Intelligent traffic information management system based on 5G

Abstract

The invention belongs to the technical field of intelligent analysis and management of traffic information, and particularly discloses and provides a 5G-based traffic information intelligent management system, which comprises the steps of positioning a traffic congestion area by identifying vehicle retention states of corresponding driving road sections and steering intersections of lanes of branch routes on a driving path of a vehicle owner; the method comprises the following steps: detecting congestion source activity time, a large vehicle proportion and a vehicle proportion of each speed interval in a traffic congestion area of each branch route on a driving path of a vehicle owner, determining predicted congestion travel path duration of each control area on each branch route, and determining a steering recommended lane for the vehicle owner; by identifying the lane changing direction of each passing vehicle in each control area on each branch route and the traveling traffic flow of the adjacent road section of each lane in the congestion period, the traffic order blocking rate of each control area on each branch route and the adjacent road section thereof is analyzed, and the traffic flow relieving effect index in each control area on each branch route is evaluated accordingly.

Description

Technical Field

[0001] The present invention belongs to the technical field of intelligent analysis and management of traffic information, and relates to a 5G-based intelligent management system for traffic information. Background Art

[0002] As urban traffic flow shows a sharp upward trend, road congestion during peak hours has become the norm in many large cities. Traditional traffic information management methods are unable to cope with the huge and complex traffic flow, making urban traffic congestion problems increasingly serious, which in turn leads to an increasingly urgent demand for society to improve traffic efficiency and reduce traffic congestion.

[0003] In the prior art, there are some solutions related to the intelligent analysis and management of traffic information. For example, Chinese Patent Publication No. CN116453360B discloses a traffic management system based on big data. It analyzes traffic information to determine future traffic congestion conditions, and then uses a navigation information push module to send navigation diversion suggestions based on future traffic congestion conditions, so that navigation users can change navigation routes in advance, thereby reducing road congestion.

[0004] Another Chinese patent, CN116721550B, discloses a traffic management system based on grid computing. It collects specified state parameter values ​​through collection means, processes and analyzes the data to obtain a traffic accident occurrence index and a traffic road safety index, and further analyzes the data to obtain a traffic management safety assessment coefficient. After comparative analysis, corresponding processing is performed to achieve the ability of autonomous decision-making and intelligent management of traffic, helping managers to monitor the system in real time to obtain the latest data and make corresponding adjustments, thereby improving the efficiency of traffic management.

[0005] While the aforementioned solutions offer some solutions for intelligent analysis and management of traffic information, they still exhibit several limitations. Specifically, existing technologies for recommending routes to navigation users based on traffic information are limited to locating and monitoring congested sections, without predicting and analyzing the congestion conditions of adjacent sections. This results in incomplete route recommendations. Furthermore, existing technologies for predicting and analyzing congestion characteristics that occur during driving tend to focus more on analyzing the probability of road accidents and lack the ability to predict congestion duration by identifying the source of congestion. This hinders the analysis of the causes of congestion and the development of solutions, as well as the accuracy of route optimization recommendations for drivers. Summary of the Invention

[0006] In view of this, in order to solve the problems raised in the above background technology, a 5G-based intelligent traffic information management system is proposed.

[0007] The purpose of the present invention can be achieved through the following technical solutions: The present invention provides a 5G-based intelligent traffic information management system, which includes: a control area screening module for obtaining the owner's target driving path, and constructing each branch route on the target driving path, detecting the corresponding road congestion level of each traffic area on each branch route, screening out each control area on each branch route, numbering each branch route as 1, 2,...i...,n, and numbering each control area as 1, 2,...j...,m.

[0008] The congestion analysis module is used to extract the traffic condition data of each control area on each branch route, identify the corresponding congestion sources of each control area on each branch route, and analyze the expected congestion duration of each control area on each branch route.

[0009] The control rule determination module is used to obtain the travel distance of each branch route, combine the expected congestion duration of each control area on each branch route, identify the corresponding path recommendation score of each branch route, and then determine the recommended lane for the driver to turn to.

[0010] The control effect evaluation module is used to track the recommended lanes that the driver turns to on the target driving path in real time, detect the vehicle conditions in each control area on each branch route of the driver's target driving path, identify the corresponding adjacent road conditions of each control area on each branch route, and evaluate the traffic relief effect indicators in each control area on each branch route.

[0011] Compared with the prior art, the present invention has the following beneficial effects: (1) The present invention locates traffic congestion areas by identifying the vehicle congestion status of the corresponding driving sections and turning intersections of each lane of each branch route on the driver's driving path, and then determines the recommended turning lane for the driver based on the expected congestion duration of each branch route, so as to avoid the congested section and help ease the vehicle traffic flow in the congested section.

[0012] (2) The present invention can provide the driver with real-time traffic status information by detecting the activity time of congestion sources, the proportion of large vehicles and the proportion of vehicles in each speed range in the traffic congestion area of ​​each branch route on the driver's driving path, and thereby determine the expected congestion duration of each control area on each branch route, so as to make the travel route optimization suggestions provided to the driver more accurate.

[0013] (3) The present invention identifies the lane change directions of each passing vehicle in each control area on each branch route and the traffic flow of each lane's adjacent sections during congestion periods, analyzes the traffic order obstruction rate of each control area and its adjacent sections on each branch route, and can more carefully map the occurrence and dissipation pattern of traffic congestion. At the same time, the present invention compares the differences in congested traffic flow of each lane in each control area on each branch route before and after congestion, revealing the long-term trend of traffic flow changes. Based on this, the traffic flow relief effect indicators in each control area on each branch route are evaluated, which can reflect the effectiveness of the implementation of traffic control by optimizing the driver's travel path, and thus provide decision support for the traffic management department. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0015] Figure 1 This is a schematic diagram of the system module connection of the present invention.

[0016] Figure 2 Schematic diagram of the corresponding intersection in the traffic area of ​​the present invention.

[0017] Figure 3 This is a display diagram of the adjacent road sections of each lane in the control area of ​​the present invention.

[0018] Reference numerals: 1. Starting stop line on the through signal indicator lane, 2. Target stop line, 3. Lanes at the corresponding intersection in the traffic area, 4. Adjacent road section of the right-turn lane in the control area. DETAILED DESCRIPTION

[0019] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making creative efforts are within the scope of protection of the present invention.

[0020] See also Figure 1 As shown, the present invention provides a 5G-based intelligent traffic information management system, which includes: a control area screening module, a congested road condition analysis module, a control rule determination module, and a control effect evaluation module.

[0021] The control area screening module is connected to the congested road condition analysis module, the congested road condition analysis module is connected to the control rule determination module, and the control rule determination module is connected to the control effect evaluation module.

[0022] The control area screening module is used to obtain the owner's target driving path, construct each branch route on the target driving path, detect the road congestion level of each traffic area on each branch route, screen out each control area on each branch route, number each branch route as 1, 2, ...i..., n, and number each control area as 1, 2, ...j..., m.

[0023] The vehicle owner's target driving route is selected by the vehicle owner's navigation.

[0024] The control areas refer to the road sections between each traffic light intersection and its adjacent traffic light intersections.

[0025] In a preferred embodiment, the construction of each branch route on the target driving path is specifically: obtaining a route map between the destination point on the owner's target driving path and the owner's positioning point from a map navigator, and obtaining traffic control rules on the route map, including restricted license plate numbers and restricted road sections.

[0026] The license plate number of the vehicle owner's vehicle is obtained, and the road section area where the restricted license plate number that is consistent with the vehicle owner's license plate number is located on the route map is eliminated, and the area belonging to the restricted road section is eliminated to obtain the branch routes that the vehicle owner's vehicle can pass on the route map, which are the branch routes on the target driving path. Then, when the vehicle owner pre-travels to the intersections of the traffic areas on the branch routes, the intersections of the corresponding traffic areas on the corresponding branch routes are used as new vehicle owner positioning points on the target driving path, and the branch routes on the target driving path corresponding to the new vehicle owner positioning points are constructed in real time.

[0027] See also Figure 2 As shown, in a further preferred embodiment, the screening of each controlled area on each branch route includes: detecting the parking time of the traffic flow corresponding to each lane in each traffic area on each branch route in real time through a road monitoring camera, and comparing it with the red light cycle setting time of the signal indicator light of the corresponding lane in the corresponding traffic area on the corresponding branch route to obtain the vehicle deceleration rate Re of each lane in each traffic area on each branch route. ija , a represents the number of the corresponding lanes in the traffic area, a=1,2,...,z.

[0028] The numerator of the ratio is the parking time of the traffic flow in each lane of each traffic area on each branch route, and the denominator is the setting time of the red light cycle of the signal indicator light in each lane of each traffic area on each branch route.

[0029] The "dwell time" refers to the total time vehicles wait for traffic signals in a traffic zone. For example, at a busy intersection, if a lane's red light cycle is set to 60 seconds, the dwell time for vehicles waiting in that lane will increase by 60 seconds during this red light cycle. If waiting vehicles in a lane fail to pass through the traffic zone within the full red light cycle of the lane's signal indicator, the dwell time for vehicles in that lane may be increased by another 60 seconds.

[0030] The vehicle deceleration rate of each lane corresponding to each traffic area on each branch route represents the congestion situation of the road section to which the traffic area belongs.

[0031] The road monitoring camera is used to extract the traffic images of the corresponding intersections in each traffic area on each branch route. The image recognition technology is used to locate the number of blocked vehicles in each lane of the corresponding intersections in each traffic area on each branch route. The number of blocked vehicles is multiplied by the preset reference blocked vehicle number ratio to obtain the vehicle crossing rate Cr of each traffic area on each branch route. ij .

[0032] The blocked vehicle refers to a vehicle that has passed the starting stop line but has not passed the target stop line in the lane corresponding to the signal indicator light when the signal indicator light for going straight or turning changes from green to red.

[0033] The vehicle crossing rate corresponding to each traffic area on each branch route represents the congestion situation at the intersection to which the traffic area belongs.

[0034] Analyze the road congestion level of each traffic area on each branch route based on the vehicle delay rate of each lane and the vehicle crossing rate of each traffic area on each branch route. z represents the number of lanes corresponding to the traffic area. The road congestion level of each traffic area on each branch route is compared with the preset road congestion level index. The traffic areas on each branch route where the road congestion level exceeds the preset road congestion level index are screened out and recorded as the controlled areas on each branch route.

[0035] The congested traffic analysis module is used to extract traffic data of each controlled area on each branch route through road monitoring cameras, identify the corresponding congestion sources of each controlled area on each branch route, and analyze the expected congestion duration of each controlled area on each branch route.

[0036] In a preferred embodiment, the traffic condition data of each controlled area on each branch route includes historical traffic condition data and real-time traffic condition data.

[0037] The historical road condition data includes regular traffic flow and driving behavior data of passing vehicles at the positioning time.

[0038] The real-time road condition data includes the real-time vehicle flow rate and the proportion of large vehicles and the proportion of vehicles in each speed range at the positioning time.

[0039] In a further preferred embodiment, the analysis of the estimated congestion duration of each control area on each branch route includes: obtaining the positioning time of the driver to each control area on each branch route;

[0040] Through the large-scale event service platform in the city, we can obtain the corresponding congestion sources and their activity time areas in each control area on each branch route. Congestion sources include station passenger flow sources and concert activity sources. The corresponding activity time areas of congestion sources are such as station passenger flow peak period and concert activity period. Then, we can extract the congestion limit time of the corresponding congestion sources in each control area on each branch route. Determine the expected congestion duration of the congestion source in each control area on each branch route Among them, tp1 indicates that the congestion limit time of the corresponding congestion source in the control area is after the positioning time, and tp2 indicates that the congestion limit time of the corresponding congestion source in the control area is before the positioning time. Indicates the preset congestion buffer duration of the corresponding congestion source in the jth control area on the i-th branch route.

[0041] The congestion limit time refers to the corresponding expected end time of the corresponding activity time area of ​​the congestion source.

[0042] Based on the real-time traffic data of each control area on each branch route, the estimated congestion duration of each control area on each branch route is analyzed.

[0043] Determine the expected congestion duration for each control area on each branch route

[0044] The estimated congested travel duration corresponding to the congestion source in each control area on each branch route and the estimated congested travel duration corresponding to the measured road conditions in each control area on each branch route are two factors affecting the congested duration on the congested route. The maximum value is selected by comparison and is used as the final estimated congested travel duration. The purpose of this analysis is to determine the impact of shortening the total congested travel time on the congested route selection when the driver turns to the recommended lane.

[0045] In a further preferred embodiment, the analysis of the time-measured road conditions of each control area on each branch route and the corresponding expected congestion duration includes: extracting the time-measured traffic flow of each control area on each branch route at the positioning time, and extracting the regular traffic flow of each control area on each branch route at the positioning time, and comparing them to obtain the expected traffic delay rate b of each control area on each branch routeij .

[0046] Extract the proportion of large vehicles and the proportion of vehicles in each speed range in each control area on each branch route at the positioning time, and record it as K′ ij , K ij→v The corresponding median speed of each speed area is recorded as sp v , calculate the vehicle type delay rate of each control area on each branch route at the positioning time Where sp0 represents the preset appropriate reference speed, v represents the number of the speed interval, and v = 1, 2, ..., x.

[0047] Analyze the time-measured road conditions in each control area on each branch route and the corresponding estimated congestion duration T0 represents the congestion duration corresponding to the preset unit delay rate.

[0048] The present invention can provide real-time traffic status information to car owners by detecting the activity time of congestion sources, the proportion of large vehicles and the proportion of vehicles in each speed range in the traffic congestion area of ​​each branch route on the car owner's driving path, and determine the expected congestion duration of each control area on each branch route based on this, so that the travel route optimization suggestions provided to the car owner are more accurate. At the same time, by analyzing the congestion-related data of each control area on each branch route, it can provide a basis for the planning and construction of traffic facilities. If it is found that a certain area is often congested due to the high proportion of large vehicles and the congestion time is long, it can be considered to widen the road, build a dedicated lane for large vehicles or improve the carrying capacity of the road; for the situation where the proportion of vehicles in the speed range is unreasonable, such as the traffic congestion caused by the excessive proportion of low-speed vehicles, reasonable speed limit areas can be set or the flatness of the road can be improved to increase the driving speed of the vehicle.

[0049] The control rule determination module is used to obtain the travel distance of each branch route, combine the expected congestion duration of each control area on each branch route, identify the corresponding path recommendation score of each branch route, and then determine the recommended lane for the driver to turn to.

[0050] The determination that the driver turns to the recommended lane is to ease the vehicle traffic flow on the congested road section and help the driver avoid the congested road section, thereby making the driving condition smoother.

[0051] In a preferred embodiment, the process of determining the recommended lane for the driver to turn to includes: obtaining the travel distance L of each branch route from the route map; i , and extract the owner's speed Calculate the travel time of each branch route based on the basic path time

[0052] The estimated congestion duration of each control area on each branch route is accumulated to obtain the corresponding estimated congestion cumulative duration T′ of each branch route. i , based on which the corresponding path recommendation score of each branch route is calculated in It represents the preset reference travel time, and e is a natural constant.

[0053] The corresponding path recommendation scores of each branch route are compared with each other, and the branch route with the maximum path recommendation score is screened out. Then, the predicted intersection turning direction of the branch route with the maximum path recommendation score is obtained from the route map, and the lane with the predicted intersection turning direction is used as the recommended turning lane for the driver.

[0054] The present invention locates traffic congestion areas by identifying the vehicle congestion status of the corresponding driving sections and turning intersections of each lane of each branch route on the driver's driving path, and then determines the recommended turning lane for the driver based on the expected congestion duration of each branch route, so as to avoid congested sections and help ease the vehicle traffic flow in congested sections.

[0055] The control effect evaluation module is used to track the recommended lanes that the driver turns to on the target driving path in real time, detect the vehicle conditions in each control area on each branch route on the driver's target driving path, identify the corresponding adjacent road conditions of each control area on each branch route, and evaluate the traffic relief effect indicators in each control area on each branch route.

[0056] In a preferred embodiment, the detection of vehicle conditions in each controlled area on each branch route includes: identifying the lane change direction of each vehicle in each controlled area on each branch route, including the lane change direction of the road turning along the same direction and the lane change direction of the road turning along the opposite direction, and obtaining the proportion of the number of vehicles in each controlled area on each branch route that change lanes along the opposite direction, and then using it as the traffic order obstruction compensation factor M of each controlled area on each branch route. ij .

[0057] The forward direction of the road turn refers to turning from the straight lane to the left or right turn lane in the slow-moving section with a signal indicator light, and the reverse direction of the road turn refers to turning from the left or right turn lane to the straight lane in the slow-moving section with a signal indicator light.

[0058] Extract the driving behavior data of vehicles passing through each control area on each branch route, including lane change frequency and sudden speed change frequency, recorded as D ij 、S ij , calculate the traffic order obstruction rate of each control area on each branch route Where D′ and S′ represent the preset reference frequencies corresponding to the frequent lane change frequency and the sudden speed change frequency, respectively.

[0059] Specifically, the purpose of identifying the traffic order obstruction compensation factor of each control area on each branch route is to focus on increasing the influence weight of the lane change situation of vehicles in the reverse direction of the road.

[0060] The lane change frequency refers to the weight of the number of vehicles changing lanes when passing through a control area on a branch route.

[0061] The sudden speed change frequency refers to the weight of the number of vehicles that suddenly accelerate or the weight of the number of vehicles that suddenly decelerate when passing through a control area on a branch route.

[0062] See also Figure 3 As shown, in a further preferred embodiment, the identification of the corresponding adjacent road conditions of each controlled area on each branch route includes: extracting the congestion period of each controlled area on each branch route through the traffic information recording terminal, and extracting the traffic flow q′ of the adjacent road sections of each lane belonging to each controlled area on each branch route during the congestion period. ij→h .

[0063] The congestion period refers to a period during which the road congestion level in the control area is in a state exceeding a preset road congestion level indicator.

[0064] The adjacent road sections of each lane belonging to the control area include the adjacent road sections of the right turn lane belonging to the control area, the adjacent road sections of the left turn lane belonging to the control area, and the adjacent road sections of the through lane belonging to the control area.

[0065] Extract the corresponding preset full-load traffic flow of the adjacent sections of each lane in each control area on each branch route, and record it as Analyze the traffic order obstruction rate of the corresponding adjacent sections of each control area on each branch route in It represents the preset overload deviation permitted traffic flow, h represents the number of the corresponding lanes in the control area, h=1,2,...,u.

[0066] In a further preferred embodiment, the evaluation of the traffic flow relief effect index in each control area on each branch route is specifically as follows: extracting the congested traffic flow q of each lane in each control area on each branch route during the congestion period through the traffic information recording terminal ij→h , and obtain the congestion source of each control area on each branch route, and extract the historical congested traffic flow q′ of each lane in the corresponding control area on the corresponding branch route under the corresponding congestion source ij ' →h .

[0067] Set the traffic flow deviation compensation weight B corresponding to the congestion source in each control area on each branch route ij , evaluate the traffic flow relief effect indicators in each control area on each branch route u represents the number of lanes corresponding to the control area.

[0068] Specifically, the corresponding traffic flow deviation compensation weights of the congestion sources in each control area on each branch route are determined by the differences in the congestion sources. For example, when the congestion source of a control area on a branch route is a celebrity's concert, its corresponding traffic flow deviation compensation weight is determined by the celebrity's online popularity, that is, the online popularity and the setting of the traffic flow deviation compensation weight are in a positive mapping relationship.

[0069] The present invention identifies the lane change direction of each passing vehicle in each controlled area on each branch route, analyzes the traffic order obstruction rate of each controlled area on each branch route, and identifies the traffic flow of the adjacent sections of each lane belonging to each controlled area on each branch route during the congested period. The traffic order obstruction rate of the corresponding adjacent sections of each controlled area on each branch route can more finely map the occurrence and dissipation pattern of traffic congestion, and at the same time compares the difference in congested traffic flow of each lane in each controlled area on each branch route before and after the congested period, revealing the long-term trend of traffic flow changes. Based on this, the traffic flow relief effect index in each controlled area on each branch route is evaluated, which can reflect the effectiveness of the implementation of traffic control by optimizing the travel path of the car owner, and thus provide decision support for the traffic management department.

[0070] The above contents are merely examples and explanations of the concept of the present invention. Those skilled in the art may make various modifications or additions to the described specific embodiments or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined by the present invention, they should all fall within the scope of protection of the present invention.

Claims

1. A 5G-based intelligent traffic information management system, characterized in that: The system includes: The control area screening module is used to obtain the target driving path of the vehicle owner, construct each branch route on the target driving path, detect the corresponding road congestion level of each traffic area on each branch route, screen out each control area on each branch route, number each branch route as 1, 2, ...i..., n, and number each control area as 1, 2, ...j..., m; The traffic congestion analysis module is used to extract the traffic condition data of each control area on each branch route, identify the corresponding congestion source of each control area on each branch route, and analyze the expected congestion duration of each control area on each branch route; The control rule determination module is used to obtain the travel distance of each branch route, combine the estimated congestion duration of each control area on each branch route, identify the corresponding path recommendation score of each branch route, and then determine the recommended lane for the driver to turn to; The control effect evaluation module is used to track the recommended lanes that the owner turns to on the target driving path in real time, detect the vehicle conditions in each control area on each branch route on the owner's target driving path, identify the corresponding adjacent road conditions of each control area on each branch route, and evaluate the traffic relief effect indicators in each control area on each branch route.

2. According to claim 1, a 5G-based intelligent traffic information management system is characterized in that: The construction of each branch route on the target driving path is specifically as follows: obtaining a route map between a destination point on the target driving path of the vehicle owner and the vehicle owner's positioning point from a map navigator, and obtaining traffic control rules on the route map, including restricted license plate numbers and restricted road sections; The license plate number of the vehicle owner is obtained, the road section area where the restricted license plate number that is consistent with the license plate number of the vehicle owner is located on the route map is eliminated, and the area belonging to the restricted section is eliminated, and the branch routes that the vehicle owner's vehicle can pass on the route map are obtained, which are the branch routes on the target driving path. Then, when the vehicle owner pre-travels to the intersections of the traffic areas on the branch routes, the intersections of the corresponding traffic areas on the corresponding branch routes are used as the newly created vehicle owner positioning points on the target driving path, and the branch routes on the target driving path corresponding to the newly created vehicle owner positioning points are constructed in real time.

3. According to claim 1, a 5G-based intelligent traffic information management system is characterized in that: The control areas on each branch route are screened out, including: The vehicle stagnation rate of each lane in each traffic area on each branch route is obtained by detecting the stagnation time of the traffic flow in each lane in each traffic area on each branch route in real time through the road monitoring camera, taking it as the numerator and comparing it with the red light cycle setting time of the signal indicator light in the corresponding lane in the corresponding traffic area on the corresponding branch route; Extract the traffic images of the corresponding intersections in each traffic area on each branch route, locate the number of blocked vehicles in each lane at the corresponding intersections in each traffic area on each branch route through image recognition technology, multiply it with the preset reference blocked vehicle number ratio, and obtain the corresponding vehicle crossing rate in each traffic area on each branch route; Based on the vehicle deceleration rate of each lane corresponding to each traffic area on each branch route and the vehicle crossing rate of each traffic area on each branch route, the road congestion degree of each traffic area on each branch route is analyzed and compared with the preset road congestion degree index, and the traffic areas on each branch route whose road congestion degree exceeds the preset road congestion degree index are screened out and recorded as the controlled areas on each branch route.

4. According to claim 1, a 5G-based intelligent traffic information management system is characterized in that: The traffic condition data of each control area on each branch route includes historical traffic condition data and real-time traffic condition data; The historical traffic data includes regular traffic flow and driving behavior data of passing vehicles at the positioning time; The real-time road condition data includes the real-time vehicle flow, the proportion of large vehicles, and the proportion of vehicles in each speed range at the positioning time.

5. According to claim 4, a 5G-based intelligent traffic information management system is characterized in that: The analysis of the estimated congestion duration of each control area on each branch route includes: Get the positioning time of the vehicle owner to each controlled area on each branch route Obtain the corresponding congestion sources and their activity time areas in each control area on each branch route, and then extract the congestion limit time of the corresponding congestion sources in each control area on each branch route Determine the expected congestion duration of congestion sources in each control area on each branch route Among them, tp1 indicates that the congestion limit time of the corresponding congestion source in the control area is after the positioning time, and tp2 indicates that the congestion limit time of the corresponding congestion source in the control area is before the positioning time. Indicates the preset congestion buffer time of the corresponding congestion source in the jth control area on the i-th branch route; Based on the real-time traffic data of each controlled area on each branch route, the estimated congestion duration of each controlled area on each branch route is analyzed. Determine the expected congestion duration for each control area on each branch route 6. A 5G-based intelligent traffic information management system according to claim 5, characterized in that: The analysis of the estimated congestion duration corresponding to the measured road conditions in each control area on each branch route includes: Extract the measured traffic flow of each control area on each branch route at the positioning time, and extract the regular traffic flow of each control area on each branch route at the positioning time, and compare them to obtain the expected traffic delay rate b of each control area on each branch route ij ; Extract the proportion of large vehicles and the proportion of vehicles in each speed range in each control area on each branch route at the positioning time, denoted as K i ' j , K ij→v , and the corresponding median speed of each speed area is recorded as sp v , calculate the vehicle type delay rate of each control area on each branch route at the positioning time Where sp0 represents the preset appropriate reference speed, v represents the number of the speed interval, v = 1, 2, ..., x; Analyze the time-measured road conditions in each control area on each branch route and the corresponding estimated congestion duration T0 represents the congestion duration corresponding to the preset unit delay rate.

7. The 5G-based intelligent traffic information management system according to claim 1 is characterized in that: The determining that the vehicle owner is turning to the recommended lane includes: Get the travel distance of each branch route, extract the vehicle owner's travel speed, and calculate the corresponding travel time of each branch route based on the basic path time The estimated congestion duration of each control area on each branch route is accumulated to obtain the corresponding estimated congestion cumulative duration T of each branch route. i ′, based on which the corresponding path recommendation score of each branch route is calculated in represents the preset reference travel time, and e is a natural constant; The corresponding path recommendation scores of each branch route are compared with each other, and the branch route with the maximum path recommendation score is screened out, and then the predicted turning direction of the intersection of the branch route with the maximum path recommendation score is obtained, and the lane with the predicted turning direction of the intersection is used as the recommended lane for the car owner to turn to.

8. According to claim 4, a 5G-based intelligent traffic information management system is characterized in that: The detection of vehicle conditions in each control area on each branch route includes: identifying the lane change direction of each passing vehicle in each control area on each branch route, including the lane change direction of the road turning along the same side and the lane change direction of the road turning against the same side, and obtaining the proportion of the number of passing vehicles in each control area on each branch route that change along the same side, and then using it as the traffic order obstruction compensation factor M of each control area on each branch route. ij ; Extract the driving behavior data of vehicles passing through each control area on each branch route, including lane change frequency and sudden speed change frequency, denoted as D ij , S ij , calculate the traffic order obstruction rate of each control area on each branch route Wherein D′ and S′ represent the preset reference frequencies corresponding to the frequent lane changing frequency and the rapid speed changing frequency respectively.

9. A 5G-based intelligent traffic information management system according to claim 8, characterized in that: The identification of the corresponding adjacent road conditions of each controlled area on each branch route includes: extracting the congestion period of each controlled area on each branch route, and extracting the traffic flow q of the adjacent road sections of each lane belonging to each controlled area on each branch route during the congestion period. i ' j→h ; Extract the corresponding preset full-load traffic flow of the adjacent sections of each lane in each control area on each branch route, denoted as Analyze the traffic order obstruction rate of the corresponding adjacent sections of each control area on each branch route in It represents the preset overload deviation permitted traffic flow, h represents the number of the corresponding lanes in the control area, h = 1, 2, ..., u.

10. A 5G-based intelligent traffic information management system according to claim 9, characterized in that: The indicators for evaluating the traffic relief effect in each control area on each branch route are specifically: Extract the congested traffic flow q of each lane in each control area on each branch route during the congestion period ij→h , and obtain the congestion source of each control area on each branch route, and extract the historical congested traffic flow q′ of each lane in the corresponding control area on the corresponding branch route under the corresponding congestion source ij ' →h ; Set the corresponding traffic flow deviation compensation weight B of each control area on each branch route ij , evaluate the traffic relief effect indicators in each control area on each branch route u represents the number of lanes corresponding to the control area.

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