Traffic flow optimization navigation method and system based on driving ability evaluation

Abstract

The present invention discloses a traffic flow optimization navigation method and system based on driving ability evaluation, the method comprising: firstly, evaluating the driving ability of navigation users to obtain the driving ability evaluation results of navigation users; then, dividing the road grades in the navigation system according to the category planning of the national transportation department and the actual needs of the navigation system; finally, according to the driving ability evaluation results of the navigation users, giving priority to recommending the corresponding road grades; during the navigation process, obtaining the real-time road network traffic congestion situation, and adjusting the route recommendation received by the navigation users. The system comprises an evaluation module, a road grade division module, a recommendation module and an adjustment module. The present invention improves the operation efficiency of urban road networks, optimizes the travel experience of motor vehicle drivers, alleviates the congestion of urban roads, improves the utilization rate of low-grade urban roads, and provides a better traffic environment for urban development.

Description

Technical Field

[0001] The present invention relates to the technical field of traffic navigation systems, and in particular to a traffic flow optimization navigation method and system based on driving ability evaluation. Background Art

[0002] With the development of social economy, the number of private cars has continued to increase, the level of urban motorization has continued to improve, and the problem of insufficient supply of road infrastructure has gradually emerged. The resulting traffic congestion has become a hot issue that needs to be solved urgently.

[0003] Traffic flow optimization aims to make traffic flow smoother through a series of measures, thereby ensuring traffic efficiency and reducing congestion based on road capacity. In the process of traffic flow optimization, many factors need to be considered, such as road conditions, traffic signals, vehicle types, driver behavior, etc. Among them, driver behavior is one of the important factors affecting traffic flow. Different drivers have different driving skills, reaction speed, judgment, etc., which directly affect the stability and safety of traffic flow. By evaluating driving ability, we can more accurately understand the actual driving level of the driver, thereby providing a strong basis for traffic flow optimization. Therefore, the traffic flow optimization method based on driving ability evaluation can more accurately adjust traffic signals and optimize road conditions, thereby improving the stability and safety of traffic flow.

[0004] At present, vehicle navigation systems have become an important part of modern urban transportation. Vehicle navigation systems provide great convenience for drivers by obtaining traffic information in real time and providing optimal route planning. However, traditional navigation systems often only consider factors such as road conditions and traffic signals, while ignoring the impact of driver behavior on traffic flow. Therefore, combining driving ability assessment with navigation systems can provide more accurate personalized navigation services, thereby improving traffic efficiency and safety. Summary of the invention

[0005] The present invention aims to provide a traffic flow optimization navigation method and system based on driving ability evaluation, which can alleviate traffic congestion, improve the operating efficiency of urban road networks, improve traffic safety, and improve navigation efficiency.

[0006] The technical solution to achieve the purpose of the present invention is: a traffic flow optimization navigation method based on driving ability evaluation, comprising the following steps:

[0007] Step 1: evaluating the driving ability of the navigation user to obtain the driving ability evaluation result of the navigation user;

[0008] Step 2: Classify roads in the navigation system;

[0009] Step 3: Based on the driving ability evaluation result of the navigation user, the corresponding road grade is recommended first;

[0010] Step 4: During navigation, obtain real-time road network traffic congestion conditions and adjust route recommendations received by navigation users.

[0011] Furthermore, in step 1, the driving ability of the navigation user is evaluated to obtain the driving ability evaluation result of the navigation user, which is as follows:

[0012] Step 1.1, collect data related to the driving ability of the navigation user, including historical accident types, historical road section driving speeds, and historical driving rapid acceleration and deceleration times;

[0013] Step 1.2: Use all data to form a database, process missing values, outliers, and duplicate data, and perform consistency and normalization to form standardized coded data that can be used directly;

[0014] Step 1.3: According to the driving ability assessment objectives, determine the driving ability evaluation criteria, including driving skills, safety awareness, compliance with traffic rules, and emergency response capabilities, and further refine the sub-criteria to form a multi-layer indicator type;

[0015] Step 1.4: Use the 1-9 scale method to quantify the relative importance of each indicator and construct a comparison matrix;

[0016] Step 1.5: Use expert survey method to calculate the weight of each indicator type to reflect the importance of each indicator in evaluating driving ability;

[0017] Step 1.6: After consistency check, the specific weights of driving ability evaluation related indicators are output, and the relevant values ​​are optimized according to the simulation experiment results;

[0018] Step 1.7: Construct a mathematical model for evaluating the driving ability of navigation users, calculate the driving level score, and classify the driving ability assessment levels of navigation users.

[0019] Furthermore, in step 1.2, SPSS, SAS, Stata or Eviews statistical analysis software, PowerBI, FineBI, YongHongBI or Tableau business intelligence software, and R, Python programming tools are used to build the database.

[0020] Furthermore, in step 1.7, a mathematical model for evaluating the driving ability of navigation users is constructed, the driving level score is calculated, and the driving ability evaluation level of navigation users is divided into the following specific levels:

[0021] The mathematical model for evaluating driving ability takes the number of historical accidents, the number of historical responsible accidents, the economic losses of historical responsible accidents, the casualties of historical responsible accidents, the historical average trip speed, the historical average number of rapid accelerations per trip, and the historical average number of rapid decelerations per trip as parameters to calculate the driving level score. The formula is:

[0022] V=100-aα-bβ-cγ-(d δ -1)+ev-ft1-gt2

[0023] α is the number of historical accidents of a certain driver, unit: times;

[0024] β is the number of historical responsible accidents of a driver, unit: times;

[0025] γ is the economic loss of historical liability accidents, unit: ten thousand yuan;

[0026] δ is the casualties of historical accidents.

[0027] v is the historical average travel speed, unit: km / h;

[0028] t1 is the historical average number of rapid accelerations in a single trip, unit: times;

[0029] t2 is the historical average number of rapid decelerations in a single trip, unit: times;

[0030] a, b, c, d, e, f, and g are the coefficients of α, β, γ, δ, v, t1, and t2, respectively. Different values ​​are taken in different road networks by using the analytic hierarchy process combined with local survey results;

[0031] The calculation result is in percentage, with 90-100 points classified as Grade A, 80-90 points as Grade B, 70-80 points as Grade C, and below 70 points as Grade D.

[0032] Furthermore, in step 2, the road levels are divided in the navigation system as follows:

[0033] According to the category planning of the national transportation department and the actual needs of the navigation system, all roads are divided into four levels: expressways, main roads, secondary roads, and branch roads based on road traffic functions, design speeds, traffic capacities, and service levels.

[0034] Furthermore, in step 3, the corresponding road grade is preferentially recommended according to the driving ability evaluation result of the navigation user, as follows:

[0035] The navigation system pushes high-level roads to navigation users with high driving ability levels and pushes low-level roads to navigation users with low driving ability levels based on the driving ability evaluation results of the navigation users obtained by the driving ability evaluation mathematical model.

[0036] Furthermore, in step 4, the real-time road network traffic congestion situation is obtained, as follows:

[0037] Through the statistical data of the transportation department, field surveys, and remote sensing monitoring methods, specific information on all road traffic conditions is collected, including road names, starting and ending points, design speeds, number of lanes, capacity, traffic flow, and road condition assessment data. Taking into account driving time, road condition information, and traffic flow factors, the generalized cost equation is used to calculate the weight of each road based on the road grade classification standards and the collected road information. The calculated road weights and other relevant information of the road are entered into the database to form a road grade database.

[0038] A traffic flow optimization navigation system based on driving ability evaluation, the system is used to implement the traffic flow optimization navigation method based on driving ability evaluation, the system includes an evaluation module, a road grade classification module, a recommendation module and an adjustment module, wherein:

[0039] An evaluation module evaluates the driving ability of the navigation user and obtains the driving ability evaluation result of the navigation user;

[0040] A road classification module is used to classify roads in the navigation system;

[0041] The recommendation module prioritizes the corresponding road grade based on the navigation user's driving ability assessment results;

[0042] The adjustment module obtains the real-time traffic congestion situation of the road network during the navigation process and adjusts the route recommendation received by the navigation user.

[0043] A mobile terminal comprises a memory, a processor and a computer program stored in the memory and executable on the processor. When the processor executes the program, the minimum distance phase ambiguity elimination direction finding method based on heterogeneous hybrid MIMO is implemented.

[0044] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps in the minimum distance phase ambiguity elimination direction finding method based on heterogeneous hybrid MIMO.

[0045] Compared with the prior art, the present invention has the following significant advantages: (1) The present invention uses the hierarchical analysis method to construct a mathematical model for driving ability evaluation, which provides a structured, systematic and flexible method for the quantitative evaluation of driving ability. It has multiple meanings such as structured and systematic evaluation process, combining subjective judgment with objective data, flexibility and adaptability, providing comprehensive and accurate results and wide applicability, which helps to promote the scientific, standardized and refined development of driving ability evaluation work; (2) The present invention prioritizes the corresponding road grade based on the driving ability evaluation results of each navigation system user, matches driving ability with road grade, can prevent potential road traffic risks, balance road network load, improve road use efficiency and optimize road resource allocation; (3) It can provide personalized navigation suggestions based on the navigation system, enhance driving confidence, improve driving experience and comfort, and can promote the application of intelligent driving assistance systems and promote the development of related technologies in the field of intelligent driving. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 It is a flow chart of the traffic flow optimization navigation method based on driving ability evaluation of the present invention.

[0047] Figure 2 It is a schematic diagram of a flow chart of the driving ability assessment for a navigation system user in the present invention.

[0048] Figure 3 It is a schematic diagram of the process of dividing road grades in the present invention.

[0049] Figure 4 It is a schematic diagram of the principle of preferentially recommending corresponding road grades according to the driving ability evaluation result of the navigation user in the present invention.

[0050] Figure 5 It is a fitting curve diagram of the driving ability evaluation mathematical model and the driver's psychological test results in the embodiment of the present invention.

[0051] Figure 6 It is a schematic diagram of the structure of the VISSIM simulation model in an embodiment of the present invention. DETAILED DESCRIPTION

[0052] Combination Figure 1 The present invention provides a traffic flow optimization navigation method based on driving ability evaluation, comprising the following steps:

[0053] Step 1: evaluating the driving ability of the navigation user to obtain the driving ability evaluation result of the navigation user;

[0054] Step 2: Classify roads in the navigation system;

[0055] Step 3: Based on the driving ability evaluation result of the navigation user, the corresponding road grade is recommended first;

[0056] Step 4: During navigation, obtain real-time road network traffic congestion conditions and adjust route recommendations received by navigation users.

[0057] As a specific example, in step 1, the driving ability of the navigation user is evaluated to obtain the driving ability evaluation result of the navigation user, such as Figure 2 As shown, the details are as follows:

[0058] Step 1.1, collect data related to the driving ability of the navigation user, including historical accident types, historical road section driving speeds, and historical driving rapid acceleration and deceleration times;

[0059] Step 1.2: Use all data to form a database, process missing values, outliers, and duplicate data, and perform consistency and normalization to form standardized coded data that can be used directly;

[0060] As a specific example, in the data cleaning process, professional tools or software are used to improve efficiency, including SPSS, SAS, Stata, Eviews statistical analysis software, PowerBI, FineBI, YongHongBI, Tableau business intelligence software, and R, Python programming tools to achieve high-level database construction.

[0061] Step 1.3: According to the driving ability assessment objectives, determine the driving ability evaluation criteria, including driving skills, safety awareness, compliance with traffic rules, and emergency response capabilities, and further refine the sub-criteria to form a multi-layer indicator type;

[0062] Step 1.4: Use the 1-9 scale or other scaling methods to quantify the relative importance of each indicator and construct a comparison matrix;

[0063] Step 1.5: Use expert survey method to calculate the weight of each indicator type to reflect the importance of each indicator in evaluating driving ability;

[0064] Step 1.6: After consistency check, the specific weights of driving ability evaluation related indicators are output, and the relevant values ​​are optimized according to the simulation experiment results;

[0065] Step 1.7: Construct a mathematical model for evaluating the driving ability of navigation users, calculate the driving level score, and classify the driving ability evaluation level of navigation users, as follows:

[0066] The mathematical model for evaluating driving ability takes the number of historical accidents, the number of historical responsible accidents, the economic losses of historical responsible accidents, the casualties of historical responsible accidents, the historical average trip speed, the historical average number of rapid accelerations per trip, and the historical average number of rapid decelerations per trip as parameters to calculate the driving level score. The formula is:

[0067] V=100-aα-bβ-cγ-(d δ -1)+ev-ft1-gt2

[0068] α: the number of historical accidents of a driver, unit: times

[0069] β: The number of accidents caused by a driver in history, unit: times

[0070] γ: Economic losses from historical liability accidents, unit: 10,000 yuan

[0071] δ: Casualties in historical accidents,

[0072] v: historical average travel speed, unit: km / h

[0073] t1: Historical average number of rapid accelerations per trip, unit: times

[0074] t2: Historical average number of rapid decelerations in a single trip, unit: times

[0075] a~g: are the coefficients of the above parameters, and different values ​​are used in different road networks by using the analytic hierarchy process combined with local survey results;

[0076] The calculation result is in percentage, with 90-100 points classified as Grade A, 80-90 points as Grade B, 70-80 points as Grade C, and below 70 points as Grade D.

[0077] As a specific example, in step 2, the road levels are divided in the navigation system according to the category planning of the national transportation department and the actual needs of the navigation system, such as Figure 3 As shown, the details are as follows:

[0078] According to the category planning of the national transportation department and the actual needs of the navigation system, all roads are divided into four levels: expressways, main roads, secondary roads, and branch roads based on road traffic functions, design speeds, traffic capacities, and service levels.

[0079] As a specific example, in step 3, the corresponding road level is recommended based on the driving ability evaluation result of the navigation user, such as Figure 4 As shown, the details are as follows:

[0080] The navigation system pushes high-level roads to navigation users with high driving ability levels and pushes low-level roads to navigation users with low driving ability levels based on the driving ability evaluation results of the navigation users obtained by the driving ability evaluation mathematical model.

[0081] As a specific example, in step 4, during the navigation process, the real-time road network traffic congestion situation is obtained, and the route recommendation received by the navigation user is adjusted, as follows:

[0082] Through the statistical data of the transportation department, field surveys, and remote sensing monitoring methods, specific information on all road traffic conditions is collected, including road names, starting and ending points, design speeds, number of lanes, capacity, traffic flow, and road condition assessment data. Taking into account driving time, road condition information, and traffic flow factors, the generalized cost equation is used to calculate the weight of each road based on the road grade classification standards and the collected road information. The calculated road weights and other relevant information of the road are entered into the database to form a road grade database.

[0083] The present invention also provides a traffic flow optimization navigation system based on driving ability evaluation, which is used to implement the traffic flow optimization navigation method based on driving ability evaluation. The system includes an evaluation module, a road grade classification module, a recommendation module and an adjustment module, wherein:

[0084] An evaluation module evaluates the driving ability of the navigation user and obtains the driving ability evaluation result of the navigation user;

[0085] A road classification module is used to classify roads in the navigation system;

[0086] The recommendation module prioritizes the corresponding road grade based on the navigation user's driving ability assessment results;

[0087] The adjustment module obtains the real-time traffic congestion situation of the road network during the navigation process and adjusts the route recommendation received by the navigation user.

[0088] The present invention also provides a mobile terminal, comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the minimum distance phase ambiguity elimination direction finding method based on heterogeneous hybrid MIMO is implemented.

[0089] The present invention also provides a computer-readable storage medium having a computer program stored thereon, and when the program is executed by a processor, the steps in the minimum distance phase ambiguity elimination direction finding method based on heterogeneous hybrid MIMO are implemented.

[0090] The present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0091] Example

[0092] One embodiment of the present invention is a traffic flow optimization navigation method based on driving ability evaluation. First, a certain amount of driver data is collected, and the driving ability evaluation score of each driver is calculated after data cleaning, analysis and consistency test, and divided into four levels: A, B, C, and D. Then, common expressways and low-level road parallel areas in cities are selected, and a model is established in VISSIM simulation software. By comparing the travel time of two parallel sections under different driver path selection states, the specific ideas of the present invention are explained, which specifically includes the following steps:

[0093] Step 1: Collect driver historical data and process and analyze the data to obtain the driving ability evaluation results of the navigation user, as follows:

[0094] 1-1) Through the questionnaire survey, we obtained the survey results of 642 drivers. The questionnaire included the number of historical accidents, the number of historical responsible accidents, the economic losses of historical responsible accidents, the casualties of historical responsible accidents, the historical average travel speed, the historical average number of rapid accelerations per trip, and the historical average number of rapid decelerations per trip. After data cleaning and consistency testing, we obtained a total of 533 valuable data.

[0095] 1-2) According to the driving ability evaluation mathematical model of the present invention and the psychological test scores of each driver, the values ​​of 7 coefficients a to g are fitted. After experiments, when a is 0.1, b is 5, c is 0.5, d is 7, e is 0.01, f is 0.1, and g is 0.1, the scores of 533 drivers are compared to form a basically consistent trend of change, such as Figure 5 Through the values ​​of the seven coefficients, it can be seen that if there are casualties in the responsible accident, the deduction will be the most serious in the ability assessment. The historical average travel speed has the lowest impact on driving ability, but it is generally believed that the higher the average speed, the better the driving ability performance.

[0096] Therefore, the driving ability evaluation mathematical model formula of the present invention is further clarified as follows:

[0097] V=100-0.1α-5β-0.5γ-(7 δ -1)+0.01v-0.1t1-0.1t2

[0098] In this embodiment, among the 533 drivers, there are 145 people with driving ability level A, accounting for 27%, 79 people with driving ability level B, accounting for 15%, 44 people with driving ability level C, accounting for 8%, and 265 people with driving ability level D, accounting for 50%.

[0099] Preferably, in order to make the driving ability assessment mathematical model of the present invention applicable to a wider range of people as much as possible, the value of the correlation coefficient should be fitted according to the collected driver historical data.

[0100] Step 2: Establish a simulation area road section model in the simulation software VISSIM and divide the road levels in the navigation system as follows:

[0101] 2-1) If Figure 6 As shown, a 660-meter-long three-lane expressway and a low-grade urban road covering three intersections are established in the VISSIM software, and the total length of the two sections is the same. In order to simplify the computer operation pressure, the modeling section distance of this embodiment is short and only simulates traffic flow in one direction, and intersection turns and the like are not taken into consideration.

[0102] 2-2) The same set of traffic lights are set at the three intersections, with a signal cycle of 60 seconds, including 20 seconds of red light and 40 seconds of green light.

[0103] 2-3) Set timers at the starting and ending points of the two roads to collect the average travel time of passing vehicles.

[0104] Step 3: Set the reference group input data and give priority to recommending the corresponding road grade according to the driving ability evaluation results of the navigation user, as follows:

[0105] 3-1) Set up three groups of reference traffic volumes, corresponding to the free flow state of the expressway and parallel trunk roads; the light congestion of the expressway and the uncongested parallel trunk roads; the heavy congestion of the expressway and the parallel trunk roads. The three groups of reference traffic volumes are 2000, 500; 6000, 1500; 10000; 2500 respectively.

[0106] 3-2) Set the ratio of drivers in four levels, A, B, C, and D, to 27%, 15%, 8%, and 50% respectively. The corresponding expected speeds on expressways are 100km / h, 90km / h, 80km / h, and 60km / h, and the expected speeds on parallel main roads are 60km / h, 54km / h, 48km / h, and 36km / h respectively.

[0107] 3-3) Set the simulation time to 1000 seconds and get the following data results:

[0108] State 1: The average travel time on the expressway is 23.1 seconds, and the average travel time on the parallel main road is 40.5 seconds.

[0109] State 2: The average travel time on the expressway is 27 seconds, and the average travel time on the parallel main road is 42.2 seconds.

[0110] State 3: The average travel time on the expressway is 34.7 seconds, and the average travel time on the parallel main road is 45.6 seconds.

[0111] 3-4) Analyze the average travel time of each path in the reference group and obtain the following data:

[0112] State 1: The average speed on the expressway is 102.86km / h, and the average speed on the parallel main road is 58.67km / h.

[0113] State 2: The average speed on the expressway is 88km / h, and the average speed on the parallel main road is 56.3km / h.

[0114] State 2: The average speed on the expressway is 68.47km / h, and the average speed on the parallel main road is 52.11km / h.

[0115] As the congestion level increases, the average vehicle speed on expressways and parallel main roads is decreasing.

[0116] Step 4: During navigation, set the experimental group input data and adjust the route recommendations received by navigation users as follows:

[0117] 4-1) Based on the driving ability evaluation model provided by the present invention, different levels of roads are recommended to drivers of different driving ability levels. Assuming that the percentage of accepting navigation path recommendations is 50%, the proportions of drivers of four levels A, B, C, and D on express roads are set to be 63.5%, 7.5%, 4%, and 25%, respectively, and the corresponding expected vehicle speeds are 100km / h, 90km / h, 80km / h, and 60km / h, respectively. The proportions of drivers of four levels A, B, C, and D on parallel trunk roads are set to be 13.5%, 57.5%, 4%, and 25%, respectively, and the corresponding expected vehicle speeds are 60km / h, 54km / h, 48km / h, and 36km / h, respectively.

[0118] 4-2) Set the simulation time to 1000 seconds and get the following data results:

[0119] State 1: The average travel time on the expressway is 23.08 seconds, and the average travel time on the parallel main road is 40.27 seconds.

[0120] State 2: The average travel time on the expressway is 25 seconds, and the average travel time on the parallel trunk road is 41.42 seconds.

[0121] State 3: The average travel time on the expressway is 31.2 seconds, and the average travel time on the parallel main road is 44.74 seconds.

[0122] 4-3) Analyze the average travel time of each path in the reference group and obtain the following data:

[0123] State 1: The average speed on the expressway is 102.95km / h, and the average speed on the parallel main road is 59km / h.

[0124] State 2: The average speed on the expressway is 95.04km / h, and the average speed on the parallel main road is 57.36km / h.

[0125] State 2: The average speed on the expressway is 76.15km / h, and the average speed on the parallel main road is 53.11km / h.

[0126] 4-4) Assuming that the percentage of accepting navigation route recommendations is 100%, the expressways are set for drivers with A-level driving ability, and the corresponding expected speed for the expressways is 100km / h. The parallel main roads are set for drivers with B-level driving ability, and the corresponding expected speed for the parallel main roads is 54km / h.

[0127] 4-5) Set the simulation time to 1000 seconds and get the following data results:

[0128] State 1: The average travel time on the expressway is 22.85 seconds, and the average travel time on the parallel trunk road is 39.87 seconds.

[0129] State 2: The average travel time on the expressway is 24.24 seconds, and the average travel time on the parallel trunk road is 40.14 seconds.

[0130] State 3: The average travel time on the expressway is 24.74 seconds, and the average travel time on the parallel trunk road is 40.48 seconds.

[0131] 4-6) Analyze the average travel time of each path in the reference group and obtain the following data:

[0132] State 1: The average speed on the expressway is 104km / h, and the average speed on the parallel main road is 59.6km / h.

[0133] State 2: The average speed on the expressway is 98km / h, and the average speed on the parallel main road is 59.2km / h.

[0134] State 2: The average speed on the expressway is 96km / h, and the average speed on the parallel main road is 58.7km / h.

[0135] Through data comparison and analysis, it can be seen that the traffic flow optimization navigation method based on driving ability evaluation provided by the present invention balances the road network load, improves road utilization efficiency, and enhances citizens' travel experience.

[0136] Although the present invention has been disclosed as above with preferred embodiments, the embodiments are not intended to limit the present invention. Any equivalent changes or modifications made without departing from the spirit and scope of the present invention also belong to the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the content defined by the claims of this application.

Claims

1. A traffic flow optimization navigation method based on driving ability evaluation, characterized in that: The following steps are involved: Step 1: evaluating the driving ability of the navigation user to obtain the driving ability evaluation result of the navigation user; Step 2: Classify roads in the navigation system; Step 3: Based on the driving ability evaluation result of the navigation user, the corresponding road grade is recommended first; Step 4: During navigation, obtain real-time road network traffic congestion conditions and adjust route recommendations received by navigation users.

2. The traffic flow optimization navigation method based on driving ability evaluation according to claim 1 is characterized in that: In step 1, the driving ability of the navigation user is evaluated to obtain the driving ability evaluation result of the navigation user, which is as follows: Step 1.1, collect data related to the driving ability of the navigation user, including historical accident types, historical road section driving speeds, and historical driving rapid acceleration and deceleration times; Step 1.2: Use all data to form a database, process missing values, outliers, and duplicate data, and perform consistency and normalization to form standardized coded data that can be used directly; Step 1.3: According to the driving ability assessment objectives, determine the driving ability evaluation criteria, including driving skills, safety awareness, compliance with traffic rules, and emergency response capabilities, and further refine the sub-criteria to form a multi-layer indicator type; Step 1.4: Use the 1-9 scale method to quantify the relative importance of each indicator and construct a comparison matrix; Step 1.5: Use expert survey method to calculate the weight of each indicator type to reflect the importance of each indicator in evaluating driving ability; Step 1.6: After consistency check, the specific weights of driving ability evaluation related indicators are output, and the relevant values ​​are optimized according to the simulation experiment results; Step 1.7: Construct a mathematical model for evaluating the driving ability of navigation users, calculate the driving level score, and classify the driving ability assessment levels of navigation users.

3. The traffic flow optimization navigation method based on driving ability evaluation according to claim 2 is characterized in that: In step 1.2, use SPSS, SAS, Stata or Eviews statistical analysis software, PowerBI, FineBI, YongHongBI or Tableau business intelligence software, and R or Python programming tools to build the database.

4. The traffic flow optimization navigation method based on driving ability evaluation according to claim 2 is characterized in that: In step 1.7, a mathematical model for evaluating the driving ability of navigation users is constructed, the driving level score is calculated, and the driving ability evaluation level of navigation users is divided into the following categories: The mathematical model for evaluating driving ability takes the number of historical accidents, the number of historical responsible accidents, the economic losses of historical responsible accidents, the casualties of historical responsible accidents, the historical average trip speed, the historical average number of rapid accelerations per trip, and the historical average number of rapid decelerations per trip as parameters to calculate the driving level score. The formula is: V=100-aα-bβ-cγ-(d δ -1)+ev-ft1-gt2 α is the number of historical accidents of a certain driver, unit: times; β is the number of historical responsible accidents of a driver, unit: times; γ is the economic loss of historical liability accidents, unit: ten thousand yuan; δ is the casualties of historical accidents. v is the historical average travel speed, unit: km / h; t1 is the historical average number of rapid accelerations in a single trip, unit: times; t2 is the historical average number of rapid decelerations in a single trip, unit: times; a, b, c, d, e, f, and g are the coefficients of α, β, γ, δ, v, t1, and t2, respectively. Different values ​​are taken in different road networks by using the analytic hierarchy process combined with local survey results; The calculation result is in percentage, with 90-100 points classified as Grade A, 80-90 points as Grade B, 70-80 points as Grade C, and below 70 points as Grade D.

5. The traffic flow optimization navigation method based on driving ability evaluation according to claim 1 is characterized in that: In step 2, the road levels are divided in the navigation system as follows: According to the category planning of the national transportation department and the actual needs of the navigation system, all roads are divided into four levels: expressways, main roads, secondary roads, and branch roads based on road traffic functions, design speeds, traffic capacities, and service levels.

6. The traffic flow optimization navigation method based on driving ability evaluation according to claim 2 is characterized in that: In step 3, the corresponding road grade is recommended based on the navigation user's driving ability evaluation result, as follows: The navigation system pushes high-level roads to navigation users with high driving ability levels and pushes low-level roads to navigation users with low driving ability levels based on the driving ability evaluation results of the navigation users obtained by the driving ability evaluation mathematical model.

7. The traffic flow optimization navigation method based on driving ability evaluation according to claim 1 is characterized in that: In step 4, the real-time road network traffic congestion situation is obtained, as follows: Through the statistical data of the transportation department, field surveys, and remote sensing monitoring methods, specific information on all road traffic conditions is collected, including road names, starting and ending points, design speeds, number of lanes, capacity, traffic flow, and road condition assessment data. Taking into account driving time, road condition information, and traffic flow factors, the generalized cost equation is used to calculate the weight of each road based on the road grade classification standards and the collected road information. The calculated road weights and other relevant information of the road are entered into the database to form a road grade database.

8. A traffic flow optimization navigation system based on driving ability evaluation, characterized in that: The system is used to implement the traffic flow optimization navigation method based on driving ability evaluation according to any one of claims 1 to 7, and the system includes an evaluation module, a road grade classification module, a recommendation module and an adjustment module, wherein: An evaluation module evaluates the driving ability of the navigation user and obtains the driving ability evaluation result of the navigation user; A road classification module is used to classify roads in the navigation system; The recommendation module prioritizes the corresponding road grade based on the navigation user's driving ability assessment results; The adjustment module obtains the real-time traffic congestion situation of the road network during the navigation process and adjusts the route recommendation received by the navigation user.

9. A mobile terminal comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the minimum distance phase ambiguity elimination direction finding method based on heterogeneous hybrid MIMO is implemented as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps in the minimum distance phase ambiguity elimination direction finding method based on heterogeneous hybrid MIMO are implemented.

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