Intelligent traffic dispersion system in urban reconstruction construction

Abstract

The invention relates to the technical field of traffic dispersion, and particularly discloses an intelligent traffic dispersion system in urban reconstruction construction, which comprises four core modules: a road image acquisition module which is used for associating a road camera video with a corresponding road section position; the construction condition judgment module is used for identifying a construction area through video frame comparison and obtaining construction type, position and construction period information in combination with a project publicity network; the travel route adjusting module is used for dynamically generating a time-phased detouring strategy according to the construction position, the duration (including the buffer period) and the road occupation proportion; and the electronic map feedback module is used for pushing the bypassing instruction to the navigation map in real time. According to the method, the construction information is automatically identified, the traffic influence is pre-judged, and the bypassing scheme is generated in advance and synchronized to the electronic map, so that a driver can know the road condition in time before going out or during driving, vehicles are effectively shunted, sudden congestion is reduced, and the road traffic efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of traffic diversion, and in particular to an intelligent traffic diversion system in urban reconstruction construction. Background Art

[0002] In the ongoing construction and periodic renovation of urban infrastructure, construction work such as repairing, renovating, or laying various pipelines on urban roads is extremely common. Whenever such projects begin, physical barriers must be erected along the relevant road sections to ensure safety and isolate the construction area. These barriers inevitably occupy some or all of the existing road space, forcing motor vehicles and non-motor vehicles passing through the section to temporarily divert or completely adjust their planned travel routes. However, current construction management practices suffer from a significant drawback: the installation of barriers is often closely synchronized with the actual start of construction activities. This means that barriers are quickly erected and lanes closed only when construction teams arrive and work officially begins. For drivers, timely and effective information channels are often lacking, both during the pre-trip planning phase and before they actually drive near the construction site, to inform them of impending road closures and restrictions. This information lag and suddenness directly lead to vehicles driving into the fenced-off areas without any preparation, which can easily cause instantaneous traffic conflicts, chaos, and bottlenecks. Large numbers of vehicles are forced to scramble lanes, seek alternative routes, or wait in queues within a short period of time and within close proximity, rapidly forming traffic jams around the construction site. This significantly increases vehicle detention time and significantly reduces overall road traffic efficiency. Ultimately, this causes time delays and efficiency losses for the vast number of travelers who rely on road transportation for commuting, errands, and freight transportation, affecting the smooth operation of daily urban operations. Summary of the Invention

[0003] The technical problem to be solved by the present invention is to address the deficiencies in the above-mentioned existing technologies and provide an intelligent traffic diversion system in urban renovation construction. The system obtains the construction location and construction information through a camera, and judges the construction duration based on the construction information. Vehicle detour information is generated based on the construction duration and construction location, and the information is fed back to the city's electronic map, so that the map prompts drivers to take detours, thereby alleviating traffic congestion caused by road construction.

[0004] To solve the above technical problems, the present invention adopts a technical solution: an intelligent traffic diversion system in urban reconstruction construction, comprising: The road image acquisition module obtains road videos taken by cameras on various roads in the city and matches each road with the road video; A construction status judgment module extracts the type of construction project from the road video and obtains construction information from the project public network based on the type of construction project and the corresponding road; a travel route adjustment module, which obtains construction duration and construction location from the construction information and determines vehicle detour information based on the construction duration and construction location; The electronic map feedback module uploads the vehicle detour information to the electronic map, and the electronic map is integrated into the electronic navigation.

[0005] Furthermore, the construction situation determination module extracts the construction project type from the road video, including: Decomposing the road video into individual video frames; Comparing each video frame with an unconstructed road image, and selecting an image having a different road surface from the unconstructed road image as a construction image; Analyze the construction images to obtain the construction project type.

[0006] Furthermore, analyzing the construction image to obtain the construction project type includes: extracting a road surface image from the construction image; Obtaining sub-images at each set position in the road surface image and extracting the color value average of each sub-image; According to the position of each sub-image and the corresponding color value mean, a three-dimensional surface is determined, and an expression of the three-dimensional surface is obtained; Determining geometric features of the three-dimensional curved surface according to an expression of the three-dimensional curved surface, and obtaining a construction project type from a pre-built project type database according to the geometric features of the three-dimensional curved surface; The project type database is used to store various project types and various geometric features corresponding to each project type.

[0007] Furthermore, determining the three-dimensional surface according to the position of each sub-image and the corresponding color value mean includes: Import the position of each sub-image into a two-dimensional coordinate system to obtain the two-dimensional coordinates of each sub-image; Add the color value corresponding to each sub-image to the two-dimensional coordinates of each sub-image to obtain the three-dimensional coordinates of each sub-image; A smooth curved surface is constructed according to the three-dimensional coordinates of each sub-image to obtain the three-dimensional curved surface.

[0008] Furthermore, the expression for obtaining the three-dimensional surface includes: Acquire each point of the three-dimensional surface and establish a point set of the three-dimensional surface; According to the point set of the three-dimensional surface, the expression of the three-dimensional surface is obtained using the spline surface method.

[0009] Furthermore, the travel route adjustment module determines vehicle detour information based on the construction duration and construction location, including: Determine the time period during which vehicles need to detour based on the construction duration; Determining a detour method for vehicles based on the construction location; The time period in which the vehicle needs to detour and the detour method of the vehicle are packaged to obtain the vehicle detour information.

[0010] Furthermore, the determining of the time period during which vehicles need to detour based on the construction duration includes: Obtaining a construction start time from the construction information; Determine a construction time period based on the construction start time and the construction duration; During the construction period, the set collection time is extended before and after to obtain the time period during which the vehicle needs to detour.

[0011] Furthermore, the method of determining the vehicle detour according to the construction location includes: Determine the number of roads occupied by the construction according to the construction location; Obtaining a road occupation ratio based on the number of lanes occupied by the construction and the original number of lanes on the road; Determine the proportion of vehicles that can pass through the road based on the road occupation ratio; A detour method for vehicles is determined according to the ratio of passable vehicles.

[0012] Furthermore, the vehicle detour method is each road time period and the road state corresponding to each road time period.

[0013] Furthermore, the method of determining the detour of vehicles according to the ratio of passable vehicles includes: Determine the number of vehicles that need to detour at each time node according to the ratio of passable vehicles; According to the real-time road video captured by the camera, the number of vehicles that have entered the road at each moment is obtained; When the number of vehicles that have entered the road is greater than the number of vehicles that need to detour, determining the road time period according to the end time of the time node segment and the current time, and determining that the road state corresponding to the road time period is a closed state; When the number of vehicles that have entered the road is less than or equal to the number of vehicles that need to detour, the road time period is determined according to the start time of the time node segment and the current time, and the road state corresponding to the road time period is determined to be an open state.

[0014] Compared with the prior art, the present invention has the following advantages: This invention provides an intelligent traffic management system for urban renovation construction. A road image acquisition module captures real-time road video data from cameras on the city's road network. A construction status assessment module intelligently analyzes the video content. First, the road video is decomposed into frame images, and construction areas are accurately identified by comparing them with historical images of unconstructed roads. The color mean of the road sub-images within the construction images is then extracted, and a three-dimensional surface model is constructed and its geometric features analyzed. This information is then used to match construction project types against a project type database. Accurate construction information (such as construction scope and process requirements) is also obtained from the project public information website. Based on this information, a travel route adjustment module extracts key parameters from the construction information—construction location and duration (including a preset buffer period for completion). By dynamically calculating the relationship between the construction road occupation ratio and road capacity and combining it with real-time traffic flow monitoring data, it intelligently generates time-based, differentiated vehicle detour strategies (for example, triggering road closures when traffic exceeds the limit). An electronic map feedback module synchronizes this detour information to the city's electronic map navigation system in real time, enabling drivers to be informed of construction road closure periods, alternative routes, and traffic recommendations during travel planning and driving. This technology chain realizes the active perception of construction information, accurate prediction of traffic impact, dynamic optimization of detour plans and real-time push of navigation prompts, thereby significantly reducing the probability of vehicles being forced to change routes when approaching construction sites, effectively diverting traffic pressure, alleviating regional traffic congestion caused by sudden road closures, and improving the overall operating efficiency of urban road networks.

[0015] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 This is a schematic diagram of the overall structure of an intelligent traffic diversion system in urban reconstruction construction provided by the present invention. DETAILED DESCRIPTION

[0017] like Figure 1 As shown, the present invention provides an intelligent traffic diversion system for urban reconstruction construction, comprising: The road image acquisition module obtains road videos taken by cameras on various roads in the city and matches each road with the road video; A construction status judgment module extracts the type of construction project from the road video and obtains construction information from the project public network based on the type of construction project and the corresponding road; a travel route adjustment module, which obtains construction duration and construction location from the construction information and determines vehicle detour information based on the construction duration and construction location; The electronic map feedback module uploads the vehicle detour information to the electronic map, and the electronic map is integrated into the electronic navigation.

[0018] The present invention solves the traffic congestion problem caused by delayed road construction information through intelligent means. The following is a detailed description of each module: The road image acquisition module is used to obtain road videos taken by cameras on various roads in the city and to match each road with the road video.

[0019] The road image acquisition module's principles and implementation: It accesses real-time video streams from cameras in the city's road network and uses geocoding technology to associate each camera ID with the coordinates of the road segment it monitors, creating a road-to-video mapping database. For example, camera C001 corresponds to the east-west lane of Renmin South Road (longitude X, latitude Y).

[0020] The road image acquisition module achieves the following effects: accurate positioning of construction sections, providing a spatial benchmark for subsequent analysis, and avoiding position deviations caused by traditional manual reporting.

[0021] The construction situation judgment module is used to extract the type of construction project from the road video, and obtain construction information from the project public network according to the type of construction project and the corresponding road.

[0022] This module is divided into two steps. The first is to extract the construction project type from the road video, and the second is to obtain construction information from the project public network based on the construction project type and the corresponding road.

[0023] The first step is to extract the construction project type from the road video, including: Decomposing the road video into individual video frames; Comparing each video frame with an unconstructed road image, and selecting an image having a different road surface from the unconstructed road image as a construction image; Analyze the construction images to obtain the construction project type.

[0024] This step utilizes construction image recognition technology. The principle is to decompose real-time video at 30 frames per second into image sequences. Images of the same road section before construction (e.g., the road surface condition 24 hours before construction) are retrieved from a historical database for pixel-level comparison. Using the OpenCV image difference algorithm, frames with differences in road color, texture, or object outline exceeding a threshold (e.g., ΔRGB > 50) are selected and marked as construction images. This effectively eliminates interference from weather and lighting, accurately capturing features such as fences and construction machinery, and achieving recognition accuracy exceeding 95%.

[0025] The step of analyzing the construction image to obtain the construction project type includes: extracting a road surface image from the construction image; Obtaining sub-images at each set position in the road surface image and extracting the color value average of each sub-image; According to the position of each sub-image and the corresponding color value mean, a three-dimensional surface is determined, and an expression of the three-dimensional surface is obtained; Determining geometric features of the three-dimensional curved surface according to an expression of the three-dimensional curved surface, and obtaining a construction project type from a pre-built project type database according to the geometric features of the three-dimensional curved surface; The project type database is used to store various project types and various geometric features corresponding to each project type.

[0026] The locations are determined based on the project type, typically fixed points within the road surface image, the midpoint, and several fixed locations within the image. The color mean of each sub-image is the mean of the color values ​​of each pixel within the sub-image, and the sub-image size is set. This invention uses geometric feature abstraction to overcome issues such as lighting changes and occlusion in construction scenes, achieving project type recognition accuracy exceeding 90%.

[0027] The step of determining the three-dimensional surface according to the position of each sub-image and the corresponding color value mean includes: Import the position of each sub-image into a two-dimensional coordinate system to obtain the two-dimensional coordinates of each sub-image; Add the color value corresponding to each sub-image to the two-dimensional coordinates of each sub-image to obtain the three-dimensional coordinates of each sub-image; A smooth curved surface is constructed according to the three-dimensional coordinates of each sub-image to obtain the three-dimensional curved surface.

[0028] This method uses 3D curve modeling. The road surface ROI is captured from the construction image and divided into 10×10 grid sub-images. The average RGB value of each sub-image is extracted (for example, the average value of grid (2,3) is R=120, G=80, B=60). A 3D coordinate point set is generated, using the grid position as the XY coordinate and the RGB value as the Z-axis height.

[0029] The expression for obtaining the three-dimensional surface includes: Acquire each point of the three-dimensional surface and establish a point set of the three-dimensional surface; According to the point set of the three-dimensional surface, the expression of the three-dimensional surface is obtained using the spline surface method.

[0030] The surface expression generation of the present invention adopts a spline surface fitting algorithm and generates a smooth surface function through control point interpolation.

[0031] Finally, the present invention uses geometric feature matching to calculate surface features such as curvature and gradient distribution, and compares them with a database of project types. For example, pipeline laying projects create "long strips of high curvature" due to trench excavation, which matches the database feature label "underground pipeline project."

[0032] The effect achieved by the construction situation judgment module of the present invention is that, through geometric feature abstraction processing, it overcomes problems such as lighting changes and occlusion in the construction scene, and the project type recognition accuracy reaches more than 90%.

[0033] The second step in the construction status assessment module, acquiring construction information, works by combining the identified project type (such as "road milling and resurfacing") with road section information as keywords. This process then crawls the government project announcement website for construction announcements, extracting structured data such as construction start and end times and road occupation extent. This integration of official and authoritative information ensures the accuracy of construction duration and location, preventing misjudgments due to camera field limitations.

[0034] The travel route adjustment module retrieves construction duration and location from the construction information and determines vehicle detour information based on the construction duration and location. The first step, retrieving the construction duration and location from the construction information, is a simple data extraction process. The second step, determining vehicle detour information based on the construction duration and location, is the key to the present invention.

[0035] In the present invention, the travel route adjustment module determines vehicle detour information based on construction duration and construction location, including: Determine the time period during which vehicles need to detour based on the construction duration; Determining a detour method for vehicles based on the construction location; The time period in which the vehicle needs to detour and the detour method of the vehicle are packaged to obtain the vehicle detour information.

[0036] The step of determining the time period during which vehicles need to detour based on the construction duration includes: Obtaining a construction start time from the construction information; Determine a construction time period based on the construction start time and the construction duration; During the construction period, the set collection time is extended before and after to obtain the time period during which the vehicle needs to detour.

[0037] This system calculates the detour time period based on the construction start and end times (e.g., 8:00 AM, June 1st - 6:00 PM, June 5th), adding the buffer periods (default 1 hour each) to generate the actual detour time period (7:00 AM, June 1st - 7:00 PM, June 5th), covering the period when construction equipment enters and leaves the site. This prevents sudden congestion caused by temporary road occupation during the construction preparation period and provides an early warning window.

[0038] Wherein, the method of determining the vehicle detour according to the construction location includes: Determine the number of roads occupied by the construction according to the construction location; Obtaining a road occupation ratio based on the number of lanes occupied by the construction and the original number of lanes on the road; Determine the proportion of vehicles that can pass through the road based on the road occupation ratio; A detour method for vehicles is determined according to the ratio of passable vehicles.

[0039] The vehicle detour method is each road time period and the road state corresponding to each road time period.

[0040] The determining of a vehicle detour method according to the ratio of passable vehicles includes: Determine the number of vehicles that need to detour at each time node according to the ratio of passable vehicles; According to the real-time road video captured by the camera, the number of vehicles that have entered the road at each moment is obtained; When the number of vehicles that have entered the road is greater than the number of vehicles that need to detour, determining the road time period according to the end time of the time node segment and the current time, and determining that the road state corresponding to the road time period is a closed state; When the number of vehicles that have entered the road is less than or equal to the number of vehicles that need to detour, the road time period is determined according to the start time of the time node segment and the current time, and the road state corresponding to the road time period is determined to be an open state.

[0041] The present invention adopts a dynamic decision-making method for detour mode decision-making, ensuring that the road is cleared in real time.

[0042] Calculate the road occupation ratio η:

[0043] If η>50% (e.g. 2 lanes occupied in a 4-lane road), the road section is marked as having a capacity reduction of 50%.

[0044] At the same time, traffic flow monitoring and diversion triggering are carried out. Traffic flow monitoring is a real-time statistics of the number of vehicles entering the construction section. (Counting by video target detection). Diversion trigger is to set the upper limit of vehicles allowed to pass in a certain period of time. .like If the actual number of vehicles exceeds the upper limit of 100 (150), a "road closure" instruction is generated; otherwise, the road remains open. The technical effect achieved is to dynamically adjust detour strategies by combining physical road limitations with real-time traffic flow, avoiding excessive diversion or worsening congestion.

[0045] The electronic map feedback module uploads the vehicle detour information to the electronic map, and combines the electronic map with the electronic navigation.

[0046] The e-map feedback module encodes detour information into an API protocol (e.g., JSON format: {road:"Renmin South Road", status:"Closed", period:"2025-06-01 07:00 to 2025-06-01 10:00"}) and pushes it to e-map service providers like AutoNavi and Baidu. The navigation system then updates traffic conditions accordingly and sends detour alerts to drivers whose planned routes pass through that section of road.

[0047] The electronic map feedback module achieves the effect of synchronizing detour information to user terminals at the minute level, providing warnings 2-4 hours in advance compared to traditional traffic broadcasts, and reducing user detour decision-making time by 70%.

[0048] This invention, through a five-level technical chain of "visual perception → surface modeling → official data fusion → dynamic diversion decision-making → real-time navigation interaction", completely solves the two major pain points of "lagging construction information" and "passive vehicle diversion" in existing technologies: Information lag: An early warning is generated 1-2 hours before construction starts through camera recognition and official data verification, which is earlier than the actual setting of the fence. Sudden traffic congestion: A dynamic detour strategy based on road occupancy ratio and real-time traffic flow allows traffic to be evenly distributed in different time periods, and the congestion index of construction sections is reduced by more than 40%.

[0049] In summary, the present invention provides an intelligent traffic diversion system for urban renovation construction, which obtains the construction location and construction information through a camera, determines the construction duration based on the construction information, and generates vehicle detour information based on the construction duration and construction location, and feeds it back to the city's electronic map, so that the map prompts drivers to take detours, thereby alleviating traffic congestion caused by road construction.

[0050] The above description is only a preferred embodiment of the present invention and does not limit the present invention in any way. Any simple modification, change and equivalent structural change made to the above embodiment based on the technical essence of the present invention shall still fall within the scope of protection of the technical solution of the present invention.

Claims

1. An intelligent traffic diversion system in urban reconstruction construction, characterized by: include: The road image acquisition module obtains road videos taken by cameras on various roads in the city and matches each road with the road video; A construction status judgment module extracts the type of construction project from the road video and obtains construction information from the project public network based on the type of construction project and the corresponding road; a travel route adjustment module, which obtains construction duration and construction location from the construction information and determines vehicle detour information based on the construction duration and construction location; The electronic map feedback module uploads the vehicle detour information to the electronic map, and the electronic map is integrated into the electronic navigation.

2. The intelligent traffic diversion system in urban reconstruction construction according to claim 1 is characterized in that: The construction situation judgment module extracts the construction project type from the road video, including: Decomposing the road video into individual video frames; Comparing each video frame with an unconstructed road image, and selecting an image having a different road surface from the unconstructed road image as a construction image; Analyze the construction images to obtain the construction project type.

3. The intelligent traffic diversion system in urban reconstruction construction according to claim 2 is characterized in that: Analyzing the construction image to obtain the construction project type includes: extracting a road surface image from the construction image; Obtaining sub-images at each set position in the road surface image and extracting the color value average of each sub-image; According to the position of each sub-image and the corresponding color value mean, a three-dimensional surface is determined, and an expression of the three-dimensional surface is obtained; Determining geometric features of the three-dimensional curved surface according to an expression of the three-dimensional curved surface, and obtaining a construction project type from a pre-built project type database according to the geometric features of the three-dimensional curved surface; The project type database is used to store various project types and various geometric features corresponding to each project type.

4. The intelligent traffic diversion system in urban reconstruction construction according to claim 3 is characterized in that: Determining the three-dimensional surface according to the position of each sub-image and the corresponding color value mean includes: Import the position of each sub-image into a two-dimensional coordinate system to obtain the two-dimensional coordinates of each sub-image; Add the color value corresponding to each sub-image to the two-dimensional coordinates of each sub-image to obtain the three-dimensional coordinates of each sub-image; A smooth curved surface is constructed according to the three-dimensional coordinates of each sub-image to obtain the three-dimensional curved surface.

5. The intelligent traffic diversion system in urban reconstruction construction according to claim 4 is characterized in that: The expression for obtaining the three-dimensional surface includes: Acquire each point of the three-dimensional surface and establish a point set of the three-dimensional surface; According to the point set of the three-dimensional surface, the expression of the three-dimensional surface is obtained using the spline surface method.

6. The intelligent traffic diversion system in urban reconstruction construction according to claim 1 is characterized in that: The travel route adjustment module determines vehicle detour information based on the construction duration and construction location, including: Determine the time period during which vehicles need to detour based on the construction duration; Determining a detour method for vehicles based on the construction location; The time period in which the vehicle needs to detour and the detour method of the vehicle are packaged to obtain the vehicle detour information.

7. The intelligent traffic diversion system in urban reconstruction construction according to claim 6 is characterized in that: Determining the time period during which vehicles need to detour based on the construction duration includes: Obtaining a construction start time from the construction information; Determine a construction time period based on the construction start time and the construction duration; During the construction period, the set collection time is extended before and after to obtain the time period during which the vehicle needs to detour.

8. The intelligent traffic diversion system in urban reconstruction construction according to claim 6 is characterized in that: The method of determining the vehicle detour according to the construction location includes: Determine the number of roads occupied by the construction according to the construction location; Obtaining a road occupation ratio based on the number of lanes occupied by the construction and the original number of lanes on the road; Determine the proportion of vehicles that can pass through the road based on the road occupation ratio; A detour method for vehicles is determined according to the ratio of passable vehicles.

9. The intelligent traffic diversion system in urban reconstruction construction according to claim 8 is characterized in that: The vehicle detour method is each road time period and the road state corresponding to each road time period.

10. The intelligent traffic diversion system in urban reconstruction construction according to claim 9 is characterized in that: The determining of a vehicle detour method according to the ratio of passable vehicles includes: Determine the number of vehicles that need to detour at each time node according to the ratio of passable vehicles; According to the real-time road video captured by the camera, the number of vehicles that have entered the road at each moment is obtained; When the number of vehicles that have entered the road is greater than the number of vehicles that need to detour, determining the road time period according to the end time of the time node segment and the current time, and determining that the road state corresponding to the road time period is a closed state; When the number of vehicles that have entered the road is less than or equal to the number of vehicles that need to detour, the road time period is determined according to the start time of the time node segment and the current time, and the road state corresponding to the road time period is determined to be an open state.

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