Vehicle blocking prevention traffic safety dispersion method for reconstruction and extension of expressway

Abstract

The invention, which relates to the technical field of traffic safety dispersion, discloses an anti-vehicle-blocking traffic safety dispersion method for reconstruction and expansion of an expressway, and the method comprises the following components: S1, constructing a multi-dimensional information collection system, S2, establishing an information intelligent transmission network, S3, carrying out a traffic jam prediction algorithm, and S4, carrying out precise early warning and intelligent guidance. According to the invention, rapid and efficient transmission of information is realized through the converged communication technology, the communication technology not only improves the speed and stability of data transmission, but also can realize intelligent switching in different signal environments, thereby providing timely and reliable traffic information for a traffic management center, enabling the traffic management center to make a response rapidly, and improving the traffic management efficiency. Accurate early warning and intelligent guidance are implemented, and traffic jam and safety accidents are effectively avoided.

Description

Technical Field

[0001] The invention relates to the technical field of traffic safety guidance, and in particular to a traffic safety guidance method for preventing traffic jams during highway reconstruction and expansion. Background Art

[0002] With the acceleration of urbanization and the continuous growth of transportation demand, highways, as an important part of the urban transportation network, are undergoing increasingly frequent reconstruction and expansion projects. However, during the reconstruction and expansion of highways, traffic congestion often occurs due to factors such as construction occupying the road and traffic flow adjustment, which seriously affects road capacity and driving safety.

[0003] Although traditional technologies can cope with the traffic management needs during the reconstruction and expansion of highways to a certain extent, they often lack the flexibility and intelligence to accurately match the various construction stages. Traditional methods mostly rely on manual monitoring and simple traffic signal control, and are unable to accurately match the traffic management needs of the various construction stages of highway reconstruction and expansion, and comprehensively collect and process multi-dimensional data on traffic flow, vehicle speed, road conditions, and construction information. Therefore, traditional technologies are difficult to accurately predict traffic congestion, and are unable to implement accurate early warning and intelligent guidance in advance, resulting in frequent traffic congestion and safety accidents during the reconstruction and expansion of highways, which seriously affects road traffic efficiency and driving safety.

[0004] In summary, traditional technologies have significant shortcomings in dealing with traffic management needs during highway reconstruction and expansion. Therefore, it is particularly important to develop a method for preventing traffic jams and safely directing traffic during highway reconstruction and expansion. Summary of the invention

[0005] The purpose of the present invention is to make up for the shortcomings of the existing technology and provide a method for traffic safety guidance during the reconstruction and expansion of expressways to prevent traffic jams. It can achieve fast and efficient transmission of information by integrating communication technology. This communication technology not only improves the rate and stability of data transmission, but also can intelligently switch under different signal environments, so that it can respond quickly, implement accurate early warning and intelligent guidance, and effectively avoid traffic congestion and safety accidents.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: a method for preventing traffic jams during highway reconstruction and expansion, the specific steps of the method are:

[0007] S1. Build a multi-dimensional information collection system

[0008] Traffic flow sensors are installed at intervals of d1 meters on the roadside and gantry infrastructure along the expressway to collect real-time traffic flow data on the expressway, and speed sensors are installed to measure the average speed of vehicles on the expressway. Road condition cameras are installed every d2 kilometers, and construction progress sensors are installed around the construction area to identify the working status of construction equipment and the stacking of construction materials, so as to accurately judge the construction progress and construction scope identification sensors. Construction information entry terminals are also installed, and construction management personnel update the estimated construction duration information in real time, so as to collect all-round information on the expressway's traffic flow, vehicle speed, road conditions and construction-related information;

[0009] S2. Establish an intelligent information transmission network

[0010] Using converged communication technology, road infrastructure sensors will send collected information to the traffic management center in a specific format through the communication protocol P at a transmission rate of v1Mbps, and the vehicle-mounted terminal equipment will upload the vehicle location, speed, and driving direction information to the traffic management center at a frequency of f1Hz;

[0011] S3. Traffic congestion prediction algorithm

[0012] In the traffic management center server, a traffic congestion prediction model is constructed. Assume that the traffic flow is F, the vehicle speed is V, the time series is t, the historical traffic congestion coefficient is α, the current road construction impact factor is β, and the road capacity correction coefficient is γ. The congestion index C is predicted by the following formula:

[0013] Where C represents the congestion index, α is the historical traffic congestion coefficient, F(t) is the actual traffic flow of the current highway collected at time t in the time series, Fmax(t) is the historical maximum traffic flow in this period, β is the impact factor of the current road construction, Λ(t) represents the average speed of vehicles on the highway at time t in the time series, Vmax(t) is the highest average speed in this period, γ is the road capacity correction coefficient, L(t) represents the number of lanes occupied by the current construction, and Ltotal(t) represents the total number of lanes in this section; is the influence weight of the i-th bad road condition point. When C exceeds the set threshold Ca, it is determined that traffic congestion occurs on this road section;

[0014] S4. Implement accurate early warning and intelligent guidance

[0015] When it is predicted that a certain road section may be congested or the construction information changes, the traffic management center sends warning information to vehicles within d3 kilometers around the predicted congestion or construction area through the vehicle-road cooperative system. The on-board terminal equipment reminds the driver through voice prompts, display screens and vibrations. The on-board navigation system uses the improved Dijkstra algorithm to re-plan the route based on the congestion index, road speed limit and real-time road conditions to guide the driver to avoid congestion and construction areas. In addition, dynamic traffic control is implemented in different construction stages of the highway reconstruction and expansion. In the construction preparation stage, based on the construction design plan and the estimated construction time, combined with historical traffic data, the traffic flow changes in different time periods and sections are predicted, and traffic diversion plans are formulated in advance. In the basic construction stage, the road structure safety status is evaluated based on the stress and displacement sensor data. When the road bearing capacity decreases or potential safety risks appear, the construction progress is adjusted or the traffic organization plan is optimized. In the pavement construction stage, traffic control measures are adjusted according to different construction processes of milling and paving. In the ancillary facilities construction stage, augmented reality and virtual reality technologies are used to provide virtual traffic guidance signs for drivers.

[0016] Furthermore, during the road construction phase, when milling operations are being carried out, if the working power of the milling equipment exceeds the set power value P1 or the noise decibel value generated exceeds the noise threshold N1, it is determined that the impact on traffic is large. At this time, the scope of sending early warning information is expanded to vehicles within kilometers around the construction area. At the same time, the construction area and recommended detour routes are highlighted in the vehicle navigation system.

[0017] Furthermore, the construction progress sensor adopts a sensor based on image recognition technology. By collecting and analyzing images of the construction site in real time, it uses a deep learning algorithm to identify the working status of construction equipment and the stacking of construction materials to accurately judge the construction progress. The sensor can identify the different stages of construction progress and accurately feedback the construction progress in the form of a percentage to the traffic management center so as to adjust the traffic diversion strategy in time.

[0018] Furthermore, in the integrated communication technology, LTE-V technology is preferentially used for information transmission in areas with weak signals to ensure the stability of information transmission. When the 5G signal strength reaches above I1, it automatically switches to the 5G network for high-speed data transmission. This switching mechanism uses fuzzy control algorithm to achieve intelligent switching through real-time monitoring and analysis of signal strength, transmission rate, and packet loss rate parameters, ensuring that information can be efficiently and stably transmitted to the traffic management center in different scenarios.

[0019] Furthermore, after receiving the warning information, the vehicle terminal device will also remind the driver through vibration. In terms of the congestion index, in addition to the original factors, the difference in the number of vehicles entering and leaving the road section per unit time and the saturation of the road section are included. When the ratio is greater than 0.7, it affects the congestion index;

[0020] In terms of road conditions, the road slope is taken into consideration. A slope greater than 5% affects speed and traffic capacity, the curvature of the curve, a radius less than 200 meters affects vehicle driving, and bad weather. The congestion index weight is adjusted according to the severity. The warning information is divided into three levels: light, medium, and heavy according to the congestion index and road traffic conditions. The vibration mode, voice prompt, and display warning icon of the vehicle terminal device are different at different levels. The navigation system also adjusts the route planning strategy accordingly to ensure driving safety and smooth roads. At the same time, the vehicle terminal device will correlate the warning information with the vehicle's driving data for analysis. If it finds that the vehicle is about to enter a congested or construction area, it will remind the driver again t1 in advance to ensure that the driver can respond in time.

[0021] Furthermore, in the improved Dijkstra algorithm, when determining the road speed limit weight, taking into account the significant difference in the proportion of historical speeding accidents before and during the expansion and reconstruction of the expressway, for the section before the expansion and reconstruction, by collecting the historical accident data of the section during the normal traffic period, the proportion of the number of accidents caused by speeding to the total number of accidents is counted, and this proportion is set as p1. At the same time, the speed limit standard V of the section is obtained. limit1 , then the speed limit weight of this road section before reconstruction and expansion;

[0022]

[0023] For the road sections in the reconstruction and expansion stage, the historical accident data during the construction period are collected separately, and the proportion of accidents caused by speeding p2 is analyzed, combined with the temporary speed limit standard V of the road section during the reconstruction and expansion stage. limit2 , calculate the road speed limit weight of the road section during the reconstruction and expansion stage;

[0024]

[0025] When the in-vehicle navigation system uses the improved Dijkstra algorithm to plan routes, for different road sections, according to whether they are in the pre-reconstruction or reconstruction stage, the corresponding road speed limit weights are used for route planning, fully considering the speed limit factors in different stages, giving priority to routes with higher speed limits and better safety performance, and improving overall traffic efficiency.

[0026] Furthermore, a traffic information database is established in the server of the traffic management center. The database not only stores the real-time collected traffic information and construction information, but also classifies and stores the historical traffic data, including traffic flow, vehicle speed, and congestion data in different time periods, seasons, and weather conditions. Through in-depth mining and analysis of historical data, it is possible to continuously optimize the parameters in the traffic congestion prediction model, such as the historical traffic congestion coefficient α and the road capacity correction coefficient γ, thereby improving the accuracy and adaptability of the prediction model.

[0027] Furthermore, when a traffic accident occurs on the roads around the construction area, the traffic management center will dynamically adjust the scope and content of the warning information according to the severity and impact range of the accident. If the accident is minor and only affects local lanes, the warning information will focus on reminding vehicles to avoid the accident scene and guide vehicles to change lanes reasonably. If the accident is serious and may cause traffic jams, the warning information will be expanded to vehicles within 4 kilometers around the accident, and drivers are advised to plan alternative routes in advance. At the same time, the traffic management center will work with the traffic police department in real time to obtain accident handling progress information and update the warning content in a timely manner.

[0028] Furthermore, in addition to collecting road condition information, the road condition camera also has an intelligent analysis function. Through image recognition technology and deep learning algorithms, it can detect obstacles, road damage, and water accumulation on the road in real time. When an obstacle is detected on the road, the road condition camera will immediately transmit the information to the traffic management center. The traffic management center will formulate a corresponding traffic diversion plan based on the location and size of the obstacle and the surrounding traffic flow conditions, and send early warning information to the driver through the vehicle-road cooperative system to guide the vehicle to avoid obstacles and ensure road safety and unobstructed traffic.

[0029] Compared with the prior art, the method for preventing traffic jams during highway reconstruction and expansion has the following beneficial effects:

[0030] 1. This method realizes fast and efficient transmission of information by integrating communication technology. This communication technology not only improves the rate and stability of data transmission, but also can switch intelligently under different signal environments, which provides timely and reliable traffic information for the traffic management center, enabling it to respond quickly, implement accurate early warning and intelligent guidance, and effectively avoid traffic congestion and safety accidents.

[0031] 2. By constructing a multi-dimensional information collection system, this method can accurately match the traffic management needs of each construction stage of the highway reconstruction and expansion, and comprehensively and in real time grasp the traffic flow, vehicle speed, road conditions and construction information of the highway, thus solving the shortcomings of traditional technology in information collection. This all-round information collection provides an accurate data basis for subsequent traffic congestion prediction and intelligent guidance, thereby effectively improving road traffic efficiency and safety during construction.

[0032] Other advantages, objectives and features of the present invention will be set forth in part in the following description and, in part, will be apparent to those skilled in the art based on an examination of the following or may be taught from the practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0034] Figure 1 This is a flow chart of a method for traffic safety guidance during highway reconstruction and expansion to prevent traffic jams. DETAILED DESCRIPTION

[0035] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the specific implementation mode, structure, characteristics and effects of the present invention are described in detail below in combination with the accompanying drawings and preferred embodiments.

[0036] Embodiment 1

[0037] This embodiment describes a section of a highway that is undergoing widening construction. The traffic conditions on this section are complex. Traffic flow sensors and vehicle speed sensors are installed every 200 meters on the roadside and on the gantry, and road condition cameras are set up every 2 kilometers. Construction progress sensors and construction scope identification sensors are deployed around the construction area, and a construction information entry terminal is installed.

[0038] A section of a highway is undergoing a widening construction project. During the construction period, the traffic flow is large and complex. In order to grasp the traffic conditions in real time, microwave radar traffic flow sensors and vehicle speed sensors are installed every 200 meters on the roadside and gantries along the construction section. Road condition cameras are set up every 2 kilometers. Construction progress sensors and construction scope identification sensors based on image recognition technology are deployed around the construction area, and construction information entry terminals are equipped. From 10:00 to 10:15 in the morning, the traffic flow sensor measures the traffic flow F(t) = 1600 vehicles / hour during this period, and the vehicle speed sensor measures the average vehicle speed V(t) = 55 kilometers / hour. The construction progress sensor feedback shows that the construction progress is 40%, and the current construction occupies a road area S(t) = 300 square meters. The total road area of ​​this section S total (t) = 1200 square meters. The construction management personnel update the estimated construction duration information through the input terminal to ensure that the information is timely and accurate.

[0039] Based on historical data, the traffic management center server obtained the historical traffic congestion coefficient α = 0.7, the current road construction impact factor β = 0.8, and the road capacity correction coefficient γ = 0.3. After analyzing the historical data, the historical maximum traffic flow F in this period max (t) = 2200 vehicles / hour, the highest average speed in history V max (t) = 75 km / h, determined through in-depth analysis of historical accident data, road maintenance records, and expert experience Calculated according to the congestion prediction formula:

[0040]

[0041]

[0042] The congestion threshold C0 is set to 0.45. Since C>C0, it is determined that traffic congestion may occur on this road section.

[0043] The traffic management center quickly sends warning information to vehicles within 3 kilometers of the construction area through the vehicle-road cooperative system. After receiving the information, the vehicle's on-board terminal equipment reminds the driver with a voice prompt "The construction section ahead will be congested, please follow the navigation prompts", a red warning icon on the display screen, and high-frequency long-term vibration. The on-board navigation system uses the improved Dijkstra algorithm to re-plan the route. For the road speed limit weight, the speed limit of a certain section is set to V limit = 80 km / h, the historical speeding accident rate of this road section is p = 0.15, then the road speed limit weight The navigation system comprehensively considers the congestion index, road speed limit, and real-time traffic weights to plan a route for the driver to avoid construction sections.

[0044] Guide the driver to the destination.

[0045] Embodiment 2

[0046] This embodiment describes that two cars have a rear-end collision on another section of the highway, occupying one lane. The traffic flow sensor and vehicle speed sensor of this section work normally, and the road condition camera captures the accident scene in time and transmits it to the traffic management center.

[0047] On another section of the highway, two cars rear-ended each other and occupied a lane. The traffic flow sensor and vehicle speed sensor of this section were working normally. The road condition camera, with its intelligent analysis function, captured the accident scene in time and transmitted it to the traffic management center through image recognition technology and deep learning algorithm. When the accident occurred, the traffic flow sensor measured the traffic flow F(t) = 1400 vehicles / hour, and the vehicle speed sensor measured the average speed V(t) = 45 kilometers / hour. Since there was no construction, the road area occupied by the construction was S(t) = 0 square meters. The total road area of ​​this section S total (t) = 1000 square meters.

[0048] In the traffic management center, according to historical data, the historical traffic congestion coefficient α = 0.6, the current road construction impact factor β = 0 (no construction), and the road capacity correction coefficient γ = 0.4. After analysis, the historical maximum traffic flow F max (t) = 1800 vehicles / hour, the highest average speed in history V max (t) = 70 km / h, considering the impact of the accident point, it is determined by analysis that ∑ i =1 n δ i =0.5 According to the congestion prediction formula calculate

[0049]

[0050] The congestion threshold C0 is set to 0.4. Since C>C0, it is determined that congestion may occur on this road section.

[0051] The traffic management center determines that the accident may affect traffic, and quickly sends warning information to vehicles within 2 kilometers of the accident, prompting "There is a traffic accident ahead, please pay attention to avoid it and change lanes appropriately." At the same time, it communicates with the traffic police department in real time to obtain information on the progress of the accident handling and update the warning content in a timely manner. After the vehicle's on-board terminal equipment receives the warning information, it reminds the driver through voice prompts, display screens showing the accident location and warning information, and low-frequency short-term vibrations. The on-board navigation system uses the improved Dijkstra algorithm to plan a route to avoid the accident section based on the warning information, guiding the driver to drive safely.

[0052] The above description is only a preferred embodiment of the present invention and does not limit the present invention in any form. Although the present invention has been disclosed as a preferred embodiment as above, it is not used to limit the present invention. Any technical personnel in this field can make some changes or modify the technical contents disclosed above into equivalent embodiments without departing from the scope of the technical solution of the present invention. However, any brief modifications, equivalent changes and modifications made to the above embodiments based on the technical essence of the present invention without departing from the content of the technical solution of the present invention are still within the scope of the technical solution of the present invention.

Claims

1. A method for preventing traffic jams during highway reconstruction and expansion, characterized in that: The specific steps of this method are: S1. Build a multi-dimensional information collection system Traffic flow sensors are installed at intervals of d1 meters on the roadside and gantry infrastructure along the expressway to collect real-time traffic flow data on the expressway, and speed sensors are installed to measure the average speed of vehicles on the expressway. Road condition cameras are installed every d2 kilometers, and construction progress sensors are installed around the construction area to identify the working status of construction equipment and the stacking of construction materials, so as to accurately judge the construction progress and construction scope identification sensors. Construction information entry terminals are also installed, and construction management personnel update the estimated construction duration information in real time, so as to collect all-round information on the expressway's traffic flow, vehicle speed, road conditions and construction-related information; S2. Establish an intelligent information transmission network Using converged communication technology, road infrastructure sensors will send collected information to the traffic management center in a specific format through the communication protocol P at a transmission rate of v1Mbps, and the vehicle-mounted terminal equipment will upload the vehicle location, speed, and driving direction information to the traffic management center at a frequency of f1Hz; S3. Traffic congestion prediction algorithm In the traffic management center server, a traffic congestion prediction model is constructed. Assume that the traffic flow is F, the vehicle speed is V, the time series is t, the historical traffic congestion coefficient is α, the current road construction impact factor is β, and the road capacity correction coefficient is Y. The congestion index C is predicted by the following formula: Where C represents the congestion index, α is the historical traffic congestion coefficient, F(t) is the actual traffic flow of the current highway collected at time t in the time series, and Fmax(t) is the historical maximum traffic flow during this period; β is the influencing factor of the current road construction, Λ(t) represents the average speed of vehicles on the highway at time t in the time series, Vmax(t) is the highest average speed in history during this period, γ is the road capacity correction coefficient, L(t) represents the number of lanes occupied by the current construction, and Ltotal(t) represents the total number of lanes in the road section. is the influence weight of the i-th bad road condition point. When C exceeds the set threshold Ca, it is determined that traffic congestion occurs on this road section; S4. Implement accurate early warning and intelligent guidance When it is predicted that a certain road section may be congested or the construction information changes, the traffic management center sends warning information to vehicles within d3 kilometers around the predicted congestion or construction area through the vehicle-road cooperative system. The on-board terminal equipment reminds the driver through voice prompts, display screens and vibrations. The on-board navigation system uses the improved Dijkstra algorithm to re-plan the route based on the congestion index, road speed limit and real-time road conditions to guide the driver to avoid congestion and construction areas. In addition, dynamic traffic control is implemented in different construction stages of the highway reconstruction and expansion. In the construction preparation stage, based on the construction design plan and the estimated construction time, combined with historical traffic data, the traffic flow changes in different time periods and sections are predicted, and traffic diversion plans are formulated in advance. In the basic construction stage, the road structure safety status is evaluated based on the stress and displacement sensor data. When the road bearing capacity decreases or potential safety risks appear, the construction progress is adjusted or the traffic organization plan is optimized. In the pavement construction stage, traffic control measures are adjusted according to different construction processes of milling and paving. In the ancillary facilities construction stage, augmented reality and virtual reality technologies are used to provide virtual traffic guidance signs for drivers.

2. A method for preventing traffic jams during highway reconstruction and expansion according to claim 1, characterized in that: During the road construction phase, when milling operations are being carried out, if the working power of the milling equipment exceeds the set power value P1 or the noise decibel value generated exceeds the noise threshold N1, it is determined that the impact on traffic is large. At this time, the warning information sending range is expanded to vehicles within kilometers around the construction area. At the same time, the construction area and recommended detour routes are highlighted in the vehicle navigation system.

3. A method for traffic safety guidance during highway reconstruction and expansion according to claim 1, characterized in that: The construction progress sensor adopts a sensor based on image recognition technology. It collects and analyzes images of the construction site in real time, and uses a deep learning algorithm to identify the working status of construction equipment and the stacking of construction materials to accurately judge the construction progress. The sensor can identify the different stages of the construction progress and accurately feedback the construction progress in the form of a percentage to the traffic management center.

4. A method for preventing traffic jams during highway reconstruction and expansion according to claim 1, characterized in that: In the above-mentioned converged communication technology, LTE-V technology is preferentially used for information transmission in areas with weak signals. When the 5G signal strength reaches above I1, it automatically switches to the 5G network for high-speed data transmission. This switching mechanism uses fuzzy control algorithm to achieve intelligent switching through real-time monitoring and analysis of signal strength, transmission rate, and packet loss rate parameters.

5. A method for preventing traffic jams during highway reconstruction and expansion according to claim 1, characterized in that: After receiving the warning information, the vehicle terminal device will also remind the driver through vibration. In terms of the congestion index, in addition to the original factors, the difference in the number of vehicles entering and leaving the road section per unit time and the saturation of the road section are included; In terms of road conditions, the road slope and curve curvature are taken into consideration, and the weight of the congestion index is adjusted according to the severity. The warning information is divided into three levels: light, medium and heavy according to the congestion index and road conditions. The vibration mode, voice prompt and display warning icon of the vehicle terminal device are different at different levels. The navigation system also adjusts the route planning strategy accordingly to ensure driving safety and smooth roads. At the same time, the vehicle terminal device will correlate the warning information with the vehicle's driving data for analysis. If it finds that the vehicle is about to enter a congested or construction area, it will remind the driver again t1 in advance to ensure that the driver can respond in time.

6. A method for preventing traffic jams during highway reconstruction and expansion according to claim 1, characterized in that: In the improved Dijkstra algorithm, when determining the road speed limit weight, taking into account the significant difference in the proportion of historical speeding accidents before and during the expansion and reconstruction of the expressway, for the section before the expansion and reconstruction, by collecting the historical accident data of the section during the normal traffic period, the proportion of the number of accidents caused by speeding to the total number of accidents is counted, and this proportion is set as p1. At the same time, the speed limit standard V of the section is obtained. limit1 , then the speed limit weight of this road section before reconstruction and expansion; For the road sections in the reconstruction and expansion stage, the historical accident data during the construction period are collected separately, and the proportion of accidents caused by speeding p2 is analyzed, combined with the temporary speed limit standard V of the road section during the reconstruction and expansion stage. limit2 , calculate the road speed limit weight of the road section during the reconstruction and expansion stage; When the in-vehicle navigation system uses the improved Dijkstra algorithm to plan routes, for different road sections, according to whether they are in the pre-reconstruction or reconstruction stage, the corresponding road speed limit weights are used for route planning, fully considering the speed limit factors in different stages, giving priority to routes with higher speed limits and better safety performance, and improving overall traffic efficiency.

7. A method for preventing traffic jams during highway reconstruction and expansion according to claim 1, characterized in that: A traffic information database is established in the server of the traffic management center. The database not only stores real-time collected traffic information and construction information, but also classifies and stores historical traffic data, including traffic flow, vehicle speed, and congestion data in different time periods, seasons, and weather conditions. Through in-depth mining and analysis of historical data, the parameters in the traffic congestion prediction model can be continuously optimized.

8. A method for preventing traffic jams during highway reconstruction and expansion according to claim 1, characterized in that: When a traffic accident occurs on roads around the construction area, the traffic management center will dynamically adjust the scope and content of the warning information according to the severity and impact range of the accident. If the accident is minor and only affects local lanes, the warning information will focus on reminding vehicles to avoid the accident scene and guide vehicles to change lanes reasonably. If the accident is serious and may cause traffic jams, the warning information will be expanded to vehicles within 4 kilometers around the accident, and drivers are advised to plan alternative routes in advance. At the same time, the traffic management center will work with the traffic police department in real time to obtain accident handling progress information and update the warning content in a timely manner.

9. A method for preventing traffic jams during highway reconstruction and expansion according to claim 1, characterized in that: In addition to collecting road condition information, the road condition camera also has an intelligent analysis function. Through image recognition technology and deep learning algorithms, it can detect obstacles, road damage, and water accumulation on the road in real time. When an obstacle is detected on the road, the road condition camera will immediately transmit the information to the traffic management center. The traffic management center will formulate a corresponding traffic diversion plan based on the location and size of the obstacle and the surrounding traffic flow conditions, and send early warning information to the driver through the vehicle-road cooperative system to guide the vehicle to avoid obstacles and ensure safe and unobstructed roads.

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