Smart city traffic operation and maintenance management system and method

Abstract

The invention provides a smart city traffic operation and maintenance management system and method, and the method comprises the steps: carrying out the analysis and calculation of the congestion degree of a current road based on the real-time collection of various road condition information (including the traffic flow, vehicle speed, occupancy monitoring data, lane number, traffic signal lamp states, variable traffic marking lines and the like of the current road) of the current road; the crowdedness degree of the current road is accurately obtained, and then the number of lanes actually required by the road condition is judged according to the crowdedness degree and the number of lanes of the current road; and finally, determining whether the lane data of the variable traffic marking meets the lane data actually required by the road condition, generating a traffic marking modification instruction according to a judgment result, and adjusting the traffic marking according to the traffic marking modification instruction. Real-time sensing and accurate analysis of traffic infrastructure states and traffic flow changes are achieved, supply and demand balance of lane resources is achieved, road communication efficiency is improved, and congestion is relieved.

Description

Technical Field

[0001] This invention relates to the technical field of smart cities and intelligent operation and maintenance, and particularly to a smart city traffic operation and maintenance management system and method. Background Technology

[0002] Smart cities represent a high level of urban informatization supported by next-generation information technology and operating within the innovative environment of a knowledge society. Their core lies in integrating urban systems and services through technologies such as the Internet of Things (IoT), big data, artificial intelligence (AI), cloud computing, and 5G to improve resource utilization efficiency, optimize urban governance and services, and ultimately enhance people's quality of life. The main application scenarios for smart cities are numerous, including smart transportation, smart healthcare, smart security, smart environmental protection, smart government affairs, and smart energy.

[0003] However, with the continuous expansion of cities and the constant increase in population, cities are facing more and more contradictions in areas such as social production, public services, and living environment. Therefore, the construction of smart cities places higher demands on information technology development. Only by applying technologies such as artificial intelligence, the Internet of Things, and big data to all aspects of the information technology construction, operation, and management of smart cities can we optimize the urban governance system and improve the management level of smart cities.

[0004] Current traffic maintenance technologies largely rely on manual inspections and fixed threshold monitoring, making it difficult to achieve real-time perception and accurate analysis of traffic infrastructure status and traffic flow changes. This results in low maintenance efficiency and difficulty in quickly resolving traffic congestion issues. Therefore, there is a need for a smart city traffic maintenance management system and methodology that deeply integrates multi-source data real-time acquisition with AI technology to achieve real-time perception and accurate analysis of traffic infrastructure status and traffic flow changes. Summary of the Invention

[0005] This paper discloses a smart city traffic operation and maintenance management system and method. Based on multi-source real-time data, with quantitative analysis as the core, and dynamic adjustment of traffic markings as a means, it not only achieves real-time perception and accurate analysis of traffic infrastructure status and traffic flow changes, but also realizes the supply and demand balance of lane resources, improves road communication efficiency, and alleviates congestion.

[0006] Firstly, this disclosure provides a traffic marking execution module, including: The real-time data acquisition module is used to collect real-time data on traffic flow, average vehicle speed, occupancy rate, number of lanes, traffic light status, and variable traffic markings on the current road within a preset time period. The data analysis module is used to input the traffic flow and the average vehicle speed into a preset lane number prediction model, and to perform saturation analysis and calculation on the traffic flow and the average vehicle speed through the preset lane number prediction model to output the actual required number of lanes. The lane data determination module is used to determine whether the data of the actually required lane is consistent with the lane data corresponding to the variable traffic markings of the current road. The traffic marking execution module is used to generate a traffic marking modification instruction when the data of the actual required lane is inconsistent with the lane data corresponding to the variable traffic markings of the current road, and to adjust the traffic markings according to the traffic marking modification instruction.

[0007] In some embodiments, the preset lane quantity prediction model includes a preset straight lane quantity prediction sub-model, a preset left-turn lane quantity prediction sub-model, and a preset right-turn lane quantity prediction sub-model; the data analysis module includes a straight lane data analysis unit, a left-turn lane data analysis unit, and a right-turn lane data analysis unit. The straight lane data analysis unit is used to input the traffic flow and average speed of the straight route into a preset straight lane number prediction sub-model, and to perform straight lane saturation analysis and calculation on the traffic flow and average speed of the straight route through the preset straight lane number prediction sub-model, so as to output the actual required number of straight lanes. The left-turn lane data analysis unit is used to input the traffic flow and average speed of the left-turn route into the preset left-turn lane number prediction sub-model, and to perform left-turn lane saturation analysis and calculation on the traffic flow and average speed of the left-turn route through the preset left-turn lane number prediction sub-model, so as to output the actual required number of left-turn lanes. The right-turn lane data analysis unit is used to input the traffic flow and average speed of the right-turn route into the preset right-turn lane number prediction sub-model, and to perform right-turn lane saturation analysis and calculation on the traffic flow and average speed of the right-turn route through the preset right-turn lane number prediction sub-model, so as to output the actual required number of right-turn lanes.

[0008] In some embodiments, the smart city traffic operation and maintenance management system further includes a traffic light duration calculation module and a traffic light control module: The traffic light duration determination module is used to determine whether the traffic flow is within a preset traffic flow range; The traffic light control module is used to adjust the traffic light duration when the traffic light duration determination module determines that the traffic flow is not within the preset traffic flow range, and to control the traffic light status according to the adjusted traffic light duration.

[0009] In some embodiments, the smart city traffic operation and maintenance management system further includes a vehicle violation identification module: The vehicle violation identification module is used to identify whether the operating vehicle is moving in accordance with the instructions of the variable traffic markings; when it is identified that the operating vehicle is not moving in accordance with the instructions of the variable traffic markings, the module determines the violating vehicle and collects the vehicle information of the violating vehicle.

[0010] In some embodiments, the smart city traffic operation and maintenance management system further includes a data preprocessing module: The data preprocessing module is used to normalize the traffic flow, average vehicle speed, occupancy monitoring data, number of lanes, traffic light status, and variable traffic markings to obtain normalized traffic flow, average vehicle speed, occupancy monitoring data, number of lanes, traffic light status, and variable traffic markings.

[0011] In some embodiments, the smart city traffic operation and maintenance management system further includes a time judgment module and a memory module: The time determination module is used to collect the current time and determine whether the current time is within the preset memory time range; The memory module is used to invoke the traffic marking modification instruction corresponding to the current time when the time judgment module determines that the current time is within the preset memory time range. The traffic marking execution module is also used to adjust the traffic markings according to the traffic marking modification instruction corresponding to the current time.

[0012] In some embodiments, the preset lane number prediction model is: in Let be the predicted number of lanes in the i-direction at time t; Let represent the congestion level of the lane in direction i at time t; Lane saturation factor; Let be the lane saturation of the lane in direction i at time t; The velocity reduction factor for lane i in the i-direction at time t; This refers to the number of lanes.

[0013] Secondly, this disclosure provides a smart city traffic operation and maintenance management method, including: Real-time data collection of traffic flow, average speed, occupancy rate, number of lanes, traffic light status, and variable traffic markings for the current road within a preset time period; The traffic flow and the average vehicle speed are input into a preset lane number prediction model. The preset lane number prediction model is used to perform saturation analysis and calculation on the traffic flow and the average vehicle speed to output the actual required number of lanes. Determine whether the data of the actually required lanes is consistent with the lane data corresponding to the variable traffic markings on the current road; When the data of the actual required lane is inconsistent with the lane data corresponding to the variable traffic markings of the current road, a traffic marking modification instruction is generated, and the traffic markings are adjusted according to the traffic marking modification instruction.

[0014] Thirdly, this disclosure provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor is used to implement the smart city traffic operation and maintenance management system described in any of the above embodiments when running the computer program.

[0015] Fourthly, this disclosure provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the smart city traffic operation and maintenance management system described in any of the above embodiments.

[0016] This disclosure provides a smart city traffic operation and maintenance management system and method. It analyzes and calculates the current road congestion level based on real-time collection of various road condition information (including traffic flow, speed, occupancy monitoring data, number of lanes, traffic light status, and variable traffic markings). This accurately determines the current road congestion level, and then combines the congestion level with the current number of lanes to determine the actual number of lanes required for the road condition. Finally, it determines whether the lane data for variable traffic markings meets the actual lane requirements, generates a traffic marking modification instruction based on the judgment result, and adjusts the traffic markings accordingly. This not only achieves real-time perception and accurate analysis of traffic infrastructure status and traffic flow changes, but also balances lane resource supply and demand, improves road communication efficiency, and alleviates congestion. Attached Figure Description

[0017] The present disclosure will be described in more detail below with reference to embodiments and the accompanying drawings; Figure 1 This is a flowchart illustrating a smart city traffic operation and maintenance management method in one embodiment; Figure 2 This is a schematic diagram of the structure of a smart city traffic operation and maintenance management system in one embodiment; Figure 3 This is a schematic diagram of the internal structure of a computer device in one embodiment.

[0018] In the accompanying drawings, the same parts are referred to by the same reference numerals, and the drawings are not drawn to scale. Detailed Implementation

[0019] To enable those skilled in the art to better understand the technical solutions of this disclosure, and to fully understand and implement the process of how this disclosure applies technical means to solve technical problems and achieve corresponding technical effects, the technical solutions in the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this disclosure, not all embodiments. The embodiments of this disclosure and the various features within them can be combined with each other without conflict, and the resulting technical solutions are all within the protection scope of this disclosure. All other embodiments obtained by those skilled in the art based on the embodiments of this disclosure without creative effort should fall within the protection scope of this disclosure.

[0020] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this disclosure are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this disclosure described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0021] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0022] Example 1 In this embodiment, as Figure 1 As shown, a smart city traffic operation and maintenance management system is provided, which includes: a real-time data acquisition module 111, a data analysis module 112, a lane data judgment module 113, and a traffic marking execution module 114. The real-time data acquisition module 111 is communicatively connected to the data analysis module 112, the lane data judgment module 113, and the traffic marking execution module 114, respectively; the data analysis module 112 is communicatively connected to the lane data judgment module 113 and the traffic marking execution module 114, respectively; and the lane data judgment module 113 is communicatively connected to the traffic marking execution module 114.

[0023] In this embodiment, the real-time acquisition module 111 is used to collect real-time traffic flow, average speed, occupancy monitoring data, number of lanes, traffic light status, and variable traffic markings of the current road within a preset time period. The data analysis module 112 is used to input the traffic flow and average speed into a preset lane number prediction model, and perform saturation analysis calculations on the traffic flow and average speed using the preset lane number prediction model to output the actual required number of lanes. The lane data judgment module 113 is used to determine whether the data of the actual required lanes is consistent with the lane data corresponding to the variable traffic markings of the current road. The traffic marking execution module 114 is used to generate a traffic marking modification instruction when the data of the actual required lanes is inconsistent with the lane data corresponding to the variable traffic markings of the current road, and adjust the traffic markings according to the traffic marking modification instruction.

[0024] Specifically, the current road is equipped with electronic variable traffic markings, LED variable traffic markings, or projected variable traffic markings. Geomagnetic vehicle monitors, millimeter-wave radar, cameras, GIS map data, and lidar are deployed across the road cross-section. The real-time data acquisition module 111 communicates with these devices.

[0025] The real-time acquisition module 111 collects current road traffic flow (including traffic flow on straight routes, left-turn routes, and right-turn routes), average speed, and occupancy monitoring data through a geomagnetic vehicle monitor, millimeter-wave radar, and camera.

[0026] The real-time acquisition module 111 then uses a camera, lidar, and real-time image recognition and spatial fitting of the current road lanes to automatically verify and obtain the number of lanes on the current road. The real-time acquisition module 111 of the smart city traffic operation and maintenance management system establishes communication with the intelligent traffic signal controller of the current road to establish a real-time data interaction link. The traffic signal controller actively reports the status of the traffic lights on the current road according to a preset cycle. The real-time acquisition module 111 then uses a camera to acquire images of the current road and further performs recognition and semantic segmentation on the images of the current road to identify the variable traffic markings of the current lane (including the shape, direction of indication, and lane affiliation of the variable traffic markings).

[0027] To achieve accurate quantification of lane resource supply and demand and avoid blindly adjusting variable lane markings, it is necessary to determine the actual number of lanes required for the current road conditions. Specifically, the data analysis module 112 predicts the actual number of lanes required through a preset lane quantity prediction model. The detailed steps are as follows: Traffic flow and average speed are input into the preset lane quantity prediction model, which distinguishes between straight-ahead, left-turn, and right-turn traffic flow. The preset lane quantity prediction model uses average speed to calculate the theoretical capacity of a single lane, and then calculates the unit lane saturation in each direction based on the theoretical capacity of a single lane, traffic flow, and the current variable lane data, i.e., performs saturation analysis to obtain the lane saturation factor. Further, the lane speed reduction factor is determined, and finally, the lane congestion level is calculated based on the lane saturation factor, lane saturation, and lane speed reduction factor. The formula for calculating lane congestion level is: in, Lane saturation factor; Let be the lane saturation of the lane in direction i at time t; Let be the speed reduction factor of the lane in the i-direction at time t.

[0028] The preset lane number model determines the actual number of lanes needed based on lane congestion levels. In this embodiment, the preset lane number prediction model is: in Let be the predicted number of lanes in the i-direction at time t; Let represent the congestion level of the lane in direction i at time t; Lane saturation factor; Let be the lane saturation of the lane in direction i at time t; The velocity reduction factor for lane i in the i-direction at time t; This refers to the number of lanes.

[0029] After the data analysis module 112 outputs the actual number of lanes required, to ensure that the lanes actually released and allowed to pass on the road match the lane functions and traffic rules indicated by the variable traffic markings, it is necessary to further determine whether the data of the actual required lanes is consistent with the lane data corresponding to the variable traffic markings on the current road. In this implementation, the data analysis module 112 transmits the output number of the actual required lanes to the lane data judgment module 113. The lane data judgment module 113 determines whether the data of the actual required lanes is consistent with the lane data corresponding to the variable traffic markings on the current road. When the data of the actual required lanes is consistent with the lane data corresponding to the variable traffic markings on the current road, it indicates that the current variable traffic markings are the optimal traffic strategy for the current traffic flow. When the data of the actual required lanes is inconsistent with the lane data corresponding to the variable traffic markings on the current road, it indicates that the current variable traffic markings need to be modified to meet the optimal traffic strategy for the current traffic flow. The lane data judgment module 113 transmits the judgment result to the traffic marking execution module 114. When the traffic marking execution module 114 determines that the data of the actual required lane is inconsistent with the lane data corresponding to the variable traffic markings of the current road, it generates a traffic marking modification instruction and adjusts the traffic markings according to the traffic marking modification instruction.

[0030] In this embodiment, the congestion level of the current road is analyzed and calculated based on real-time collection of various road condition information (including traffic flow, speed, occupancy monitoring data, number of lanes, traffic light status, and variable traffic markings). This accurately determines the current road congestion level, and then, by combining the congestion level with the current number of lanes, the actual number of lanes required for the road condition is determined. Finally, it is determined whether the lane data for the variable traffic markings meets the actual lane requirements, and based on the determination result, a traffic marking modification instruction is generated. The traffic markings are then adjusted according to this instruction. This not only achieves real-time perception and accurate analysis of traffic infrastructure status and traffic flow changes, but also achieves a balance between lane resource supply and demand, improves road communication efficiency, and alleviates congestion.

[0031] Specific limitations regarding the smart city traffic operation and maintenance management system can be found in the limitations of the smart city traffic operation and maintenance management methods below, and will not be repeated here. Each unit in the aforementioned smart city traffic operation and maintenance management system can be implemented entirely or partially through software, hardware, or a combination thereof. These units can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the corresponding operations of each unit.

[0032] In one embodiment, the data analysis module 112 includes a straight-ahead lane data analysis unit, a left-turn lane data analysis unit, and a right-turn lane data analysis unit.

[0033] The system includes the following sub-models: a straight-ahead lane data analysis unit, a left-turn lane data analysis unit, and a right-turn lane data analysis unit. The former inputs the traffic flow and average speed of the straight-ahead route into a preset straight-ahead lane quantity prediction sub-model. The latter performs a straight-ahead lane saturation analysis based on these parameters to output the actual required number of straight-ahead lanes.

[0034] In this embodiment, the preset lane number prediction model can be used to simultaneously output the actual required number of straight lanes, left-turn lanes, and right-turn lanes; it can also be used to output the actual required number of straight lanes, left-turn lanes, or right-turn lanes individually. Dividing the data analysis module 112 into a straight lane data analysis unit, a left-turn lane data analysis unit, and a right-turn lane data analysis unit, and using the corresponding data analysis unit to independently complete lane saturation analysis to obtain the actual required number of lanes in different directions, is to improve the system's modularity and enhance its stability and scalability.

[0035] In practical applications, the preset lane quantity prediction model can include a preset sub-model for predicting the number of straight lanes, a preset sub-model for predicting the number of left-turn lanes, and a preset sub-model for predicting the number of right-turn lanes. The straight lane data analysis unit calls the preset straight lane quantity prediction sub-model when calculating the required number of straight lanes; the left-turn lane data analysis unit calls the preset left-turn lane quantity prediction sub-model when calculating the actual required number of left-turn lanes; and the right-turn lane data analysis unit calls the preset right-turn lane quantity prediction sub-model when calculating the actual required number of right-turn lanes.

[0036] The preset sub-model for predicting the number of straight lanes is: in Let be the predicted number of lanes for the straight-ahead lanes at time t; Let be the level of congestion in the straight lane at time t; Lane saturation factor; Let be the lane saturation of the lane in direction i at time t; The velocity reduction factor for lane i in the i-direction at time t; This refers to the number of lanes.

[0037] The preset left-turn lane number prediction sub-model is as follows: in Let be the predicted number of left-turn lanes at time t; Let be the congestion level of the left-turn lane at time t; Lane saturation factor; Let be the lane saturation of the lane in direction i at time t; The velocity reduction factor for lane i in the i-direction at time t; This refers to the number of lanes.

[0038] The preset right-turn lane number prediction sub-model is as follows: in Let be the predicted number of left-turn lanes at time t; Let be the congestion level of the left-turn lane at time t; Lane saturation factor; Let be the lane saturation of the lane in direction i at time t; The velocity reduction factor for lane i in the i-direction at time t; This refers to the number of lanes.

[0039] In one embodiment, the smart city traffic operation and maintenance management system further includes a traffic light duration calculation module and a traffic light control module.

[0040] The traffic light duration determination module is used to determine whether the traffic flow is within a preset traffic flow range. The traffic light control module is used to adjust the traffic light duration when the traffic light duration determination module determines that the traffic flow is not within the preset traffic flow range, and to control the traffic light status according to the adjusted traffic light duration.

[0041] In this embodiment, the traffic light duration determination module determines whether the traffic flow is within a preset traffic flow range. Specifically, it first determines whether the straight-ahead traffic flow is within the preset straight-ahead traffic flow range, then determines whether the left-turning traffic flow is within the preset left-turning traffic flow range, and finally determines whether the right-turning traffic flow is within the preset traffic flow range. When the traffic light duration determination module determines that the traffic flow is within the preset traffic flow range, it means that the current traffic light duration is suitable for the current traffic demand, and the existing traffic light duration parameters can remain unchanged, continuing to control the traffic light state according to the original duration. When the traffic light duration determination module determines that the traffic flow is not within the preset traffic flow range, it means that the current traffic light duration does not match the actual traffic demand; at this time, a traffic light duration adjustment event is triggered. In practical applications, when the traffic light duration determination module determines that the traffic flow is not within the preset traffic flow range, a traffic light duration adjustment event is triggered. The traffic light control module adjusts the traffic light duration according to the traffic light duration adjustment event and controls the traffic light state according to the adjusted traffic light duration.

[0042] In this embodiment, adaptive and closed-loop control of traffic light duration is achieved without manual intervention, and it can cope with different traffic scenarios such as morning peak, evening peak, off-peak, or sudden high traffic volume.

[0043] In one embodiment, the smart city traffic operation and maintenance management system further includes a vehicle violation identification module.

[0044] The vehicle violation identification module is used to identify whether the operating vehicle is moving in accordance with the instructions of the variable traffic markings; when it is identified that the operating vehicle is not moving in accordance with the instructions of the variable traffic markings, the vehicle is identified as violating the rules and the vehicle information of the violating vehicle is collected.

[0045] In this embodiment, the vehicle violation identification module acquires real-time status data of the variable traffic markings (including lane driving direction, lane permitted traffic type, and lane prohibited behavior indication information) to determine the permitted driving trajectory, turning behavior, and driving constraints of vehicles in each lane on the current road. Then, it acquires real-time perception data of vehicles operating within the target road segment using image acquisition equipment, radar monitoring equipment, or vehicle-to-infrastructure communication units deployed on the current road. It performs real-time monitoring, positioning, and continuous tracking of all vehicles operating within the target road segment to obtain the real-time position, speed, heading, trajectory, and lane information of each vehicle, forming a continuous motion dataset for a single vehicle. Finally, it iterates through the continuous motion dataset of a single vehicle, comparing each vehicle's real-time driving behavior, trajectory, and lane with the permitted driving trajectory, turning behavior, and driving constraints of vehicles in the corresponding lane to identify whether the vehicle is moving according to the instructions of the variable traffic markings.

[0046] When a vehicle's driving behavior and trajectory conform to the permitted driving trajectory, turning behavior, and driving constraints of the corresponding lane, the vehicle violation identification module determines that the vehicle is driving in compliance with regulations. When a vehicle's driving behavior and trajectory do not conform to the permitted driving trajectory, turning behavior, and driving constraints of the corresponding lane, the vehicle violation identification module determines that the vehicle is driving illegally and further collects the vehicle information corresponding to the illegal vehicle to provide a data basis for subsequent penalties for the illegal vehicle.

[0047] In this embodiment, by identifying the consistency between the driving behavior of vehicles on the current road and the instructions of variable traffic markings, vehicles that do not follow the markings are accurately identified and their relevant information is collected. On the one hand, this enables automatic monitoring and evidence collection of traffic violations in the variable traffic marking control area, ensuring the control effectiveness of variable traffic markings and maintaining road traffic order and safety. On the other hand, it provides objective and complete data support for traffic law enforcement, traffic control strategy optimization, and traffic operation analysis.

[0048] In one embodiment, the smart city traffic operation and maintenance management system further includes a data preprocessing module.

[0049] The data preprocessing module is used to normalize the traffic flow, average vehicle speed, occupancy monitoring data, number of lanes, traffic light status, and variable traffic markings to obtain normalized traffic flow, average vehicle speed, occupancy monitoring data, number of lanes, traffic light status, and variable traffic markings.

[0050] In this embodiment, traffic flow, average vehicle speed, occupancy monitoring data, number of lanes, traffic light status, and variable traffic markings are normalized. This eliminates differences in the dimensions and numerical ranges of different data, achieves standardization of multi-source traffic data, reduces the complexity of subsequent calculations, improves the accuracy of data analysis and control decisions, enhances the data compatibility, robustness, and scalability of the smart city traffic operation and maintenance management system, and provides standardized and reliable data support for intelligent traffic dynamic control.

[0051] In one embodiment, the smart city traffic operation and maintenance management system further includes a time judgment module and a memory module.

[0052] The time determination module collects the current time and determines whether it falls within a preset memory time range. The memory module, when the time determination module determines that the current time is within the preset memory time range, invokes the traffic marking modification command corresponding to the current time.

[0053] In this embodiment, the smart city traffic operation and maintenance management system stores preset memory time ranges and corresponding variable traffic marking modification instructions for each preset memory time range. The time judgment module obtains the standard current time of the smart city traffic operation and maintenance management system in real time. This current time can be provided by the built-in clock module of the smart city traffic operation and maintenance management system or through a network module. The time judgment module compares the obtained current time with each preset memory time range to determine whether the current time falls within any preset memory time range. If the current time is not within any preset memory time range, it is determined that memory control will not be triggered. If the current time is within any preset memory time range, it is determined that the conditions for triggering memory control are met. The memory module then locks the successfully matched preset memory time range and the corresponding variable traffic marking modification instructions. The memory module transmits the variable traffic marking modification instructions to the traffic marking execution module. The traffic marking execution module adjusts the traffic markings according to the traffic marking modification instructions.

[0054] In this embodiment, by determining whether the current time is within the preset memory time range, and directly calling the corresponding traffic marking modification command during matching, the variable traffic markings can be accurately and automatically switched within the preset time period. This ensures the stable implementation of time-based traffic control strategies, reduces redundant calculations in the smart city traffic operation and maintenance management system, improves the response speed and reliability of variable traffic marking control, and forms a dual control mechanism that combines fixed time-based control with real-time dynamic regulation.

[0055] Example 2 In this embodiment, as Figure 2 As shown, a smart city traffic operation and maintenance management method is provided, including: Step S211: Real-time collection of traffic flow, average vehicle speed, occupancy monitoring data, number of lanes, traffic light status, and variable traffic markings for the current road within a preset time period.

[0056] In this embodiment, the current road is equipped with electronic variable traffic markings, LED variable traffic markings, or projected variable traffic markings. Geomagnetic vehicle monitors, millimeter-wave radar, cameras, GIS map data, and lidar are deployed on the cross-section of the current road.

[0057] The system collects traffic flow data (including traffic flow in straight, left-turn, and right-turn lanes), average speed, and occupancy data for the current road using geomagnetic vehicle monitors, millimeter-wave radar, and cameras. It also automatically verifies and obtains the number of lanes by performing image recognition and spatial fitting on real-time lane views using cameras and lidar. Furthermore, it proactively reports the status of traffic lights at preset intervals. Finally, it acquires images of the current road using cameras and performs further recognition and semantic segmentation on these images to identify variable lane markings (including the shape, direction, and lane affiliation of the variable lane markings).

[0058] Step S212: Input the traffic flow and the average vehicle speed into the preset lane number prediction model, and perform saturation analysis calculation on the traffic flow and the average vehicle speed through the preset lane number prediction model to output the actual required number of lanes.

[0059] Traffic flow and average speed are input into a preset lane quantity prediction model. This model distinguishes between straight-ahead, left-turn, and right-turn traffic flow. It calculates the theoretical capacity of a single lane using average speed, and then calculates the lane saturation in each direction based on the theoretical capacity, traffic flow, and current road variable lane data—a saturation analysis—to obtain the lane saturation factor. Further, it determines the lane speed reduction factor, and finally calculates the lane congestion level based on the lane saturation factor, lane saturation, and lane speed reduction factor. The formula for calculating lane congestion level is: in, Lane saturation factor; Let be the lane saturation of the lane in direction i at time t; Let be the speed reduction factor of the lane in the i-direction at time t.

[0060] The preset lane number model determines the actual number of lanes needed based on lane congestion levels. In this embodiment, the preset lane number prediction model is: in Let be the predicted number of lanes in the i-direction at time t; Let represent the congestion level of the lane in direction i at time t; Lane saturation factor; Let be the lane saturation of the lane in direction i at time t; The velocity reduction factor for lane i in the i-direction at time t; This refers to the number of lanes.

[0061] Step S213: Determine whether the data of the actual required lane is consistent with the lane data corresponding to the variable traffic markings of the current road.

[0062] In this embodiment, when the data of the actual required lane is consistent with the lane data corresponding to the current variable traffic markings, it indicates that the current variable traffic markings are the optimal traffic flow strategy for the current traffic volume; when the data of the actual required lane is inconsistent with the lane data corresponding to the current variable traffic markings, it indicates that the current variable traffic markings need to be modified to meet the optimal traffic flow strategy for the current traffic volume.

[0063] Step S214: When the data of the actual required lane is inconsistent with the lane data corresponding to the variable traffic markings of the current road, a traffic marking modification instruction is generated, and the traffic markings are adjusted according to the traffic marking modification instruction.

[0064] In this embodiment, the congestion level of the current road is analyzed and calculated based on real-time collection of various road condition information (including traffic flow, speed, occupancy monitoring data, number of lanes, traffic light status, and variable traffic markings). This accurately determines the current road congestion level, and then, by combining the congestion level with the current number of lanes, the actual number of lanes required for the road condition is determined. Finally, it is determined whether the lane data for the variable traffic markings meets the actual lane requirements, and based on the determination result, a traffic marking modification instruction is generated. The traffic markings are then adjusted according to this instruction. This not only achieves real-time perception and accurate analysis of traffic infrastructure status and traffic flow changes, but also achieves a balance between lane resource supply and demand, improves road communication efficiency, and alleviates congestion.

[0065] It should be understood that, although Figure 2 The steps in the flowchart are shown sequentially as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order in which these steps are executed, and they can be performed in other orders. Figure 2 At least some of the steps in the process may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least some of the sub-steps or stages of other steps.

[0066] In one embodiment, the preset lane number prediction model includes a preset straight lane number prediction sub-model, a preset left-turn lane number prediction sub-model, and a preset right-turn lane number prediction sub-model.

[0067] The process of calculating the current total number of vehicles based on the traffic flow and the average vehicle speed, calculating the current road congestion level based on the current total number of vehicles and the occupancy monitoring data, and determining the actual number of lanes required for the road condition by combining the congestion level and the number of lanes includes: 1-1) Input the traffic flow of the straight route and the average vehicle speed into the preset straight lane number prediction sub-model, and perform straight lane saturation analysis and calculation on the traffic flow of the straight route and the average vehicle speed through the preset straight lane number prediction sub-model to output the actual required number of straight lanes.

[0068] 1-2) Input the traffic flow of the left-turn route and the average vehicle speed into the preset left-turn lane number prediction sub-model, and perform left-turn lane saturation analysis and calculation on the traffic flow of the left-turn route and the average vehicle speed through the preset left-turn lane number prediction sub-model to output the actual required number of left-turn lanes.

[0069] 1-3) Input the traffic flow of the right-turn route and the average vehicle speed into the preset right-turn lane number prediction sub-model, and perform right-turn lane saturation analysis and calculation on the right-turn route traffic flow and the average vehicle speed through the preset right-turn lane number prediction sub-model to output the actual required number of right-turn lanes.

[0070] In one embodiment, the smart city traffic operation and maintenance management method further includes: 2-1) Determine whether the traffic flow is within the preset traffic flow range.

[0071] 2-2) When the traffic light duration determination module determines that the traffic flow is not within the preset traffic flow range, the traffic light duration is adjusted, and the traffic light status is controlled according to the adjusted traffic light duration.

[0072] In one embodiment, the smart city traffic operation and maintenance management method further includes: identifying whether the operating vehicle is moving in accordance with the instructions of the variable traffic markings; when it is identified that the operating vehicle is not moving in accordance with the instructions of the variable traffic markings, identifying the violating vehicle and collecting the vehicle information of the violating vehicle.

[0073] In one embodiment, the smart city traffic operation and maintenance management method further includes: normalizing the traffic flow, average vehicle speed, occupancy monitoring data, number of lanes, traffic light status, and variable traffic markings to obtain normalized traffic flow, average vehicle speed, occupancy monitoring data, number of lanes, traffic light status, and variable traffic markings.

[0074] In one embodiment, the smart city traffic operation and maintenance management method further includes: 3-1) Collect the current time and determine whether the current time is within the preset memory time range.

[0075] 3-2) When the time determination module determines that the current time is within the preset memory time range, it calls the traffic marking modification instruction corresponding to the current time.

[0076] 3-3) Adjust the traffic markings according to the traffic marking modification instruction corresponding to the current time.

[0077] In one embodiment, the preset lane number prediction model is: in Let be the predicted number of lanes in the i-direction at time t; Let represent the congestion level of the lane in direction i at time t; Lane saturation factor; Let be the lane saturation of the lane in direction i at time t; The velocity reduction factor for lane i in the i-direction at time t; This refers to the number of lanes.

[0078] Example 3 In this embodiment, a computer device is provided. Its internal structure diagram can be shown as follows: Figure 3 As shown, the computer device includes a processor, memory, network interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system and computer programs, and also houses a database for storing all data related to the smart city traffic operation and maintenance management system and method. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The network interface is used to communicate with other computer devices that have deployed application software. When the processor executes the computer program, it implements a smart city traffic operation and maintenance management system and method. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad mounted on the computer device casing, or an external keyboard, touchpad, or mouse.

[0079] Those skilled in the art will understand that Figure 3The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0080] In one embodiment, an electronic device is provided, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the smart city traffic operation and maintenance management system and method described in any of the above embodiments.

[0081] Example 4 In this embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, it implements the smart city traffic operation and maintenance management system and method described in any of the above embodiments.

[0082] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0083] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0084] The above embodiments merely illustrate several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and all such modifications and improvements fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A smart city traffic operation and maintenance management system, characterized in that, include: The real-time data acquisition module is used to collect real-time data on traffic flow, average vehicle speed, occupancy rate, number of lanes, traffic light status, and variable traffic markings on the current road within a preset time period. The data analysis module is used to input the traffic flow and the average vehicle speed into a preset lane number prediction model, and to perform saturation analysis and calculation on the traffic flow and the average vehicle speed through the preset lane number prediction model to output the actual required number of lanes. The lane data determination module is used to determine whether the data of the actually required lane is consistent with the lane data corresponding to the variable traffic markings of the current road. The traffic marking execution module is used to generate a traffic marking modification instruction when the data of the actual required lane is inconsistent with the lane data corresponding to the variable traffic markings of the current road, and to adjust the traffic markings according to the traffic marking modification instruction.

2. The smart city traffic operation and maintenance management system according to claim 1, characterized in that, The preset lane quantity prediction model includes a preset straight lane quantity prediction sub-model, a preset left-turn lane quantity prediction sub-model, and a preset right-turn lane quantity prediction model; the data analysis module includes a straight lane data analysis unit, a left-turn lane data analysis unit, and a right-turn lane data analysis unit. The straight lane data analysis unit is used to input the traffic flow and average speed of the straight route into a preset straight lane number prediction sub-model, and to perform straight lane saturation analysis and calculation on the traffic flow and average speed of the straight route through the preset straight lane number prediction sub-model, so as to output the actual required number of straight lanes. The left-turn lane data analysis unit is used to input the traffic flow and average speed of the left-turn route into the preset left-turn lane number prediction sub-model, and to perform left-turn lane saturation analysis and calculation on the traffic flow and average speed of the left-turn route through the preset left-turn lane number prediction sub-model, so as to output the actual required number of left-turn lanes. The right-turn lane data analysis unit is used to input the traffic flow and average speed of the right-turn route into the preset right-turn lane number prediction sub-model, and to perform right-turn lane saturation analysis and calculation on the traffic flow and average speed of the right-turn route through the preset right-turn lane number prediction sub-model, so as to output the actual required number of right-turn lanes.

3. The smart city traffic operation and maintenance management system according to claim 1, characterized in that, The smart city traffic operation and maintenance management system also includes a traffic light duration calculation module and a traffic light control module: The traffic light duration determination module is used to determine whether the traffic flow is within a preset traffic flow range; The traffic light control module is used to adjust the traffic light duration when the traffic light duration determination module determines that the traffic flow is not within the preset traffic flow range, and to control the traffic light status according to the adjusted traffic light duration.

4. The smart city traffic operation and maintenance management system according to claim 1, characterized in that, The smart city traffic operation and maintenance management system also includes a vehicle violation identification module: The vehicle violation identification module is used to identify whether the operating vehicle is moving in accordance with the instructions of the variable traffic markings; when it is identified that the operating vehicle is not moving in accordance with the instructions of the variable traffic markings, the module determines the violating vehicle and collects the vehicle information of the violating vehicle.

5. The smart city traffic operation and maintenance management system according to claim 1, characterized in that, The smart city traffic operation and maintenance management system also includes a data preprocessing module: The data preprocessing module is used to normalize the traffic flow, average vehicle speed, occupancy monitoring data, number of lanes, traffic light status, and variable traffic markings to obtain normalized traffic flow, average vehicle speed, occupancy monitoring data, number of lanes, traffic light status, and variable traffic markings.

6. The smart city traffic operation and maintenance management system according to any one of claims 1-3, characterized in that, The smart city traffic operation and maintenance management system also includes a time judgment module and a memory module: The time determination module is used to collect the current time and determine whether the current time is within the preset memory time range; The memory module is used to invoke the traffic marking modification instruction corresponding to the current time when the time judgment module determines that the current time is within the preset memory time range. The traffic marking execution module is also used to adjust the traffic markings according to the traffic marking modification instruction corresponding to the current time.

7. The smart city traffic operation and maintenance management system according to any one of claims 2, characterized in that, The preset lane number prediction model is as follows: in Let be the predicted number of lanes in the i-direction at time t; Let represent the congestion level of the lane in direction i at time t; Lane saturation factor; Let be the lane saturation of the lane in direction i at time t; The velocity reduction factor for lane i in the i-direction at time t; This refers to the number of lanes.

8. A smart city traffic operation and maintenance management method, characterized in that, include: Real-time data collection of traffic flow, average speed, occupancy rate, number of lanes, traffic light status, and variable traffic markings for the current road within a preset time period; The traffic flow and the average vehicle speed are input into a preset lane number prediction model. The preset lane number prediction model is used to perform saturation analysis and calculation on the traffic flow and the average vehicle speed to output the actual required number of lanes. Determine whether the data of the actually required lanes is consistent with the lane data corresponding to the variable traffic markings on the current road; When the data of the actual required lane is inconsistent with the lane data corresponding to the variable traffic markings of the current road, a traffic marking modification instruction is generated, and the traffic markings are adjusted according to the traffic marking modification instruction.

9. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, The processor is used to implement the smart city traffic operation and maintenance management system according to any one of claims 1 to 7 when running the computer program.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the smart city traffic operation and maintenance management system as described in any one of claims 1 to 7.

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