expressway network typically consists of multiple expressway lines and branch lines, traversing different mountain ranges, hills, and valleys, connecting cities, county towns, and various important economic zones.

[0003] The resilience assessment of mountain highway networks primarily focuses on infrastructure stability, traffic flow responsiveness, and emergency transportation capabilities. First, it's necessary to evaluate the structural design and construction quality of the highways, as well as their adaptability to extreme environmental conditions, particularly the safety and durability of critical infrastructure such as roadbeds and tunnels in the event of natural disasters or severe weather. Second, it's crucial to assess the highway system's ability to handle traffic flow during peak travel periods, post-disaster situations, or emergencies, including rapid traffic diversion, the construction of temporary access routes, and the coordinated transport of different modes of transportation. Finally, the effectiveness of the emergency management system, rescue forces, and resources for mountain highways during emergencies must be measured, including real-time data feedback from monitoring systems, the timeliness of alarm systems, and the efficiency of traffic guidance measures.

[0004] Currently, traditional highway network resilience assessment methods rely on historical data, static models, and assumptions based on conventional scenarios. When facing peak travel periods, they often cannot accurately predict changes in traffic flow on mountainous highway networks, resulting in low accuracy of resilience assessments. They are unable to dynamically adjust traffic flow and vehicle management, making it difficult to provide timely support for decision-making. Summary of the Invention

[0005] (a) Technical problems to be solved

[0006] To address the shortcomings of existing technologies, this invention provides a method for assessing the resilience of mountainous expressway networks during peak travel periods. This method has advantages such as high accuracy in multi-dimensional assessment and strong practicality in dynamic adjustment, solving the problems of low accuracy and inability to dynamically adjust traffic flow and vehicle management in traditional expressway network resilience assessment methods.

[0007] (II) Technical Solution

[0008] To achieve the above objectives, the present invention provides the following technical solution: a method for assessing the resilience of mountainous expressway networks during peak travel periods, comprising the following steps:

[0009] Step 1: Connect the highway traffic management system, sensing devices, and monitoring devices via the network to acquire management data for all mountain road sections, traffic flow data for all mountain entrances and exits, and environmental monitoring data for all mountain road sections, and classify them into road network datasets, environmental datasets, and traffic flow datasets.

[0010] Step 2: Based on the road network dataset and environmental dataset, analyze the slope PD and curvature QL of each mountain road segment, and generate the corresponding flatness coefficient Ptx;

[0011] Step 3: Set target points based on the traffic data set, obtain all driving paths between target points, and then combine the road network data set and the environment data set to analyze the total number of curves S, road length variability Cbdl, and average width of each driving path. Road width variability (Kbdl), average vegetation height Average rainfall average humidity Temperature fluctuation rate Wbdl and average flow rate And generate the corresponding risk coefficient Fxx;

[0012] Step 4: Set up different types of test vehicles, and then combine the road network dataset and the environment dataset to predict the travel time YZT of different types of test vehicles under the condition of unified driving path;

[0013] Step 5: Set fixed ranges for flatness threshold PTY, risk threshold FXY, and duration threshold SCY. Then, combine the flatness coefficient Ptx, risk coefficient Fxx, and travel duration YZT to assess the resilience level of the mountainous expressway network, the resilience level of each travel route, and the adaptability of a single travel route for different types of vehicles, according to three dimensions: road surface conditions, traffic status, and vehicle type. Output the corresponding assessment results and management recommendations.

[0014] Preferably, in step one, the expression for the road network dataset is {D1}. h D2 h D3 h ... Dn h}, D1 h To Dn h These are the management data for the first to the nth mountain road sections. The management data includes road length, road width, turning radius, road surface void ratio, construction time, and turning arc length. h represents the altitude of each mountain road section. Each mountain road section contains only one curve.

[0015] Preferably, in step one, the expression for the environmental dataset is {H1}. m H2 m H3 m ... Hj m}, H1 m To Hj m These are the environmental monitoring data for the first to the nth mountain road sections, respectively. The environmental monitoring data includes vegetation coverage, roadside vegetation height, rainfall, humidity, and temperature. m represents the specific time point at which the environmental monitoring data for each mountain road section was obtained.

[0016] Preferably, in step one, the expression for the traffic dataset is {K1}. s K2 s K3 s ...Ku s}, K1 s To Ko s These represent traffic flow data for the first to the 0th mountain entrances / exits, including both pedestrian and vehicle traffic. s represents the specific time point at which traffic flow data for each mountain entrance / exit was obtained.

[0017] Preferably, in step two, the flatness coefficient Ptx is calculated as follows:

[0018] S11. Based on the road network dataset, extract the management data for the i-th mountain road segment and mark the turning radius of the i-th mountain road segment as WR. i The pavement roughness of the i-th mountain road segment is denoted as MK. i The construction time of the i-th mountain road section is marked as JS. i The altitude of the starting point of the i-th mountain road segment is marked as qi. h Mark the altitude of the end point of the i-th mountain road segment as zi. h Then, using GPS positioning technology, the horizontal distance from the start to the end of the i-th mountain road segment is marked as SP. i ;

[0019] S12. Based on the environmental dataset, extract the environmental monitoring data for the i-th mountain road segment and label the vegetation coverage rate of the i-th mountain road segment as ZF. i ;

[0020] S13. Calculate the slope PD of the i-th mountain road segment. i The calculation formula is as follows:

[0021]

[0022] In the formula, zi h -qi hThis represents the altitude difference between the start and end points of the i-th mountain road segment;

[0023] S14. Calculate the curvature QL of the i-th mountain road segment. i The calculation formula is as follows:

[0024]

[0025] S15. Calculate the flatness coefficient Ptx of the i-th mountain road segment. i The calculation formula is as follows:

[0026]

[0027] In the formula, BPD represents the standard slope, used to measure the steepness of mountain road sections; α1 represents the evaluation weight for the ratio of slope to standard slope; BQL represents the standard curvature, used to measure the curvature of curves in mountain road sections; α2 represents the evaluation weight for the ratio of curvature to standard curvature; BMK represents the standard porosity, used to measure the density of road surface in mountain road sections; α3 represents the evaluation weight for the ratio of pavement porosity to standard porosity; BJS represents the standard duration, used to measure the aging of road surface in mountain road sections; α4 represents the evaluation weight for the ratio of construction time to standard duration; BZF represents the standard coverage rate, used to measure the density of vegetation coverage in mountain road sections; α5 represents the evaluation weight for the ratio of vegetation coverage to standard coverage rate; and α1 + α2 + α3 + α4 + α5 = 1. This represents the flatness coefficient Ptx of the i-th mountain road segment, calculated using weights α1, α2, α3, α4, and α5. i .

[0028] Preferably, in step three, the risk coefficient Fxx is calculated as follows:

[0029] S21. Based on the traffic data set, set target point 1 and target point 2. Both target point 1 and target point 2 are entrances and exits in the mountainous area. They are different but connected. Then, use map software to obtain all driving paths between target point 1 and target point 2 and mark them as {L1, L2, L3, ..., Lp}. L1 to Lp are the first to the pth driving paths between target point 1 and target point 2, respectively. Each driving path is composed of several mountain road segments.

[0030] S22. Extract the k-th driving route between target point 1 and target point 2, wherein the k-th driving route consists of the a-th mountain road segment, the b-th mountain road segment, the c-th mountain road segment, and the d-th mountain road segment;

[0031] S23. Based on the road network dataset, mark the road surface length of the a-th mountain road segment as cd.a The length of the b-th mountain road segment is marked as cd. b The length of the c-th mountain road segment is marked as cd. c The road surface length of the d-th mountain road segment is marked as cd. d Then calculate the total length ZC of the kth travel path. k Average length of mountain road sections and the volatility Cbdl of the road surface length of the kth driving path k ;

[0032] ZC k =cd a +cd b +cd c +cd d

[0033]

[0034] In the formula, S k S represents the total number of mountain road sections in the k-th driving route, i.e., the total number of curves in the k-th driving route. k =4, This represents the ratio of the total path length to the total number of road segments, which is the average length of the mountain road segments in the k-th travel path. cd l Let represent the road surface length of any one of the following mountain road segments: the a-th, b-th, c-th, and d-th mountain road segments. This represents the volatility Cbdl of the road surface length of the k-th driving path, obtained using the standard deviation formula. k ;

[0035] S24. Based on the road network dataset, mark the road width of the a-th mountain road segment as kd. a The road width of the b-th mountain road section is marked as kd. b The road width of the c-th mountain road segment is marked as kd. c The road width of the d-th mountain road segment is marked as kd. d Then calculate the total width ZK of the k-th driving path. k Average width of mountain road sections and the volatility Kbdl of the road width of the kth driving path k ;

[0036] ZK k =kd a +kd b +kd c +kd d

[0037]

[0038] In the formula, This represents the ratio of the total width of the route to the total number of road segments, which is the average width of the mountain road segments in the k-th travel route. kd q Let represent the road width of any one of the following mountain road segments: a-th, b-th, c-th, and d-th. This represents the volatility Kbdl of the road width for the k-th driving path, calculated using the standard deviation formula. k ;

[0039] S25. Based on the environmental dataset, label the roadside vegetation height of the a-th mountain road segment as gd. a The height of the roadside vegetation in the b-th mountainous section is marked as gd. b The height of the roadside vegetation in the c-th mountain road section is marked as gd. c The height of the roadside vegetation in the d-th mountain road section is marked as gd. d Then calculate the average vegetation height of the mountainous section in the k-th driving route.

[0040]

[0041] S26. Based on the environmental dataset, label the rainfall of the a-th mountain road segment as yu. a The humidity of the a-th mountain road section is marked as sd. a The rainfall in the b-th mountain road section is marked as yu. b The humidity of the b-th mountain road section is marked as sd. b The rainfall in the c-th mountain road section is marked as yu. c The humidity of the c-th mountain road section is marked as sd. c The rainfall in the d-th mountain road section is marked as yu. d The humidity of the d-th mountain road section is marked as sd. d Then calculate the average rainfall in the mountainous section of the k-th driving route. and average humidity drop

[0042]

[0043] S27. Based on the environmental dataset, label the temperature of the a-th mountain road segment as wd. a The temperature of the b-th mountain road section is marked as wd. b The temperature of the c-th mountain road segment is marked as wd. cThe temperature of the d-th mountain road section is marked as wd. d Then calculate the average temperature of the mountain section in the k-th driving route. and the temperature fluctuation rate Wbdl in mountainous road sections k ;

[0044]

[0045] In the formula, wd e This represents the temperature of any one of the following mountain road segments: the a-th, b-th, c-th, and d-th. This represents the temperature fluctuation rate Wbdl in the mountainous section of the k-th driving route, calculated using the standard deviation formula. k ;

[0046] S28. Based on the traffic data set, mark the pedestrian traffic at target point 1 as rl. mu1 Mark the traffic flow at target point 1 as cl mu1 Mark the pedestrian flow at target point two as rl mu2 Mark the traffic flow at target point two as cl mu2 Then calculate the total flow rate ZL between target point one and target point two, as well as the average flow rate for each travel path.

[0047] ZL = rl mu1 +cl mu1 +rl mu2 +cl mu2

[0048]

[0049] S29. Calculate the risk coefficient Fxx of the k-th driving path between target point 1 and target point 2. k The calculation formula is as follows:

[0050] If the average rainfall in the mountainous sections of the k-th travel route is... =0,

[0051]

[0052] In the formula, β1 represents the evaluation weight for the total number of mountain road sections in the driving route, and β2 represents the evaluation weight for the road surface length fluctuation rate in the driving route. β1 represents the standard road width, used to measure the width of road surfaces in mountainous areas. β3 represents the evaluation weight for the ratio of average width to standard road width; β4 represents the evaluation weight for road width fluctuation during the driving route; β5 represents the evaluation weight for average vegetation height during the driving route; β6 represents the evaluation weight for average humidity during the driving route; β7 represents the evaluation weight for temperature fluctuation during the driving route in mountainous areas; β8 represents the evaluation weight for average traffic flow. β1 + β2 + β3 + β4 + β5 + β6 + β7 + β8 = 1. This indicates that the risk coefficient Fxx of the k-th driving path is obtained by applying the weights β1, β2, β3, β4, β5, β6, β7, and β8. k ;

[0053] If the average rainfall in the mountainous sections of the k-th travel route is... Not 0,

[0054]

[0055] In the formula, This indicates the evaluation weight for the total number of mountainous road sections in the driving route. This indicates the evaluation weight for the road surface length fluctuation rate during the driving path. This indicates the evaluation weight for the ratio of average width to standard road width. This indicates the evaluation weight for road width fluctuation rate during the driving path. This indicates the evaluation weight of the average vegetation height along the driving route. This indicates the assessment weight of the average rainfall along the travel route. This indicates the evaluation weight of average humidity along the driving route. This indicates the assessment weight for temperature fluctuation rate in mountainous road sections during the driving route. This indicates the evaluation weight for average flow. Indicates according to and The weights are used to obtain the risk coefficient Fxx of the k-th driving path. k .

[0056] Preferably, in step four, the calculation process for the passage time YZT is as follows:

[0057] S31. The types of test vehicles include x1, x2 and x3. Among them, the body length of x1 test vehicles is <6 meters, 6 meters ≤ x2 test vehicles have a body length ≤9 meters, and the body length of x3 test vehicles is >9 meters.

[0058] S32. Presumably, the x-th test vehicle travels on the k-th travel path between target point 1 and target point 2, starting from either target point 1 or target point 2. The x-th test vehicle belongs to class x1. The length of the x-th test vehicle is denoted as che. x ;

[0059] S33. Connect to the highway traffic management system via network and obtain the speed limit marker xv for the kth travel path between target point one and target point two. k ;

[0060] S34. Based on the road network dataset, the turning arc length of the a-th mountain road segment will be labeled as hl. a Mark the turning arc length of the b-th mountain road segment as hl. b Mark the turning arc length of the c-th mountain road segment as hl. c Mark the turning arc length of the d-th mountain road segment as hl. d Then predict the cornering time (GWT) of the xth test vehicle. x and the duration of YZT x ;

[0061]

[0062] In the formula, μ represents the delay factor, μ×che x This indicates the delay time for the xth test vehicle when it takes a turn, based on the vehicle's length. This indicates that the cornering time (GWT) of the xth test vehicle is predicted based on the delay duration. x ;

[0063] If the average rainfall in the mountainous sections of the k-th travel route is... =0,

[0064]

[0065] In the formula, θ represents the base time required for the x-th test vehicle to complete the k-th travel path under speed-limited conditions, where θ represents the delay factor. This indicates the delay time of the x-th test vehicle traveling on the k-th travel path, based on the average humidity. This indicates that the predicted passage time YZT for the xth test vehicle is obtained based on the base duration and the delay duration. x ;

[0066] If the average rainfall in the mountainous sections of the k-th travel route is... Not 0,

[0067]

[0068] In the formula, Represents the delay factor. This indicates the delay time of the x-th test vehicle traveling on the k-th route, based on the average rainfall. This indicates that the predicted transit time YZT for the xth vehicle is obtained based on the base duration and the delay duration. x .

[0069] Preferably, in step five, the flatness coefficient Ptx of each mountain road segment is compared with the flatness threshold PTY. If the flatness coefficient Ptx of a single mountain road segment is lower than the flatness threshold PTY, it means that the single mountain road segment has reached the maintenance condition. Then, the number of mountain road segments that have reached the maintenance condition is counted, and the resilience level of the mountain expressway network is evaluated according to the dimension of road surface conditions.

[0070] If the number of mountain road sections that have reached the maintenance conditions is less than 20% of the total number of mountain road sections, the resilience level of the mountain expressway network is Level 1, and it is recommended to carry out road surface construction during off-peak travel periods.

[0071] If the number of mountain road sections that have met the maintenance conditions is less than 50% of the total number of mountain road sections, the resilience level of the mountain expressway network is level two. It is recommended to carry out road construction immediately and prohibit vehicles with a load of more than 6 tons from entering the mountain expressway network.

[0072] If the number of mountain road sections that have reached the maintenance conditions is greater than or equal to 50% of the total number of mountain road sections, the resilience level of the mountain expressway network is level three. It is recommended to carry out road construction immediately and prohibit vehicles with a load of more than 3 tons from entering the mountain expressway network.

[0073] The pavement conditions of a mountain expressway network with a first-level toughness rating are better than those of a second-level toughness rating, and the pavement conditions of a second-level toughness rating are better than those of a third-level toughness rating.

[0074] Preferably, in step five, the risk coefficient Fxx of each driving path between target point one and target point two is compared with the risk threshold FXY, and the resilience level of each driving path is evaluated according to the dimension of traffic status.

[0075] If the risk coefficient Fxx of a single driving route is lower than the risk threshold FXY, it means that the traffic status of the single driving route is not saturated, the resilience level of the single driving route is level one, no traffic diversion is required, and all vehicles are allowed to continue to pass at the speed limit.

[0076] If the risk coefficient Fxx of a single driving route is included in the risk threshold FXY, it means that the traffic status of the single driving route is not saturated, the resilience level of the single driving route is level two, and it is recommended to carry out traffic management and reduce the speed limit of the driving route by 20%.

[0077] If the risk coefficient Fxx of a single driving route exceeds the risk threshold FXY, it indicates that the traffic flow of the single driving route is saturated, the resilience level of the single driving route is level three, and it is recommended to strengthen traffic management, prohibit vehicles from passing, and start limiting traffic flow and queuing.

[0078] The traffic conditions of a Level 1 resilience driving route are better than those of a Level 2 resilience driving route, and the traffic conditions of a Level 2 resilience driving route are better than those of a Level 3 resilience driving route.

[0079] Preferably, in step five, under the condition of a unified driving route, the travel time YZT of test vehicles of categories x1, x2 and x3 is compared with the time threshold SCY, and the resilience level of the driving route is evaluated according to the dimension of vehicle type.

[0080] If the travel time YZT of a single type of test vehicle is lower than the travel time threshold SCY, it means that the resilience level of the travel route is Level 1 for that single type of test vehicle, and it is recommended that all vehicles of that single type prioritize choosing that travel route for travel.

[0081] If the travel time YZT of a single type of test vehicle is included in the travel time threshold SCY, it means that for a single type of test vehicle, the resilience level of the travel route is level two, allowing all vehicles of the single type to choose the travel route for travel;

[0082] If the travel time YZT of a single type of test vehicle exceeds the time threshold SCY, it means that the resilience level of the driving path is level three for the single type of test vehicle. The body length of the single type of test vehicle increases the risk of traffic congestion at the curves of the driving path, and all vehicles corresponding to the single type should be prohibited from entering the driving path.

[0083] For a single type of test vehicle, the adaptability of Level 1 toughness driving paths is better than that of Level 2 toughness driving paths, and the adaptability of Level 2 toughness driving paths is better than that of Level 3 toughness driving paths.

[0084] Compared with existing technologies, this invention provides a method for assessing the resilience of mountainous expressway networks during peak travel periods, which has the following advantages: high accuracy of multi-dimensional assessment and strong practicality of dynamic adjustment.

[0085] 1. This invention connects a highway traffic management system, sensing devices, and monitoring devices via a network to acquire management data for all mountain road sections, traffic flow data for all mountain entrances and exits, and environmental monitoring data for all mountain road sections. These data are then categorized into road network datasets, environmental datasets, and traffic flow datasets. Based on these datasets, the slope (PD) and curvature (QL) of each mountain road section are analyzed, generating a corresponding flatness coefficient (Ptx). A higher flatness coefficient (Ptx) indicates better road conditions, directly impacting driving experience and vehicle performance. Target points are set based on the traffic flow dataset, and all driving paths between these points are acquired. By considering multiple factors, the risk coefficient (Fxx) of each driving path is analyzed, helping to identify potentially high-risk driving paths or road sections in advance. This provides early warnings for traffic management, reducing the occurrence of emergencies. Different types of test vehicles are set up, and under the same driving path, the travel time (YZT) of different types of test vehicles is predicted. Based on the prediction results of different types of test vehicles, refined traffic flow management is achieved, ensuring appropriate vehicles enter specific road sections and avoiding traffic bottlenecks caused by incompatible vehicle types. The multi-dimensional evaluation has high accuracy.

[0086] 2. This invention sets fixed-range flatness thresholds PTY, risk thresholds FXY, and duration thresholds SCY, and combines them with flatness coefficients Ptx, risk coefficients Fxx, and travel duration YZT. Based on three dimensions—road surface conditions, traffic conditions, and vehicle type—it assesses the resilience level of mountain highway networks, the resilience level of each travel route, and the adaptability of individual travel routes to different vehicle types. It outputs corresponding assessment results and management recommendations, scientifically evaluating the emergency response capabilities, traffic flow management capabilities, and long-term sustainable development capabilities of mountain highway networks. This optimizes road use strategies, helps formulate more precise peak-hour traffic plans, effectively reduces congestion and accidents, and ensures the balance and smoothness of traffic flow during peak travel periods. Its dynamic adjustment is highly practical. Attached Figure Description

[0087] Figure 1 This is a diagram illustrating the steps of the method of the present invention. Detailed Implementation

[0088] 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 embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0089] Example 1

[0090] Please see Figure 1This invention provides a method for assessing the resilience of mountainous expressway networks during peak travel periods, comprising the following steps:

[0091] Step 1: By connecting to the highway traffic management system, sensors, and monitoring devices via the network, management data for all mountain road sections, traffic flow data for all mountain entrances and exits, and environmental monitoring data for all mountain road sections are acquired. These data are then categorized into road network datasets, environmental datasets, and traffic flow datasets. The real-time updates and classification of multi-dimensional data can accurately reflect traffic flow and environmental changes in mountainous areas. Comprehensive collection of key information is crucial for subsequent prediction and adjustment of traffic conditions during peak travel periods.

[0092] Step 2: Based on the road network dataset and environmental dataset, analyze the slope PD and curvature QL of each mountain road segment, and generate the corresponding flatness coefficient Ptx. For mountain highways, the flatness coefficient Ptx directly affects the driving experience and vehicle performance.

[0093] Step 3: Set target points based on the traffic data set, obtain all driving paths between target points, and then combine the road network data set and the environment data set to analyze the total number of curves S, road length variability Cbdl, and average width of each driving path. Road width variability (Kbdl), average vegetation height Average rainfall average humidity Temperature fluctuation rate Wbdl and average flow rate It generates a corresponding risk coefficient Fxx, and by taking into account multiple factors, evaluates the risk coefficient Fxx of each driving route. This helps to identify potentially high-risk driving routes or road sections in advance, providing early warnings for traffic management and reducing the occurrence of emergencies.

[0094] Step 4: Set up different types of test vehicles, and combine them with road network datasets and environmental datasets. Under the condition of unified driving path, predict the travel time YZT of different types of test vehicles. Based on the prediction results of different types of test vehicles, it helps to refine traffic flow management, ensure that appropriate vehicles enter specific road sections, avoid traffic bottlenecks caused by unsuitable vehicle types, and achieve high accuracy in multi-dimensional evaluation.

[0095] Step 5: Set fixed-range flatness threshold PTY, risk threshold FXY, and duration threshold SCY. Then, combine the flatness coefficient Ptx, risk coefficient Fxx, and travel duration YZT to assess the resilience level of the mountain expressway network, the resilience level of each travel route, and the adaptability of a single travel route for different types of vehicles, according to three dimensions: road surface conditions, traffic status, and vehicle type. Output the corresponding assessment results and management suggestions to scientifically evaluate the emergency response capability, traffic flow capability, and long-term sustainable development capability of the mountain expressway network. The dynamic adjustment is highly practical.

[0096] The flatness coefficient Ptx of each mountain road segment is compared with the flatness threshold PTY. If the flatness coefficient Ptx of a single mountain road segment is lower than the flatness threshold PTY, it means that the single mountain road segment has reached the maintenance condition. The number of mountain road segments that have reached the maintenance condition is then counted, and the resilience level of the mountain expressway network is evaluated according to the dimension of road surface conditions.

[0097] If the number of mountain road sections that have reached the maintenance conditions is less than 20% of the total number of mountain road sections, the resilience level of the mountain expressway network is Level 1, and it is recommended to carry out road surface construction during off-peak travel periods.

[0098] If the number of mountain road sections that have met the maintenance conditions is less than 50% of the total number of mountain road sections, the resilience level of the mountain expressway network is level two. It is recommended to carry out road construction immediately and prohibit vehicles with a load of more than 6 tons from entering the mountain expressway network.

[0099] If the number of mountain road sections that have reached the maintenance conditions is greater than or equal to 50% of the total number of mountain road sections, the resilience level of the mountain expressway network is level three. It is recommended to carry out road construction immediately and prohibit vehicles with a load of more than 3 tons from entering the mountain expressway network.

[0100] The pavement conditions of a mountain expressway network with a first-level toughness rating are better than those of a second-level toughness rating, and the pavement conditions of a second-level toughness rating mountain expressway network are better than those of a third-level toughness rating mountain expressway network.

[0101] Compare the risk coefficient Fxx of each driving path between target point 1 and target point 2 with the risk threshold FXY, and assess the resilience level of each driving path according to the dimension of traffic status.

[0102] If the risk coefficient Fxx of a single driving route is lower than the risk threshold FXY, it means that the traffic status of the single driving route is not saturated, the resilience level of the single driving route is level one, no traffic diversion is required, and all vehicles are allowed to continue to pass at the speed limit.

[0103] If the risk coefficient Fxx of a single driving route is included in the risk threshold FXY, it means that the traffic status of the single driving route is not saturated, the resilience level of the single driving route is level two, and it is recommended to carry out traffic management and reduce the speed limit of the driving route by 20%.

[0104] If the risk coefficient Fxx of a single driving route exceeds the risk threshold FXY, it indicates that the traffic flow of the single driving route is saturated, the resilience level of the single driving route is level three, and it is recommended to strengthen traffic management, prohibit vehicles from passing, and start limiting traffic flow and queuing.

[0105] The traffic conditions of a Level 1 resilience driving route are better than those of a Level 2 resilience driving route, and the traffic conditions of a Level 2 resilience driving route are better than those of a Level 3 resilience driving route.

[0106] Under the condition of a unified driving route, the travel time YZT of test vehicles of categories x1, x2 and x3 is compared with the time threshold SCY, and the resilience level of the driving route is evaluated according to the dimension of vehicle type.

[0107] If the travel time YZT of a single type of test vehicle is lower than the travel time threshold SCY, it means that the resilience level of the travel route is Level 1 for that single type of test vehicle, and it is recommended that all vehicles of that single type prioritize choosing that travel route for travel.

[0108] If the travel time YZT of a single type of test vehicle is included in the travel time threshold SCY, it means that for a single type of test vehicle, the resilience level of the travel route is level two, allowing all vehicles of the single type to choose the travel route for travel;

[0109] If the travel time YZT of a single type of test vehicle exceeds the time threshold SCY, it means that the resilience level of the driving path is level three for the single type of test vehicle. The body length of the single type of test vehicle increases the risk of traffic congestion at the curves of the driving path, and all vehicles corresponding to the single type should be prohibited from entering the driving path.

[0110] For a single type of test vehicle, the adaptability of the Level 1 toughness driving path is better than that of the Level 2 toughness driving path, and the adaptability of the Level 2 toughness driving path is better than that of the Level 3 toughness driving path.

[0111] In this embodiment, by integrating data from the traffic management system, sensors, and monitoring equipment, a comprehensive understanding of the real-time status of mountain highways is ensured, providing rich foundational information for subsequent analysis. The slope (PD) and curvature (QL) of each mountain road segment are analyzed, and the corresponding flatness coefficient (Ptx) is generated. Excessively steep slopes or sharp bends may increase the risk of accidents, thus identifying hazardous mountain road segments and ensuring traffic safety for people and vehicles. Target points are set, and all driving paths between these points are acquired. The risk coefficient (Fxx) of each driving path is analyzed, which helps predict congestion during peak hours, providing more comprehensive information for drivers and traffic management. By providing safer and more efficient driving route suggestions and setting up different types of test vehicles along a unified driving path, the system predicts the travel time (YZT) for different types of test vehicles. This accurately assesses the adaptability and driving ability of different types of vehicles on mountain highways. Based on three dimensions—road conditions, traffic status, and vehicle type—the system evaluates the resilience level of the mountain highway network, the resilience level of each driving path, and the adaptability of a single driving path for different types of vehicles. This optimizes road use strategies, helps to develop more precise peak-hour traffic planning, effectively reduces congestion and accidents, and ensures the balance and smoothness of traffic flow during peak travel periods.

[0112] Example 2

[0113] Please refer to Table 1. This embodiment is based on the explanation of Embodiment 1. Specifically, the flatness coefficient Ptx calculation process is as follows:

[0114] S11. Based on the road network dataset, extract the management data for the i-th mountain road segment and mark the turning radius of the i-th mountain road segment as WR. i The pavement roughness of the i-th mountain road segment is denoted as MK. i The construction time of the i-th mountain road section is marked as JS. i The altitude of the starting point of the i-th mountain road segment is marked as qi. h Mark the altitude of the end point of the i-th mountain road segment as zi. h Then, using GPS positioning technology, the horizontal distance from the start to the end of the i-th mountain road segment is marked as SP. i That is, the straight-line distance from the starting point to the ending point in the horizontal direction;

[0115] S12. Based on the environmental dataset, extract the environmental monitoring data for the i-th mountain road segment and label the vegetation coverage rate of the i-th mountain road segment as ZF. i ;

[0116] S13. Calculate the slope PD of the i-th mountain road segment. i The calculation formula is as follows:

[0117]

[0118] In the formula, zi h -qi h This represents the difference in elevation between the start and end points of the i-th mountain road segment. The smaller the gradient PD, the flatter the road. Novice drivers should try to avoid highways with steep gradients to ensure the stability and safety of the vehicle at high speeds.

[0119] S14. Calculate the curvature QL of the i-th mountain road segment. i The calculation formula is as follows:

[0120]

[0121] The smaller the curvature QL, the gentler the curve and the flatter the road surface. Mountain highways usually choose a larger radius for curves to reduce the curvature of the turn, thereby improving the driving experience.

[0122] S15. Calculate the flatness coefficient Ptx of the i-th mountain road segment. i The calculation formula is as follows:

[0123]

[0124] In the formula, BPD represents the standard slope, used to measure the steepness of mountain road sections; α1 represents the evaluation weight for the ratio of slope to standard slope; BQL represents the standard curvature, used to measure the curvature of curves in mountain road sections; α2 represents the evaluation weight for the ratio of curvature to standard curvature; BMK represents the standard porosity, used to measure the density of road surface in mountain road sections; α3 represents the evaluation weight for the ratio of pavement porosity to standard porosity; BJS represents the standard duration, used to measure the aging of road surface in mountain road sections; α4 represents the evaluation weight for the ratio of construction time to standard duration; BZF represents the standard coverage rate, used to measure the density of vegetation coverage in mountain road sections; α5 represents the evaluation weight for the ratio of vegetation coverage to standard coverage rate; and α1 + α2 + α3 + α4 + α5 = 1. This means that the flatness coefficient Ptxi of the i-th mountain road segment is obtained according to the weights α1, α2, α3, α4 and α5. The higher the flatness coefficient Ptx, the better the road surface conditions.

[0125] Specifically, in Table 1, the standard slope is 10%, the standard curvature is 10 degrees / meter, the standard porosity is 50%, the standard duration is 20 years, the standard coverage is 80%, and in the formula for calculating the flatness coefficient Ptx, α1=0.1, α2=0.1, α3=0.3, α4=0.3, α5=0.2;

[0126]

[0127]

[0128] Table 1

[0129] The flatness threshold PTY is set to 5.5-6.0. The total number of mountain road sections is 3. It is determined that 2 mountain road sections have reached the maintenance conditions. The resilience level of the mountain expressway network is level 3. It is recommended to carry out road surface construction immediately and prohibit vehicles with a load of more than 3 tons from entering the mountain expressway network.

[0130] In this embodiment, factors affecting the flatness coefficient Ptx of mountain road sections also include pavement porosity, construction time, and vegetation coverage. The porosity affects the density, durability, and waterproofness of the pavement. The lower the porosity of the pavement material, the higher the density, which can more effectively prevent water infiltration and reduce the damage to the material's toughness caused by freeze-thaw cycles. The pavement material of mountain highways needs to take into account long-term natural aging factors such as ultraviolet radiation and oxidation. Materials that age more slowly can maintain their toughness for a longer period of time. In addition, mountain highways with high vegetation coverage may indirectly affect the pavement flatness, especially when the soil is relatively loose or the roadbed is not solid enough. Root growth may cause pavement undulations, which in turn affect the long-term stability of the pavement structure.

[0131] Example 3

[0132] This embodiment is based on the explanation of Embodiment 1. Specifically, the calculation process for the risk coefficient Fxx is as follows:

[0133] S21. Based on the traffic data set, set target point 1 and target point 2. Both target point 1 and target point 2 are entrances and exits in mountainous areas. They are different but connected. Then, use map software to obtain all driving routes between target point 1 and target point 2 and mark them as {L1, L2, L3, ..., Lp}. L1 to Lp are the first to the pth driving routes between target point 1 and target point 2, respectively. Each driving route consists of several mountain road segments, which helps to compare the safety and traffic conditions of different routes in the future, and thus provides basic data for subsequent risk assessment.

[0134] S22. Extract the k-th driving route between target point 1 and target point 2, wherein the k-th driving route consists of the a-th mountain road segment, the b-th mountain road segment, the c-th mountain road segment, and the d-th mountain road segment;

[0135] S23. Based on the road network dataset, mark the road surface length of the a-th mountain road segment as cd. a The length of the b-th mountain road segment is marked as cd. b The length of the c-th mountain road segment is marked as cd. cThe road surface length of the d-th mountain road segment is marked as cd. d Then calculate the total length ZC of the kth travel path. k Average length of mountain road sections and the volatility Cbdl of the road surface length of the kth driving path k ;

[0136] ZC k =cd a +cd b +cd c +cd d

[0137]

[0138] In the formula, S k S represents the total number of mountain road sections in the k-th driving route, i.e., the total number of curves in the k-th driving route. k =4, This represents the ratio of the total path length to the total number of road segments, which is the average length of the mountain road segments in the k-th travel path. cd l Let represent the road surface length of any one of the following mountain road segments: the a-th, b-th, c-th, and d-th mountain road segments. This represents the volatility Cbdl of the road surface length of the k-th driving path, obtained using the standard deviation formula. k The calculation of average length helps to assess the complexity and stability of driving routes. Longer road sections may increase risks, while areas with large fluctuations in road length may have higher unpredictability.

[0139] S24. Based on the road network dataset, mark the road width of the a-th mountain road segment as kd. a The road width of the b-th mountain road section is marked as kd. b The road width of the c-th mountain road segment is marked as kd. c The road width of the d-th mountain road segment is marked as kd. d Then calculate the total width ZK of the k-th driving path. k Average width of mountain road sections and the volatility Kbdl of the road width of the kth driving path k ;

[0140] ZK k =kd a +kd b +kd c +kd d

[0141]

[0142] In the formula, This represents the ratio of the total width of the route to the total number of road segments, which is the average width of the mountain road segments in the k-th travel route. kd q Let represent the road width of any one of the following mountain road segments: a-th, b-th, c-th, and d-th. This represents the volatility Kbdl of the road width for the k-th driving path, calculated using the standard deviation formula. k Road width is crucial for driving safety. Narrow roads can lead to traffic difficulties, especially on mountain roads. Calculating the road width and variability of each road segment can help analyze potential congestion and safety hazards along the driving route.

[0143] S25. Based on the environmental dataset, label the roadside vegetation height of the a-th mountain road segment as gd. a The height of the roadside vegetation in the b-th mountainous section is marked as gd. b The height of the roadside vegetation in the c-th mountain road section is marked as gd. c The height of the roadside vegetation in the d-th mountain road section is marked as gd. d Then calculate the average vegetation height of the mountainous section in the k-th driving route.

[0144]

[0145] The height of roadside vegetation affects visibility and road visibility. Tall vegetation may obstruct the view and increase driving difficulty. By calculating the average height of vegetation on a road segment, the visibility safety of each driving path can be assessed. Tall vegetation may also bring more ecological risks, such as landslides or falling objects.

[0146] S26. Based on the environmental dataset, label the rainfall of the a-th mountain road segment as yu. a The humidity of the a-th mountain road section is marked as sd. a The rainfall in the b-th mountain road section is marked as yu. b The humidity of the b-th mountain road section is marked as sd. b The rainfall in the c-th mountain road section is marked as yu. c The humidity of the c-th mountain road section is marked as sd. c The rainfall in the d-th mountain road section is marked as yu. d The humidity of the d-th mountain road section is marked as Sd. d Then calculate the average rainfall in the mountainous section of the k-th driving route. and average humidity drop

[0147]

[0148] Rainfall and humidity have a significant impact on road slipperiness, visibility, and driving stability. Higher rainfall and humidity may make the road surface more slippery, increasing the risk of accidents. By calculating the average rainfall and humidity of each road segment, the impact of weather conditions on driving safety can be assessed more accurately.

[0149] S27. Based on the environmental dataset, label the temperature of the a-th mountain road segment as wd. a The temperature of the b-th mountain road section is marked as wd. b The temperature of the c-th mountain road segment is marked as wd. c The temperature of the d-th mountain road section is marked as wd. d Then calculate the average temperature of the mountain section in the k-th driving route. and the temperature fluctuation rate Wbdl in mountainous road sections k ;

[0150]

[0151] In the formula, wd e This represents the temperature of any one of the following mountain road segments: the a-th, b-th, c-th, and d-th. This represents the temperature fluctuation rate Wbdl in the mountainous section of the k-th driving route, calculated using the standard deviation formula. k Temperature also affects road conditions and driving experience. Temperatures that are too high or too low may cause road damage or increase the risk of ice and snow formation. Areas with large temperature fluctuations may have greater uncertainty. The greater the temperature fluctuation, the higher the risk of tire blowout.

[0152] S28. Based on the traffic data set, mark the pedestrian traffic at target point 1 as rl. mu1 Mark the traffic flow at target point 1 as cl mu1 Mark the pedestrian flow at target point two as rl mu2 Mark the traffic flow at target point two as cl mu2 Then calculate the total flow rate ZL between target point one and target point two, as well as the average flow rate for each travel path.

[0153] ZL = rl mu1 +cl mu1 +rl mu2 +cl mu2

[0154]

[0155] The calculation of pedestrian and vehicle traffic can provide information on traffic density, which is crucial for assessing the congestion level and potential dangers of travel routes. The higher the average traffic volume, the higher the risk of traffic accidents.

[0156] S29. Calculate the risk coefficient Fxx of the k-th driving path between target point 1 and target point 2. k The calculation formula is as follows:

[0157] If the average rainfall in the mountainous sections of the k-th travel route is... =0,

[0158]

[0159] In the formula, β1 represents the evaluation weight for the total number of mountain road sections in the driving route, and β2 represents the evaluation weight for the road surface length fluctuation rate in the driving route. β1 represents the standard road width, used to measure the width of road surfaces in mountainous areas. β3 represents the evaluation weight for the ratio of average width to standard road width; β4 represents the evaluation weight for road width fluctuation during the driving route; β5 represents the evaluation weight for average vegetation height during the driving route; β6 represents the evaluation weight for average humidity during the driving route; β7 represents the evaluation weight for temperature fluctuation during the driving route in mountainous areas; β8 represents the evaluation weight for average traffic flow. β1 + β2 + β3 + β4 + β5 + β6 + β7 + β8 = 1.

[0160] This indicates that the risk coefficient Fxx of the k-th driving path is obtained by applying the weights β1, β2, β3, β4, β5, β6, β7, and β8. k ;

[0161] If the average rainfall in the mountainous sections of the k-th travel route is... Not 0,

[0162]

[0163] In the formula, This indicates the evaluation weight for the total number of mountainous road sections in the driving route. This indicates the evaluation weight for the road surface length fluctuation rate during the driving path. This indicates the evaluation weight for the ratio of average width to standard road width. This indicates the evaluation weight for road width fluctuation rate during the driving path. This indicates the evaluation weight of the average vegetation height along the driving route. This indicates the assessment weight of the average rainfall along the travel route. This indicates the evaluation weight of average humidity along the driving route. This indicates the assessment weight for temperature fluctuation rate in mountainous road sections during the driving route. This indicates the evaluation weight for average flow. Indicates according to and The weights are used to obtain the risk coefficient Fxx of the k-th driving path. k ;

[0164] In this embodiment, the risk coefficient Fxx of each driving path between target point one and target point two is calculated in a targeted manner. The risk coefficient Fxx is generated by quantitatively integrating diverse factors such as road segment characteristics, environmental conditions, and traffic flow, and can intuitively reflect the risk level of different paths. Then, the resilience level of each driving path is evaluated based on the traffic status, which can provide a reliable basis for traffic management and decision-making.

[0165] Example 4

[0166] Please refer to Table 2. This embodiment is an explanation based on Embodiment 1. Specifically, the calculation process for the passage time YZT is as follows:

[0167] S31. Test vehicles are categorized into x1, x2, and x3 categories. Among them, x1 category test vehicles have a body length of less than 6 meters, corresponding to small vehicles, suitable for urban driving and short-distance travel; x2 category test vehicles have a body length of 6 meters to 9 meters, corresponding to medium-sized vehicles, which typically offer greater comfort and space, suitable for long-distance driving and family use; and x3 category test vehicles have a body length of more than 9 meters, corresponding to large vehicles, such as large sedans, commercial vehicles, trucks, and buses, suitable for carrying passengers or goods, with stronger load-bearing capacity and larger driving space.

[0168] S32. Presumably, the x-th test vehicle travels on the k-th travel path between target point 1 and target point 2, starting from either target point 1 or target point 2. The x-th test vehicle belongs to class x1. The length of the x-th test vehicle is denoted as che. x By clearly defining the impact of vehicle body length on driving, the performance of different vehicles can be simulated more accurately.

[0169] S33. Connect to the highway traffic management system via network and obtain the speed limit marker xv for the kth travel path between target point one and target point two. k Speed ​​limits are a key factor affecting vehicle travel time. They can accurately predict the travel time required for each type of vehicle on the travel path, which helps to optimize route selection.

[0170] S34. Based on the road network dataset, the turning arc length of the a-th mountain road segment will be labeled as hl. a Mark the turning arc length of the b-th mountain road segment as hl. b Mark the turning arc length of the c-th mountain road segment as hl. c Mark the turning arc length of the d-th mountain road segment as hl. d Then predict the cornering time (GWT) of the xth test vehicle. x and the duration of YZT x ;

[0171]

[0172] In the formula, μ represents the delay factor, μ×che x This indicates the delay time for the xth test vehicle when it takes a turn, based on the vehicle's length. This indicates that the cornering time (GWT) of the xth test vehicle is predicted based on the delay duration. x It accurately simulates the vehicle's performance on different road sections. A larger turning radius and vehicle length will cause the vehicle to slow down and increase the travel time. Predicting the turning time can help determine the difficulty of the driving path.

[0173] If the average rainfall in the mountainous sections of the k-th travel route is... The value is 0, indicating good road conditions.

[0174]

[0175] In the formula, θ represents the base time required for the x-th test vehicle to complete the k-th travel path under speed-limited conditions, where θ represents the delay factor. This indicates the delay time of the x-th test vehicle traveling on the k-th travel path, based on the average humidity. This indicates that the predicted passage time YZY for the xth test vehicle is obtained based on the base duration and the delay duration. x ;

[0176] If the average rainfall in the mountainous sections of the k-th travel route is... A value not equal to 0 indicates that slippery road surfaces increase braking distance, reduce vehicle speed, and consequently prolong travel time.

[0177]

[0178] In the formula, Represents the delay factor. This indicates the delay time of the x-th test vehicle traveling on the k-th route, based on the average rainfall. This indicates that the predicted transit time YZT for the xth vehicle is obtained based on the base duration and the delay duration. x By combining actual driving environments and calculating delay factors, accurate prediction of driving time for different types of vehicles is ensured.

[0179] Specifically, in Table 2, test vehicle X1 belongs to category x1, test vehicle X2 belongs to category x2, and test vehicle X3 belongs to category x3. The test route consists of the first mountain road section, the second mountain road section, and the third mountain road section. Test vehicles X1, X2, and X3 are arranged to drive on the route consisting of the first mountain road section, the second mountain road section, and the third mountain road section in sequence. In the formulas for calculating the cornering time GWT and the passing time YZT, μ = 0.2 and θ = 0.1. The speed limit for this route is 40,000 m / h;

[0180]

[0181] Table 2

[0182] The duration threshold SCY is set to 5-6 hours. Based on the assessment, the resilience level of this driving route is Level 2 for X1 and X2 test vehicles, allowing all small and medium-sized vehicles to choose this driving route. For X3 test vehicles, the resilience level of this driving route is Level 3. The body length of large vehicles increases the risk of traffic congestion at the curves of the driving route, and all large vehicles should be prohibited from entering this driving route.

[0183] In this embodiment, through detailed analysis of factors such as road conditions and weather, the travel time required for different types of vehicles can be accurately predicted based on actual conditions. A detailed assessment of a single travel route can help traffic management departments predict the traffic capacity of different types of vehicles under different weather and road conditions, thereby making reasonable traffic scheduling or road section design decisions. The dynamic adjustment is highly practical.

[0184] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for assessing the resilience of mountainous expressway networks during peak travel periods, characterized by: Includes the following steps: Step 1: Connect the highway traffic management system, sensing devices, and monitoring devices via the network to acquire management data for all mountain road sections, traffic flow data for all mountain entrances and exits, and environmental monitoring data for all mountain road sections, and classify them into road network datasets, environmental datasets, and traffic flow datasets. Step 2: Based on the road network dataset and environmental dataset, analyze the slope PD and curvature QL of each mountain road segment, and generate the corresponding flatness coefficient Ptx; Step 3: Set target points based on the traffic data set, obtain all driving paths between target points, and then combine the road network data set and the environment data set to analyze the total number of curves S, road length variability Cbdl, and average width of each driving path. Road width variability (Kbdl), average vegetation height Average rainfall average humidity Temperature fluctuation rate Wbdl and average flow rate And generate the corresponding risk coefficient Fxx; Step 4: Set up different types of test vehicles, and then combine the road network dataset and the environment dataset to predict the travel time YZT of different types of test vehicles under the condition of unified driving path; Step 5: Set fixed ranges for flatness threshold PTY, risk threshold FXY, and duration threshold SCY. Then, combine the flatness coefficient Ptx, risk coefficient Fxx, and travel duration YZT to assess the resilience level of the mountainous expressway network, the resilience level of each travel route, and the adaptability of a single travel route for different types of vehicles, according to three dimensions: road surface conditions, traffic status, and vehicle type. Output the corresponding assessment results and management recommendations.

2. The method for assessing the resilience of mountainous expressway networks during peak travel periods according to claim 1, characterized in that: In step one, the expression for the road network dataset is {D1}. h D2 h D3 h ... Dn h }, D1 h To Dn h These are the management data for the first to the nth mountain road sections. The management data includes road length, road width, turning radius, road surface void ratio, construction time, and turning arc length. h represents the altitude of each mountain road section. Each mountain road section contains only one curve.

3. The method for assessing the resilience of mountainous expressway networks during peak travel periods according to claim 2, characterized in that: In step one, the expression for the environmental dataset is {H1}. m H2 m H3 m ... Hj m }, H1 m To Hj m These are the environmental monitoring data for the first to the nth mountain road sections, respectively. The environmental monitoring data includes vegetation coverage, roadside vegetation height, rainfall, humidity, and temperature. m represents the specific time point at which the environmental monitoring data for each mountain road section was obtained.

4. The method for assessing the resilience of mountainous expressway networks during peak travel periods according to claim 3, characterized in that: In step one, the expression for the traffic dataset is {K1}. s K2 s K3 s ...Ku s }, K1 s To Ko s These represent traffic flow data for the first to the 0th mountain entrances / exits, including both pedestrian and vehicle traffic. s represents the specific time point at which traffic flow data for each mountain entrance / exit was obtained.

5. The method for assessing the resilience of mountainous expressway networks during peak travel periods according to claim 4, characterized in that: In step two, the flatness coefficient Ptx is calculated as follows: S11. Based on the road network dataset, extract the management data for the i-th mountain road segment and mark the turning radius of the i-th mountain road segment as WR. i The pavement roughness of the i-th mountain road segment is denoted as MK. i The construction time of the i-th mountain road section is marked as JS. i The altitude of the starting point of the i-th mountain road segment is marked as q. ih Mark the altitude of the end point of the i-th mountain road segment as zi. h Then, using GPS positioning technology, the horizontal distance from the start to the end of the i-th mountain road segment is marked as SP. i ; S12. Based on the environmental dataset, extract the environmental monitoring data for the i-th mountain road segment and label the vegetation coverage rate of the i-th mountain road segment as ZF. i ; S13. Calculate the slope PD of the i-th mountain road segment. i The calculation formula is as follows: In the formula, zi h -qi h This represents the altitude difference between the start and end points of the i-th mountain road segment; S14. Calculate the curvature QL of the i-th mountain road segment. i The calculation formula is as follows: S15. Calculate the flatness coefficient Ptx of the i-th mountain road segment. i The calculation formula is as follows: In the formula, BPD represents the standard slope, used to measure the steepness of mountain road sections; α1 represents the evaluation weight for the ratio of slope to standard slope; BQL represents the standard curvature, used to measure the curvature of curves in mountain road sections; α2 represents the evaluation weight for the ratio of curvature to standard curvature; BMK represents the standard porosity, used to measure the density of road surface in mountain road sections; α3 represents the evaluation weight for the ratio of pavement porosity to standard porosity; BJS represents the standard duration, used to measure the aging of road surface in mountain road sections; α4 represents the evaluation weight for the ratio of construction time to standard duration; BZF represents the standard coverage rate, used to measure the density of vegetation coverage in mountain road sections; α5 represents the evaluation weight for the ratio of vegetation coverage to standard coverage rate; and α1 + α2 + α3 + α4 + α5 = 1. This represents the flatness coefficient Ptx of the i-th mountain road segment, calculated using weights α1, α2, α3, α4, and α5. i .

6. The method for assessing the resilience of mountainous expressway networks during peak travel periods according to claim 5, characterized in that: In step three, the calculation process for the risk coefficient Fxx is as follows: S21. Based on the traffic data set, set target point 1 and target point 2. Both target point 1 and target point 2 are entrances and exits in the mountainous area. They are different but connected. Then, use map software to obtain all driving paths between target point 1 and target point 2 and mark them as {L1, L2, L3, ..., Lp}. L1 to Lp are the first to the pth driving paths between target point 1 and target point 2, respectively. Each driving path is composed of several mountain road segments. S22. Extract the k-th driving route between target point 1 and target point 2, wherein the k-th driving route consists of the a-th mountain road segment, the b-th mountain road segment, the c-th mountain road segment, and the d-th mountain road segment; S23. Based on the road network dataset, mark the road surface length of the a-th mountain road segment as cd. a The length of the b-th mountain road segment is marked as cd. b The length of the c-th mountain road segment is marked as cd. c The road surface length of the d-th mountain road segment is marked as cd. d Then calculate the total length ZC of the kth travel path. k Average length of mountain road sections and the volatility Cbdl of the road surface length of the kth driving path k ; ZC k =continued a +continued b +continued c +continued d In the formula, S k S represents the total number of mountain road sections in the k-th driving route, i.e., the total number of curves in the k-th driving route. k =4, This represents the ratio of the total path length to the total number of road segments, which is the average length of the mountain road segments in the k-th travel path. cd l Let represent the road surface length of any one of the following mountain road segments: the a-th, b-th, c-th, and d-th mountain road segments. This represents the volatility Cbdl of the road surface length of the k-th driving path, obtained using the standard deviation formula. k ; S24. Based on the road network dataset, mark the road width of the a-th mountain road segment as kd. a The road width of the b-th mountain road section is marked as kd. b The road width of the c-th mountain road segment is marked as kd. c The road width of the d-th mountain road segment is marked as kd. d Then calculate the total width ZK of the k-th driving path. k Average width of mountain road sections and the volatility Kbdl of the road width of the kth driving path k ; ZK k =kd a +kd b +kd c +kd d In the formula, This represents the ratio of the total width of the route to the total number of road segments, which is the average width of the mountain road segments in the k-th travel route. kd q Let represent the road width of any one of the following mountain road segments: a-th, b-th, c-th, and d-th. This represents the volatility Kbdl of the road width for the k-th driving path, calculated using the standard deviation formula. k ; S25. Based on the environmental dataset, label the roadside vegetation height of the a-th mountain road segment as gd. a The height of the roadside vegetation in the b-th mountainous section is marked as gd. b The height of the roadside vegetation in the c-th mountain road section is marked as gd. c The height of the roadside vegetation in the d-th mountain road section is marked as gd. d Then calculate the average vegetation height of the mountainous section in the k-th driving route. S26. Based on the environmental dataset, label the rainfall of the a-th mountain road segment as yu. a The humidity of the a-th mountain road section is marked as sd. a The rainfall in the b-th mountain road section is marked as yu. b The humidity of the b-th mountain road section is marked as sd. b The rainfall in the c-th mountain road section is marked as yu. c The humidity of the c-th mountain road section is marked as sd. c The rainfall in the d-th mountain road section is marked as yu. d The humidity of the d-th mountain road section is marked as sd. d Then calculate the average rainfall in the mountainous section of the k-th driving route. and average humidity drop S27. Based on the environmental dataset, label the temperature of the a-th mountain road segment as wd. a The temperature of the b-th mountain road section is marked as wd. b The temperature of the c-th mountain road segment is marked as wd. c The temperature of the d-th mountain road section is marked as wd. d Then calculate the average temperature of the mountain section in the k-th driving route. and the temperature fluctuation rate Wbdl in mountainous road sections k ; In the formula, wd e This represents the temperature of any one of the following mountain road segments: the a-th, b-th, c-th, and d-th. This represents the temperature fluctuation rate Wbdl in the mountainous section of the k-th driving route, calculated using the standard deviation formula. k ; S28. Based on the traffic data set, mark the pedestrian traffic at target point 1 as rl. mu1 Mark the traffic flow at target point 1 as cl mu1 Mark the pedestrian flow at target point two as rl mu2 Mark the traffic flow at target point two as cl mu2 Then calculate the total flow rate ZL between target point one and target point two, as well as the average flow rate for each travel path. ZL=rl mu1 +cl mu1 +rl mu2 +cl mu2 S29. Calculate the risk coefficient Fxx of the k-th driving path between target point 1 and target point 2. k The calculation formula is as follows: If the average rainfall in the mountainous sections of the k-th travel route is... =0, In the formula, β1 represents the evaluation weight for the total number of mountain road sections in the driving route, and β2 represents the evaluation weight for the road surface length fluctuation rate in the driving route. β1 represents the standard road width, used to measure the width of road surfaces in mountainous areas. β3 represents the evaluation weight for the ratio of average width to standard road width; β4 represents the evaluation weight for road width fluctuation during the driving route; β5 represents the evaluation weight for average vegetation height during the driving route; β6 represents the evaluation weight for average humidity during the driving route; β7 represents the evaluation weight for temperature fluctuation during the driving route in mountainous areas; β8 represents the evaluation weight for average traffic flow. β1 + β2 + β3 + β4 + β5 + β6 + β7 + β8 = 1. This indicates that the risk coefficient Fxx of the k-th driving path is obtained by applying the weights β1, β2, β3, β4, β5, β6, β7, and β8. k ; If the average rainfall in the mountainous sections of the k-th travel route is... Not 0, In the formula, This indicates the evaluation weight for the total number of mountainous road sections in the driving route. This indicates the evaluation weight for the road surface length fluctuation rate during the driving path. This indicates the evaluation weight for the ratio of average width to standard road width. This indicates the evaluation weight for road width fluctuation rate during the driving path. This indicates the evaluation weight of the average vegetation height along the driving route. This indicates the assessment weight of the average rainfall along the travel route. This indicates the evaluation weight of average humidity along the driving route. This indicates the assessment weight for temperature fluctuation rate in mountainous road sections during the driving route. This indicates the evaluation weight for average flow. Indicates according to and The weights are used to obtain the risk coefficient Fxx of the k-th driving path. k .

7. The method for assessing the resilience of mountainous expressway networks during peak travel periods according to claim 6, characterized in that: In step four, the calculation process for the passage time YZT is as follows: S31. The types of test vehicles include x1, x2 and x3. Among them, the body length of x1 test vehicles is <6 meters, 6 meters ≤ x2 test vehicles have a body length ≤9 meters, and the body length of x3 test vehicles is >9 meters. S32. Presumably, the x-th test vehicle travels on the k-th travel path between target point 1 and target point 2, starting from either target point 1 or target point 2. The x-th test vehicle belongs to class x1. The length of the x-th test vehicle is denoted as che. x ; S33. Connect to the highway traffic management system via network and obtain the speed limit marker xv for the kth travel path between target point one and target point two. k ; S34. Based on the road network dataset, mark the turning arc length of the a-th mountain road segment as hl. a Mark the turning arc length of the b-th mountain road segment as hl. b Mark the turning arc length of the c-th mountain road segment as hl. c Mark the turning arc length of the d-th mountain road segment as hl. d Then predict the cornering time (GWT) of the xth test vehicle. x and the duration of YZT x ; In the formula, μ represents the delay factor, μ×che x This indicates the delay time for the xth test vehicle when it takes a turn, based on the vehicle's length. This indicates that the cornering time (GWT) of the xth test vehicle is predicted based on the delay duration. x ; If the average rainfall in the mountainous sections of the k-th travel route is... =0, In the formula, θ represents the base time required for the x-th test vehicle to complete the k-th travel path under speed-limited conditions, where θ represents the delay factor. This indicates the delay time of the x-th test vehicle traveling on the k-th travel path, based on the average humidity. This indicates that the predicted passage time YZT for the xth test vehicle is obtained based on the base duration and the delay duration. x ; If the average rainfall in the mountainous sections of the k-th travel route is... Not 0, In the formula, Represents the delay factor. This indicates the delay time of the x-th test vehicle traveling on the k-th route, based on the average rainfall. This indicates that the predicted transit time YZT for the xth vehicle is obtained based on the base duration and the delay duration. x .

8. The method for assessing the resilience of mountainous expressway networks during peak travel periods according to claim 7, characterized in that: In step five, the flatness coefficient Ptx of each mountain road segment is compared with the flatness threshold PTY. If the flatness coefficient Ptx of a single mountain road segment is lower than the flatness threshold PTY, it means that the single mountain road segment has reached the maintenance condition. Then, the number of mountain road segments that have reached the maintenance condition is counted, and the resilience level of the mountain expressway network is evaluated according to the dimension of road surface conditions. If the number of mountain road sections that have reached the maintenance conditions is less than 20% of the total number of mountain road sections, the resilience level of the mountain expressway network is Level 1, and it is recommended to carry out road surface construction during off-peak travel periods. If the number of mountain road sections that have met the maintenance conditions is less than 50% of the total number of mountain road sections, the resilience level of the mountain expressway network is level two. It is recommended to carry out road construction immediately and prohibit vehicles with a load of more than 6 tons from entering the mountain expressway network. If the number of mountain road sections that have reached the maintenance conditions is greater than or equal to 50% of the total number of mountain road sections, the resilience level of the mountain expressway network is level three. It is recommended to carry out road construction immediately and prohibit vehicles with a load of more than 3 tons from entering the mountain expressway network. The pavement conditions of a mountain expressway network with a first-level toughness rating are better than those of a second-level toughness rating, and the pavement conditions of a second-level toughness rating are better than those of a third-level toughness rating.

9. The method for assessing the resilience of mountainous expressway networks during peak travel periods according to claim 8, characterized in that: In step five, the risk coefficient Fxx of each driving path between target point one and target point two is compared with the risk threshold FXY, and the resilience level of each driving path is evaluated according to the dimension of traffic status. If the risk coefficient Fxx of a single driving route is lower than the risk threshold FXY, it means that the traffic status of the single driving route is not saturated, the resilience level of the single driving route is level one, no traffic diversion is required, and all vehicles are allowed to continue to pass at the speed limit. If the risk coefficient Fxx of a single driving route is included in the risk threshold FXY, it means that the traffic status of the single driving route is not saturated, the resilience level of the single driving route is level two, and it is recommended to carry out traffic management and reduce the speed limit of the driving route by 20%. If the risk coefficient Fxx of a single driving route exceeds the risk threshold FXY, it indicates that the traffic flow of the single driving route is saturated, the resilience level of the single driving route is level three, and it is recommended to strengthen traffic management, prohibit vehicles from passing, and start limiting traffic flow and queuing. The traffic conditions of a Level 1 resilience driving route are better than those of a Level 2 resilience driving route, and the traffic conditions of a Level 2 resilience driving route are better than those of a Level 3 resilience driving route.

10. A method for assessing the resilience of mountainous expressway networks during peak travel periods, as described in claim 9, is characterized in that: In step five, under the condition of a unified driving route, the travel time YZT of test vehicles of categories x1, x2 and x3 is compared with the time threshold SCY, and the resilience level of the driving route is evaluated according to the vehicle type dimension. If the travel time YZT of a single type of test vehicle is lower than the travel time threshold SCY, it means that the resilience level of the travel route is Level 1 for that single type of test vehicle, and it is recommended that all vehicles of that single type prioritize choosing that travel route for travel. If the travel time YZT of a single type of test vehicle is included in the travel time threshold SCY, it means that for a single type of test vehicle, the resilience level of the travel route is level two, allowing all vehicles of the single type to choose the travel route for travel; If the travel time YZT of a single type of test vehicle exceeds the time threshold SCY, it means that the resilience level of the driving path is level three for the single type of test vehicle. The body length of the single type of test vehicle increases the risk of traffic congestion at the curves of the driving path, and all vehicles corresponding to the single type should be prohibited from entering the driving path. For a single type of test vehicle, the adaptability of the Level 1 toughness driving path is better than that of the Level 2 toughness driving path, and the adaptability of the Level 2 toughness driving path is better than that of the Level 3 toughness driving path.

Images