A method and system for relieving traffic on a congested road by opening an emergency lane

Abstract

This invention discloses a method and system for easing traffic congestion by opening emergency lanes on congested road sections. The method includes: integrating multi-source highway operation data, collecting and calculating key characteristic parameters of traffic operation on target road sections, and constructing a traffic operation status characteristic dataset. Based on historical data, a congestion prediction model is trained, traffic operation thresholds are calculated, congestion types are distinguished, and open road sections are selected, outputting scenario-specific emergency lane opening rules. A multi-department collaborative mechanism is activated, synchronizing data and instructions through multi-entity information interaction, configuring traffic guidance resources, and pushing traffic prompts to guide vehicles to pass through the emergency lane in an orderly manner. Emergency events are detected and lane closure instructions are triggered, activating upstream diversion to ensure rescue channels; the recovery status of the road section is monitored, and the emergency lane function is restored after the criteria are met. During periods of highway congestion, this improves the traffic efficiency and resource utilization of highways.

Description

Technical Field

[0001] This invention belongs to the field of intelligent traffic management technology for highways, and more specifically, relates to a method and system for easing traffic congestion by opening emergency lanes in congested road sections. Background Technology

[0002] With the continuous increase in highway traffic volume, traffic congestion has become increasingly prominent, especially during holidays, peak commuting hours, and inclement weather. The duration and scope of congestion are constantly expanding, not only reducing traffic efficiency and increasing travel costs, but also potentially triggering chain traffic accidents. Emergency lanes, as crucial channels for ensuring the rapid passage of rescue vehicles, while strictly controlled under normal circumstances to ensure emergency response efficiency, fail to fully utilize their traffic diversion potential in severe congestion scenarios, resulting in low utilization of road resources.

[0003] Currently, some areas are attempting to temporarily open emergency lanes to alleviate traffic congestion, but a scientific and systematic implementation framework is lacking. Most decisions to open lanes rely on manual judgment or simple traffic flow statistics, making it difficult to accurately match congestion types, road conditions, and traffic demand. This easily leads to problems such as inappropriate timing of openings and unreasonable selection of road sections. Furthermore, the multi-departmental coordination mechanism is imperfect, with poor information sharing between traffic management, road maintenance, and emergency management departments. This results in a lack of coordination in traffic guidance, law enforcement, and emergency preparedness, not only affecting the effectiveness of traffic management but also potentially causing safety hazards due to chaotic control.

[0004] Furthermore, the lack of a dynamic response mechanism for switching between opening and closing emergency lanes makes it difficult to quickly adapt to sudden emergencies. When emergency lanes are opened for traffic management, if emergencies such as traffic accidents or medical rescues occur, the inability to close the lanes and activate diversion plans in a timely manner may delay rescue efforts. The existing model fails to effectively balance the traffic management function of emergency lanes with their core mission of emergency support, resulting in their limited effectiveness in alleviating congestion and ensuring emergency response. Therefore, it is urgent to develop a scientific and efficient method for opening and managing emergency lanes on congested road sections to address issues such as inaccurate decision-making regarding opening lanes, poor inter-departmental coordination, and untimely function switching, thereby improving road resource utilization and traffic efficiency while ensuring that the core support function of emergency lanes remains unaffected. Summary of the Invention

[0005] This invention aims to address issues such as low utilization rate of emergency lanes during highway congestion periods, inaccurate decision-making regarding their opening, poor inter-departmental coordination, and untimely function switching. By scientifically assessing congestion conditions, formulating scenario-specific opening rules, establishing collaborative interaction mechanisms, and dynamically responding to emergency needs, it achieves a reasonable balance between the emergency lane's traffic management and emergency support functions, thereby improving road traffic efficiency and resource utilization, and ensuring safe and orderly traffic operations.

[0006] In view of the above-mentioned defects or improvement needs of the prior art, as a first aspect of the present invention, the present invention provides a method for easing traffic congestion by opening emergency lanes in congested road sections, comprising: S1. By integrating relevant highway operation data through multi-source data fusion methods, key characteristic parameters of traffic operation of the target road section are obtained through data collection and calculation, and a traffic operation status characteristic dataset is constructed. S2. Based on historical traffic data, a congestion prediction model is trained and generated, and traffic operation thresholds are dynamically calculated. Different congestion types are distinguished by scene recognition methods, and open road sections are selected by road segment evaluation methods. The rules for opening emergency lanes in different scenarios are output. S3. Activate a multi-departmental collaboration mechanism and achieve data and instruction synchronization through multi-entity information exchange methods; configure traffic guidance resources and push traffic prompts, and use preset control methods to guide vehicles to pass through the emergency lane in an orderly manner; S4. Detect emergency events using emergency event identification methods and trigger lane closure commands to activate upstream traffic diversion plans to ensure unobstructed access to rescue channels; monitor road segment recovery based on traffic condition assessment methods, and when preset conditions are met, guide vehicles to leave the emergency lane and restore its function.

[0007] Furthermore, the multi-source data fusion method in S1 is specifically as follows: Based on vehicle passage record data collected by the highway toll system Real-time traffic image data collected by the monitoring system Traffic flow trend data output by the traffic flow prediction system Environmental impact data recorded by the meteorological system , The level of weather impact on traffic is represented by a value of 0-1, where 0 indicates no impact and 1 indicates the greatest impact. First, use the target road segment mileage markers. and timestamp To unify the benchmark, a spatiotemporal alignment formula is used to... , , , Mapped to the same spatiotemporal coordinates : , , in, The starting point of the road section is the chainage number. Station number interval For station index, To calculate the start time, For time intervals, For time indexing; Then, through data complementarity verification calculations, image data is used. Vehicle model category set obtained from vehicle contour recognition With passage record data The collection of vehicle categories Perform intersection operation To verify the accuracy of the vehicle model data; Combining traffic flow trend data Traffic volume per unit time in real-time traffic data absolute value of the difference If the difference is less than a preset threshold Then retain Otherwise and arithmetic mean Correction ; Using environmental impact data Judgment, when When the threshold is exceeded, it is marked as an abnormal working condition due to severe weather in the traffic operation data; Finally, through feature association extraction, the number of vehicles passing through per unit time is calculated. Average driving speed ,in, Spacetime coordinates The instantaneous speed of the vehicle, The number of vehicles at this coordinate; the length of the vehicle queue. ,in, Spacetime coordinates A segment showing the length of the vehicle queue. Extract the time point of occurrence of abnormal events. , Meets image data The timestamps that identify accident or fault characteristics will be used to identify the accident or fault characteristics. , , , The system integrates and verifies the corrected vehicle models and traffic flow data to construct a traffic operation status feature dataset.

[0008] Furthermore, the key characteristic parameters in S1 include: traffic flow intensity, average traffic speed, congestion spread length, and abnormal event impact coefficient; each parameter is derived through the following mathematical formulas: Based on spatiotemporally aligned multi-source data, traffic flow intensity ,in, Spacetime coordinates Vehicle passage record data at the location. For time intervals, The effective cross-sectional area of ​​the target road segment is the number of sections that can be used for traffic. This parameter reflects the traffic carrying capacity pressure of the road segment per unit time. Average traffic speed ,in, Spacetime coordinates The instantaneous speed of the vehicle. The average speed is calculated by accumulating the instantaneous speed of vehicles with the corresponding number of vehicles, thus intuitively reflecting the traffic efficiency of the road segment. Congestion Spread Length ,in, This refers to the station number index corresponding to the farthest station in the congested area. The mileage marker at the beginning of the congested area corresponds to the mileage marker value. The congestion coverage area is determined by the difference between the mileage markers at both ends of the congested area. Abnormal event impact coefficient ,in, The duration of the abnormal event. The point in time when the abnormal event occurred. This parameter represents the total number of vehicles in the affected area when the abnormal event occurs. It quantifies the degree of disruption the abnormal event caused to traffic operations. Indicates the first The first time node The basic values ​​of each traffic characteristic parameter.

[0009] Furthermore, the construction process of the congestion prediction model in S2 is as follows: Based on the constructed traffic operation status feature dataset, traffic flow intensity is extracted from historical data. Average traffic speed Length of congestion spread and the impact coefficient of abnormal events As input features for the model; Using whether a road segment triggers congestion mitigation needs as the output label, a training set and a validation set are defined. The input features are then reconstructed using a sliding time window method to obtain... to , to , to , to The time-series characteristic sequence, where, For the current moment, Using a time step, it can capture the changing trends of traffic operation status; A parameter-free mapping model is constructed based on time-series feature sequences, and the Euclidean distance between the current feature sequence and the feature sequences before the congestion occurred is calculated: , in, These are the characteristic parameters corresponding to the historical congestion events before they occurred. Each corresponds to , , , ;when If the similarity is less than a preset threshold, it is determined that there is a risk of congestion in the current road segment. By combining the critical values ​​of characteristic parameters when congestion occurs in historical data, the collaborative threshold range of each characteristic parameter is determined by taking the intersection operation, thus forming a congestion prediction model.

[0010] Furthermore, the method for identifying congestion types in S2 is specifically as follows: Based on the traffic operation status feature dataset, the current traffic flow intensity is extracted. Average traffic speed Duration of congestion and the impact coefficient of abnormal events ; By combining the characteristic parameter ranges corresponding to different congestion types in historical data, the difference between the current traffic flow intensity and the historical average flow intensity for the same period is calculated using a formula. : , in, This represents the average traffic flow intensity for the same period in history. The difference between the current congestion duration and the historical average congestion duration is calculated using a formula. : , in, This refers to the historical average duration of congestion. when and and At that time, it was determined to be a fixed peak congestion type, among which The allowable threshold for traffic fluctuations, This is the allowable threshold for duration fluctuations; when or or At that time, it was determined to be a sudden type of congestion. in, This is the allowable threshold for traffic fluctuations, meaning that traffic fluctuations within this range are considered normal. This is the allowable threshold for congestion duration fluctuations; congestion duration fluctuations within this range are considered normal fluctuations.

[0011] Furthermore, the process for confirming the open road segment in S2 is as follows: Based on the constructed traffic operation status feature dataset and the traffic operation thresholds output by the congestion prediction model, the basic road parameters of the target road segment are first extracted using the road segment assessment method. Facility configuration parameters ,in, Includes the number of lanes Effectiveness of emergency lanes Straightness of the route , Includes the number of monitoring device coverage points Spacing between emergency stations ; Traffic flow intensity in traffic operation parameters Average traffic speed Traffic operation threshold , Compare: When and When a road segment is determined to be congested, a judgment formula is then used. , , Select road sections that meet the basic requirements, among which, The minimum emergency lane width required for vehicle passage. The minimum number of monitoring points required for real-time monitoring of the entire road section. To ensure the maximum permissible spacing between service stations for rapid emergency response, eligible road sections are marked as candidate open road sections; Based on the congestion type output by the scene recognition method, the open area is determined by formula under a fixed peak congestion scenario: , in, This is the furthest station in the historically congested area. This is the starting point of the historically congested area; In the event of sudden traffic congestion, the open area is determined using a formula: , in, To address the congestion at the core mileage station, For upstream flow stabilization column number and The difference, For downstream flow stabilization, the station number and The difference; Finally, the traffic carrying capacity is verified using a formula: , , in, Traffic flow intensity of candidate open road sections, , These represent the traffic flow intensity of adjacent road sections upstream and downstream, To ensure a smooth flow of traffic, the maximum allowable difference in traffic volume is considered, and road sections that meet the verification criteria are confirmed as the final open road sections.

[0012] Furthermore, the multi-subject information interaction method in S3 is specifically as follows: Based on the emergency lane opening rules, a shared information exchange framework for multiple collaborating entities is established. This framework has functions for information uploading, instruction issuance, and synchronous verification. Each entity uploads information related to the opening rules through the framework, and the uploaded information is associated with a unified identifier. The framework generates and pushes control instructions based on open rules, with key open parameters embedded in the instructions. The synchronization status is determined by the time difference between the verification information and the transmission of the instructions, and a retransmission is triggered if the timeout occurs. The framework enables the sharing of feedback information among various entities, forming a closed-loop interaction, ensuring consistent control actions, supporting traffic guidance and orderly passage, and adapting to the needs of emergency scenario instruction adjustment.

[0013] Furthermore, the traffic state assessment method in S4 is specifically as follows: Based on the constructed traffic operation status feature dataset, the current traffic flow intensity is extracted. Average traffic speed Length of congestion spread and the impact coefficient of abnormal events Combining the scope of open road sections and control enforcement information from multi-stakeholder interactions, the traffic status assessment value is calculated using a formula: , in, This is a comprehensive traffic condition assessment index. Reflecting the current traffic capacity of the road section, Reflects traffic efficiency. Indicating the degree of congestion spread, Quantify the interference of abnormal events; Evaluation value With preset grading threshold , Compare: When If the current situation is deemed to be smooth traffic flow, the existing opening rules and control methods will be maintained; when When the situation is determined to be slow-moving, a guidance command for optimization is triggered; when If the situation is determined to be congested, the emergency lane closure and traffic diversion plan will be activated.

[0014] As a second aspect of the present invention, a traffic management system for opening emergency lanes in congested road sections is also provided, comprising: The data fusion and feature extraction unit is used to integrate highway-related operational data through multi-source data fusion methods, obtain key traffic operation feature parameters of the target road section through data collection and calculation, and construct a traffic operation status feature dataset. The threshold calculation and rule generation unit is used to train and generate a congestion prediction model based on historical traffic data, dynamically calculate traffic operation thresholds, distinguish different congestion types through scene recognition methods, select open road segments by combining road segment evaluation methods, and output scenario-based emergency lane opening rules. The collaborative control and traffic guidance unit is used to activate a multi-departmental collaborative mechanism and achieve data and instruction synchronization through multi-entity information exchange methods; configure traffic guidance resources and push traffic prompts; and guide vehicles to pass through the emergency lane in an orderly manner using preset control methods. The emergency response and lane closure unit is used to detect emergency events through emergency event identification methods and trigger lane closure commands to activate upstream traffic diversion plans to ensure the smooth flow of rescue channels; it monitors the road section recovery status based on traffic condition assessment methods, and guides vehicles to leave the emergency lane and restores the emergency lane function when preset conditions are met.

[0015] As a third aspect of the invention, a computer-readable storage medium is also provided, on which a computer program is stored, which is executed by a processor as described in any one of the inventions, a method for easing traffic congestion by opening an emergency lane on a congested road section.

[0016] In summary, compared with the prior art, the above-described technical solutions conceived by this invention can achieve the following beneficial effects: 1. The present invention provides a method for easing traffic congestion by opening emergency lanes on congested road sections. This method integrates multi-source highway operation data, collects and calculates key characteristic parameters such as traffic flow intensity and average traffic speed of target road sections, and constructs a traffic operation status characteristic dataset. Based on historical data, a congestion prediction model is trained to dynamically calculate traffic operation thresholds. Simultaneously, it distinguishes between fixed peak congestion and sudden congestion types, and selects open road sections based on road conditions and facility configuration, outputting scenario-specific emergency lane opening rules. This technical feature enables accurate assessment of traffic congestion status, ensuring a high degree of compatibility between the road sections, time periods, and congestion types for opening emergency lanes, avoiding resource waste or traffic chaos caused by indiscriminate opening, and providing a scientific basis for subsequent orderly traffic management.

[0017] 2. The traffic management method for opening emergency lanes in congested road sections according to the present invention activates a multi-departmental collaborative mechanism and establishes a multi-entity information interaction framework to achieve real-time synchronization of data and instructions. Based on scenario-specific opening rules, traffic guidance resources are rationally allocated, precise traffic prompts are pushed to vehicles, and standardized management methods are used to guide vehicles to enter and exit the emergency lane in an orderly manner. This technical feature breaks down information barriers between departments, ensures consistency in traffic guidance and law enforcement, effectively improves the traffic efficiency of emergency lanes, and reduces the risk of vehicles illegally occupying or passing through disorderly, ensuring a safe and orderly traffic management process.

[0018] 3. The traffic management method for opening emergency lanes in congested road sections according to the present invention detects sudden emergency events in real time through an emergency event identification method, promptly triggers an emergency lane closure command, and simultaneously activates an upstream traffic diversion plan to ensure unobstructed access for rescue operations. Based on a traffic state assessment method, the method continuously monitors the road section's traffic recovery status. When traffic parameters meet preset recovery conditions, vehicles are guided to leave the emergency lane in an orderly manner, quickly restoring its emergency support function. This technical feature enables flexible switching between the "traffic management" and "emergency support" functions of the emergency lane, relieving traffic pressure during congested periods and quickly reverting to its core support function in the event of a sudden emergency, thus balancing traffic efficiency and emergency needs. Attached Figure Description

[0019] Figure 1 This is a flowchart illustrating a method for clearing congested road sections by opening emergency lanes, as described in an embodiment of the present invention. Figure 2 This is a schematic diagram of a congestion prediction model according to an embodiment of the present invention; Figure 3 This is a system unit diagram of an embodiment of the present invention. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention. Furthermore, the technical features involved in the various embodiments of this invention described below can be combined with each other as long as they do not conflict with each other.

[0021] Example 1 Please refer to Figure 1 This embodiment 1 provides a method for easing traffic congestion by opening emergency lanes in congested road sections, including: S1. By integrating relevant highway operation data through multi-source data fusion methods, key characteristic parameters of traffic operation of the target road section are obtained through data collection and calculation, and a traffic operation status characteristic dataset is constructed. S2. Based on historical traffic data, a congestion prediction model is trained and generated, and traffic operation thresholds are dynamically calculated. Different congestion types are distinguished by scene recognition methods, and open road sections are selected by road segment evaluation methods. The rules for opening emergency lanes in different scenarios are output. S3. Activate a multi-departmental collaboration mechanism and achieve data and instruction synchronization through multi-entity information exchange methods; configure traffic guidance resources and push traffic prompts, and use preset control methods to guide vehicles to pass through the emergency lane in an orderly manner; S4. Detect emergency events using emergency event identification methods and trigger lane closure commands to activate upstream traffic diversion plans to ensure unobstructed access to rescue channels; monitor road segment recovery based on traffic condition assessment methods, and when preset conditions are met, guide vehicles to leave the emergency lane and restore its function.

[0022] This embodiment 1 further elaborates on the above steps.

[0023] (1) Data fusion and feature extraction Given the increasingly prominent problem of highway traffic congestion, in order to achieve the scientific opening and efficient management of emergency lanes, it is first necessary to integrate multi-source operational data to accurately grasp the traffic situation. Specifically, this involves collecting vehicle passage record data through the highway toll system, collecting real-time road condition image data through the monitoring system, obtaining traffic flow trend data through the traffic flow prediction system, and recording environmental impact data (which is used to characterize the level of weather impact on traffic) through the meteorological system.

[0024] Using the mileage markers and timestamps of the target road segment as a unified benchmark, the aforementioned data types are mapped to the same spatiotemporal coordinates. First, the vehicle type category identified from the vehicle outline in the image data is compared and verified with the vehicle type category in the traffic record data to ensure the accuracy of the vehicle type data. Then, the traffic flow trend data and the unit time traffic volume in the real-time traffic condition data are combined. If the difference between the two is within a reasonable range, the real-time traffic volume data is retained; otherwise, it is corrected. At the same time, environmental impact data is used to determine whether there are severe weather or abnormal operating conditions, and these are marked in the traffic operation data.

[0025] Specifically, the source data fusion method is as follows: Based on vehicle passage record data collected by the highway toll system Real-time traffic image data collected by the monitoring system Traffic flow trend data output by the traffic flow prediction system Environmental impact data recorded by the meteorological system , The level of weather impact on traffic is represented by a value of 0-1, where 0 indicates no impact and 1 indicates the greatest impact. First, use the target road segment mileage markers. and timestamp To unify the benchmark, a spatiotemporal alignment formula is used to... , , , Mapped to the same spatiotemporal coordinates : , , in, The starting point of the road section is the chainage number. Station number interval For station index, To calculate the start time, For time intervals, For time indexing; Then, through data complementarity verification calculations, image data is used. Vehicle model category set obtained from vehicle contour recognition With passage record data The collection of vehicle categories Perform intersection operation To verify the accuracy of the vehicle model data; Combining traffic flow trend data Traffic volume per unit time in real-time traffic data absolute value of the difference If the difference is less than a preset threshold Then retain Otherwise and arithmetic mean Correction ; Using environmental impact data Judgment, when When the threshold is exceeded, it is marked as an abnormal working condition due to severe weather in the traffic operation data; Finally, through feature association extraction, the number of vehicles passing through per unit time is calculated. Average driving speed ,in, Spacetime coordinates The instantaneous speed of the vehicle, The number of vehicles at this coordinate; the length of the vehicle queue. ,in, Spacetime coordinates A segment showing the length of the vehicle queue. Extract the time point of occurrence of abnormal events. , Meets image data The timestamps that identify accident or fault characteristics will be used to identify the accident or fault characteristics. , , , The system integrates and verifies the corrected vehicle models and traffic flow data to construct a traffic operation status feature dataset.

[0026] Subsequently, based on this processed data, the number of vehicles passing through per unit time, average driving speed, and vehicle queue length are calculated, and the time points of abnormal events are extracted. These data, along with validated and corrected vehicle type and traffic flow data, are integrated to construct a traffic operation status feature dataset. In this dataset, traffic flow intensity reflects the traffic carrying capacity of a road segment per unit time, average traffic speed intuitively reflects the traffic efficiency of the road segment, congestion spread length clearly defines the congestion coverage area, and the abnormal event impact coefficient quantifies the degree of interference of abnormal events on traffic operation. These key feature parameters provide core data support for subsequent congestion prediction, scene identification, and the formulation of emergency lane opening rules.

[0027] The parameters are derived using the following mathematical formulas: Based on spatiotemporally aligned multi-source data, traffic flow intensity ,in, Spacetime coordinates Vehicle passage record data at the location. For time intervals, The effective cross-sectional area of ​​the target road segment is the number of sections that can be used for traffic. This parameter reflects the traffic carrying capacity pressure of the road segment per unit time. Average traffic speed ,in, Spacetime coordinates The instantaneous speed of the vehicle. The average speed is calculated by accumulating the instantaneous speed of vehicles with the corresponding number of vehicles, thus intuitively reflecting the traffic efficiency of the road segment. Congestion Spread Length ,in, This refers to the station number index corresponding to the farthest station in the congested area. The mileage marker at the beginning of the congested area corresponds to the mileage marker value. The congestion coverage area is determined by the difference between the mileage markers at both ends of the congested area. Abnormal event impact coefficient ,in, The duration of the abnormal event. The point in time when the abnormal event occurred. This parameter represents the total number of vehicles in the affected area when the abnormal event occurs. It quantifies the degree of disruption the abnormal event caused to traffic operations. Indicates the first The first time node The basic values ​​of each traffic characteristic parameter.

[0028] (2) Threshold calculation and rule generation Given the urgent need to address highway congestion, in order to achieve precise opening of emergency lanes, it is necessary to train congestion prediction models based on historical traffic data and formulate opening rules for different scenarios.

[0029] Please refer to Figure 2 First, a congestion prediction model is constructed, and the construction process is as follows: Based on the constructed traffic operation status feature dataset, traffic flow intensity is extracted from historical data. Average traffic speed Length of congestion spread and the impact coefficient of abnormal events As input features for the model; Using whether a road segment triggers congestion mitigation needs as the output label, a training set and a validation set are defined. The input features are then reconstructed using a sliding time window method to obtain... to , to , to , to The time-series characteristic sequence, where, For the current moment, Using a time step, it can capture the changing trends of traffic operation status; A parameter-free mapping model is constructed based on time-series feature sequences, and the Euclidean distance between the current feature sequence and the feature sequences before the congestion occurred is calculated: , in, These are the characteristic parameters corresponding to the historical congestion events before they occurred. Each corresponds to , , , ;when If the similarity is less than a preset threshold, it is determined that there is a risk of congestion in the current road segment. By combining the critical values ​​of characteristic parameters when congestion occurs in historical data, the collaborative threshold range of each characteristic parameter is determined by taking the intersection operation, thus forming a congestion prediction model.

[0030] Next, congestion type identification is performed. Based on the traffic operation status feature dataset, the current traffic flow intensity, average traffic speed, congestion duration, and abnormal event impact coefficient are extracted. The specific method for congestion type identification is as follows: Based on the traffic operation status feature dataset, the current traffic flow intensity is extracted. Average traffic speed Duration of congestion and the impact coefficient of abnormal events ; By combining the characteristic parameter ranges corresponding to different congestion types in historical data, the difference between the current traffic flow intensity and the historical average flow intensity for the same period is calculated using a formula. : , in, This represents the average traffic flow intensity for the same period in history. The difference between the current congestion duration and the historical average congestion duration is calculated using a formula. : , in, This refers to the historical average duration of congestion. when and and At that time, it was determined to be a fixed peak congestion type, among which The allowable threshold for traffic fluctuations, This is the allowable threshold for duration fluctuations; when or or At that time, it was determined to be a sudden type of congestion. in, This is the allowable threshold for traffic fluctuations, meaning that traffic fluctuations within this range are considered normal. This is the allowable threshold for congestion duration fluctuations; congestion duration fluctuations within this range are considered normal fluctuations.

[0031] Finally, the open road sections are selected, and the confirmation process for the open road sections is as follows: Based on the constructed traffic operation status feature dataset and the traffic operation thresholds output by the congestion prediction model, the basic road parameters of the target road segment are first extracted using the road segment assessment method. Facility configuration parameters ,in, Includes the number of lanes Effectiveness of emergency lanes Straightness of the route , Includes the number of monitoring device coverage points Spacing between emergency stations ; Traffic flow intensity in traffic operation parameters Average traffic speed Traffic operation threshold , Compare: When and When a road segment is determined to be congested, a judgment formula is then used. , , Select road sections that meet the basic requirements, among which, The minimum emergency lane width required for vehicle passage. The minimum number of monitoring points required for real-time monitoring of the entire road section. To ensure the maximum permissible spacing between service stations for rapid emergency response, eligible road sections are marked as candidate open road sections; Based on the congestion type output by the scene recognition method, the open area is determined by formula under a fixed peak congestion scenario: , in, This is the furthest station in the historically congested area. This is the starting point of the historically congested area; In the event of sudden traffic congestion, the open area is determined using a formula: , in, To address the congestion at the core mileage station, For upstream flow stabilization column number and The difference, For downstream flow stabilization, the station number and The difference; Finally, the traffic carrying capacity is verified using a formula: , , in, Traffic flow intensity of candidate open road sections, , These represent the traffic flow intensity of adjacent road sections upstream and downstream, To ensure a smooth flow of traffic, the maximum allowable difference in traffic volume is considered, and road sections that meet the verification criteria are confirmed as the final open road sections.

[0032] (3) Collaborative management and traffic guidance To ensure efficient and orderly traffic management after the emergency lane is opened, it is necessary to establish a multi-departmental collaborative mechanism to achieve real-time synchronization of data and instructions and closed-loop management of the entire process.

[0033] At the inter-departmental collaboration level, a "one road, three parties" emergency lane opening coordination mechanism has been established, comprising the highway operation company, highway traffic police, and comprehensive traffic law enforcement. The core responsibilities of each department are clearly defined: the highway operation company utilizes road network monitoring equipment to conduct real-time traffic flow monitoring, accurately capturing traffic flow trends; the highway traffic police implement accident monitoring through road patrols and monitoring systems to quickly identify traffic incidents; and the comprehensive traffic law enforcement is responsible for road asset monitoring, promptly grasping the operational status of road facilities. The three parties summarize their respective monitoring information in the "one road, three parties" comprehensive analysis stage, jointly determining the conditions for opening or closing the emergency lane based on dimensions such as traffic flow, accident situation, and road asset status. If the opening conditions are met, the highway traffic police issue an opening order, and the operation company immediately implements traffic guidance measures, pushing traffic prompts through variable message signs and navigation apps, and deploying traffic guidance signs at key nodes to guide vehicles to enter the emergency lane in an orderly manner. After opening, the "one road, three parties" simultaneously conduct joint patrols, with the highway traffic police strengthening on-site enforcement, the operation company ensuring the integrity of road facilities, and the comprehensive traffic law enforcement maintaining road asset order. If the conditions for closure are triggered, the highway traffic police will issue a closure order, and the operating company will quickly cancel all guidance measures and close the emergency lane entrance. After closure, the "one road, three parties" will continue to work together to dynamically monitor the traffic flow and adjust the regular lane control strategy in a timely manner.

[0034] At the information exchange level, a shared information exchange framework for multiple collaborating entities is built around the emergency lane opening rules, enabling information uploading, instruction issuance, and synchronous verification. Various participating entities, including traffic management, road maintenance, and emergency management, upload information related to the opening rules through this framework. Uploaded information uses a unified identifier to ensure traceability and relevance. The framework generates control instructions based on the opening rules and pushes them synchronously. These instructions embed key opening parameters, ensuring all entities are clearly aware of the execution details. Synchronization status is determined by the time difference between the verification information and the instruction transmission; if a timeout occurs, a retransmission mechanism is triggered to ensure timely information delivery. Simultaneously, the framework enables information sharing among various entities, forming a closed-loop interaction. This ensures consistency in traffic guidance and law enforcement control actions, effectively supporting the implementation of preset control methods for traffic guidance resource allocation, traffic prompts, and orderly vehicle passage through emergency lanes, and is adaptable to the need for instruction adjustments in emergency scenarios.

[0035] At the technical support level, a real-time information sharing platform is built using a 4G intercom system. Combined with the big data analysis capabilities of the "Jiaotong E-Command" intelligent platform, real-time sharing and visual analysis of data such as traffic flow, accident handling, and road property status are achieved. The platform can automatically link historical congestion data with current operating parameters, providing intelligent suggestions on the duration and scope of emergency lane openings for all parties involved, significantly improving decision-making efficiency. Simultaneously, based on this mechanism, a high-traffic tiered management plan and emergency response plan for sudden incidents are jointly developed: multiple emergency lane opening plans are preset for scenarios such as holiday peaks and severe weather, clearly defining a tiered strategy of "segmented opening for mild congestion and full opening for severe congestion"; in the event of a sudden traffic accident, the platform can trigger emergency lane closure and upstream diversion commands with a single click, ensuring unobstructed rescue channels.

[0036] This collaborative mechanism, characterized by "clearly defined responsibilities, scientific analysis, rapid response, and closed-loop coordination," breaks down information barriers through deep integration of departmental collaboration, information exchange, and technical support. It enables highly efficient coordination of the emergency lane throughout the entire process of "decision-execution-control," maximizing its traffic management effectiveness during congestion periods and enabling rapid switching to emergency support mode in case of emergencies, thus providing solid support for the safe and efficient operation of highways.

[0037] (4) Emergency response and lane closure While ensuring the emergency lane's traffic management function, its core mission of emergency response must also be considered. Emergency event identification methods can promptly detect sudden emergencies. Once an emergency such as an accident or medical rescue is identified, the emergency lane closure order is immediately triggered, and upstream traffic diversion plans are simultaneously activated to ensure unobstructed access for rescue operations. Specifically, upstream diversion nodes are accurately determined by combining traffic operation status characteristic datasets and real-time road condition information. At the diversion nodes, diversion prompts are pushed to vehicles through variable message signs, navigation apps, and other channels, guiding vehicles to enter adjacent parallel highways or local roads in advance. Simultaneously, highway police direct traffic at the diversion nodes, and the operating company cooperates by setting up temporary diversion signs to ensure orderly vehicle diversion. This effectively disperses upstream traffic, avoids traffic congestion caused by the closure of the emergency lane, opens a clear lifeline for rescue vehicles, ensures rapid arrival of rescue forces at the scene, and improves emergency response efficiency.

[0038] During the road segment recovery phase, traffic conditions are continuously monitored based on traffic condition assessment methods. Using a traffic operation status feature dataset as a foundation, current traffic flow intensity, average traffic speed, congestion spread length, and abnormal event impact coefficients are extracted. Simultaneously, combined with the scope of the open road segment and control implementation information from multi-stakeholder interaction, a comprehensive traffic condition assessment index is calculated. Specifically, the traffic condition assessment method is as follows: Based on the constructed traffic operation status feature dataset, the current traffic flow intensity is extracted. Average traffic speed Length of congestion spread and the impact coefficient of abnormal events Combining the scope of open road sections and control enforcement information from multi-stakeholder interactions, the traffic status assessment value is calculated using a formula: , in, This is a comprehensive traffic condition assessment index. Reflecting the current traffic capacity of the road section, Reflects traffic efficiency. Indicating the degree of congestion spread, Quantify the interference of abnormal events; This index comprehensively reflects the current traffic capacity, traffic efficiency, congestion spread, and the degree of disruption from abnormal events on the road segment. The evaluation values... With preset grading threshold , Compare: When If the current situation is deemed to be smooth traffic flow, the existing opening rules and control methods will be maintained; when When the situation is determined to be slow-moving, a guidance command for optimization is triggered; when If the situation is determined to be congested, the emergency lane closure and traffic diversion plan will be activated.

[0039] When the road section's traffic restoration status meets the preset conditions, vehicles are guided to leave the emergency lane in an orderly manner, quickly restoring its emergency support function. This allows for flexible switching between the emergency lane's traffic guidance function and its emergency support function, balancing traffic efficiency and emergency needs.

[0040] The method for opening emergency lanes to alleviate traffic congestion in this embodiment has broad application prospects in the field of traffic management. With the continuous increase in highway traffic volume, congestion problems are becoming increasingly prominent. This method, through scientific assessment of congestion status, formulation of scenario-specific opening rules, and the establishment of a multi-departmental collaborative interaction mechanism, can effectively improve road resource utilization and alleviate traffic congestion pressure. It provides traffic management departments with a systematic and efficient congestion mitigation solution, which can be widely applied to congestion management on highways across the country. Especially during peak congestion periods such as holidays and commuting peaks, it can significantly improve road traffic efficiency and enhance the public's travel experience.

[0041] Furthermore, this method also has significant application value in the field of emergency management. Its dynamic response mechanism enables flexible switching between the "traffic management" and "emergency support" functions of emergency lanes. While ensuring effective traffic management during congestion periods, it can quickly close lanes and activate diversion plans during sudden emergencies, creating unobstructed passages for rescue vehicles and facilitating efficient emergency rescue operations. This is of great significance for improving the highway emergency management system and enhancing emergency response capabilities. It is expected to be promoted and applied nationwide in highway emergency management work in the future, providing solid support for road traffic safety and emergency support.

[0042] Example 2 Please refer to Figure 3 This embodiment 2 provides a traffic management system for opening emergency lanes in congested road sections, including: The data fusion and feature extraction unit is used to integrate highway-related operational data through multi-source data fusion methods, obtain key traffic operation feature parameters of the target road section through data collection and calculation, and construct a traffic operation status feature dataset. The threshold calculation and rule generation unit is used to train and generate a congestion prediction model based on historical traffic data, dynamically calculate traffic operation thresholds, distinguish different congestion types through scene recognition methods, select open road segments by combining road segment evaluation methods, and output scenario-based emergency lane opening rules. The collaborative control and traffic guidance unit is used to activate a multi-departmental collaborative mechanism and achieve data and instruction synchronization through multi-entity information exchange methods; configure traffic guidance resources and push traffic prompts; and guide vehicles to pass through the emergency lane in an orderly manner using preset control methods. The emergency response and lane closure unit is used to detect emergency events through emergency event identification methods and trigger lane closure commands to activate upstream traffic diversion plans to ensure the smooth flow of rescue channels; it monitors the road section recovery status based on traffic condition assessment methods, and guides vehicles to leave the emergency lane and restores the emergency lane function when preset conditions are met.

[0043] Example 3 This embodiment 3 also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, can implement any step of a traffic management method for opening emergency lanes on congested road sections.

[0044] The computer-readable storage medium may include various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0045] For a description of the computer-readable storage medium provided in this application, please refer to the above method embodiments; further details will not be repeated here.

[0046] Those skilled in the art will readily understand that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for easing traffic congestion by opening emergency lanes in congested road sections, characterized in that, include: S1. By integrating relevant highway operation data through multi-source data fusion methods, key characteristic parameters of traffic operation of the target road section are obtained through data collection and calculation, and a traffic operation status characteristic dataset is constructed. S2. Based on historical traffic data, a congestion prediction model is trained and generated, and traffic operation thresholds are dynamically calculated. Different congestion types are distinguished by scene recognition methods, and open road sections are selected by road segment evaluation methods. The rules for opening emergency lanes in different scenarios are output. S3. Activate a multi-departmental collaboration mechanism and achieve data and instruction synchronization through multi-entity information exchange methods; configure traffic guidance resources and push traffic prompts, and use preset control methods to guide vehicles to pass through the emergency lane in an orderly manner; S4. Detect emergency events using emergency event identification methods and trigger lane closure commands to activate upstream traffic diversion plans to ensure unobstructed access to rescue channels; monitor road segment recovery based on traffic condition assessment methods, and when preset conditions are met, guide vehicles to leave the emergency lane and restore its function.

2. The method for easing traffic congestion by opening emergency lanes in congested road sections according to claim 1, characterized in that, The multi-source data fusion method in S1 is specifically as follows: Based on vehicle passage record data collected by the highway toll system ; Real-time traffic image data collected by the monitoring system Traffic flow trend data output by the traffic flow prediction system Environmental impact data recorded by the meteorological system , The level of weather impact on traffic is represented by a value of 0-1, where 0 indicates no impact and 1 indicates the greatest impact. First, use the target road segment mileage markers. and timestamp To unify the benchmark, a spatiotemporal alignment formula is used to... , , , Mapped to the same spatiotemporal coordinates : ， ， in, The starting point of the road section is the chainage number. Station number interval For station index, To calculate the start time, For time intervals, For time indexing; Then, through data complementarity verification calculations, image data is used. Vehicle model category set obtained from vehicle contour recognition With passage record data The collection of vehicle categories Perform intersection operation To verify the accuracy of the vehicle model data; Combining traffic flow trend data Traffic volume per unit time in real-time traffic data absolute value of the difference If the difference is less than a preset threshold Then retain Otherwise and arithmetic mean Correction ; Using environmental impact data Judgment, when When the threshold is exceeded, it is marked as an abnormal working condition due to severe weather in the traffic operation data; Finally, through feature association extraction, the number of vehicles passing through per unit time is calculated. Average driving speed ,in, Spacetime coordinates The instantaneous speed of the vehicle, The number of vehicles at this coordinate; the length of the vehicle queue. ,in, Spacetime coordinates A segment showing the length of the vehicle queue. Extract the time point of occurrence of abnormal events. , Meets image data The timestamps that identify accident or fault characteristics will be used to identify the accident or fault characteristics. , , , The system integrates and verifies the corrected vehicle models and traffic flow data to construct a traffic operation status feature dataset.

3. The method for easing traffic congestion by opening emergency lanes in congested road sections according to claim 1, characterized in that, The key characteristic parameters in S1 include: traffic flow intensity, average traffic speed, congestion spread length, and abnormal event impact coefficient; each parameter is derived through the following mathematical formulas: Based on spatiotemporally aligned multi-source data, traffic flow intensity ,in, Spacetime coordinates Vehicle passage record data at the location. For time intervals, The effective cross-sectional area of ​​the target road segment is the number of sections that can be used for traffic. This parameter reflects the traffic carrying capacity pressure of the road segment per unit time. Average traffic speed ,in, Spacetime coordinates The instantaneous speed of the vehicle. The average speed is calculated by accumulating the instantaneous speed of vehicles with the corresponding number of vehicles, thus intuitively reflecting the traffic efficiency of the road segment. Congestion Spread Length ,in, This refers to the station number index corresponding to the farthest station in the congested area. The mileage marker at the beginning of the congested area corresponds to the mileage marker value. The congestion coverage area is determined by the difference between the mileage markers at both ends of the congested area. Abnormal event impact coefficient ,in, The duration of the abnormal event. The point in time when the abnormal event occurred. This parameter represents the total number of vehicles in the affected area when the abnormal event occurs. It quantifies the degree of disruption the abnormal event caused to traffic operations. Indicates the first The first time node The basic values ​​of each traffic characteristic parameter.

4. A method for easing traffic congestion by opening emergency lanes in congested road sections according to claim 1, characterized in that, The construction process of the congestion prediction model in S2 is as follows: Based on the constructed traffic operation status feature dataset, traffic flow intensity is extracted from historical data. Average traffic speed Length of congestion spread and the impact coefficient of abnormal events As input features for the model; Using whether a road segment triggers congestion mitigation needs as the output label, a training set and a validation set are defined. The input features are then reconstructed using a sliding time window method to obtain... to , to , to , to The time-series characteristic sequence, where, For the current moment, Using a time step, it can capture the changing trends of traffic operation status; A parameter-free mapping model is constructed based on time-series feature sequences, and the Euclidean distance between the current feature sequence and the feature sequences before the congestion occurred is calculated: ， in, These are the characteristic parameters corresponding to the historical congestion events before they occurred. Each corresponds to , , , ;when If the similarity is less than a preset threshold, it is determined that there is a risk of congestion in the current road segment. By combining the critical values ​​of characteristic parameters when congestion occurs in historical data, the collaborative threshold range of each characteristic parameter is determined by taking the intersection operation, thus forming a congestion prediction model.

5. A method for easing traffic congestion by opening emergency lanes in congested road sections according to claim 1, characterized in that, The specific method for identifying congestion types in S2 is as follows: Based on the traffic operation status feature dataset, the current traffic flow intensity is extracted. Average traffic speed Duration of congestion and the impact coefficient of abnormal events ; Combine the characteristic parameter ranges corresponding to different congestion types in historical data; The difference between the current flow intensity and the historical average flow intensity for the same period is calculated using a formula. : ， in, This represents the average traffic flow intensity for the same period in history. The difference between the current congestion duration and the historical average congestion duration is calculated using a formula. : ， in, This refers to the historical average duration of congestion. when and and At that time, it was determined to be a fixed peak congestion type, among which The allowable threshold for traffic fluctuations, This is the allowable threshold for duration fluctuations; when or or At that time, it was determined to be a sudden type of congestion. in, This is the allowable threshold for traffic fluctuations, meaning that traffic fluctuations within this range are considered normal. This is the allowable threshold for congestion duration fluctuations; congestion duration fluctuations within this range are considered normal fluctuations.

6. A method for easing traffic congestion by opening emergency lanes in congested road sections according to claim 1, characterized in that, The process for confirming the open road section in S2 is as follows: Based on the constructed traffic operation status feature dataset and the traffic operation thresholds output by the congestion prediction model, the basic road parameters of the target road segment are first extracted using the road segment assessment method. Facility configuration parameters ,in, Includes the number of lanes Effectiveness of emergency lanes Straightness of the route , Includes the number of monitoring device coverage points Spacing between emergency stations ; Traffic flow intensity in traffic operation parameters Average traffic speed Traffic operation threshold , Compare: When and When a road segment is determined to be congested, a judgment formula is then used. , , Select road sections that meet the basic requirements, among which, The minimum emergency lane width required for vehicle passage. The minimum number of monitoring points required for real-time monitoring of the entire road section. To ensure the maximum permissible spacing between service stations for rapid emergency response, eligible road sections are marked as candidate open road sections; Based on the congestion type output by the scene recognition method, the open area is determined by formula under a fixed peak congestion scenario: ， in, This is the furthest station in the historically congested area. This is the starting point of the historically congested area; In the event of sudden traffic congestion, the open area is determined using a formula: ， in, To address the congestion at the core mileage station, For upstream flow stabilization column number and The difference, For downstream flow stabilization, the station number and The difference; Finally, the traffic carrying capacity is verified using a formula: ， ， in, Traffic flow intensity of candidate open road sections, , These represent the traffic flow intensity of adjacent road sections upstream and downstream, To ensure a smooth flow of traffic, the maximum allowable difference in traffic volume is considered, and road sections that meet the verification criteria are confirmed as the final open road sections.

7. A method for easing traffic congestion by opening emergency lanes in congested road sections according to claim 1, characterized in that, The multi-subject information interaction method in S3 is specifically as follows: Based on the emergency lane opening rules, a shared information exchange framework for multiple collaborating entities is established. This framework has functions for information uploading, instruction issuance, and synchronous verification. Each entity uploads information related to the opening rules through the framework, and the uploaded information is associated with a unified identifier. The framework generates and pushes control instructions based on open rules, with key open parameters embedded in the instructions. The synchronization status is determined by the time difference between the verification information and the transmission of the instructions, and a retransmission is triggered if the timeout occurs. The framework enables the sharing of feedback information among various entities, forming a closed-loop interaction, ensuring consistent control actions, supporting traffic guidance and orderly passage, and adapting to the needs of emergency scenario instruction adjustment.

8. A method for easing traffic congestion by opening emergency lanes in congested road sections according to claim 1, characterized in that, The traffic condition assessment method in S4 is specifically as follows: Based on the constructed traffic operation status feature dataset, the current traffic flow intensity is extracted. Average traffic speed Length of congestion spread and the impact coefficient of abnormal events This is combined with the control and enforcement information from the open road sections and the feedback from multiple stakeholders. The traffic condition assessment value is calculated using the following formula: ， in, This is a comprehensive traffic condition assessment index. Reflecting the current traffic capacity of the road section, Reflects traffic efficiency. Indicating the degree of congestion spread, Quantify the interference of abnormal events; Evaluation value With preset grading threshold , Compare: When If the current situation is deemed to be smooth traffic flow, the existing opening rules and control methods will be maintained; when When the situation is determined to be slow-moving, a guidance command for optimization is triggered; when If the situation is determined to be congested, the emergency lane closure and traffic diversion plan will be activated.

9. A traffic management system for opening emergency lanes in congested road sections, characterized in that, include: The data fusion and feature extraction unit is used to integrate highway-related operational data through multi-source data fusion methods, obtain key traffic operation feature parameters of the target road section through data collection and calculation, and construct a traffic operation status feature dataset. The threshold calculation and rule generation unit is used to train and generate a congestion prediction model based on historical traffic data and dynamically calculate traffic operation thresholds. Different types of congestion are distinguished by scene recognition methods, and open road sections are selected by road segment assessment methods to output scenario-based emergency lane opening rules. The collaborative control and traffic guidance unit is used to activate a multi-departmental collaborative mechanism and achieve data and instruction synchronization through multi-entity information exchange methods; configure traffic guidance resources and push traffic prompts; and guide vehicles to pass through the emergency lane in an orderly manner using preset control methods. The emergency response and lane closure unit is used to detect emergency events through emergency event identification methods and trigger lane closure commands to activate upstream traffic diversion plans to ensure unobstructed rescue channels. Based on traffic condition assessment methods, the road section recovery status is monitored, and when preset conditions are met, vehicles are guided to leave the emergency lane, restoring the emergency lane function.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, The computer program is executed by a processor as described in any one of claims 1-8, a method for easing traffic congestion by opening emergency lanes on congested road sections.

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