An emergency control method, system, medium and product for emergency events in the exit direction

Abstract

A method, system, medium, and product for emergency control of exit-direction emergencies, relating to the field of intelligent transportation Internet of Things, include: obtaining event alarm data uploaded by a user terminal, identifying the event location and event type in the event alarm data; obtaining traffic flow data for the exit area corresponding to the event location, and generating event impact results based on the event type and traffic flow data; generating a traffic guidance plan and a signal timing adjustment plan based on the event impact results and traffic flow data; generating navigation guidance for multiple sections surrounding the event location based on the traffic guidance plan, and sending the navigation guidance to the user terminal; and adjusting the traffic signal device in the exit area corresponding to the event location based on the signal timing adjustment plan. Implementing this application can improve the efficiency of traffic congestion relief control.

Description

Technical Field

[0001] The present application relates to the field of intelligent transportation Internet of Things, and in particular to an emergency control method, system, medium and product for emergency events in the exit direction. Background Art

[0002] With the acceleration of urbanization and the continued growth of traffic volume, problems such as traffic congestion and traffic accidents on urban roads have become increasingly prominent, seriously affecting the efficiency and safety of urban transportation. In particular, the ability of traffic signal control systems to quickly respond and effectively adjust during emergencies (such as traffic accidents, natural disasters, and road construction) plays a crucial role in alleviating traffic pressure and ensuring road safety.

[0003] In related technologies, some systems adjust traffic light timing plans through preset event response modes. When a specific traffic event is detected, the system will make fixed adjustments to the traffic lights according to pre-programmed rules to prioritize traffic flow in a specific direction. Generally, flashing yellow lights are used instead of red and green lights on the main lines for warnings, cooperating with on-site staff to divert traffic.

[0004] However, the fixed signal timing in related technologies lacks accurate perception of real-time traffic flow changes, and there are delays in the arrival of staff, resulting in low efficiency in alleviating and controlling traffic congestion. Summary of the Invention

[0005] The present application provides an exit-direction emergency control method, system, medium and product for improving the efficiency of traffic congestion relief control.

[0006] In the first aspect, the present application provides an emergency control method for emergency events in the exit direction, which is applied to a traffic control system. The method includes: obtaining event alarm data uploaded by a user terminal, identifying the event location and event type in the event alarm data; obtaining traffic flow data of the exit area corresponding to the event location, and generating event impact results based on the event type and traffic flow data; generating a traffic guidance plan and a signal timing adjustment plan based on the event impact results and the traffic flow data; generating navigation guidance for multiple sections around the corresponding event location based on the traffic guidance plan, and sending the navigation guidance to the user terminal; adjusting the traffic signal device in the exit area corresponding to the event location according to the signal timing adjustment plan.

[0007] In the above embodiment, the traffic control system obtains event alarm data in real time and identifies location types, combines traffic flow data to generate impact assessments and response plans, and coordinates navigation guidance and signal timing, thereby achieving rapid response and precise control of emergency events; it can dynamically adjust traffic diversion strategies according to actual conditions, avoiding the limitations of traditional fixed models and improving traffic management efficiency and road capacity.

[0008] In combination with some embodiments of the first aspect, in some embodiments, the steps of obtaining event alarm data uploaded by the user terminal and identifying the event location and event type in the event alarm data specifically include: obtaining event alarm data uploaded by the user terminal and identifying the initial location in the event alarm data; cross-validating the event alarm data with the real-time monitoring data of the initial location and the traffic event information provided by a third-party platform to obtain a verification result; determining the credibility score of the event alarm data based on the verification result, and screening out event alarm data with a credibility score higher than a preset credibility threshold as an alarm mark event; and determining the event location and event type corresponding to the alarm mark event based on the real-time monitoring data and traffic event information.

[0009] In the above embodiment, the traffic control system ensures the accuracy and reliability of event alarm data through multi-source data cross-validation and credibility scoring mechanism, combined with real-time monitoring and third-party platform information; effectively reduces the impact of false alarms and improves the pertinence and timeliness of system responses.

[0010] In combination with some embodiments of the first aspect, in some embodiments, the steps of obtaining traffic flow data of the exit area corresponding to the event location and generating event impact results based on the event type and traffic flow data specifically include: obtaining traffic flow data and road characteristics of the exit area corresponding to the event location, inputting the traffic flow data and road characteristics into a traffic status assessment model to obtain an estimated impact range; determining the time of event occurrence based on event alarm data, and determining the corresponding secondary traffic impact effect based on the event type, event location and event occurrence time, combined with historical traffic data of the exit area; and generating event impact results based on the estimated impact range and secondary traffic impact effect.

[0011] In the above embodiment, the traffic control system integrates traffic flow data, road characteristics and historical data, uses an evaluation model to predict the impact range, and combines the secondary traffic contagion effect to achieve a comprehensive assessment of the impact of the event, thereby improving the accuracy and predictability of the warning.

[0012] In combination with some embodiments of the first aspect, in some embodiments, before the step of obtaining event alarm data uploaded by the user terminal and identifying the event location and event type in the event alarm data, the method also includes: obtaining real-time traffic status data of multiple exit areas; determining emergencies based on real-time traffic status data and historical traffic flow data; and sending prompt information including emergencies to the management terminal.

[0013] In the above embodiment, the traffic control system realizes active identification and early warning of emergencies by continuously monitoring the real-time traffic status of multiple exit areas and analyzing and comparing historical data, thereby improving the system's prevention capabilities and management efficiency.

[0014] In combination with some embodiments of the first aspect, in some embodiments, after the step of determining the emergency event based on real-time traffic status data and historical traffic flow data, the method also includes: obtaining the license plate information of the vehicle corresponding to the emergency event; determining the owner information based on the license plate information, and connecting the mobile terminal corresponding to the owner information to the management terminal.

[0015] In the above embodiment, the traffic control system achieves accurate docking and efficient communication in emergency handling by quickly locating the vehicle involved and establishing a direct communication channel with the vehicle owner, thereby improving the efficiency of emergency handling.

[0016] In combination with some embodiments of the first aspect, in some embodiments, after the step of adjusting the traffic signal device of the exit area corresponding to the event location according to the signal timing adjustment plan, the method also includes: obtaining traffic flow adjustment data of the exit area corresponding to the event location; and correcting the signal timing adjustment plan according to the traffic flow adjustment data.

[0017] In the above embodiment, the traffic control system achieves continuous optimization and fine-tuning of the control strategy by acquiring traffic flow adjustment data in real time and dynamically correcting the signal timing plan, thereby ensuring the real-time and effectiveness of traffic control.

[0018] In combination with some embodiments of the first aspect, in some embodiments, after the step of obtaining traffic flow adjustment data of the exit area corresponding to the event location, the method also includes: determining the traffic guidance effect of the exit area based on the traffic flow adjustment data; and generating a traffic diversion record after the processing of the traffic guidance effect characterization event is completed.

[0019] In the above embodiment, the traffic control system establishes a complete event processing process archive through real-time evaluation and recording of traffic guidance effects, provides data support for subsequent optimization, and improves the system's continuous improvement capabilities.

[0020] In a second aspect, an embodiment of the present application provides a traffic control system, which includes: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code includes computer instructions, and the one or more processors call the computer instructions to enable the traffic control system to execute the method described in the first aspect and any possible implementation of the first aspect.

[0021] In a third aspect, an embodiment of the present application provides a computer program product comprising instructions, which, when executed on a traffic control system, enables the traffic control system to execute the method described in the first aspect and any possible implementation of the first aspect.

[0022] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium comprising instructions. When the instructions are executed on a traffic control system, the traffic control system executes the method described in the first aspect and any possible implementation of the first aspect.

[0023] It is understood that the traffic control system provided in the second aspect, the computer program product provided in the third aspect, and the computer storage medium provided in the fourth aspect are all used to execute the methods provided in the embodiments of the present application. Therefore, the beneficial effects that can be achieved can be referenced to the beneficial effects of the corresponding methods and will not be repeated here.

[0024] One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages:

[0025] 1. Due to the adoption of a multi-dimensional dynamic response mechanism based on real-time event alarm data, combined with precise location identification, traffic flow analysis and adaptive control strategies, it is possible to quickly perceive and accurately locate emergency events, and generate the optimal diversion plan based on actual conditions. This effectively solves the problems of delayed response and extensive regulation caused by reliance on preset modes in related technologies, thereby achieving real-time, precise and efficient traffic control, and improving the efficiency of emergency handling and road traffic capacity.

[0026] 2. Due to the adoption of an active early warning mechanism based on multi-region real-time traffic status monitoring and historical data comparison, combined with the emergency identification and management terminal push function, it is possible to promptly detect anomalies and take corresponding measures at the early stage of an incident, effectively solving the problem of passively waiting for alarms and missing the best time to deal with them in related technologies, thereby achieving early warning and rapid response to emergencies, and improving the predictability and initiative of traffic management.

[0027] 3. Due to the adoption of a dynamic optimization mechanism based on real-time traffic flow adjustment data, combined with the continuous correction function of the signal timing plan, it is possible to continuously adjust and optimize the control strategy according to the actual traffic conditions, effectively solving the problem of rigid control schemes in related technologies and their inability to adapt to dynamic changes, thereby achieving refined and adaptable traffic control and ensuring the continued effectiveness of control measures. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 This is a flow chart of an emergency control method for an emergency in the exit direction according to an embodiment of the present application;

[0029] Figure 2 This is another flow chart of the method for emergency control of an emergency in the exit direction according to an embodiment of the present application;

[0030] Figure 3It is a schematic diagram of the structure of a physical device of a traffic control system in an embodiment of the present application. DETAILED DESCRIPTION

[0031] The terms used in the following examples of the present application are only for the purpose of describing specific embodiments and are not intended to limit the present application. As used in the specification of the present application, the singular expressions "a", "an", "above", "the", and "this" are intended to include plural expressions as well, unless the context clearly indicates otherwise. It should also be understood that the term "and / or" used in the present application refers to any or all possible combinations of one or more of the listed items.

[0032] In the following, the terms "first" and "second" are used for descriptive purposes only and should not be understood to imply or suggest relative importance or implicitly indicate the number of the technical features indicated. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the embodiments of this application, unless otherwise specified, "plurality" means two or more.

[0033] For ease of understanding, the application scenarios of the embodiments of the present application are introduced below.

[0034] Congestion often occurs at urban expressway exit ramps due to traffic accidents or vehicle breakdowns. This is especially true during peak hours, when an incident can quickly cause widespread traffic delays. For example, after a traffic accident on the exit ramp of the South Second Ring Road in a certain city, traffic could not be cleared in a timely manner, resulting in widespread congestion on the main road and ramps. Delays lasted over two hours, affecting tens of thousands of commuters. In such situations, how to quickly respond to emergencies and achieve efficient traffic flow control becomes a pressing issue.

[0035] In related technologies, emergency response to exit area emergencies can be achieved by using a preset signal timing scheme and manual on-site guidance. The following describes a scenario in which the related technology's exit direction emergency control method is used.

[0036] In the prior art, when an accident occurs on the South Second Ring Road exit ramp, the system can only activate a pre-set emergency plan: switching the upstream traffic light on the main road to a flashing yellow light while waiting for traffic police to arrive and manually direct traffic. This approach has obvious flaws: the pre-set plan cannot be flexibly adjusted to actual traffic conditions, and the flashing yellow light may cause even more traffic chaos. Furthermore, it takes time for manual traffic controllers to arrive at the scene, missing the optimal time to intervene and causing the congestion to continue to expand.

[0037] The method for controlling exit-direction emergencies in the embodiments of this application, through multi-source data verification, real-time traffic flow analysis, and dynamic optimization control, enables precise and rapid emergency response, not only improving handling efficiency but also preventing secondary congestion. The following describes scenarios in which the method for controlling exit-direction emergencies in this application is used.

[0038] After adopting this application solution, upon receiving an accident alarm, the system immediately verifies the accident location and type through multi-source data verification, while also analyzing the real-time traffic conditions of the surrounding road network. Based on traffic flow characteristics, the system automatically generates an optimal traffic diversion plan: adjusting signal timing at multiple upstream intersections to divert some traffic in advance; sending personalized navigation suggestions to vehicles on surrounding roads to avoid congested areas; and predicting the risk of secondary congestion to adjust the traffic capacity of relevant road sections in advance. This intelligent, collaborative management and control improves traffic diversion efficiency.

[0039] It can be seen that the use of the exit direction emergency control method in the embodiment of the present application can not only achieve rapid response, but also effectively solve the problem of low efficiency of traditional fixed mode handling, thereby realizing intelligent and refined traffic management.

[0040] For ease of understanding, the following describes the process of the method provided by this implementation in combination with the above scenario. Figure 1 , which is a flow chart of the emergency control method for emergency events in the exit direction in an embodiment of the present application.

[0041] S101: Acquire event alarm data uploaded by a user terminal, and identify event locations and event types in the event alarm data.

[0042] Among them, user terminal refers to a mobile device installed with a traffic event alarm application, such as a smartphone, vehicle-mounted device, etc.; event alarm data refers to a data packet uploaded by the user through the terminal containing information such as the time, location, and type of the event, which is used to report traffic events to the traffic control system; event location refers to the specific geographical coordinate location where the traffic event occurred, including latitude and longitude information, road name, kilometer pile number, etc.; event type refers to the specific category of traffic events, such as traffic accidents, vehicle breakdowns, road construction, etc.

[0043] The traffic control system begins this step when it detects that a user terminal has triggered an event alarm. Specifically, the system first receives the event alarm request from the user terminal and parses the initial event information contained in the request. It then verifies the event information by combining real-time data collected by video surveillance, electronic police, and other equipment at the incident site. It also queries third-party platforms (such as traffic police and rescue services) to determine if the relevant alarm has been received. It then verifies the authenticity of the event through multi-source data comparison and analysis. Finally, based on the verification results, it pinpoints the incident location and determines the type of event based on the actual situation at the scene.

[0044] In some embodiments, the acquisition and identification of event alarm data can be achieved in a variety of ways: optionally, the traffic control system receives structured event data uploaded by the user terminal through a dedicated API, extracts the location and type fields in the data packet, combines the location mapping with the geographic information system, and determines the event type through a preset event classification model; optionally, the traffic control system uses a deep learning model to analyze the monitoring video footage, identify abnormal events, extract event features, and cross-validate with user alarm information to ultimately determine the event location and type. It is understandable that event alarm data can also be simple call data, and other data collection and analysis methods can also be used to achieve the acquisition and identification of event information, which is not limited here.

[0045] S102: Obtain traffic flow data of an exit area corresponding to the event location, and generate event impact results based on the event type and traffic flow data.

[0046] Among them, the exit area refers to the traffic section where the incident occurs and its upstream and downstream related sections, including the main road, ramps and surrounding road networks; traffic flow data refers to various data indicators reflecting the regional traffic operation status, including traffic volume, speed, occupancy rate, density, etc.; the event impact result refers to the comprehensive assessment result of the scope, degree and duration of the traffic impact that the incident may cause.

[0047] After identifying the location and type of an incident, the traffic control system immediately begins assessing its impact. Specifically, it first collects real-time traffic flow data using vehicle detectors, video surveillance, and other equipment within the area. It then builds a traffic status assessment model based on static parameters such as road geometry and capacity. It also analyzes historical data for similar incidents to extract impact characteristics. Using deep learning algorithms, it then predicts the spatial and temporal impact of the incident. Finally, it integrates external factors such as weather and time of day to generate a multi-dimensional impact assessment.

[0048] It should be noted that the traffic state assessment model is trained based on historical traffic flow data (traffic volume, speed, density, etc.) and road characteristic data (number of lanes, design speed, etc.). Parameters are optimized by minimizing the error between the predicted and actual states. The model utilizes a deep neural network architecture consisting of a temporal feature extraction layer, a spatial feature extraction layer, and a multi-layer perceptron, capturing feature correlations through an attention mechanism. During use, the model inputs real-time traffic flow data and road parameters, and outputs evaluation indicators such as traffic state level, congestion level, and capacity, as well as short-term evolution trends, to support traffic control decisions. Training criteria include state classification accuracy (≥95%), congestion prediction accuracy (error ≤10%), and evolution trend prediction accuracy (≥90%).

[0049] In some embodiments, event impact assessment can be achieved through a variety of methods: Optionally, based on traffic wave propagation theory, an event impact propagation model can be established, using Monte Carlo simulation to predict the scope and extent of impact under different scenarios, and combining factors such as traffic density and speed distribution to calculate the congestion risk of key road sections. Optionally, spatiotemporal big data analysis methods can be used, combined with a historical case library, to extract the impact patterns of similar events, and machine learning algorithms can be used to predict the evolution of the impact of the current event. It is understood that other mathematical models and algorithms can also be used to achieve accurate assessment of event impact, and this is not limited here.

[0050] It should be noted that the event impact propagation model is trained using a library of historical event cases (including information such as event type, location, impact range, and duration). Parameters are optimized by comparing predicted impact ranges with actual records. The model employs a hybrid architecture that combines physics-driven and data-driven models, including a traffic wave propagation theory module and a machine learning module, incorporating factors such as road network topology, traffic flow characteristics, and external conditions. Basic event information and the current road network state are input, and the model outputs the spatiotemporal evolution of the impact, including diffusion speed, congestion level, and secondary effect predictions. Training criteria include spatial prediction accuracy (≥90%) and temporal duration prediction error (≤15 minutes).

[0051] S103: Generate a traffic guidance plan and a signal timing adjustment plan based on the event impact results and traffic flow data.

[0052] Among them, the traffic guidance plan represents the traffic diversion strategy formulated to alleviate traffic congestion, including diversion routes, detour suggestions, etc.; the signal timing adjustment plan refers to the timing and phase adjustment plan of traffic lights in the affected area; the plan generation process involves solving multi-objective optimization problems.

[0053] After receiving the impact assessment results, the traffic control system begins developing a response plan. Specifically, the system first divides the control area based on the impact results and identifies key control nodes. It then designs a feasible diversion plan based on the regional road network topology. It also optimizes signal timing parameters based on the remaining capacity of each road section. Traffic simulations are then used to verify the feasibility of the plan. Finally, a complete control plan with specific implementation steps is developed.

[0054] In some embodiments, control solutions can be generated through a variety of methods: Optionally, a multi-objective optimization model can be constructed using a heuristic algorithm to determine optimal signal timing and diversion strategies based on objectives such as minimizing overall delays and maximizing service levels on critical sections. Alternatively, a regional traffic coordination control model can be established based on reinforcement learning methods, continuously optimizing control strategies through online learning to achieve intelligent management of complex road networks. It is understood that other intelligent optimization algorithms can also be used to generate control solutions, and this is not limited here.

[0055] It should be noted that the traffic control system utilizes a target optimization approach, integrating constraints such as road capacity, traffic demand distribution, and safety margin within the event-affected area to construct a mathematical optimization model. The model's objective function encompasses multiple dimensions, including minimizing overall delay and maximizing the service level of key sections. First, the current saturation of each road section is determined based on the flow-density relationship, and the remaining capacity is calculated. Traffic flow is then prioritized based on the impact of the event, and prioritized routes are allocated to different traffic flows. A genetic algorithm is then used to determine the optimal route combination, while the Webster method is used to calculate the corresponding signal timing parameters, including cycle length, green-to-signal ratio, and phase difference. Finally, simulations are performed to verify the feasibility of the proposed solution, and parameters are fine-tuned as necessary. For example, if an accident occurs on a ramp, the system prioritizes the remaining capacity of the surrounding road network, selects three to four diversion channels with optimal traffic conditions, and coordinates signal timing adjustments to guide vehicle diversion.

[0056] S104: Generate navigation guidance for multiple sections around the corresponding event location based on the traffic guidance plan, and send the navigation guidance to the user terminal.

[0057] Among them, navigation guidance refers to personalized route recommendation information provided to users, including the optimal detour route, estimated travel time, real-time traffic conditions, etc.; surrounding sections refer to related road sections affected by the event, including upstream confluence sections, downstream diversion sections, etc.; user terminals include in-vehicle navigation devices, mobile phone navigation apps, etc.

[0058] After determining the traffic guidance plan, the traffic control system begins delivering personalized navigation suggestions. Specifically, the system first selects suitable diversion routes based on the vehicle's current location and destination information. It then calculates the real-time traffic conditions for each route, including congestion levels and estimated travel time. It also generates navigation instructions with detailed routes and key tips based on the user's personalized needs, such as preference for highways or surface roads. Finally, the instructions are sent to the relevant user terminals via a push notification mechanism.

[0059] In some embodiments, navigation guidance can be generated and delivered through a variety of methods: Optionally, multi-source traffic data fusion technology, combined with historical travel patterns, can be used to develop differentiated navigation strategies for different vehicle types, ensuring the timeliness of navigation suggestions through real-time traffic updates; Optionally, a collaborative navigation model based on swarm intelligence algorithms can be established to optimize the overall road network load distribution and avoid the formation of new congestion points. It is understood that other intelligent navigation algorithms can also be used to generate personalized guidance, and this is not limited here.

[0060] It should be noted that the traffic control system, based on travel time reliability theory, integrates factors such as the real-time traffic status of road sections, historical travel time distribution, and personalized user needs to construct a precise route planning model. First, using Bayesian estimation methods and combining historical data, the expected travel time and reliability indicators of each road section in the current time period are calculated. Then, using a multi-constrained shortest path algorithm, with the goal of minimizing total travel time, while also taking into account path reliability requirements, several candidate route plans are generated. Next, based on the user's historical trajectory characteristics, their route preferences, such as preference for highways or secondary roads, are extracted and the candidate plans are personalized and ranked. Finally, the recommended route is converted into segmented navigation instructions, including information such as key turning points and estimated travel time. For example, the system will recommend two or three suitable detour routes based on the vehicle's destination and update traffic conditions in real time.

[0061] S105. Adjust the traffic signal device at the exit area corresponding to the event location according to the signal timing adjustment plan.

[0062] Among them, traffic signal devices refer to signal light equipment that controls vehicle traffic, including main road signal lights, ramp signal lights, etc.; signal timing adjustment involves dynamic adjustment of parameters such as cycle, phase, and green-to-signal ratio; the exit area includes all signal-controlled intersections within the impact range of the event.

[0063] After generating a timing plan, the traffic control system immediately implements signal adjustments. Specifically, it first determines the control authority for the affected signal intersections; then, based on priority, it gradually adjusts the signal parameters at each intersection; simultaneously, it monitors the actual control effects of the adjusted parameters; then, based on feedback data, it dynamically optimizes the timing plan; and finally, it achieves coordinated control of regional signal timing while ensuring safety.

[0064] In some embodiments, signal timing adjustment can be achieved through a variety of methods: Optionally, an adaptive control algorithm can be used to dynamically adjust signal cycles and phase ratios based on real-time detection data, continuously optimizing control effectiveness through a feedback mechanism; Optionally, a regional coordinated control model can be established to achieve timing linkage across multiple intersections, improving regional traffic efficiency through signal optimization algorithms. It is understood that other traffic control algorithms can also be used to achieve intelligent signal timing adjustment, and this is not limited here.

[0065] In the above embodiment, efficient emergency response is achieved through real-time data analysis and multi-dimensional collaborative control. In practical applications, this method can dynamically adjust control strategies based on different scenarios, ensuring the targeted and effective response plan. The following supplements the scenarios of this embodiment.

[0066] In one specific application, the system not only implemented basic traffic flow management functions but also demonstrated strong scalability. Upon detecting a ramp incident that could affect a subway station exit, the system automatically coordinated with the subway operations department to adjust passenger flow management plans at the relevant stations. Simultaneously, based on a historical case database, the system anticipated potential chain reactions and deployed emergency resources at key nodes. The system also dynamically optimized control strategies through real-time evaluation of traffic flow management effectiveness, ultimately reducing the estimated traffic flow management time from two hours to 45 minutes, fully demonstrating the advanced nature of this solution.

[0067] After combining the above scenarios, the following is a more detailed description of the process of the method provided by this implementation. Figure 2 , is another flow chart of the emergency control method for emergency events in the exit direction in an embodiment of the present application.

[0068] S201. Acquire real-time traffic status data of multiple exit areas.

[0069] Among them, the exit area refers to the intersection of the city's main roads and expressways and its surrounding areas, including ramps, auxiliary roads and connecting lines; real-time traffic status data refers to various dynamic data reflecting the current traffic operation conditions, including traffic volume, average speed, vehicle density, queue length, etc.; data acquisition equipment includes coil detectors, video surveillance, radar detectors, etc.

[0070] The traffic control system continuously performs this step during daily operation. Specifically, it first collects basic traffic parameters in real time through multiple types of detection equipment distributed at each exit area. It then cleans, verifies, and completes the collected raw data. It also integrates floating vehicle data from a third-party platform. The processed data is then organized and stored according to time and space dimensions. Finally, a real-time data stream is established to ensure continuous updates of traffic status information.

[0071] In some embodiments, the acquisition and processing of real-time traffic status data can be achieved through a variety of methods: optionally, a distributed data acquisition architecture can be adopted to establish a dynamic self-organizing network of data acquisition nodes to achieve intelligent scheduling of acquisition equipment and real-time data transmission, while using data cleaning algorithms to remove outliers and integrating multi-source information through data fusion algorithms; optionally, based on edge computing technology, an intelligent processing unit can be deployed at the data acquisition end to achieve data preprocessing and preliminary analysis, reduce the transmission load through data compression algorithms, and optimize data acquisition efficiency through adaptive sampling strategies. It is understandable that other data acquisition and processing methods can also be used to achieve real-time monitoring of traffic status, which are not limited here.

[0072] S202: Determine an emergency based on real-time traffic status data and historical traffic flow data.

[0073] Among them, historical traffic flow data refers to the traffic operation data collected by the system over a long period of time, including traffic flow characteristics under different time periods, weather conditions, holidays and other conditions; emergencies refer to various events that cause abnormal traffic conditions, including traffic accidents, vehicle failures, traffic congestion, etc.; the determination process involves anomaly detection and event identification.

[0074] The traffic control system continuously performs this step after acquiring real-time traffic status data. Specifically, it first performs a time series analysis on the real-time data to extract the changing characteristics of key traffic parameters. It then compares this data with historical data from the same period to establish a baseline model of normal traffic conditions. It also incorporates external factors such as weather and activity to build a multi-dimensional anomaly discrimination model. It then uses deep learning algorithms to identify abnormal patterns. Finally, based on the severity and duration of the anomaly, it determines whether it constitutes an emergency.

[0075] In some embodiments, emergency event detection and identification can be achieved through a variety of methods: Optionally, a time series prediction model based on a long short-term memory (LSTM) network can be constructed. By comparing the deviation between actual traffic conditions and predicted values, an adaptive threshold can be set for anomaly detection, and multi-dimensional feature vectors can be used for event classification and identification. Optionally, a reasoning mechanism based on a knowledge graph can be used to construct a knowledge base of historical event cases, and event attribution analysis of the current abnormal state can be achieved through similarity matching and causal reasoning. It is understood that other intelligent analysis methods can also be used to achieve automatic identification of emergencies, which are not limited here.

[0076] In some embodiments, the traffic control system will contact the vehicle owner for processing, that is, the traffic control system will obtain the license plate information of the vehicle corresponding to the emergency; determine the owner information based on the license plate information, and connect the mobile terminal corresponding to the owner information to the management terminal.

[0077] Among them, license plate information refers to the unique identification code of the motor vehicle, including license plate number, vehicle type, etc.; owner information refers to the owner information of the vehicle registration, including contact information, identity information, etc.; mobile terminal refers to the communication equipment used by the owner, such as mobile phones, vehicle-mounted equipment, etc.; management terminal refers to the control equipment used by the traffic management department.

[0078] The traffic control system executes this process after identifying the vehicle involved. Specifically, it first captures a clear license plate image using a high-definition camera or electronic police equipment; then it accesses the vehicle management database for license plate recognition and information matching; simultaneously, it queries the vehicle owner's valid contact information; then it attempts to contact the vehicle owner through various channels; and finally, while ensuring information security, it establishes a real-time communication link between the vehicle owner's mobile terminal and the management terminal to facilitate remote negotiation and resolution.

[0079] In some embodiments, vehicle owner contact and communication can be established through a variety of methods: Optionally, an automatic license plate recognition system based on intelligent recognition technology can be constructed, using deep learning algorithms to accurately extract license plate characters, rapidly locating vehicle owner information in conjunction with a vehicle database, and pushing contact requests through encrypted channels. Optionally, a multimodal communication system can be used to establish a contact mechanism that includes multiple methods such as text messages, phone calls, and app push notifications, ensuring communication security through identity authentication, and enabling instant communication between the management terminal and the vehicle owner. It is understood that other communication methods can also be used to establish vehicle owner contact, and this is not limited here.

[0080] S203: Send prompt information including the emergency event to the management terminal.

[0081] Among them, the prompt information represents a detailed description of the emergency event, including key information such as event type, location, time, and impact; the management terminal refers to the control equipment used by the traffic management department, including the workstation of the traffic command center, mobile law enforcement terminal, etc.; the information sending process involves data transmission and display.

[0082] The traffic control system immediately executes this step after confirming an emergency. Specifically, it first integrates the multi-dimensional information of the emergency and generates a standardized event description report. It then prioritizes information push based on the incident level. It also generates differentiated information content for different levels of management. The information is then pushed to the relevant management terminals via secure channels. Finally, the status of information receipt is tracked to ensure that important information is processed promptly.

[0083] In some embodiments, prompt information generation and delivery can be achieved through a variety of methods: Optionally, a multi-level warning mechanism based on event severity can be established, with intelligent text generation technology automatically composing event descriptions, combined with a geographic information system to generate visual displays, and encrypted channels ensuring secure information transmission. Optionally, a distributed message push system can be established to achieve targeted push notifications to management terminals at different levels, ensuring information delivery through a message confirmation mechanism, and supporting real-time information updates and interactive feedback. It is understood that other information processing methods can also be used to achieve intelligent push notifications for event prompts, which are not limited here.

[0084] S204: Acquire event alarm data uploaded by the user terminal, and identify the event location and event type in the event alarm data.

[0085] Referring to step S101 , the traffic control system identifies the event location and event type.

[0086] In some embodiments, the traffic control system will perform alarm verification, that is, the traffic control system will obtain the event alarm data uploaded by the user terminal and identify the initial position in the event alarm data; cross-verify the event alarm data with the real-time monitoring data of the initial position and the traffic event information provided by the third-party platform to obtain a verification result; determine the credibility score of the event alarm data based on the verification result, and screen out event alarm data with a credibility score higher than a preset credibility threshold as an alarm mark event; determine the event location and event type corresponding to the alarm mark event based on the real-time monitoring data and traffic event information.

[0087] Among them, the initial location refers to the geographic location information when the user reports the event; real-time monitoring data refers to information such as video images collected by on-site monitoring equipment; the third-party platform refers to the information system of relevant departments such as traffic police and rescue; the credibility score represents a quantitative assessment value of the authenticity of the event; the preset credibility threshold refers to the minimum score standard for judging the credibility of an event.

[0088] The traffic control system immediately executes this process upon receiving an event alarm. Specifically, it first analyzes the location information contained in the alarm data; then calls upon all monitoring resources surrounding that location; simultaneously queries real-time information from third-party platforms; then integrates this information using a multi-source data fusion algorithm; further calculates a credibility score based on evidence theory; and finally, accurately locates and classifies high-credibility events to form a standardized event description.

[0089] In some embodiments, alarm information verification and processing can be achieved through a variety of methods: Optionally, a multi-source information fusion model based on DS evidence theory can be constructed. By assigning credibility weights to different information sources, a comprehensive credibility score can be calculated, and a spatial clustering algorithm can be used to determine the precise location of the event. Optionally, a multimodal verification system incorporating video analysis and text understanding can be established using deep learning methods. Accurately identify the event type through feature extraction and pattern matching. It is understood that other data analysis methods can also be used to assess the credibility of alarm information, and these are not limited here.

[0090] S205 : Obtain traffic flow data and road characteristics of the exit area corresponding to the event location, input the traffic flow data and road characteristics into a traffic state assessment model, and obtain an estimated impact range.

[0091] Among them, road characteristics refer to the static attribute parameters of the road, including the number of lanes, design speed, and capacity; the traffic state assessment model refers to the mathematical model used to analyze the traffic operation status, including flow-density relationship, wave propagation characteristics, etc.; the estimated impact range is used to represent the spatial range and degree of the possible impact of an event.

[0092] The traffic control system performs this step immediately after determining the incident location. Specifically, it first extracts static feature data of the road network surrounding the incident location; then obtains real-time traffic flow parameters for all detection points in the area; and combines this with topological features such as road grade and interchange type. This data is then fed into a pre-trained evaluation model. Finally, simulation calculations are performed to predict the spatiotemporal distribution of the incident's impact.

[0093] In some embodiments, traffic status assessment and impact range prediction can be achieved through various approaches: Optionally, a cellular automaton-based traffic flow evolution model can be established. By setting different boundary conditions and parameters, the propagation of event disturbances on traffic flow can be simulated, and changes in the level of service at key sections can be calculated. Alternatively, a graph neural network can be used to construct a road network status prediction model, using road sections as nodes and intersections as edges. Using a message passing mechanism, the spatial correlation of traffic flow can be simulated to predict the spread of the event's impact. It is understood that other traffic flow theories and models can also be used to achieve accurate prediction of the impact range, and this is not limited here.

[0094] It should be noted that the traffic control system, based on traffic flow propagation theory, establishes a hybrid assessment model that incorporates both macroscopic and microscopic features. At the macro level, the LWR model is used to describe the relationship between traffic density, speed, and flow rate, and basic flow characteristics are derived through numerical solutions. At the micro level, a cellular automaton model is introduced to characterize the following behavior and lane-changing characteristics of individual vehicles. Model inputs include road geometry (number of lanes, slope, etc.), traffic flow parameters (flow rate, speed, occupancy, etc.), and environmental parameters (weather, road conditions, etc.). A Kalman filter algorithm is used to enable real-time parameter correction, improving prediction accuracy. Model outputs include key indicators such as spatial impact range, congestion level, and propagation speed. For example, if an accident occurs somewhere on a major road, the system can accurately predict the specific extent of the congestion within 10-15 minutes.

[0095] S206: Determine the time of occurrence of the event based on the event alarm data, and determine the corresponding secondary traffic impact based on the event type, event location, and event time, combined with historical traffic data of the exit area.

[0096] Among them, the event time indicates the exact moment when the traffic incident occurred; the secondary traffic impact effect refers to the chain reaction triggered by the initial event, including the spread of congestion in upstream sections and the load transfer of surrounding road networks; historical traffic data contains traffic operation records under similar conditions.

[0097] The traffic control system performs this step after obtaining basic information about the incident. Specifically, it first determines the exact time of the incident through cross-validation of multi-source data; then searches for similar cases in the historical database; and, taking into account the current road network load and available capacity in the surrounding area, develops a model for the impact and propagation of the incident; and finally, simulates and predicts the various chain reactions that the incident may trigger.

[0098] In some embodiments, the prediction of secondary effects can be achieved through various methods: Optionally, a shock wave propagation model based on traffic flow theory can be constructed. By calculating the upstream queue growth rate and downstream relief capacity, combined with the road network topology, the congestion diffusion process can be predicted and the overflow risk at key nodes can be assessed. Optionally, a Bayesian network can be used to establish an event impact causal chain model, and conditional probability distributions can be trained using historical data to achieve probabilistic prediction of chain reactions. It is understood that other prediction methods can also be used to evaluate secondary effects, and these are not limited here.

[0099] It should be noted that the traffic control system utilizes an event impact propagation model based on a causal network. First, a Bayesian network is constructed, encompassing nodes such as the road network topology, traffic demand, and operating status. The conditional probability distribution between nodes is learned using historical data. The network's initial state is then set based on information such as event type, location, and time, and a probabilistic inference algorithm is used to predict the state transition probabilities of each associated road segment. The model specifically focuses on spillover risks at key nodes (such as interchanges and major intersections), validating the evolution of these processes through simulations under various scenarios. Finally, a complete prediction is generated, encompassing factors such as the impact propagation path, impact severity, and duration. For example, the system can predict that congestion on a main road could lead to a 30% increase in ground road traffic volume and adjust signal control strategies accordingly.

[0100] S207. Generate event impact results based on the estimated impact range and secondary traffic spillover effects.

[0101] Among them, the event impact result represents the comprehensive assessment result of the overall impact of the event, including quantitative indicators such as the scope of impact, duration, and severity; the impact result generation process involves the fusion analysis of multi-dimensional data.

[0102] The traffic control system performs this step after completing the impact range estimation and secondary effect analysis. Specifically, the system first integrates the spatial impact range and temporal evolution characteristics; then evaluates the service level changes of each affected road section; calculates the overall increase in regional delays; then analyzes the risk level of key nodes; and finally generates a comprehensive impact report that includes multiple assessment dimensions.

[0103] In some embodiments, impact results can be generated through a variety of methods: Optionally, a multi-level evaluation indicator system can be constructed, with weights for each indicator determined using the Analytic Hierarchy Process (AHP) and combined with fuzzy comprehensive evaluation methods to quantitatively characterize the impact level. Optionally, multiple possible impact development scenarios can be designed based on scenario analysis, with Monte Carlo simulations used to calculate the probability of occurrence and impact level of each scenario. It is understood that other evaluation methods can also be used to quantitatively express impact results, and these are not limited here.

[0104] S208: Generate a traffic guidance plan and a signal timing adjustment plan based on the event impact results and traffic flow data.

[0105] Referring to step S103 , the traffic control system generates a traffic guidance plan and a signal timing adjustment plan.

[0106] S209: Generate navigation guidance for multiple sections around the corresponding event location based on the traffic guidance plan, and send the navigation guidance to the user terminal.

[0107] Referring to step S104 , the traffic control system sends navigation instructions to the user terminal.

[0108] S210. Adjust the traffic signal device at the exit area corresponding to the event location according to the signal timing adjustment plan.

[0109] Referring to step S105 , the traffic control system controls and adjusts the traffic signal device.

[0110] S211. Obtain traffic flow adjustment data for the exit area corresponding to the event location.

[0111] Among them, traffic flow adjustment data refers to the traffic status change data after the implementation of control measures, including changes in traffic volume, speed, queue length, etc.; the data acquisition process involves real-time monitoring and effect evaluation.

[0112] The traffic control system continues this process after implementing traffic control measures. Specifically, it first collects real-time traffic parameters through a regional sensor network; then calculates the change in each indicator relative to pre-control conditions; simultaneously monitors load transfer within the surrounding road network; then evaluates the effectiveness of the control measures; and finally generates a detailed traffic status adjustment report.

[0113] In some embodiments, monitoring the effects of traffic flow adjustments can be achieved through a variety of methods: Optionally, a real-time evaluation system based on multi-source data fusion can be established. By setting multiple effect evaluation indicators, the degree of improvement after implementation of measures can be calculated in real time, and the degree of achievement can be analyzed in conjunction with the expected goals. Optionally, a comparative analysis method can be used to establish a dynamic comparison model that includes data before and after the control, and the control effects can be quantified through spatiotemporal evolution analysis. It is understood that other evaluation methods can also be used to accurately quantify the effects of traffic adjustments, and this is not limited here.

[0114] In some embodiments, the traffic control system records data, that is, the traffic control system determines the traffic guidance effect of the exit area based on the traffic flow adjustment data; after the traffic guidance effect characterization event processing is completed, a traffic diversion record is generated.

[0115] Among them, the traffic guidance effect refers to the actual improvement degree after the implementation of traffic control measures; the completion of event handling refers to the restoration of traffic order to normal; and the traffic diversion record refers to a data document that fully records the entire process of event handling, including time nodes, handling measures, effect evaluation and other information.

[0116] The traffic control system continues this process after implementing control measures. Specifically, it first collects traffic status data from all detection points in the area; then calculates the change in key indicators compared to before the incident; simultaneously evaluates the actual effectiveness of the measures; then determines whether traffic order has returned to normal; and finally, standardizes and organizes all types of data from the entire handling process to form a complete traffic diversion record.

[0117] In some embodiments, effect evaluation and record generation can be achieved through a variety of methods: Optionally, a multi-dimensional evaluation indicator system can be established. By setting quantitative indicators such as traffic efficiency and delay reduction, combined with time series data analysis, the overall improvement effect can be evaluated and an evaluation report can be automatically generated. Optionally, knowledge graph technology can be used to construct a complete process chain for event handling, and semantic analysis can be used to achieve structured storage of information to support subsequent experience summary and pattern mining. It is understood that other data processing methods can also be used to achieve effect evaluation and record generation, which are not limited here.

[0118] S212. Modify the signal timing adjustment plan based on the traffic flow adjustment data.

[0119] Among them, the correction process means dynamically optimizing and adjusting the original timing plan based on the actual results, including updating parameters such as cycle length, phase difference, and green-to-signal ratio; the plan correction involves multi-objective optimization solution.

[0120] The traffic control system performs this step after acquiring traffic flow adjustment data. Specifically, it first evaluates the actual control effect of the existing timing plan; then identifies key parameters that require optimization; and, taking into account the coordination requirements of upstream and downstream intersections, calculates a new timing plan using an optimization algorithm. Finally, it gradually implements parameter adjustments to ensure a smooth transition.

[0121] In some embodiments, dynamic adjustment of the timing scheme can be achieved through various methods: Optionally, an adaptive control algorithm can be employed to establish a feedback adjustment mechanism based on real-time traffic conditions. By setting multiple control objective functions, signal timing parameters can be optimized in real time to achieve rapid response to changes in traffic demand. Alternatively, a reinforcement learning approach can be used to model signal timing optimization as a sequential decision-making problem, continuously improving the control strategy through continuous interaction with the environment. It is understood that other optimization algorithms can also be employed to achieve dynamic adjustment of the timing scheme, and these are not limited here.

[0122] It should be noted that the traffic control system uses reinforcement learning to dynamically optimize the timing plan. The control area is modeled as a Markov decision process, with the state space containing traffic flow parameters for each road section and the action space consisting of adjustable signal timing parameters. By designing a reward function based on factors such as vehicle delays and the number of stops, the control strategy is continuously optimized using a deep Q-learning algorithm. The system evaluates the control effect every 3-5 minutes and adjusts the parameters based on the actual improvement. To ensure control stability, a soft update mechanism is used to gradually adjust the timing plan. For example, if the diversion effect is detected to be less than expected, the system will appropriately increase the green signal ratio, but the change will not exceed 15% of the original plan.

[0123] In the embodiments of the present application, due to the use of innovative technologies such as an event verification mechanism based on multi-source data fusion, an impact assessment model for real-time traffic flow analysis, a dynamically optimized signal control strategy, and personalized navigation guidance push, it is possible to achieve rapid perception, accurate positioning, precise assessment, and coordinated handling of emergency events, effectively solving the problems of delayed response, inaccurate assessment, and extensive control in traditional technologies, thereby realizing intelligent, precise, and efficient traffic management, improving the efficiency of handling emergencies in exit areas, and providing strong support for ensuring safe and smooth urban traffic.

[0124] The following describes the traffic control system in the embodiment of the present invention from the perspective of hardware processing. Figure 3 , is a schematic diagram of the structure of a physical device of a traffic control system in an embodiment of the present application.

[0125] It should be noted that Figure 3 The structure of the traffic control system shown is only an example and should not limit the functions and scope of use of the embodiments of the present invention.

[0126] like Figure 3 As shown, the traffic control system includes a CPU 301, which can perform various appropriate actions and processes according to programs stored in a ROM 302 or programs loaded from a storage unit 308 into a RAM 303, such as executing the methods described in the above embodiments. The RAM 303 also stores various programs and data required for system operation. The CPU 301, ROM 302, and RAM 303 are interconnected via a bus 304. An I / O interface 305 is also connected to the bus 304.

[0127] The following components are connected to the I / O interface 305: an input section 306 including an audio input device, push button switches, and the like; an output section 307 including a liquid crystal display (LCD), an audio output device, indicator lights, and the like; a storage section 308 including a hard disk and the like; and a communication section 309 including a network interface card such as a LAN (Local Area Network) card or a modem. The communication section 309 performs communication processing via a network such as the Internet. A drive 310 is also connected to the I / O interface 305 as needed. Removable media 311, such as a magnetic disk, an optical disk, a magneto-optical disk, or a semiconductor memory, is installed in the drive 310 as needed, so that computer programs read from the removable media can be installed in the storage section 308 as needed.

[0128] In particular, according to an embodiment of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, an embodiment of the present invention includes a computer program product comprising a computer program carried on a computer-readable medium, the computer program including a computer program for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network via the communication section 309 and / or installed from the removable medium 311. When the computer program is executed by the CPU 301, the various functions defined in the present invention are performed.

[0129] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present invention. Each box in the flowchart or block diagram can represent a module, program segment, or part of the code, and the above-mentioned module, program segment, or part of the code contains one or more executable instructions for implementing the specified logical functions. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in an order different from that marked in the accompanying drawings.

[0130] Specifically, the traffic control system of this embodiment includes a processor and a memory. The memory stores a computer program. When the computer program is executed by the processor, the emergency control method for emergency events in the exit direction provided by the above embodiment is implemented.

[0131] As another aspect, the present invention further provides a computer-readable storage medium, which may be included in the traffic control system described in the above embodiments, or may exist independently and not be incorporated into the traffic control system. The storage medium carries one or more computer programs, which, when executed by a processor of the traffic control system, enable the traffic control system to implement the exit-direction emergency response control method provided in the above embodiments.

[0132] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application.

[0133] As used in the above embodiments, the term “when” may be interpreted to mean “if” or “after” or “in response to determining that” or “in response to detecting that”, depending on the context. Similarly, the phrases “upon determining that” or “if (stated condition or event) is detected” may be interpreted to mean “if determining that” or “in response to determining that” or “upon detecting (stated condition or event)” or “in response to detecting (stated condition or event)”, depending on the context.

Claims

1. A method for controlling emergency events in an exit direction, characterized in that: Applied to a traffic control system, the method comprises: Obtain the event alarm data uploaded by the user terminal, and identify the event location and event type in the event alarm data; the step of obtaining the event alarm data uploaded by the user terminal and identifying the event location and event type in the event alarm data specifically includes: obtaining the event alarm data uploaded by the user terminal, and identifying the initial location in the event alarm data; cross-validating the event alarm data with the real-time monitoring data of the initial location and the traffic event information provided by a third-party platform to obtain a verification result; determining the credibility score of the event alarm data based on the verification result, and screening out the event alarm data with the credibility score higher than a preset credibility threshold as an alarm marked event; determining the event location and event type corresponding to the alarm marked event based on the real-time monitoring data and the traffic event information; Obtaining traffic flow data for an exit area corresponding to the event location, and generating an event impact result based on the event type and the traffic flow data; the step of obtaining traffic flow data for an exit area corresponding to the event location, and generating an event impact result based on the event type and the traffic flow data specifically includes: obtaining traffic flow data and road characteristics for the exit area corresponding to the event location, inputting the traffic flow data and the road characteristics into a traffic state assessment model to obtain an estimated impact range; determining an event occurrence time based on the event alarm data, and determining a corresponding secondary traffic impact effect based on the event type, the event location, and the event occurrence time, in combination with historical traffic data of the exit area; and generating an event impact result based on the estimated impact range and the secondary traffic impact effect; generating a traffic guidance plan and a signal timing adjustment plan based on the event impact results and the traffic flow data; generating navigation guidance corresponding to a plurality of sections around the event location based on the traffic guidance plan, and sending the navigation guidance to the user terminal; The traffic signal device in the exit area corresponding to the event location is adjusted according to the signal timing adjustment plan.

2. The method according to claim 1, characterized in that Before the step of obtaining the event alarm data uploaded by the user terminal and identifying the event location and event type in the event alarm data, the method further includes: Obtain real-time traffic status data for multiple exit areas; Determining an emergency based on the real-time traffic status data and the historical traffic flow data; Sending prompt information including the emergency event to the management terminal.

3. The method according to claim 2, characterized in that After the step of determining an emergency based on the real-time traffic status data and the historical traffic flow data, the method further includes: Obtaining the license plate information of the vehicle corresponding to the emergency event; The vehicle owner information is determined based on the license plate information, and the mobile terminal corresponding to the vehicle owner information is connected to the management terminal.

4. The method according to claim 1, wherein After the step of adjusting the traffic signal device at the exit area corresponding to the event location according to the signal timing adjustment plan, the method further includes: Obtaining traffic flow adjustment data of an exit area corresponding to the event location; The signal timing adjustment plan is modified according to the traffic flow adjustment data.

5. The method according to claim 4, characterized in that After the step of obtaining traffic flow adjustment data of the exit area corresponding to the event location, the method further includes: determining a traffic guidance effect of the exit area based on the traffic flow adjustment data; After the traffic guidance effect representation event is processed, a traffic diversion record is generated.

6. A traffic control system, characterized in that: The traffic control system includes: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code includes computer instructions, and the one or more processors call the computer instructions to enable the traffic control system to execute the method described in any one of claims 1-5.

7. A computer-readable storage medium comprising instructions, characterized in that: When the instruction is executed on a traffic control system, the traffic control system is caused to execute the method according to any one of claims 1 to 5.

8. A computer program product, characterized in that When the computer program product is run on a traffic control system, the traffic control system is caused to perform the method according to any one of claims 1 to 5.

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