Mountain area expressway network toughness evaluation method for travel peak period

Abstract

The invention relates to the technical field of traffic safety management, and discloses a mountain area expressway network toughness evaluation method for a travel peak period, and the method comprises the following steps: 1, obtaining the management data of a mountain area road section, the traffic flow data of a mountain area entrance and exit, and the environment detection data of the mountain area road section; 2, analyzing the gradient, curvature and flatness coefficient of each mountain road section; 3, setting target points, obtaining all driving paths between the target points, and analyzing the risk coefficient of each driving path; 4, different types of test vehicles are set, the passing duration of the different types of test vehicles is predicted under the condition that the path is unified, and the multi-dimensional evaluation accuracy is high; and step 5, according to the road condition, the traffic state and the vehicle type dimension, evaluating the toughness grade of the highway network in the mountainous area, the toughness grade of each driving path and the adaptation degree of different types of vehicles and single driving paths, and outputting an evaluation result and a management suggestion, so that the dynamic adjustment practicability is high.

Description

Technical Field

[0001] The present invention relates to the technical field of traffic safety management, and in particular to a method for assessing the resilience of a mountain highway network during a travel peak period. Background Art

[0002] Mountain expressway network refers to the expressway system in complex terrains such as mountains and hills, especially in mountainous areas. Due to the particularity of mountainous terrain, the construction of expressways faces more technical challenges and engineering difficulties. Mountain expressways are not only an important part of regional transportation development, but also an important guarantee for promoting economic, social and cultural development and promoting regional integration. Mountain expressway network is usually composed of multiple expressway lines and branches, crossing different mountains, hills and valleys, connecting cities, counties and various important economic zones.

[0003] The resilience assessment of the mountain highway network mainly focuses on infrastructure stability, traffic flow resilience and emergency transportation capacity. First, it is necessary to evaluate the structural design and construction quality of the highway, as well as its adaptability to extreme environmental conditions, especially the safety and durability of key infrastructure such as roadbeds and tunnels when encountering natural disasters or severe weather. Secondly, it is necessary to evaluate the ability of the highway system to cope with traffic flow during peak travel periods, after disasters or when emergencies occur, including the rapid diversion of traffic flow, the construction of temporary channels, and the coordinated transportation of different means of transportation. Finally, it is necessary to measure the effectiveness of the emergency management system, rescue forces and resources of mountain highways in emergencies, including real-time data feedback from the monitoring system, the timeliness of the alarm system, and the efficiency of traffic guidance measures.

[0004] At present, traditional highway network resilience assessment methods rely on historical data, static models and assumptions of conventional scenarios. During peak travel periods, they are often unable to accurately predict changes in traffic flow in mountain highway networks, resulting in low accuracy in resilience assessments, inability to dynamically adjust traffic flow and vehicle diversion, and difficulty in providing timely support for decision-making. Summary of the invention

[0005] 1. Technical issues to be resolved

[0006] In view of the shortcomings of the existing technology, the present invention provides a method for assessing the resilience of mountain highway networks during peak travel periods, which has the advantages of high accuracy in multi-dimensional assessment and strong practicality in dynamic adjustment. It solves the problem that traditional highway network resilience assessment methods have low accuracy and cannot dynamically adjust traffic flow and vehicle diversion.

[0007] (II) Technical solution

[0008] To achieve the above object, the present invention provides the following technical solution: a method for evaluating the resilience of a mountain highway network during peak travel periods, comprising the following steps:

[0009] Step 1: Connect the highway traffic management system, sensor devices and monitoring devices through the network to obtain the management data of all mountain road sections, the traffic flow data of all mountain entrances and exits, and the environmental detection data of all mountain road sections, and classify them into road network data sets, environmental data sets and flow data sets;

[0010] Step 2: According to the road network dataset and the environmental dataset, the slope PD and curvature QL of each mountainous road section are analyzed, and the corresponding flatness coefficient Ptx is generated;

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

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

[0013] Step 5: Set a fixed range of flatness threshold PTY, risk threshold FXY and duration threshold SCY, and then combine the flatness coefficient Ptx, risk coefficient Fxx and travel time YZT to evaluate the resilience level of the mountain highway network, the resilience level of each driving path, and the adaptability of a single driving path to different types of vehicles based on three dimensions: road conditions, traffic status and vehicle types, and output corresponding evaluation results and management recommendations.

[0014] Preferably, in step 1, the expression of the road network data set is {D1 h 、D2 h 、D3 h 、...、Dn h}, D1 h To Dn h They are the management data of the first to the nth mountain road sections, including road length, road width, turning radius, road porosity, construction time and turning arc length. h represents the altitude of each mountain road section, and each mountain road section contains only one bend.

[0015] Preferably, in step 1, the expression of the environmental data set is {H1 m 、H2 m 、H3 m 、...、Hj m}, H1 m To Hj m They are the environmental monitoring data of the first to the nth mountain road sections, respectively. The environmental monitoring data include vegetation coverage, roadside vegetation height, rainfall, humidity and temperature. m represents the specific time point for obtaining the environmental monitoring data of each mountain road section.

[0016] Preferably, in step 1, the expression of the flow data set is {K1 s , K2 s 、K3 s , ..., Ku s}, K1 s To Ko s They are the traffic flow data of the first to the oth mountain entrances and exits, respectively. The traffic flow data includes pedestrian flow and vehicle flow. s represents the specific time point for obtaining the traffic flow data of each mountain entrance and exit.

[0017] Preferably, in step 2, the calculation process of the flatness coefficient Ptx is as follows:

[0018] S11. Extract the management data of the i-th mountain road section according to the road network data set, and mark the turning radius of the i-th mountain road section as WR i , the road surface porosity of the i-th mountain road section is marked as MK i , mark the construction time of the i-th mountain road section as JS i , mark the altitude of the starting point of the i-th mountain section as qi h , mark the altitude of the end point of the i-th mountain section as zi h , and then use GPS positioning technology to mark the horizontal distance from the starting point to the end point of the i-th mountain section as SP i ;

[0019] S12. Extract the environmental detection data of the i-th mountain road section according to the environmental data set, and mark the vegetation coverage rate of the i-th mountain road section as ZF i ;

[0020] S13, calculate the slope PD of the i-th mountain road section i , and its calculation formula is as follows:

[0021]

[0022] In the formula, z h -qi hrepresents the altitude difference between the starting point and the end point of the i-th mountain section;

[0023] S14. Calculate the curvature QL of the i-th mountain road section i , and its calculation formula is as follows:

[0024]

[0025] S15, calculate the flatness coefficient Ptx of the i-th mountain road section i , and its calculation formula is as follows:

[0026]

[0027] In the formula, BPD represents the standard slope, which is 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, which is used to measure the curvature of mountain road sections. α2 represents the evaluation weight for the ratio of curvature to standard curvature. BMK represents the standard porosity, which is used to measure the compactness of the road surface in mountainous areas. α3 represents the evaluation weight for the ratio of road surface porosity to standard porosity. BJS represents the standard duration, which is used to measure the aging degree of the road surface in mountainous areas. α4 represents the evaluation weight for the ratio of construction duration to standard duration. BZF represents the standard coverage, which is used to measure the density of vegetation coverage in mountainous areas. α5 represents the evaluation weight for the ratio of vegetation coverage to standard coverage. α1+α2+α3+α4+α5=1. Indicates that according to the weights α1, α2, α3, α4 and α5, the flatness coefficient Ptx of the i-th mountainous road section is obtained i .

[0028] Preferably, in step 3, the risk factor Fxx calculation process is as follows:

[0029] S21. According to the traffic data set, target point 1 and target point 2 are set. Both target point 1 and target point 2 are entrances and exits of the mountain area, which are different but connected. Then, map software is used to obtain all driving paths between target point 1 and target point 2, and they are marked as {L1, L2, L3, ..., Lp}. L1 to Lp are the first to pth driving paths between target point 1 and target point 2, respectively, wherein each driving path is composed of several mountain road sections;

[0030] S22, extracting the kth driving path between the target point 1 and the target point 2, wherein the kth driving path consists of the ath mountain road section, the bth mountain road section, the cth mountain road section and the dth mountain road section;

[0031] S23. According to the road network dataset, the road length of the a-th mountainous road section is marked as cda , mark the road length of the b-th mountain section as cd b , mark the road length of the cth mountain section as cd c , mark the road length of the d-th mountain road section as cd d , and then calculate the total length ZC of the kth driving path k , the average length of mountainous road sections And the volatility Cbdl of the road 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 sections in the k-th driving path, that is, the total number of bends in the k-th driving path. k =4, It represents the ratio of the total length of the path to the total number of road sections, that is, the average length of the mountain road section in the kth driving path cd l represents the road length of any mountain road section among the a-th mountain road section, the b-th mountain road section, the c-th mountain road section and the d-th mountain road section, According to the standard deviation formula, the volatility Cbdl of the road length of the kth driving path is obtained k ;

[0035] S24. According to the road network dataset, the road width of the a-th mountain road section is marked as kd a , mark the road width of the b-th mountain section as kd b , mark the road width of the c-th mountain road section as kd c , mark the road width of the d-th mountain road section as kd d , and then calculate the total width ZK of the k-th driving path k , the average width of mountainous road sections And the fluctuation rate of the road width of the kth driving path Kbdl k ;

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

[0037]

[0038] In the formula, It represents the ratio of the total width of the path to the total number of road sections, that is, the average width of the mountain road section in the kth driving path kd q represents the road width of any mountain road section among the a-th mountain road section, the b-th mountain road section, the c-th mountain road section and the d-th mountain road section, According to the standard deviation formula, the fluctuation rate Kbdl of the road width of the kth driving path is obtained k ;

[0039] S25. According to the environmental data set, the roadside vegetation height of the a-th mountainous road section is marked as gd a , mark the roadside vegetation height of the b-th mountain road section as gd b , mark the roadside vegetation height of the cth mountain road section as gd c , mark the roadside vegetation height of the d-th mountain road section as gd d , and then calculate the average vegetation height of the mountainous section in the kth driving path

[0040]

[0041] S26. According to the environmental data set, the rainfall of the a-th mountain section is marked as yu a , mark the humidity of the a-th mountain road section as sd a , mark the rainfall of the b-th mountain section as yu b , mark the humidity of the b-th mountain section as sd b , mark the rainfall of the cth mountain section as yu c , the humidity of the c-th mountain road section is marked as sd c , mark the rainfall of the d-th mountain section as yu d , the humidity of the d-th mountain road section is marked as sd d , and then calculate the average rainfall in the mountainous section of the k-th driving path and average humidity

[0042]

[0043] S27. According to the environmental data set, the temperature of the a-th mountain road section is marked as wd a , mark the temperature of the b-th mountain section as wd b , mark the temperature of the cth mountain section as wd c, mark the temperature of the d-th mountain section as wd d , and then calculate the average temperature of the mountainous section in the kth driving path And the fluctuation rate of temperature in mountainous road sections Wbdl k ;

[0044]

[0045] In the formula, wd e represents the temperature of any mountain road section among the a-th mountain road section, the b-th mountain road section, the c-th mountain road section and the d-th mountain road section, According to the standard deviation formula, the fluctuation rate of the temperature in the mountainous section of the k-th driving path is Wbdl k ;

[0046] S28. According to the traffic data set, the traffic flow of target point 1 is marked as rl mu1 , mark the traffic flow of target point 1 as cl mu1 , mark the flow of people at target point 2 as rl mu2 , mark the traffic flow of target point 2 as cl mu2 , and then calculate the total flow ZL between target point 1 and target point 2, as well as the average flow of each driving path

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

[0048]

[0049] S29. Calculate the risk factor Fxx of the kth driving path between target point 1 and target point 2 k , and its calculation formula is as follows:

[0050] If the average rainfall in the mountainous section of the k-th driving route is is 0,

[0051]

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

[0053] If the average rainfall in the mountainous section of the k-th driving route is Not 0,

[0054]

[0055] In the formula, Represents the evaluation weight for the total number of mountain sections in the driving route. It represents the evaluation weight of the road length fluctuation rate in the driving path. represents the evaluation weight for the ratio of average width to standard road width, It represents the evaluation weight of the road width fluctuation rate in the driving path. Represents the evaluation weight of the average vegetation height in the driving path. Represents the evaluation weight of the average rainfall along the driving route. Represents the evaluation weight of the average humidity in the driving path, It represents the evaluation weight of the temperature fluctuation rate of the mountainous road section during the driving route. represents the evaluation weight for the average flow, Indicates according to and Weight, get the risk factor Fxx of the kth driving path k .

[0056] Preferably, in step 4, the calculation process of the travel time YZT is as follows:

[0057] S31. The types of test vehicles include x1, x2 and x3. The body length of the x1 test vehicle is less than 6 meters, 6 meters ≤, the body length of the x2 test vehicle is less than 9 meters, and the body length of the x3 test vehicle is greater than 9 meters.

[0058] S32: The xth test vehicle is assumed to travel on the kth driving path between the target point 1 and the target point 2, and starts from the target point 1 or the target point 2. The xth test vehicle belongs to the x1 category, and the body length of the xth test vehicle is marked as che. x ;

[0059] S33, connecting to the highway traffic management system through the network, obtaining the speed limit of the k-th driving path between the target point 1 and the target point 2, marked as xv k ;

[0060] S34. According to the road network data set, the turning arc length of the a-th mountain road section is marked as hl a , mark the turning arc length of the b-th mountain road section as hl b , mark the turning arc length of the cth mountain road section as hl c , mark the turning arc length of the dth mountain road section as hl d , and then predict the turning time of the xth test car GWT x And the passing time YZT x ;

[0061]

[0062] In the formula, μ represents the delay factor, μ×che x It means that according to the length of the vehicle body, the delay time of the xth test vehicle when turning is obtained. Indicates that according to the delay time, the cornering time GWT of the x-th test vehicle is predicted x ;

[0063] If the average rainfall in the mountainous section of the k-th driving route is is 0,

[0064]

[0065] In the formula, represents the basic time required for the x-th test vehicle to complete the k-th driving path under speed limit, θ represents the delay factor, It means that according to the average humidity, the delay time of the x-th test vehicle traveling on the k-th driving path is obtained. It indicates that according to the basic duration and delay duration, the passing time YZT of the x-th test vehicle is predicted x ;

[0066] If the average rainfall in the mountainous section of the k-th driving route is Not 0,

[0067]

[0068] In the formula, represents the delay factor, It means that according to the average rainfall, the delay time of the x-th test vehicle traveling on the k-th driving path is obtained. It indicates that according to the basic duration and delay duration, the passing time YZT of the x-th vehicle is predicted x .

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

[0070] If the number of mountain road sections that have reached maintenance conditions is less than 20% of the total number of mountain road sections, the resilience level of the mountain highway network is level 1, and it is recommended to carry out road construction during the travel trough period;

[0071] If the number of mountain road sections that have reached maintenance conditions is less than 50% of the total number of mountain road sections, the resilience level of the mountain highway network is level 2, and it is recommended to immediately carry out road construction, and vehicles with a load of more than 6 tons are prohibited from entering the mountain highway 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 highway network is level 3, and it is recommended to immediately carry out road construction, and vehicles with a load of more than 3 tons are prohibited from entering the mountain highway network;

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

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

[0075] If the risk factor Fxx of a single driving path is lower than the risk threshold FXY, it means that the traffic status of the single driving path is not saturated, and the resilience level of the single driving path is level 1. Traffic diversion is not required, and all vehicles are allowed to continue to pass at the speed limit.

[0076] If the risk factor Fxx of a single driving path is included in the risk threshold FXY, it means that the traffic status of the single driving path is not saturated, and the resilience level of the single driving path is level 2. It is recommended to conduct traffic diversion and reduce the speed limit of the driving path by 20%;

[0077] If the risk factor Fxx of a single driving path exceeds the risk threshold FXY, it means that the traffic status of the single driving path has been saturated, and the resilience level of the single driving path is level three. It is recommended to strengthen traffic diversion, prohibit vehicles from passing, and start limiting queuing;

[0078] The traffic condition of a driving path with the first-level toughness grade is better than that of a driving path with the second-level toughness grade, and the traffic condition of a driving path with the second-level toughness grade is better than that of a driving path with the third-level toughness grade.

[0079] Preferably, in step 5, when the driving route is unified, the travel time YZT of the test vehicles of category x1, category x2 and category x3 is compared with the time threshold SCY, and the toughness 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 for a single type of test vehicle, the toughness level of this driving path is level 1, and it is recommended that all vehicles corresponding to a single type give priority to this driving path for travel;

[0081] If the travel time YZT of a single type of test vehicle is included in the time threshold SCY, it means that for a single type of test vehicle, the toughness level of this driving path is level 2, and all vehicles corresponding to a single type are allowed to choose this driving path for travel;

[0082] If the travel time YZT of a single type of test vehicle exceeds the time threshold SCY, it means that for the single type of test vehicle, the toughness level of the driving path is level three. The body length of the single type of test vehicle increases the risk of traffic congestion at the bend of the driving path. 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 the first-level toughness level driving path is better than that of the second-level toughness level driving path, and the adaptability of the second-level toughness level driving path is better than that of the third-level toughness level driving path.

[0084] Compared with the prior art, the present invention provides a method for evaluating the resilience of a mountain highway network during peak travel periods, which has the following beneficial effects: high multi-dimensional evaluation accuracy and strong practicality of dynamic adjustment.

[0085] 1. The present invention connects the highway traffic management system, the sensor device and the monitoring device through a network, obtains the management data of all mountain sections, the traffic flow data of all mountain entrances and exits and the environmental detection data of all mountain sections, and classifies them into a road network data set, an environmental data set and a flow data set. According to the road network data set and the environmental data set, the slope PD and the curvature QL of each mountain section are analyzed, and the corresponding flatness coefficient Ptx is generated. The higher the flatness coefficient Ptx, the better the road condition. The level of the flatness coefficient Ptx directly affects the driving experience and vehicle performance. According to the flow data set, the target point is set, all the driving paths between the target points are obtained, and the risk coefficient Fxx of each driving path is analyzed by combining multiple factors, which is helpful to identify the driving paths or sections with high risks in advance, can provide early warning for traffic management, reduce the occurrence of emergencies, set different types of test vehicles, and predict the travel time YZT of different types of test vehicles under the condition of unified driving paths. According to the prediction results of different types of test vehicles, it is helpful to manage the traffic flow in a refined manner, can ensure that appropriate vehicles enter specific sections, avoid traffic bottlenecks caused by incompatible vehicle types, and have high multi-dimensional evaluation accuracy.

[0086] 2. The present invention sets a fixed range of flat threshold PTY, risk threshold FXY and duration threshold SCY, and then combines the flat coefficient Ptx, risk coefficient Fxx and travel time YZT to evaluate the resilience level of the mountain highway network, the resilience level of each driving path, and the adaptability of a single driving path to different types of vehicles according to three dimensions: road conditions, traffic status and vehicle types, and outputs corresponding evaluation results and management suggestions. It scientifically evaluates the emergency response capability, traffic diversion capability and long-term sustainable development capability of the mountain highway network, optimizes road use strategies, and helps to formulate more accurate peak traffic planning, effectively reducing congestion and accidents, and ensuring the balance and smoothness of traffic flow during peak travel periods. The dynamic adjustment is highly practical. BRIEF DESCRIPTION OF THE DRAWINGS

[0087] Figure 1 It is a step diagram of the method of the present invention. DETAILED DESCRIPTION

[0088] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0089] Example 1

[0090] See also Figure 1The present invention provides a method for evaluating the resilience of a mountain highway network during a travel peak period, comprising the following steps:

[0091] Step 1: Connect the highway traffic management system, sensor devices and monitoring devices through the network to obtain the management data of all mountain road sections, the traffic flow data of all mountain entrances and exits, and the environmental detection data of all mountain road sections, and classify them into road network data sets, environmental data sets and traffic data sets. The real-time update and classification of multi-dimensional data can accurately reflect the traffic flow and environmental changes in mountainous areas, and comprehensively collect key information, which is crucial for the subsequent prediction and adjustment of traffic conditions during peak travel periods;

[0092] Step 2: According to the road network dataset and the environmental dataset, the slope PD and curvature QL of each mountain road section are analyzed, and the corresponding flatness coefficient Ptx is generated. For mountain highways, the flatness coefficient Ptx directly affects the driving experience and vehicle performance;

[0093] Step 3: Set target points according to the traffic data set, obtain all driving paths between the target points, and then combine the road network data set and the environmental data set to analyze the total number of curves S, road length fluctuation rate Cbdl, and average width in each driving path. Road width fluctuation rate Kbdl, average vegetation height Average rainfall Average humidity Temperature fluctuation rate Wbdl and average flow rate The corresponding risk coefficient Fxx is generated. By integrating various factors, the risk coefficient Fxx of each driving route is evaluated, which helps to identify driving routes or road sections with high risks in advance, provide early warning for traffic management, and reduce the occurrence of emergencies.

[0094] Step 4: Set up different types of test vehicles, and then combine the road network dataset and the environmental dataset to predict the travel time YZT of different types of test vehicles under the unified driving path. The prediction results of different types of test vehicles are helpful for fine-grained management of traffic flow, ensuring that appropriate vehicles enter specific sections of the road, avoiding traffic bottlenecks caused by inappropriate vehicle types, and achieving high multi-dimensional evaluation accuracy.

[0095] Step 5: Set a fixed range of flat threshold PTY, risk threshold FXY and duration threshold SCY, and then combine the flat coefficient Ptx, risk coefficient Fxx and travel time YZT to evaluate the resilience level of the mountain highway network, the resilience level of each driving path, and the adaptability of a single driving path to different types of vehicles according to the three dimensions of road conditions, traffic status and vehicle types, and output the corresponding evaluation results and management suggestions, scientifically evaluate the emergency response capacity, diversion capacity and long-term sustainable development capacity of the mountain highway network, and make dynamic adjustments with strong practicality;

[0096] Compare the flatness coefficient Ptx of each mountain road section with the flatness threshold PTY. If the flatness coefficient Ptx of a single mountain road section is lower than the flatness threshold PTY, it means that the single mountain road section has reached the maintenance condition. Then count the number of mountain road sections that have reached the maintenance condition, and evaluate the resilience level of the mountain highway network according to the dimension of road surface conditions.

[0097] If the number of mountain road sections that have reached maintenance conditions is less than 20% of the total number of mountain road sections, the resilience level of the mountain highway network is level 1, and it is recommended to carry out road construction during the travel trough period;

[0098] If the number of mountain road sections that have reached maintenance conditions is less than 50% of the total number of mountain road sections, the resilience level of the mountain highway network is level 2, and it is recommended to immediately carry out road construction, and vehicles with a load of more than 6 tons are prohibited from entering the mountain highway 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 highway network is level 3, and it is recommended to immediately carry out road construction, and vehicles with a load of more than 3 tons are prohibited from entering the mountain highway network;

[0100] The pavement conditions of the highway network in the mountainous area with the first-level toughness are better than those of the highway network in the mountainous area with the second-level toughness, and the pavement conditions of the highway network in the mountainous area with the second-level toughness are better than those of the highway network in the mountainous area with the third-level toughness;

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

[0102] If the risk factor Fxx of a single driving path is lower than the risk threshold FXY, it means that the traffic status of the single driving path is not saturated, and the resilience level of the single driving path is level 1. Traffic diversion is not required, and all vehicles are allowed to continue to pass at the speed limit.

[0103] If the risk factor Fxx of a single driving path is included in the risk threshold FXY, it means that the traffic status of the single driving path is not saturated, and the resilience level of the single driving path is level 2. It is recommended to conduct traffic diversion and reduce the speed limit of the driving path by 20%;

[0104] If the risk factor Fxx of a single driving path exceeds the risk threshold FXY, it means that the traffic status of the single driving path has been saturated, and the resilience level of the single driving path is level three. It is recommended to strengthen traffic diversion, prohibit vehicles from passing, and start limiting queuing;

[0105] The traffic condition of the first-level toughness driving path is better than that of the second-level toughness driving path, and the traffic condition of the second-level toughness driving path is better than that of the third-level toughness driving path;

[0106] In the case of a unified driving route, the travel time YZT of the x1, x2 and x3 test vehicles 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 for a single type of test vehicle, the toughness level of this driving path is level 1, and it is recommended that all vehicles corresponding to a single type give priority to this driving path for travel;

[0108] If the travel time YZT of a single type of test vehicle is included in the time threshold SCY, it means that for a single type of test vehicle, the toughness level of this driving path is level 2, and all vehicles corresponding to a single type are allowed to choose this driving path for travel;

[0109] If the travel time YZT of a single type of test vehicle exceeds the time threshold SCY, it means that for the single type of test vehicle, the toughness level of the driving path is level three. The body length of the single type of test vehicle increases the risk of traffic congestion at the bend of the driving path. 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 driving path with the first-level toughness level is better than that of the driving path with the second-level toughness level, and the adaptability of the driving path with the second-level toughness level is better than that of the driving path with the third-level toughness level.

[0111] In this embodiment, by integrating the data of the traffic management system, sensors and monitoring equipment, it is ensured that the real-time status of the mountain highway can be fully grasped, providing rich basic information for subsequent analysis, analyzing the slope PD and curvature QL of each mountain road section, and generating the corresponding flatness coefficient Ptx. Too steep a slope or a sharp bend in curvature may increase the risk of accidents, identify dangerous mountain road sections, ensure the traffic safety of people and vehicles when passing through, set target points, and then obtain all driving paths between the target points. The risk coefficient Fxx of each driving path is analyzed, which helps to predict the congestion during peak hours and provide more information for car owners and traffic management. It adds safe and efficient driving route suggestions, sets up different types of test vehicles, and predicts the travel time YZT of different types of test vehicles under the unified driving route. It accurately evaluates the adaptability and driving ability of different types of vehicles on mountain highways, and 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 according to the three dimensions of road conditions, traffic status and vehicle type. It optimizes road use strategies, helps to formulate more accurate peak-hour traffic plans, 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 an explanation based on Embodiment 1. Specifically, the calculation process of the flatness coefficient Ptx is as follows:

[0114] S11. Extract the management data of the i-th mountain road section according to the road network data set, and mark the turning radius of the i-th mountain road section as WR i , the road surface porosity of the i-th mountain road section is marked as MK i , mark the construction time of the i-th mountain road section as JS i , mark the altitude of the starting point of the i-th mountain section as qi h , mark the altitude of the end point of the i-th mountain section as zi h , and then use GPS positioning technology to mark the horizontal distance from the starting point to the end point of the i-th mountain section as SP i , which is the straight-line distance from the starting point to the end point in the horizontal direction;

[0115] S12. Extract the environmental detection data of the i-th mountain road section according to the environmental data set, and mark the vegetation coverage rate of the i-th mountain road section as ZF i ;

[0116] S13, calculate the slope PD of the i-th mountain road section i , and its calculation formula is as follows:

[0117]

[0118] In the formula, z h -qi h It represents the altitude difference between the starting point and the end point of the i-th mountain road section. The smaller the slope PD is, the flatter the road is. Novice drivers should try to avoid highways with steep slopes to ensure the stability and safety of the vehicle when driving at high speeds.

[0119] S14. Calculate the curvature QL of the i-th mountain road section i , and its calculation formula is as follows:

[0120]

[0121] The smaller the curvature QL, the gentler the curve and the flatter the road surface. Curves on mountain highways usually choose larger radii 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 section i , and its calculation formula is as follows:

[0123]

[0124] In the formula, BPD represents the standard slope, which is 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, which is used to measure the curvature of mountain road sections. α2 represents the evaluation weight for the ratio of curvature to standard curvature. BMK represents the standard porosity, which is used to measure the compactness of the road surface in mountainous areas. α3 represents the evaluation weight for the ratio of road surface porosity to standard porosity. BJS represents the standard duration, which is used to measure the aging degree of the road surface in mountainous areas. α4 represents the evaluation weight for the ratio of construction duration to standard duration. BZF represents the standard coverage, which is used to measure the density of vegetation coverage in mountainous areas. α5 represents the evaluation weight for the ratio of vegetation coverage to standard coverage. α1+α2+α3+α4+α5=1. It means that according to the weights of α1, α2, α3, α4 and α5, the flatness coefficient Ptxi of the i-th mountainous road section is obtained. The higher the flatness coefficient Ptx, the better the road condition;

[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 calculation formula of 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 flat threshold PTY is set to 5.5-6.0. The total number of mountain road sections is 3. After judgment, the number of mountain road sections that have reached the maintenance conditions is 2. The toughness level of the mountain highway network is level three. It is recommended to carry out road construction immediately, and vehicles with a load of more than 3 tons are prohibited from entering the mountain highway network.

[0130] In this embodiment, the factors affecting the flatness coefficient Ptx of the mountain road section also include the porosity of the road surface, the construction time and the vegetation coverage rate. The porosity will affect the density, durability, waterproofness and other characteristics of the road surface. The lower the porosity of the road surface material and the higher the density, the more effective it can be to prevent water from penetrating and reduce the damage to the toughness of the material caused by freeze-thaw. The pavement materials of mountain highways need to take into account long-term natural aging factors such as ultraviolet rays and oxidation. Materials that age more slowly can maintain toughness for a longer time. In addition, mountain highways with high vegetation coverage may indirectly affect the flatness of the road surface, especially when the soil is relatively soft or the roadbed construction is not strong enough. The growth of roots may cause the road surface to fluctuate, thereby affecting the long-term stability of the road surface structure.

[0131] Example 3

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

[0133] S21. According to the traffic data set, target point 1 and target point 2 are set. Both target point 1 and target point 2 are entrances and exits of the mountain area, which are different but connected. Then, map software is used to obtain all driving paths between target point 1 and target point 2, and they are marked as {L1, L2, L3, ..., Lp}. L1 to Lp are the first to pth driving paths between target point 1 and target point 2, respectively. Each driving path is composed of several mountain road sections, which is helpful for subsequent comparison of the safety and traffic conditions of different routes, and thus provides basic data for subsequent risk assessment;

[0134] S22, extracting the kth driving path between the target point 1 and the target point 2, wherein the kth driving path consists of the ath mountain road section, the bth mountain road section, the cth mountain road section and the dth mountain road section;

[0135] S23. According to the road network dataset, the road length of the a-th mountainous road section is marked as cd a , mark the road length of the b-th mountain section as cd b , mark the road length of the cth mountain section as cd c, mark the road length of the d-th mountain road section as cd d , and then calculate the total length ZC of the kth driving path k , the average length of mountainous road sections And the volatility Cbdl of the road 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 sections in the k-th driving path, that is, the total number of bends in the k-th driving path. k =4, It represents the ratio of the total length of the path to the total number of road sections, that is, the average length of the mountain road section in the kth driving path cd l represents the road length of any mountain road section among the a-th mountain road section, the b-th mountain road section, the c-th mountain road section and the d-th mountain road section, According to the standard deviation formula, the volatility Cbdl of the road length of the kth driving path is obtained k , quantitatively assess the complexity and stability of driving routes. Long sections may increase risk, while areas with large fluctuations in section length may have higher unpredictability. The calculation of average length helps to assess the driving difficulty of different sections;

[0139] S24. According to the road network dataset, the road width of the a-th mountain road section is marked as kd a , mark the road width of the b-th mountain section as kd b , mark the road width of the c-th mountain road section as kd c , mark the road width of the d-th mountain road section as kd d , and then calculate the total width ZK of the k-th driving path k , the average width of mountainous road sections And the fluctuation rate of the road width of the kth driving path Kbdl k ;

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

[0141]

[0142] In the formula, It represents the ratio of the total width of the path to the total number of road sections, that is, the average width of the mountain road section in the kth driving path kd q represents the road width of any mountain road section among the a-th mountain road section, the b-th mountain road section, the c-th mountain road section and the d-th mountain road section, According to the standard deviation formula, the fluctuation rate Kbdl of the road width of the kth driving path is obtained k ,The road width is crucial to driving safety. Narrow roads may cause difficulties in passing, especially on mountain roads. Calculating the road width and fluctuation rate of each road section can help analyze possible congestion and safety hazards in the driving path;

[0143] S25. According to the environmental data set, the roadside vegetation height of the a-th mountainous road section is marked as gd a , mark the roadside vegetation height of the b-th mountain road section as gd b , mark the roadside vegetation height of the cth mountain road section as gd c , mark the roadside vegetation height of the d-th mountain road section as gd d , and then calculate the average vegetation height of the mountainous section in the kth driving path

[0144]

[0145] The height of roadside vegetation affects sight distance and road visibility. Higher vegetation may block the line of sight and increase driving difficulty. By calculating the average height of vegetation on a road section, the sight distance safety of each driving path can be evaluated. Higher vegetation may also bring more ecological risks, such as landslides or falling objects.

[0146] S26. According to the environmental data set, the rainfall of the a-th mountain section is marked as yu a , mark the humidity of the a-th mountain road section as sd a , mark the rainfall of the b-th mountain section as yu b , mark the humidity of the b-th mountain section as sd b , mark the rainfall of the cth mountain section as yu c , the humidity of the c-th mountain road section is marked as sd c , mark the rainfall of the d-th mountain section as yu d , the humidity of the d-th mountain section is marked as Sd d , and then calculate the average rainfall in the mountainous section of the k-th driving path and average humidity

[0147]

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

[0149] S27. According to the environmental data set, the temperature of the a-th mountain road section is marked as wd a , mark the temperature of the b-th mountain section as wd b , mark the temperature of the cth mountain section as wd c , mark the temperature of the d-th mountain section as wd d , and then calculate the average temperature of the mountainous section in the kth driving path And the fluctuation rate of temperature in mountainous road sections Wbdl k ;

[0150]

[0151] In the formula, wd e represents the temperature of any mountain road section among the a-th mountain road section, the b-th mountain road section, the c-th mountain road section and the d-th mountain road section, According to the standard deviation formula, the fluctuation rate of the temperature in the mountainous section of the k-th driving path is Wbdl k Temperature also affects road conditions and driving experience. Too high or too low temperatures may cause road damage or increase the risk of ice and snow. Areas with large temperature fluctuations may have greater uncertainty. The greater the temperature fluctuation, the higher the risk of vehicle tire blowout.

[0152] S28. According to the traffic data set, the traffic flow of target point 1 is marked as rl mu1 , mark the traffic flow of target point 1 as cl mu1 , mark the flow of people at target point 2 as rl mu2 , mark the traffic flow of target point 2 as cl mu2 , and then calculate the total flow ZL between target point 1 and target point 2, as well as the average flow of each driving path

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

[0154]

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

[0156] S29. Calculate the risk factor Fxx of the kth driving path between target point 1 and target point 2 k , and its calculation formula is as follows:

[0157] If the average rainfall in the mountainous section of the k-th driving route is is 0,

[0158]

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

[0160] Indicates that the risk coefficient Fxx of the kth driving path is obtained according to the weights of β1, β2, β3, β4, β5, β6, β7 and β8 k ;

[0161] If the average rainfall in the mountainous section of the k-th driving route is Not 0,

[0162]

[0163] In the formula, Represents the evaluation weight for the total number of mountain sections in the driving route. It represents the evaluation weight of the road length fluctuation rate in the driving path. represents the evaluation weight for the ratio of average width to standard road width, It represents the evaluation weight of the road width fluctuation rate in the driving path. Represents the evaluation weight of the average vegetation height in the driving path. Represents the evaluation weight of the average rainfall along the driving route. Represents the evaluation weight of the average humidity in the driving path, It represents the evaluation weight of the temperature fluctuation rate of the mountainous road section during the driving route. represents the evaluation weight for the average flow, Indicates according to and Weight, get the risk factor Fxx of the kth 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 specifically, and diversified factors such as road section characteristics, environmental conditions, and traffic flow are quantified and integrated. The generated risk coefficient Fxx can intuitively reflect the risk level of different paths, and 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 of the travel time YZT is as follows:

[0167] S31. The types of test vehicles include x1, x2 and x3. Among them, the body length of x1 test vehicles is less than 6 meters, corresponding to small vehicles, suitable for urban driving and short-distance driving; the body length of x2 test vehicles is 6 meters or less and 9 meters or less, corresponding to medium-sized vehicles, usually with higher comfort and space, suitable for long-distance driving and family use; the body length of x3 test vehicles is greater than 9 meters, corresponding to large vehicles, such as large sedans, commercial vehicles, trucks, buses, suitable for carrying passengers or goods, with stronger carrying capacity and larger driving space;

[0168] S32: The xth test vehicle is assumed to travel on the kth driving path between the target point 1 and the target point 2, and starts from the target point 1 or the target point 2. The xth test vehicle belongs to the x1 category, and the body length of the xth test vehicle is marked as che. x , clarify the impact of vehicle length on driving, and be able to more accurately simulate the performance of different vehicles;

[0169] S33, connecting to the highway traffic management system through the network, obtaining the speed limit of the k-th driving path between the target point 1 and the target point 2, marked as xv k ,Speed ​​limit is a key factor affecting vehicle travel time. It can accurately predict the travel time required for each vehicle on the driving path, which helps to optimize route selection;

[0170] S34. According to the road network data set, the turning arc length of the a-th mountain road section is marked as hl a , mark the turning arc length of the b-th mountain road section as hl b , mark the turning arc length of the cth mountain road section as hl c , mark the turning arc length of the dth mountain road section as hl d , and then predict the turning time of the xth test car GWT x And the passing time YZT x ;

[0171]

[0172] In the formula, μ represents the delay factor, μ×che x It means that according to the length of the vehicle body, the delay time of the xth test vehicle when turning is obtained. Indicates that according to the delay time, the cornering time GWT of the x-th test vehicle is predicted x , accurately simulate the performance of vehicles on different road sections. Larger turning radius and vehicle length will cause the vehicle to slow down and increase driving time. Predicting the turning time can help determine the difficulty of the driving path;

[0173] If the average rainfall in the mountainous section of the k-th driving route is 0, the road condition is good.

[0174]

[0175] In the formula, represents the basic time required for the x-th test vehicle to complete the k-th driving path under speed limit, θ represents the delay factor, It means that according to the average humidity, the delay time of the x-th test vehicle traveling on the k-th driving path is obtained. It means that according to the basic time and delay time, the predicted passing time of the x-th test vehicle is YZY x ;

[0176] If the average rainfall in the mountainous section of the k-th driving route is Not 0, slippery roads will increase braking distance, reduce vehicle speed, and thus extend driving time.

[0177]

[0178] In the formula, represents the delay factor, It means that according to the average rainfall, the delay time of the x-th test vehicle traveling on the k-th driving path is obtained. It indicates that according to the basic duration and delay duration, the passing time YZT of the x-th vehicle is predicted x ,Combined with the actual driving environment, the delay factor is calculated to ensure accurate prediction of the driving time of different types of vehicles;

[0179] Specifically, in Table 2, the test vehicle numbered X1 belongs to the x1 category, the test vehicle numbered X2 belongs to the x2 category, and the test vehicle numbered X3 belongs to the x3 category. The first mountain road section, the second mountain road section, and the third mountain road section constitute the driving path to be tested. The test vehicles numbered X1, X2, and X3 are arranged to travel on the driving path composed of the first mountain road section, the second mountain road section, and the third mountain road section in sequence. In the calculation formulas of the cornering time GWT and the passing time YZT, μ=0.2, θ=0.1, The speed limit on this route is 40,000 m / h;

[0180]

[0181] Table 2

[0182] The duration threshold SCY is set to 5-6 hours. After judgment, for x1 and x2 test vehicles, the toughness level of this driving path is level 2, allowing all small and medium-sized vehicles to choose this driving path for passage; for x3 test vehicles, the toughness level of this driving path is level 3. The body length of large vehicles increases the risk of traffic congestion at the bends of the driving path, and all large vehicles should be prohibited from entering this driving path.

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

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

Claims

1. A method for assessing the resilience of a mountain highway network during peak travel periods, characterized by: The following steps are involved: Step 1: Connect the highway traffic management system, sensor devices and monitoring devices through the network to obtain the management data of all mountain road sections, the traffic flow data of all mountain entrances and exits, and the environmental detection data of all mountain road sections, and classify them into road network data sets, environmental data sets and traffic data sets; Step 2: According to the road network dataset and the environmental dataset, the slope PD and curvature QL of each mountainous road section are analyzed, and the corresponding flatness coefficient Ptx is generated; Step 3: Set target points according to the traffic data set, obtain all driving paths between the target points, and then combine the road network data set and the environmental data set to analyze the total number of curves S, road length fluctuation rate Cbdl, and average width in each driving path. Road width fluctuation rate Kbdl, average vegetation height Average rainfall Average humidity Temperature fluctuation rate Wbdl and average flow rate And generate the corresponding risk factor Fxx; Step 4: Set up different types of test vehicles, and then combine the road network dataset and the environmental dataset to predict the travel time YZT of different types of test vehicles under the unified driving path; Step 5: Set a fixed range of flatness threshold PTY, risk threshold FXY and duration threshold SCY, and then combine the flatness coefficient Ptx, risk coefficient Fxx and travel time YZT to evaluate the resilience level of the mountain highway network, the resilience level of each driving path, and the adaptability of a single driving path to different types of vehicles based on three dimensions: road conditions, traffic status and vehicle types, and output corresponding evaluation results and management recommendations.

2. The method for evaluating the resilience of a mountain highway network during peak travel periods according to claim 1 is characterized by: In step 1, the expression of the road network dataset is {D1 h 、D2 h 、D3 h 、...、Dn h }, D1 h To Dn h They are the management data of the first to the nth mountain road sections, including road length, road width, turning radius, road porosity, construction time and turning arc length. h represents the altitude of each mountain road section, and each mountain road section contains only one bend.

3. The method for evaluating the resilience of a mountain highway network during peak travel periods according to claim 2 is characterized by: In step 1, the expression of the environmental data set is {H1 m 、H2 m 、H3 m 、...、Hj m }, H1 m To Hj m They are the environmental monitoring data of the first to the nth mountain road sections, respectively. The environmental monitoring data include vegetation coverage, roadside vegetation height, rainfall, humidity and temperature. m represents the specific time point for obtaining the environmental monitoring data of each mountain road section.

4. The method for evaluating the resilience of a mountain highway network during peak travel periods according to claim 3 is characterized by: In step 1, the expression of the flow data set is {K1 s , K2 s 、K3 s 、...、Ku s }, K1 s To Ko s They are the traffic flow data of the first to the oth mountain entrances and exits, respectively. The traffic flow data includes pedestrian flow and vehicle flow. s represents the specific time point for obtaining the traffic flow data of each mountain entrance and exit.

5. The method for evaluating the resilience of a mountain highway network during peak travel periods according to claim 4 is characterized by: In step 2, the calculation process of the flatness coefficient Ptx is as follows: S11. Extract the management data of the i-th mountain road section according to the road network data set, and mark the turning radius of the i-th mountain road section as WR i , the road surface porosity of the i-th mountain road section is marked as MK i , mark the construction time of the i-th mountain road section as JS i , mark the altitude of the starting point of the i-th mountain section as q ih , mark the altitude of the end point of the i-th mountain section as zi h , and then use GPS positioning technology to mark the horizontal distance from the starting point to the end point of the i-th mountain section as SP i ; S12. Extract the environmental detection data of the i-th mountain road section according to the environmental data set, and mark the vegetation coverage rate of the i-th mountain road section as ZF i ; S13, calculate the slope PD of the i-th mountain road section i , and its calculation formula is as follows: In the formula, z h -qi h represents the altitude difference between the starting point and the end point of the i-th mountain section; S14. Calculate the curvature QL of the i-th mountain road section i , and its calculation formula is as follows: S15, calculate the flatness coefficient Ptx of the i-th mountain road section i , and its calculation formula is as follows: In the formula, BPD represents the standard slope, which is 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, which is used to measure the curvature of mountain road sections. α2 represents the evaluation weight for the ratio of curvature to standard curvature. BMK represents the standard porosity, which is used to measure the compactness of the road surface in mountainous areas. α3 represents the evaluation weight for the ratio of road surface porosity to standard porosity. BJS represents the standard duration, which is used to measure the aging degree of the road surface in mountainous areas. α4 represents the evaluation weight for the ratio of construction duration to standard duration. BZF represents the standard coverage, which is used to measure the density of vegetation coverage in mountainous areas. α5 represents the evaluation weight for the ratio of vegetation coverage to standard coverage. α1+α2+α3+α4+α5=1. Indicates that according to the weights α1, α2, α3, α4 and α5, the flatness coefficient Ptx of the i-th mountainous road section is obtained i .

6. The method for evaluating the resilience of a mountain highway network during peak travel periods according to claim 5 is characterized by: In step 3, the risk factor Fxx calculation process is as follows: S21. According to the traffic data set, target point 1 and target point 2 are set. Both target point 1 and target point 2 are entrances and exits of the mountain area, which are different but connected. Then, map software is used to obtain all driving paths between target point 1 and target point 2, and they are marked as {L1, L2, L3, ..., Lp}. L1 to Lp are the first to pth driving paths between target point 1 and target point 2, respectively, wherein each driving path is composed of several mountain road sections; S22, extracting the kth driving path between the target point 1 and the target point 2, wherein the kth driving path consists of the ath mountain road section, the bth mountain road section, the cth mountain road section and the dth mountain road section; S23. According to the road network dataset, the road length of the a-th mountain road section is marked as cd a , mark the road length of the b-th mountain section as cd b , mark the road length of the cth mountain section as cd c , mark the road length of the d-th mountain road section as cd d , and then calculate the total length ZC of the kth driving path k , the average length of mountainous road sections And the volatility Cbdl of the road 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 sections in the k-th driving path, that is, the total number of bends in the k-th driving path. k =4, It represents the ratio of the total length of the path to the total number of road sections, that is, the average length of the mountain road section in the kth driving path cd l represents the road length of any mountain road section among the a-th mountain road section, the b-th mountain road section, the c-th mountain road section and the d-th mountain road section, According to the standard deviation formula, the volatility Cbdl of the road length of the kth driving path is obtained k ; S24. According to the road network dataset, the road width of the a-th mountain road section is marked as kd a , mark the road width of the b-th mountain road section as kd b , mark the road width of the c-th mountain road section as kd c , mark the road width of the d-th mountain road section as kd d , and then calculate the total width ZK of the k-th driving path k , the average width of mountainous road sections And the fluctuation rate of the road width of the kth driving path Kbdl k ; ZK k =kd a +kd b +kd c +kd d In the formula, It represents the ratio of the total width of the path to the total number of road sections, that is, the average width of the mountain road section in the kth driving path kd q represents the road width of any mountain road section among the a-th mountain road section, the b-th mountain road section, the c-th mountain road section and the d-th mountain road section, According to the standard deviation formula, the fluctuation rate Kbdl of the road width of the kth driving path is obtained k ; S25. According to the environmental data set, the roadside vegetation height of the a-th mountainous road section is marked as gd a , mark the roadside vegetation height of the b-th mountain road section as gd b , mark the roadside vegetation height of the cth mountain road section as gd c , mark the roadside vegetation height of the d-th mountain road section as gd d , and then calculate the average vegetation height of the mountainous section in the kth driving path S26. According to the environmental data set, the rainfall of the a-th mountain section is marked as yu a , the humidity of the a-th mountain road section is marked as sd a , mark the rainfall of the b-th mountain section as yu b , mark the humidity of the b-th mountain section as sd b , mark the rainfall of the cth mountain section as yu c , the humidity of the c-th mountain road section is marked as sd c , mark the rainfall of the d-th mountain section as yu d , the humidity of the d-th mountain road section is marked as sd d , and then calculate the average rainfall in the mountainous section of the k-th driving path and average humidity S27. According to the environmental data set, the temperature of the a-th mountain road section is marked as wd a , mark the temperature of the b-th mountain section as wd b , mark the temperature of the cth mountain section as wd c , mark the temperature of the d-th mountain section as wd d , and then calculate the average temperature of the mountainous section in the kth driving path And the fluctuation rate of temperature in mountainous road sections Wbdl k ; In the formula, wd e represents the temperature of any mountain road section among the a-th mountain road section, the b-th mountain road section, the c-th mountain road section and the d-th mountain road section, According to the standard deviation formula, the fluctuation rate of the temperature in the mountainous section of the k-th driving path is Wbdl k ; S28. According to the traffic data set, the traffic flow of target point 1 is marked as rl mu1 , mark the traffic flow of target point 1 as cl mu1 , mark the flow of people at target point 2 as rl mu2 , mark the traffic flow of target point 2 as cl mu2 , and then calculate the total flow ZL between target point 1 and target point 2, as well as the average flow of each driving path ZL=rl mu1 +cl mu1 +rl mu2 +cl mu2 S29. Calculate the risk factor Fxx of the kth driving path between target point 1 and target point 2 k , and its calculation formula is as follows: If the average rainfall in the mountainous section of the k-th driving route is is 0, In the formula, β1 represents the evaluation weight for the total number of mountainous road sections in the driving route, and β2 represents the evaluation weight for the fluctuation rate of road length in the driving route. represents the standard width of the road surface, which is used to measure the width of the road surface in mountainous areas. β3 represents the evaluation weight for the ratio of the average width to the standard width of the road surface. β4 represents the evaluation weight for the fluctuation rate of the road surface width during the driving path. β5 represents the evaluation weight for the average vegetation height during the driving path. β6 represents the evaluation weight for the average humidity during the driving path. β7 represents the evaluation weight for the temperature fluctuation rate of the mountainous road section during the driving path. β8 represents the evaluation weight for the average flow rate. β1+β2+β3+β4+β5+β6+β7+β8=1. Indicates that the risk coefficient Fxx of the kth driving path is obtained according to the weights of β1, β2, β3, β4, β5, β6, β7 and β8 k ; If the average rainfall in the mountainous section of the k-th driving route is Not 0, In the formula, Represents the evaluation weight for the total number of mountain sections in the driving route. It represents the evaluation weight of the road length fluctuation rate in the driving path. represents the evaluation weight for the ratio of average width to standard road width, It represents the evaluation weight of the road width fluctuation rate in the driving path. Represents the evaluation weight of the average vegetation height in the driving path. Represents the evaluation weight of the average rainfall along the driving route. Represents the evaluation weight of the average humidity in the driving path, It represents the evaluation weight of the temperature fluctuation rate of the mountainous road section during the driving route. represents the evaluation weight for the average flow, Indicates according to and Weight, get the risk factor Fxx of the kth driving path k .

7. The method for evaluating the resilience of a mountain highway network during peak travel periods according to claim 6 is characterized by: In step 4, the calculation process of the travel time YZT is as follows: S31. The types of test vehicles include x1, x2 and x3. The body length of the x1 test vehicle is less than 6 meters, 6 meters ≤, the body length of the x2 test vehicle is less than 9 meters, and the body length of the x3 test vehicle is greater than 9 meters. S32: The xth test vehicle is assumed to travel on the kth driving path between the target point 1 and the target point 2, and starts from the target point 1 or the target point 2. The xth test vehicle belongs to the x1 category, and the body length of the xth test vehicle is marked as che. x ; S33, connecting to the highway traffic management system through the network, obtaining the speed limit of the k-th driving path between the target point 1 and the target point 2, marked as xv k ; S34. According to the road network dataset, the turning arc length of the a-th mountain road section is marked as hl a , mark the turning arc length of the b-th mountain road section as hl b , mark the turning arc length of the cth mountain road section as hl c , mark the turning arc length of the dth mountain road section as hl d , and then predict the turning time of the xth test car GWT x And the passing time YZT x ; In the formula, μ represents the delay factor, μ×che x It means that according to the length of the vehicle body, the delay time of the xth test vehicle when turning is obtained. Indicates that according to the delay time, the cornering time GWT of the x-th test vehicle is predicted x ; If the average rainfall in the mountainous section of the k-th driving route is is 0, In the formula, represents the basic time required for the x-th test vehicle to complete the k-th driving path under speed limit, θ represents the delay factor, It means that according to the average humidity, the delay time of the x-th test vehicle traveling on the k-th driving path is obtained. It indicates that according to the basic duration and delay duration, the passing time YZT of the x-th test vehicle is predicted x ; If the average rainfall in the mountainous section of the k-th driving route is Not 0, In the formula, represents the delay factor, It means that according to the average rainfall, the delay time of the x-th test vehicle traveling on the k-th driving path is obtained. It indicates that according to the basic duration and delay duration, the passing time YZT of the x-th vehicle is predicted x .

8. The method for evaluating the resilience of a mountain highway network during peak travel periods according to claim 7 is characterized by: In the step 5, the flatness coefficient Ptx of each mountain road section is compared with the flatness threshold PTY. If the flatness coefficient Ptx of a single mountain road section is lower than the flatness threshold PTY, it means that the single mountain road section has reached the maintenance condition. Then, the number of mountain road sections that have reached the maintenance condition is counted, and the resilience level of the mountain highway network is evaluated according to the dimension of road surface conditions. If the number of mountain road sections that have reached maintenance conditions is less than 20% of the total number of mountain road sections, the resilience level of the mountain highway network is level 1, and it is recommended to carry out road construction during the travel trough period; If the number of mountain road sections that have reached maintenance conditions is less than 50% of the total number of mountain road sections, the resilience level of the mountain highway network is level 2, and it is recommended to immediately carry out road construction, and vehicles with a load of more than 6 tons are prohibited from entering the mountain highway 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 highway network is level 3, and it is recommended to immediately carry out road construction, and vehicles with a load of more than 3 tons are prohibited from entering the mountain highway network; The pavement conditions of the first-level toughness grade mountain expressway network are better than those of the second-level toughness grade mountain expressway network, and the pavement conditions of the second-level toughness grade mountain expressway network are better than those of the third-level toughness grade mountain expressway network.

9. The method for evaluating the resilience of a mountain highway network during peak travel periods according to claim 8, characterized in that: In the step 5, the risk coefficient Fxx of each driving path between the target point 1 and the target point 2 is compared with the risk threshold FXY, and the resilience level of each driving path is evaluated according to the dimension of the traffic state; If the risk factor Fxx of a single driving path is lower than the risk threshold FXY, it means that the traffic status of the single driving path is not saturated, and the resilience level of the single driving path is level 1. Traffic diversion is not required, and all vehicles are allowed to continue to pass at the speed limit. If the risk factor Fxx of a single driving path is included in the risk threshold FXY, it means that the traffic status of the single driving path is not saturated, and the resilience level of the single driving path is level 2. It is recommended to conduct traffic diversion and reduce the speed limit of the driving path by 20%; If the risk factor Fxx of a single driving path exceeds the risk threshold FXY, it means that the traffic status of the single driving path has been saturated, and the resilience level of the single driving path is level three. It is recommended to strengthen traffic diversion, prohibit vehicles from passing, and start limiting queuing; The traffic condition of a driving path with the first-level toughness grade is better than that of a driving path with the second-level toughness grade, and the traffic condition of a driving path with the second-level toughness grade is better than that of a driving path with the third-level toughness grade.

10. The method for evaluating the resilience of a mountain highway network during peak travel periods according to claim 9, characterized in that: In step 5, in the case of a unified driving route, the travel time YZT of the test vehicles of category x1, category x2 and category 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; If the travel time YZT of a single type of test vehicle is lower than the travel time threshold SCY, it means that for a single type of test vehicle, the toughness level of this driving path is level 1, and it is recommended that all vehicles corresponding to a single type give priority to this driving path for travel; If the travel time YZT of a single type of test vehicle is included in the time threshold SCY, it means that for a single type of test vehicle, the toughness level of this driving path is level 2, and all vehicles corresponding to a single type are allowed to choose this driving path for travel; If the travel time YZT of a single type of test vehicle exceeds the time threshold SCY, it means that for the single type of test vehicle, the toughness level of the driving path is level three. The body length of the single type of test vehicle increases the risk of traffic congestion at the bend of the driving path. 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 driving path with the first-level toughness level is better than that of the driving path with the second-level toughness level, and the adaptability of the driving path with the second-level toughness level is better than that of the driving path with the third-level toughness level.

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