ETC data-based highway infrastructure operation load flow real-time atlas method and system, and storage medium

Abstract

The invention discloses a highway infrastructure operation load flow implementation atlas method and system based on ETC (Electronic Toll Collection) data, and a storage medium. Statistics of seven parameter indexes of traffic flow Q, truck proportion P, overload truck number Qo, maximum overload tonnage Wm, equivalent axle load Weff, lane load correction coefficient eta s and fatigue load correction coefficient eta f of each ETC portal station is carried out to carry out grade evaluation of station operation load flow level, and grade division is carried out through colors. Transport load grades of all traffic routes in areas near the ETC portal are presented in real time, a road network operation load flow real-time map is drawn by combining ETC portal space coordinate information, road network traffic load distribution conditions are observed from the perspective of infrastructure operation risks, infrastructure managers can accurately master the traffic load flow level of each area of a road network, and the traffic load flow level of each area of the road network is improved. And important support is provided for operation management and maintenance resource planning of infrastructures.

Description

Technical Field

[0001] The present invention relates to the field of highways, and in particular to a real-time graph method, system and storage medium for highway infrastructure operation load flow based on ETC data. Background Art

[0002] By the end of 2023, there are more than 1 million highway bridges in my country, and the number of bridges built is still growing rapidly every year. The number of trucks is 11.6666 million, the tonnage of trucks is 169.6733 million tons, and the average truck load is 145,435 tons, indicating that the highway transportation infrastructure continues to face the role of heavy-load transportation, which also makes many bridges that have been in service for many years face operational safety risks. At present, with the development of sensor and electronic information technology, there are many technical means to obtain the actual operating load information of bridges, such as dynamic weighing technology of pavement and dynamic weighing technology of bridges, but these technical means can only carry out load survey statistics for a single bridge, and cannot be applied to a large number of bridge groups on highways. At present, there is no technical means that can present the traffic load transportation status of highway network in real time, so that highway operating units cannot well grasp the transportation load status of bridges in the road network area, and cannot conduct timely traffic load response analysis and load control of road network bridge groups.

[0003] In the transportation industry, with the technical support of the global positioning system, the monitoring of road network traffic flow has been realized very early, allowing transportation users to quickly analyze the time required for route selection and the smoothness of the road, which provides good support for transportation control and road network accessibility. Transportation infrastructure is the carrier that supports transportation. At present, there is a lack of effective means to grasp the transportation load status of road network transportation infrastructure in real time, which has caused great obstacles to infrastructure maintenance and management personnel. If the transportation load status of road network infrastructure can be grasped in real time, according to the road network traffic status analysis method in the transportation field, the transportation load status of all areas can be presented in real time, so that the transportation load level of each area can be quickly grasped. Through the red, orange, yellow, blue, green and other map presentation methods, transportation infrastructure managers can quickly grasp the operational safety risk level of road network infrastructure, which is of great significance for timely load control and extending the service life of infrastructure. Summary of the invention

[0004] The present invention aims to overcome at least one defect of the above-mentioned prior art, and provides a real-time map of highway infrastructure operation load flow based on ETC data, which utilizes the traffic load information recorded by the ETC gantry to present in real time the transport load levels of all traffic routes in the area near the ETC gantry, observes the road network traffic load distribution status from the perspective of infrastructure operation risk, and solves the technical problem that the prior art lacks effective means to grasp the transport load status of road network traffic infrastructure in real time.

[0005] The present invention provides a real-time mapping method of highway infrastructure operation load flow based on ETC data, which is characterized by comprising the following steps:

[0006] Step 1: Capture the real-time sensing data of all ETC gantries in the highway network system at regular intervals, and extract the traffic information of all sensed vehicles within the time interval;

[0007] Step 2: Use automated procedures to count the traffic flow Q, truck ratio P, and number of overloaded vehicles Q at each ETC gantry station o , Maximum overload tonnage W m , equivalent axle weight W eff , Lane load correction factor η s , Fatigue load correction factor η f Seven parameter indicators;

[0008] Step 3: Based on the relationship between the calculation results of the seven parameter indicators and their thresholds, a grade evaluation of the operational load flow level of the site is formed, and the road section is divided into red, yellow, blue or green colors according to the evaluation grade;

[0009] Step 4: Based on the evaluation results of the seven indicators of each ETC station and combined with the spatial coordinate information of the ETC gantry, a real-time map of the road network operation load flow is drawn, and different colors are used to distinguish the real-time status of the load flow in each area.

[0010] The real-time sensing data of the ETC gantry in step 1 includes the number reflecting the spatial coordinates of the ETC gantry, the vehicle license plate, the vehicle color, the vehicle travel time, the vehicle model, the number of vehicle axles and the vehicle weight information.

[0011] The seven parameter indicators described in step 2 specifically include:

[0012] The traffic flow Q is the total number of vehicles passing through the lane per unit time;

[0013] The truck ratio P is the ratio of the total number of truck vehicles to the total number of all vehicles;

[0014] The number of overloaded vehicles Q o It is the total number of vehicles with loads exceeding national standards per unit time;

[0015] The maximum overload tonnage W m It is the maximum weight among all overloaded vehicles that exceeds the regulatory limit;

[0016] The equivalent axle weight W eff =0.935A2+1.925A3+3.025A4+4.315A5+4.207A6, where A2~A6 are the ratios of the number of two-axle trucks, three-axle trucks, four-axle trucks, five-axle trucks, and six-axle and above trucks in the passing vehicles to the number of all passing vehicles;

[0017] The lane load correction factor η s It is the proportionality coefficient between the maximum loading response of the fleet of passing vehicles on the typical influence line of the bridge and the calculated response of the standard lane load model;

[0018] The fatigue load correction factor η f It is the proportionality coefficient between the equivalent response of a fleet of passing vehicles loaded on the typical influence line of the bridge and the equivalent response calculated by the standard fatigue load model.

[0019] The lane load correction factor η s The fatigue load correction factor η f The calculation steps include:

[0020] Step 2.1, the real-time sensing data of the ETC gantry is supplemented with axle weight, wheelbase and speed information according to different types of vehicles through a data enhancement method, so as to form a traffic flow load sequence of the bridge deck starting from the first vehicle and according to the known wheelbase and speed information;

[0021] Step 2.2: Use an automated program to calculate the load effect of the fleet load sequence on the mid-span bending moment influence line of the simply supported beam (span 5-100m), the mid-span bending moment influence line of the two-span simply supported beam, and the support shear force influence line (span 20-120m), and obtain the measured maximum load response value S for each influence line. ci (i=1,2,…,N, N is the number of influence lines), and the measured equivalent load response value D calculated based on Miner linear damage accumulation criterion ci (i=1,2,…,N, N is the number of influence lines);

[0022] Step 2.3: Use the lane load model and fatigue load model of the bridge design specification to load the influence line respectively, and obtain the specification maximum load response value S of the corresponding influence line. di And the code equivalent load response value D di (i=1,2,…,N, N is the number of influence lines);

[0023] Step 2.4: Calculate the measured maximum load response value S corresponding to each influence line ci Compared with the maximum load response value S di The ratio S ci / S di , take the maximum value of all influence line calculation ratios as the lane load correction factor η s ;

[0024] Step 2.5: Calculate the measured equivalent load response value D corresponding to each influence line ci Equivalent load response value D di The ratio D ci / D di , take the maximum value of all influence line calculation ratios as the fatigue load correction factor η f .

[0025] The name of the automation program is the MSCA hybrid simulation program, and the specific execution steps of the MSCA hybrid simulation program include:

[0026] S1. Input the real-time sensing data of the ETC gantry, use the statistical probability model based on the statistical results of bridge vehicle information over a long period of time as the data source, use the data enhancement method to supplement the axle weight, wheelbase and speed of vehicles according to different types, and form a traffic flow load sequence of the bridge deck starting from the first vehicle according to the known wheelbase and speed information;

[0027] S2. Prepare the influence line data of bridges with different spans according to the mechanical model, iterate the traffic load sequence according to the time step, calculate the load effect value at each time step by superimposing the load sequence of the current time step with the influence line value, and form a load response time history. On this basis, the rain flow method is calculated based on the Miner linear damage accumulation criterion to obtain the equivalent load response of the corresponding influence line;

[0028] S3. Find the maximum value of the load response time corresponding to different influence lines as the maximum load effect value of the influence line. The same is true for the equivalent load response value.

[0029] S4. Compare with the standard value to obtain the lane load correction factor η s and fatigue load correction factor η f .

[0030] In step 3: according to the size of the traffic flow Q, the traffic density is evaluated into four levels: green (<300 vehicles / lane / hour), blue (300-600 vehicles / lane / hour), yellow (600-900 vehicles / lane / hour) and red (>900 vehicles / lane / hour);

[0031] According to the size of the truck ratio P, the heavy-load transport level is assessed into four levels: green (<20%), blue (20% to 50%), yellow (50% to 80%), and red (>80%).

[0032] According to the number of overloaded vehicles Q o , which are rated into four levels: green (<1 vehicle / hour), blue (1-5 vehicles / hour), yellow (5-10 vehicles / hour) and red (>10 vehicles / hour);

[0033] According to the maximum overload tonnage W m , the overload transport level is assessed into four levels: green (<5 tons), blue (5-10 tons), yellow (10-20 tons) and red (>20 tons);

[0034] According to the equivalent axle weight W eff The road damage risk is assessed into four levels: green (<5 tons), blue (5-10 tons), yellow (10-15 tons) and red (>15 tons).

[0035] According to the lane load correction factor η s The bridge strength damage risk is assessed into four levels: green (<0.5), blue (0.5-0.8), yellow (0.8-1.0) and red (>1.0).

[0036] According to the fatigue load correction factor η f The fatigue damage risk of bridges is assessed according to the size of the fatigue damage, which is divided into four levels: green (<0.5), blue (0.5-0.8), yellow (0.8-1.0) and red (>1.0).

[0037] The seven parameter indicators of the ETC gantry station are evaluated according to their corresponding threshold values ​​and quantitatively regulated according to different levels;

[0038] If the indicator is graded as green, it is set as 1; if the indicator is graded as blue, it is set as 3; if the indicator is graded as yellow, it is set as 5; and if the indicator is graded as red, it is set as 7.

[0039] The seven parameter indicators make an overall assessment of the operational load flow level of the corresponding station. The overall assessment is a quantitative weighted sum of the seven parameter indicators. The seven parameter indicators are weighted according to their importance as follows: traffic flow Q accounts for 8%, truck proportion P accounts for 8%, and the number of overloaded vehicles Q accounts for 8%. o 12%, maximum overload tonnage W m 12% of the equivalent axle weight W eff 20%, Lane load correction factor η s 20%, fatigue load correction factor ηf The weighted sum of the quantitative values ​​of the seven indicators and their weights is 20%, which is the comprehensive evaluation index;

[0040] According to the comprehensive evaluation index, it is evaluated into four levels: green (<2), blue (2-4), yellow (4-6) and red (>6).

[0041] The real-time map of the road network operation load flow has seven performance indicators. Under each performance indicator, according to the spatial coordinate position of the ETC gantry, half of the road route between the gantry and all nearby gantries is painted with corresponding colors according to its evaluation level, presenting a real-time map of the road network area load under the evaluation indicator.

[0042] At the same time, the seven performance indicators are evaluated comprehensively. According to the spatial coordinate position of the ETC gantry, half of the road route of the gantry and all nearby gantries are painted with corresponding colors according to their evaluation levels, presenting a real-time map of regional loads under the overall road network operation level.

[0043] The present invention also provides a real-time graph system for highway infrastructure operation load flow based on ETC data, comprising:

[0044] Acquisition module: used to capture the real-time perception data of all ETC gantries in the highway network system at regular intervals;

[0045] Extraction module: used to extract the traffic information of all sensed vehicles within the time interval;

[0046] Statistics module: Count the traffic flow Q, truck ratio P, and number of overloaded vehicles Q at each ETC gantry station through automated programs o , Maximum overload tonnage W m , equivalent axle weight W eff , Lane load correction factor η s , Fatigue load correction factor η f Seven parameter indicators;

[0047] Evaluation module: Based on the relationship between the calculation results of the seven parameter indicators and their thresholds, a grade evaluation of the operational load flow level of the site is formed respectively;

[0048] A division module: divides the road section into red, yellow, blue or green colors according to the evaluation level;

[0049] Map drawing module: Based on the evaluation results of the seven indicators of each ETC station and combined with the spatial coordinate information of the ETC gantry, a real-time map of the road network operation load flow is drawn, and different colors are used to distinguish the real-time status of the load flow in each area.

[0050] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when processed and executed, implements the steps of a real-time mapping method for highway infrastructure operation load flow based on ETC data.

[0051] The beneficial effects of the present invention are as follows: the method, system and storage medium for real-time mapping of highway infrastructure operation load flow based on ETC data utilize the traffic load information recorded by the ETC gantry to present in real time the transport load levels of all traffic routes in the area near the ETC gantry, and draw a real-time map of the road network operation load flow in combination with the spatial coordinate information of the ETC gantry, so as to observe the distribution of road network traffic load from the perspective of infrastructure operation risk, which is beneficial for infrastructure managers to accurately grasp the traffic load flow levels in various areas of the road network, and provide important support for infrastructure operation management and maintenance resource planning. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0053] Figure 1 It is a flow chart of a real-time mapping method of highway infrastructure operation load flow based on ETC data of the present invention.

[0054] Figure 2 is the maximum load effect value S of a real-time atlas method for highway infrastructure operation load flow based on ETC data in Example 2 of the present invention. ci Calculation process diagram.

[0055] Figure 3 This is an example table of ETC gantry data fragments (the license plate has been desensitized) of a real-time mapping method for highway infrastructure operation load flow based on ETC data in Example 2 of the present invention.

[0056] Figure 4 This is an example table of traffic flow statistics and evaluation results of a real-time mapping method for highway infrastructure operation load flow based on ETC data according to Example 2 of the present invention.

[0057] Figure 5 This is an example table of truck ratio statistics and evaluation results of a real-time mapping method for highway infrastructure operation load flow based on ETC data according to Example 2 of the present invention.

[0058] Figure 6This is an example table of overload vehicle statistics and evaluation results of a real-time mapping method for highway infrastructure operation load flow based on ETC data according to Example 2 of the present invention.

[0059] Figure 7 This is an example table of maximum overload tonnage statistics and assessment results of a real-time mapping method for highway infrastructure operation load flow based on ETC data according to Example 2 of the present invention.

[0060] Figure 8 This is an example table of equivalent axle weight statistics and assessment results of a real-time mapping method for highway infrastructure operation load flow based on ETC data according to Example 2 of the present invention.

[0061] Fig. 9 This is an example table of lane load correction coefficient statistics and evaluation results of a real-time mapping method for highway infrastructure operation load flow based on ETC data in an embodiment of the present invention.

[0062] Fig.10 This is an example table of fatigue load correction coefficient statistics and evaluation results of a real-time atlas method for highway infrastructure operation load flow based on ETC data in an embodiment of the present invention.

[0063] Fig.11 It is a schematic diagram of the expressway network and ETC gantry positions of a real-time mapping method of expressway infrastructure operation load flow based on ETC data in an embodiment of the present invention.

[0064] Fig.12 It is a schematic diagram of a real-time map of highway network operation traffic load of a method for real-time map of highway infrastructure operation load flow based on ETC data in an embodiment of the present invention. DETAILED DESCRIPTION

[0065] In order to make the purpose, technical solution and advantages of the present invention more clear, the embodiments of the present invention will be further described in detail below with reference to the accompanying drawings:

[0066] An embodiment of the present invention provides an embodiment of a real-time mapping method, system and storage medium for highway infrastructure operation load flow based on ETC data. Although the logical order is shown in the flow chart, under certain data, the steps shown or described can be completed in an order different from that shown here.

[0067] Example 1: Reference Figure 1

[0068] like Figure 1As shown in the schematic diagram of the steps of the method for real-time mapping of highway infrastructure operation load flow based on ETC data, the present invention provides a method for real-time mapping of highway infrastructure operation load flow based on ETC data, comprising the following steps:

[0069] Step 1: Capture the real-time sensing data of all ETC gantries in the highway network system at regular intervals, and extract the traffic information of all sensed vehicles within the time interval;

[0070] Step 2: Use automated procedures to count the traffic flow Q, truck ratio P, and number of overloaded vehicles Q at each ETC gantry station o , Maximum overload tonnage W m , equivalent axle weight W eff , Lane load correction factor η s , Fatigue load correction factor η f Seven parameter indicators;

[0071] Step 3: Based on the relationship between the calculation results of the seven parameter indicators and their thresholds, a grade evaluation of the operational load flow level of the site is formed, and the road section is divided into red, yellow, blue or green colors according to the evaluation grade;

[0072] Step 4: Based on the evaluation results of the seven indicators of each ETC station and combined with the spatial coordinate information of the ETC gantry, a real-time map of the road network operation load flow is drawn, and different colors are used to distinguish the real-time status of the load flow in each area.

[0073] Example 2: Reference Figure 2 to Figure 10

[0074] Compared with Example 1, the difference is that the seven parameter indicators in step 2 specifically include:

[0075] The traffic flow Q is the total number of vehicles passing through the lane per unit time;

[0076] The truck ratio P is the ratio of the total number of truck vehicles to the total number of all vehicles;

[0077] The number of overloaded vehicles Q o It is the total number of vehicles with loads exceeding national standards per unit time;

[0078] The maximum overload tonnage W m It is the maximum weight among all overloaded vehicles that exceeds the regulatory limit;

[0079] The equivalent axle weight W eff=0.935A2+1.925A3+3.025A4+4.315A5+4.207A6, where A2~A6 are the ratios of the number of two-axle trucks, three-axle trucks, four-axle trucks, five-axle trucks, and six-axle and above trucks in the passing vehicles to the number of all passing vehicles;

[0080] The lane load correction factor η s It is the proportionality coefficient between the maximum loading response of the fleet of passing vehicles on the typical influence line of the bridge and the calculated response of the standard lane load model;

[0081] The fatigue load correction factor η f It is the proportionality coefficient between the equivalent response of a fleet of passing vehicles loaded on the typical influence line of the bridge and the equivalent response calculated by the standard fatigue load model.

[0082] The lane load correction factor η s The fatigue load correction factor η f The calculation steps include:

[0083] Step 2.1, the real-time sensing data of the ETC gantry is supplemented with axle weight, wheelbase and speed information according to different types of vehicles through a data enhancement method, so as to form a traffic flow load sequence of the bridge deck starting from the first vehicle and according to the known wheelbase and speed information;

[0084] Step 2.2: Use an automated program to calculate the load effect of the fleet load sequence on the mid-span bending moment influence line of the simply supported beam (span 5-100m), the mid-span bending moment influence line of the two-span simply supported beam, and the support shear force influence line (span 20-120m), and obtain the measured maximum load response value S for each influence line. ci (i=1,2,…,N, N is the number of influence lines), and the measured equivalent load response value D calculated based on Miner linear damage accumulation criterion ci (i=1,2,…,N, N is the number of influence lines);

[0085] Step 2.3: Use the lane load model and fatigue load model of the bridge design specification to load the influence line respectively, and obtain the specification maximum load response value S of the corresponding influence line. di And the code equivalent load response value D di (i=1,2,…,N, N is the number of influence lines);

[0086] Step 2.4: Calculate the measured maximum load response value S corresponding to each influence line ci Compared with the maximum load response value S di The ratio S ci / S di , take the maximum value of all influence line calculation ratios as the lane load correction factor ηs ;

[0087] Step 2.5: Calculate the measured equivalent load response value D corresponding to each influence line ci Equivalent load response value D di The ratio D ci / D di , take the maximum value of all influence line calculation ratios as the fatigue load correction factor η f .

[0088] like Figure 2 As shown, the ETC gantry sensing information is first input, and the axle weight, wheelbase and speed information of different types of vehicles are supplemented by data enhancement method to form a traffic flow load sequence of the bridge deck starting from the first vehicle according to the known wheelbase and speed information. Then, the obtained fleet load sequence is used to calculate the load effect of the influence lines of bridges with different spans to obtain the load effect value. By comparing the load effect values ​​of different influence lines, the maximum load effect value S is obtained. ci .

[0089] The name of the automation program is MSCA hybrid simulation program, such as Figure 2 The specific execution steps of the MSCA hybrid simulation program include:

[0090] S1. Input the real-time sensing data of the ETC gantry, use the statistical probability model based on the statistical results of bridge vehicle information over a long period of time as the data source, use the data enhancement method to supplement the axle weight, wheelbase and speed of vehicles according to different types, and form a traffic flow load sequence of the bridge deck starting from the first vehicle according to the known wheelbase and speed information;

[0091] S2. Prepare the influence line data of bridges with different spans according to the mechanical model, iterate the traffic load sequence according to the time step, calculate the load effect value at each time step by superimposing the load sequence of the current time step with the influence line value, and form a load response time history. On this basis, the rain flow method is calculated based on the Miner linear damage accumulation criterion to obtain the equivalent load response of the corresponding influence line;

[0092] S3. Find the maximum value of the load response time corresponding to different influence lines as the maximum load effect value of the influence line. The same is true for the equivalent load response value.

[0093] S4. Compare with the standard value to obtain the lane load correction factor η s and fatigue load correction factor η f .

[0094] In step 3: according to the size of the traffic flow Q, the traffic density is evaluated into four levels: green (<300 vehicles / lane / hour), blue (300-600 vehicles / lane / hour), yellow (600-900 vehicles / lane / hour) and red (>900 vehicles / lane / hour);

[0095] According to the size of the truck ratio P, the heavy-load transport level is assessed into four levels: green (<20%), blue (20% to 50%), yellow (50% to 80%), and red (>80%).

[0096] According to the number of overloaded vehicles Q o , which are rated into four levels: green (<1 vehicle / hour), blue (1-5 vehicles / hour), yellow (5-10 vehicles / hour) and red (>10 vehicles / hour);

[0097] According to the maximum overload tonnage W m , the overload transport level is assessed into four levels: green (<5 tons), blue (5-10 tons), yellow (10-20 tons) and red (>20 tons);

[0098] According to the equivalent axle weight W eff The road damage risk is assessed into four levels: green (<5 tons), blue (5-10 tons), yellow (10-15 tons) and red (>15 tons).

[0099] According to the lane load correction factor η s The bridge strength damage risk is assessed into four levels: green (<0.5), blue (0.5-0.8), yellow (0.8-1.0) and red (>1.0).

[0100] According to the fatigue load correction factor η f The fatigue damage risk of bridges is assessed according to the size of the fatigue damage, which is divided into four levels: green (<0.5), blue (0.5-0.8), yellow (0.8-1.0) and red (>1.0).

[0101] The interval time used in this embodiment is 10 minutes, and the time period ① 6:00-7:00 and the time period ② 16:00-17:00 are counted. Figure 3 Shown is an example table of ETC gantry data segments (the license plate has been desensitized) of a real-time mapping method for highway infrastructure operation load flow based on ETC data, which includes a data segment instance table numbered 5876 reflecting the spatial coordinates of the ETC gantry, including the vehicle license plate, vehicle color, vehicle travel time, vehicle model, vehicle axle number and vehicle weight information.

[0102] The traffic flow Q is the total number of vehicles passing through the lane per unit time, such as Figure 4 The table shown reflects an example of traffic flow Q statistics and evaluation results. Traffic flow statistics are performed on the time period ① and time period ② to obtain the traffic flow Q and classify it.

[0103] The truck ratio P is the ratio of the total number of truck vehicles to the total number of all vehicles. Figure 5 The table shown reflects an example of truck proportion statistics and evaluation results. Truck statistics are conducted for the time periods ① and ② to obtain the truck proportion P, which is then graded.

[0104] The number of overloaded vehicles Q o It is the total number of vehicles with a load exceeding the national standard per unit time, such as Figure 6 The table showing the example of the statistics and evaluation results of the number of overloaded vehicles is shown. The number of overloaded vehicles Q0 in the time period ① and the time period ② is counted and graded.

[0105] The maximum overload tonnage W m It is the maximum weight among all overloaded vehicles that exceeds the regulatory limit, such as Figure 7 The figure shows an example of the maximum overload tonnage statistics and assessment results, which counts the maximum overload tonnage W in the time period ① and time period ②. m , and classify them into levels.

[0106] The equivalent axle weight W eff =0.935A2+1.925A3+3.025A4+4.315A5+4.207A6, where A2~A6 are the proportions of two-axle trucks, three-axle trucks, four-axle trucks, five-axle trucks, and six-axle and above trucks in the passing vehicles. Figure 8 The figure shows an example of equivalent axle weight statistics and evaluation results, and the equivalent axle weight W of the time period ① and time period ② is counted. eff , and classify them into levels.

[0107] The lane load correction factor η s It is the proportionality coefficient between the maximum loading response of the passing vehicle fleet on the typical influence line of the bridge and the calculated response of the standard lane load model, such as Fig. 9 The figure shows an example of lane load correction coefficient statistics and evaluation results, and the lane load correction coefficient η for the time period ① and time period ② is obtained by statistics and calculation. s , and classify them into levels.

[0108] The fatigue load correction factor η f It is the proportionality coefficient between the equivalent response of the vehicle fleet loaded on the typical influence line of the bridge and the equivalent response calculated by the standard fatigue load model, such as Fig.10 As shown, it reflects the example of fatigue load correction coefficient statistics and evaluation results, and the lane load correction coefficient η of the time period ① and time period ② is obtained by statistics and calculation. f , and classify them into levels.

[0109] Example 3: Reference Figure 11-12

[0110] Compared with the above embodiment, the difference is that the seven parameter indicators of the ETC gantry station are evaluated according to the relationship between their corresponding thresholds and then quantitatively specified according to different levels;

[0111] If the indicator is graded as green, it is set as 1; if the indicator is graded as blue, it is set as 3; if the indicator is graded as yellow, it is set as 5; and if the indicator is graded as red, it is set as 7.

[0112] The seven parameter indicators make an overall assessment of the operational load flow level of the corresponding station. The overall assessment is a quantitative weighted sum of the seven parameter indicators. The seven parameter indicators are weighted according to their importance as follows: traffic flow Q accounts for 8%, truck proportion P accounts for 8%, and the number of overloaded vehicles Q accounts for 8%. o 12%, maximum overload tonnage W m 12% of the equivalent axle weight W eff 20%, Lane load correction factor η s 20%, fatigue load correction factor η f The weighted sum of the quantitative values ​​of the seven indicators and their weights is 20%, which is the comprehensive evaluation index;

[0113] According to the comprehensive evaluation index, it is evaluated into four levels: green (<2), blue (2-4), yellow (4-6) and red (>6).

[0114] The real-time graph of the road network operation load flow has seven performance indicators. Under each performance indicator, according to the spatial coordinate position of the ETC gantry, such as Fig.11 As shown, the expressway network and ETC gantry locations are displayed, and half of the road route between the gantry and all nearby gantries are painted with corresponding colors according to their evaluation levels, presenting a real-time map of the road network area load under the evaluation index.

[0115] At the same time, the seven performance indicators are evaluated comprehensively. According to the spatial coordinate position of the ETC gantry, half of the road route between the gantry and all nearby gantries is painted with corresponding colors according to its evaluation level, such as Fig.12 As shown, a real-time map of regional load under the overall road network operation level is presented.

Claims

1. A real-time mapping method for highway infrastructure operation load flow based on ETC data, characterized in that: The following steps are included: Step 1: Capture the real-time sensing data of all ETC gantries in the highway network system at regular intervals, and extract the traffic information of all sensed vehicles within the time interval; Step 2: Use automated procedures to count the traffic flow Q, truck ratio P, and number of overloaded vehicles Q at each ETC gantry station o , Maximum overload tonnage W m , equivalent axle weight W eff , Lane load correction factor η s , Fatigue load correction factor η f Seven parameter indicators; Step 3: Based on the relationship between the calculation results of the seven parameter indicators and their thresholds, a grade evaluation of the operational load flow level of the site is formed, and the road section is divided into red, yellow, blue or green colors according to the evaluation grade; Step 4: Based on the evaluation results of the seven indicators of each ETC station and combined with the spatial coordinate information of the ETC gantry, a real-time map of the road network operation load flow is drawn, and different colors are used to distinguish the real-time status of the load flow in each area.

2. According to claim 1, a real-time mapping method of highway infrastructure operation load flow based on ETC data is characterized in that: The ETC gantry real-time sensing data includes the number reflecting the spatial coordinates of the ETC gantry, vehicle license plate, vehicle color, vehicle travel time, vehicle model, vehicle axle number and vehicle weight information.

3. According to the method of claim 1, the method is characterized by: The seven parameter indicators described in step 2 specifically include: The traffic flow Q is the total number of vehicles passing through the lane per unit time; The truck ratio P is the ratio of the total number of truck vehicles to the total number of all vehicles; The number of overloaded vehicles Q o It is the total number of vehicles with loads exceeding national standards per unit time; The maximum overload tonnage W m It is the maximum weight among all overloaded vehicles that exceeds the regulatory limit; The equivalent axle weight W eff =0.935A2+1.925A3+3.025A4+4.315A5+4.207A6, where A2~A6 are the ratios of the number of two-axle trucks, three-axle trucks, four-axle trucks, five-axle trucks, and six-axle and above trucks in the passing vehicles to the number of all passing vehicles; The lane load correction factor η s It is the proportionality coefficient between the maximum loading response of the fleet of passing vehicles on the typical influence line of the bridge and the calculated response of the standard lane load model; The fatigue load correction factor η f It is the proportionality coefficient between the equivalent response of a fleet of passing vehicles loaded on the typical influence line of the bridge and the equivalent response calculated by the standard fatigue load model.

4. A real-time mapping method for highway infrastructure operation load flow based on ETC data according to claim 3, characterized in that: The lane load correction factor η s The fatigue load correction factor η f The calculation steps include: Step 2.1, the real-time sensing data of the ETC gantry is supplemented with axle weight, wheelbase and speed information according to different types of vehicles through a data enhancement method, so as to form a traffic flow load sequence of the bridge deck starting from the first vehicle and according to the known wheelbase and speed information; Step 2.2: Use an automated program to calculate the load effect of the fleet load sequence on the mid-span bending moment influence line of the simply supported beam (span 5-100m), the mid-span bending moment influence line of the two-span simply supported beam, and the support shear force influence line (span 20-120m), and obtain the measured maximum load response value S for each influence line. ci (i=1,2,…,N, N is the number of influence lines), and the measured equivalent load response value D calculated based on Miner linear damage accumulation criterion ci (i=1,2,…,N, N is the number of influence lines); Step 2.3: Use the lane load model and fatigue load model of the bridge design specification to load the influence line respectively, and obtain the specification maximum load response value S of the corresponding influence line. di And the code equivalent load response value D di (i=1,2,…,N, N is the number of influence lines); Step 2.4: Calculate the measured maximum load response value S corresponding to each influence line ci Compared with the maximum load response value S di The ratio S ci / S di , take the maximum value of all influence line calculation ratios as the lane load correction factor η s ; Step 2.5: Calculate the measured equivalent load response value D corresponding to each influence line ci Equivalent load response value D di The ratio D ci / D di , take the maximum value of all influence line calculation ratios as the fatigue load correction factor η f .

5. The method for real-time mapping of highway infrastructure operation load flow based on ETC data according to claim 1 is characterized by: According to the size of the traffic flow Q, the traffic density is evaluated into four levels: green (<300 vehicles / lane / hour), blue (300-600 vehicles / lane / hour), yellow (600-900 vehicles / lane / hour) and red (>900 vehicles / lane / hour); According to the size of the truck ratio P, the heavy-load transport level is evaluated into four levels: green (<20%), blue (20% to 50%), yellow (50% to 80%) and red (>80%); According to the number of overloaded vehicles Q o , which are rated into four levels: green (<1 vehicle / hour), blue (1-5 vehicles / hour), yellow (5-10 vehicles / hour) and red (>10 vehicles / hour); According to the maximum overload tonnage W m , the overload transport level is assessed into four levels: green (<5 tons), blue (5-10 tons), yellow (10-20 tons) and red (>20 tons); According to the equivalent axle weight W eff The road damage risk is assessed into four levels: green (<5 tons), blue (5-10 tons), yellow (10-15 tons) and red (>15 tons). According to the lane load correction factor η s The bridge strength damage risk is assessed into four levels: green (<0.5), blue (0.5-0.8), yellow (0.8-1.0) and red (>1.0). According to the fatigue load correction factor η f The fatigue damage risk of bridges is assessed according to the size of the fatigue damage, which is divided into four levels: green (<0.5), blue (0.5-0.8), yellow (0.8-1.0) and red (>1.0).

6. The method for real-time mapping of highway infrastructure operation load flow based on ETC data according to claim 1 is characterized in that: The seven parameter indicators of the ETC gantry station are evaluated according to their corresponding threshold values ​​and quantitatively regulated according to different levels; If the indicator is graded as green, it is set as 1; if the indicator is graded as blue, it is set as 3; if the indicator is graded as yellow, it is set as 5; and if the indicator is graded as red, it is set as 7.

7. The method for real-time mapping of highway infrastructure operation load flow based on ETC data according to claim 1 is characterized by: The seven parameter indicators provide an overall assessment of the operational load flow level of the corresponding site; The overall evaluation method is the quantitative weighted sum of seven parameter indicators, and the seven parameter indicators are weighted according to their importance as follows: traffic flow Q accounts for 8%, truck proportion P accounts for 8%, number of overloaded vehicles Q accounts for 8%, and the number of overloaded vehicles Q accounts for 8%. o 12%, maximum overload tonnage W m 12% of the equivalent axle weight W eff 20%, Lane load correction factor η s 20%, fatigue load correction factor η f The weighted sum of the quantitative values ​​of the seven indicators and their weights is 20%, which is the comprehensive evaluation index; According to the comprehensive evaluation index, it is evaluated into four levels: green (<2), blue (2-4), yellow (4-6) and red (>6).

8. The method for real-time mapping of highway infrastructure operation load flow based on ETC data according to claim 1 is characterized in that: The real-time map of the road network operation load flow has seven performance indicators. Under each performance indicator, according to the spatial coordinate position of the ETC gantry, half of the road route between the gantry and all nearby gantries is drawn with a corresponding color according to its evaluation level, presenting a real-time map of the road network area load under the evaluation indicator; At the same time, the seven performance indicators are evaluated comprehensively. According to the spatial coordinate position of the ETC gantry, half of the road route of the gantry and all nearby gantries are painted with corresponding colors according to their evaluation levels, presenting a real-time map of regional loads under the overall road network operation level.

9. A real-time graph system for highway infrastructure operation load flow based on ETC data, characterized in that: include: Acquisition module: used to capture the real-time perception data of all ETC gantries in the highway network system at regular intervals; Extraction module: used to extract the traffic information of all sensed vehicles within the time interval; Statistics module: Count the traffic flow Q, truck ratio P, and number of overloaded vehicles Q at each ETC gantry station through automated programs o , Maximum overload tonnage W m , equivalent axle weight W eff , Lane load correction factor η s , Fatigue load correction factor η f Seven parameter indicators; Evaluation module: Based on the relationship between the calculation results of the seven parameter indicators and their thresholds, a grade evaluation of the operational load flow level of the site is formed respectively; A division module: divides the road section into red, yellow, blue or green colors according to the evaluation level; Map drawing module: Based on the evaluation results of the seven indicators of each ETC station and combined with the spatial coordinate information of the ETC gantry, a real-time map of the road network operation load flow is drawn, and different colors are used to distinguish the real-time status of the load flow in each area.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is processed and executed, the steps of a real-time mapping method of highway infrastructure operation load flow based on ETC data as described in any one of claims 1-8 are implemented.

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