1220results about "Controlling traffic signals" patented technology

The invention relates to the technical field of signal devices, and discloses a traffic signal control method and system based on vehicle-road cloud multi-modal data fusion, and the method comprises the steps: collecting multi-modal traffic data synchronously in real time through a vehicle-end sensor, road-side sensing equipment and a cloud Internet platform; fusing the heterogeneous data by adopting a space-time alignment algorithm, and constructing a standardized space-time feature matrix; traffic flow prediction is carried out based on a multi-layer space-time diagram neural network trained by a federated learning mechanism, and a signal control instruction is generated through reinforcement learning and a multi-objective optimization model; and issuing the green wave parameter, the dynamic timing scheme and the cross-domain coordination strategy to a roadside signal machine through the cloud edge coordination architecture to execute control. The problems that in the prior art, low-delay private network communication cannot be achieved, the data fusion efficiency is low, unmanned driving is not supported, and the deployment cost is high are solved, and the purposes of low-delay communication, high reliability and low risk are achieved.

The invention belongs to the technical field of intelligent traffic systems, and particularly relates to an intersection signal timing dynamic coordination system and method based on real-time traffic prediction.The method comprises the steps of collecting multi-source traffic data, generating a space-time prediction digital twin model, dynamically defining an intersection coordination cluster and executing cluster coordination optimization control. And signal timing and closed loop feedback are carried out. By adopting the technical scheme, the cooperative operation efficiency and the intelligent management level of the urban intersection group can be effectively improved, and the fundamental conversion from local and reactive active cooperative control to global and predictive active cooperative control is realized.

The invention discloses an urban traffic intelligent regulation and control system and method based on digital twinning. The method comprises the following steps: S1, collecting and fusing multi-source data in real time; s2, constructing a digital twinborn model; s3, traffic flow prediction and optimization; s4, multi-system cooperative control is carried out; the system comprises the following modules: a multi-source data real-time acquisition and fusion module which comprises a video data acquisition and processing unit, a geomagnetic sensor network construction unit and a floating car data integration unit; the digital twinborn model building module comprises a three-dimensional road network model building unit and a data fusion and calibration unit; the traffic flow prediction and optimization module comprises an intelligent prediction model unit and a real-time optimization control unit; and the multi-system cooperative control module comprises an emergency priority passing system unit, a reversible lane dynamic adjustment unit and a parking lot induction unit. The traffic efficiency can be remarkably improved, the cooperative response speed is greatly increased, and carbon emission is effectively reduced.

The invention discloses a traffic operation and maintenance fault intelligent scheduling method and system based on an AI large model, and belongs to the technical field of traffic control. The method comprises the following steps: collecting a vehicle driving track GPS coordinate set, a traffic flow density matrix, a vehicle-mounted camera monitoring image frame sequence and a fault vehicle owner speed anomaly detection result in real time; constructing a traffic operation state analysis model, and outputting real-time traffic operation state characteristics; generating a traffic fault probability distribution curved surface in a future time window; generating a comprehensive fault positioning confidence coefficient matrix; and planning an optimal maintenance resource path according to the pheromone updating rule, and updating the optimal maintenance resource path to the visual scheduling platform in real time. According to the method, space-time diagram convolutional network dynamic modeling is constructed according to multi-source data, so that the limitation of a space blind area of a single data source is broken through, the fault positioning speed is improved, and the problems of incomplete coverage and low positioning speed in the prior art are solved.

The invention relates to the technical field of traffic scheduling, in particular to a road traffic analysis scheduling method and system based on event driving. The method comprises the following steps: collecting traffic state data in real time, and generating an original traffic data flow; feature extraction is carried out on the original traffic data flow, a space-time traffic flow graph is constructed, and a dynamic traffic network graph is generated; performing analysis and space-time correlation analysis on the dynamic traffic network diagram to generate a traffic event list; triggering an intelligent scheduling response based on the traffic event list, calling a corresponding scheduling strategy template according to the event type, and generating a candidate scheme set; a multi-objective optimization model is constructed for optimization, and an optimal scheduling scheme is generated; issuing the optimal scheduling scheme to road control equipment, and executing traffic scheduling; and collecting traffic feedback data, evaluating a scheduling effect, and if an expected target is not reached, adjusting scheduling parameters and regenerating an optimization scheme until the traffic state is improved. According to the invention, accurate analysis and efficient scheduling of road traffic can be realized.

The invention discloses an intelligent interpretable traffic signal adaptive control method. The method comprises the following steps: training an intelligent agent through reinforcement learning according to environment state information of an intersection; a timing decision of each phase of the intersection is generated by using the intelligent agent; guiding the first large language model to generate pre-training data by the timing decision and the cue word to perform LoRA fine tuning on the second large language model, and inputting the cue word into the fine-tuned second large language model to enable the second large language model to generate a plurality of reasoning tracks to generate positive samples and negative samples; performing all-parameter fine tuning on the second large language model to obtain a traffic control signal decision model; and inputting the constructed cue word into a traffic control signal decision model to obtain each phase timing scheme of the intersection. According to the method, the defects that an intelligent traffic signal control algorithm based on deep reinforcement learning lacks interpretability and the cross-scene generalization ability is poor are overcome, and the method has important significance in improving the decision credibility and the deployment efficiency.

The invention provides a 5G communication-based vehicle state sensing and intelligent traffic cooperative scheduling method, and relates to the technical field of intelligent traffic, and the method comprises the steps: receiving high-frequency vehicle state sensing data transmitted by a vehicle-mounted unit in a target road section in real time through a 5G base station cluster; collecting traffic environment sensing data of a target road section through a roadside sensing unit, fusing the high-frequency vehicle state sensing data with the traffic environment sensing data, and constructing a multi-dimensional real-time traffic situation map; performing collaborative analysis based on the multi-dimensional real-time traffic situation map to generate a traffic control instruction set; and issuing to a road side execution unit and a vehicle-mounted unit for cooperative scheduling control of the traffic flow. The technical problems that a traffic system in the prior art often depends on periodic data acquisition and a fixed signal control scheme, traffic control adjustment cannot be carried out in real time according to changing traffic conditions, traffic flow fluctuation or emergencies cannot be rapidly coped with, and then the traffic system is low in efficiency and unstable are solved.

The invention provides a traffic signal lamp intelligent control method based on a fiber bragg grating sensing network, and relates to the technical field of traffic signal lamps, and the method comprises the steps: obtaining the detection data of each fiber bragg grating sensing line, and enabling the fiber bragg grating sensing lines to be transversely laid at a selected traffic crossroad along a lane, three fiber bragg grating sensing lines parallel to the stop line are laid on each road; determining the length range of the motor vehicle queuing length of each lane according to the detection data of each fiber Bragg grating sensing line; and controlling the working state of the corresponding traffic signal lamp according to the length range of the motor vehicle queuing length of each lane. The actual traffic flow can be determined through the detection data of the fiber Bragg grating sensing lines, the green light time can be dynamically allocated based on the actual traffic flow, the vehicle queuing time is shortened, and the passing efficiency can be effectively improved.

The invention relates to a multi-type emergency vehicle signal dynamic priority control method based on vehicle-road cloud cooperation, and belongs to the technical field of intelligent traffic control. The method comprises the following steps: acquiring an emergency vehicle state and traffic environment data in real time through cooperation of a vehicle-mounted terminal, roadside equipment and a cloud control platform; the cloud control platform dynamically calculates priority scores based on vehicle types, task emergency degrees, predicted arrival time, real-time traffic influences and path complexity multi-dimensional factors; when multiple vehicles have conflicts, collaborative decision making is carried out based on scores; and finally, an optimized signal control strategy is generated and executed. The system effectively solves the problems that a traditional priority control mode is extensive, and traffic jam and multi-vehicle conflicts are easily caused, achieves the purpose that interference to social traffic is minimized while efficient passing of emergency vehicles is guaranteed, and improves the overall efficiency and safety of an urban traffic system.

The invention discloses an emergency vehicle rescue control method under a virtual emergency special lane, and solves the problems that an emergency vehicle is difficult to pass in time due to traffic jam, the lane changing efficiency is low, and the path interference factor is complex in the existing traffic environment. The method comprises an emergency vehicle rescue dynamic control model; establishing a simulation scene, deploying a model and performing training; vehicle and road information is obtained, an emergency vehicle lane changing strategy is obtained according to the emergency vehicle autonomous lane changing decision and the optimal emergency lane selection model, and an optimal virtual emergency lane is selected; and guiding social vehicles to give way according to the lane dynamic emptying frame model. According to the invention, lane traffic density is monitored, an emergency vehicle rescue control strategy is triggered, an emergency vehicle lane change decision is optimized by using an emergency vehicle autonomous lane change decision model, an optimal virtual emergency lane is dynamically selected based on an optimal emergency lane selection model, social vehicles are guided to give way through a lane emptying frame, and rapid passing of the emergency vehicles is ensured.

The invention relates to the technical field of traffic management, in particular to an urban road intersection passage intelligent optimization method based on multi-modal information, which comprises the following steps: acquiring real-time sensing data of pedestrians and non-motor vehicles through a video camera, a millimeter wave radar and a laser radar, and generating fusion sensing data through timestamp synchronization and coordinate system unification; identifying and generating a target list with category labels by using a detection and clustering algorithm, and obtaining a stable motion trail and intensity by combining with multi-target tracking; predicting a crossing intention and a path based on time sequence deep learning, calculating an interleaving point and quantifying a conflict risk; and according to a comparison result of the conflict risk coefficient and a threshold value, generating a strategy control instruction of different time periods, different paths or a mixed mode, and in combination with execution time window information, forming an optimized timing scheme through cooperative execution of an intelligent prompt identifier, a telescopic isolation belt and a signal control machine, so as to realize cooperative passage. The method improves the recognition precision, reduces the conflict risk, and improves the passing efficiency.

The invention discloses a traffic signal global collaborative prediction method based on a quantum entanglement state, and the method comprises the steps: building a quantization model of a dynamic evolution path of a target traffic network in a time window in the future, and constructing a space-time diagram model; mapping the signal phase state of the intersection into a time-space integrated quantum state by adopting a layered quantum coding scheme; on the basis of a preset traffic optimization target, constructing Hamiltonian including spatial coupling, time evolution and a cost item, solving a ground state of the Hamiltonian through a mixed quantum-classical calculation method, and determining an optimal dynamic evolution path; coding the actual state of the current traffic network into an initial state, carrying out sequential measurement through an optimal evolution operator, decoding a cooperative signal control strategy of each time point in the future, and executing the cooperative signal control strategy through a rolling time domain control framework; according to the invention, global optimization is carried out by using quantum parallelism, the optimal cooperation strategy in the whole space-time range can be obtained at one time, and the overall operation efficiency, predictability and robustness of the traffic network are improved.

Aspects of the disclosed technology relate to a system and methods for optimizing traffic flow along a roadway. The systems receive, from a connected-automated vehicle (CAV), a request for a travel time along a roadway having a managed lane and a general purpose lane. The system determines real-time traffic data, and using a trained machine learning model, predicts future traffic data along the roadway, determines a time saved by using the managed lane, communicates with the CAV, receives an acknowledgement that the vehicle will use the managed lane, and updates the predicted traffic data. Segment agents may be responsible for discrete segments of the roadway and may communicate with a coordination agent to result in a multi-agent reinforced traffic machine learning system. The system provides a mobility service to CAVs by implementing a reinforcement learning process to understand CAV behavior to optimize and maintain the service level along a roadway.

The invention provides an intersection accident evacuation scheduling method based on cooperation of an unmanned aerial vehicle and a large language model, and relates to the technical field of intelligent traffic management, unmanned aerial vehicle application and artificial intelligence multi-mode fusion. The method comprises the following steps: generating an optimized patrol route based on a large language model; controlling an unmanned aerial vehicle cluster to perform multi-modal data acquisition on urban road network intersection nodes; fusing multi-source heterogeneous data by using a multi-modal fusion module enhanced by a large language model; inputting a multi-modal large language model based on a traffic-dedicated Prompt template, and generating a semantic report including accident positioning, influence evaluation and resource requirements; natural language decisions output by the large model are converted into structured control instructions, and signal lamp timing and lane allocation strategies are elastically adjusted in combination with real-time loads of a road network; and synchronously issuing a multi-mode guide instruction through the variable information board, the vehicle-mounted terminal and the navigation APP.

The invention belongs to the technical field of tunnel catastrophe, and particularly relates to an intelligent feedback type tunnel catastrophe monitoring method, which comprises the following steps of: 1, deploying a tunnel monitoring system; a temperature sensor, a smoke sensor, an air velocity sensor and a gas concentration sensor are arranged in the tunnel; the temperature sensor is used for monitoring temperature changes in the tunnel. According to the intelligent feedback type tunnel catastrophe monitoring method, the safety and the emergency response capability of the tunnel are effectively improved through real-time data monitoring and analysis and an automatic control mechanism. Firstly, through temperature, smoke, gas concentration and air velocity sensors, the system can detect and identify potential catastrophe risks in a tunnel in time, such as fire, smoke diffusion and harmful gas leakage. Once an abnormality occurs, the system performs catastrophe prediction through an algorithm model, and performs accurate evaluation on a fire propagation path and smoke diffusion in combination with a simulation analysis result, thereby providing reliable data support for quick response.

The invention provides a traffic corridor signal cooperative control method based on single agent reinforcement learning, and relates to the technical field of traffic management and control, and the control method comprises the steps: obtaining the real-time traffic state data of a traffic corridor comprising a plurality of signal intersections; acquiring a current signal control scheme of the traffic corridor; constructing a state vector according to the real-time traffic state data and the current signal control scheme; inputting the state vector into a pre-trained single-agent reinforcement learning model to obtain a corresponding action vector; based on the action vectors, phase division of all the signal intersections is synchronously adjusted, and a new signal control scheme is generated; according to the invention, signal timing of all signal intersections in a traffic corridor is cooperatively controlled by adopting a centralized single-agent architecture, so that the problems of system complexity and training instability caused by local observation, distributed decision and communication coordination among agents in a multi-agent scheme are fundamentally avoided.

The invention discloses an urban elevated expressway congestion relieving method and system based on detector data. The method comprises the steps that multi-dimensional traffic parameters of key sections of an elevated expressway are collected in real time through arranged traffic detectors; establishing a multi-level threshold matrix based on the multi-dimensional traffic parameters, and dynamically dividing traffic congestion levels; when the real-time traffic parameters meet the threshold condition of any congestion level, a disposal instruction of the corresponding level is automatically triggered; according to the processing instruction, executing a multi-device cooperative control strategy covering a congestion point space correlation area; and dynamically optimizing the threshold matrix or the cooperative control strategy according to traffic parameter change feedback after strategy execution. According to the invention, by monitoring the traffic data in real time, dynamically setting the congestion threshold and automatically matching the optimal mitigation strategy, rapid early warning and accurate intervention of traffic congestion can be realized, and the traffic efficiency and intelligent management level of the elevated expressway are improved.

The invention is suitable for the technical field of traffic control, and provides a road section pedestrian crossing signal control method and system based on human and vehicle postures. The method comprises the following steps: acquiring instantaneous speed and position data of a head vehicle on each lane in front of a pedestrian crossing in real time, and calculating the maximum crossing time by combining the length of the pedestrian crossing and the type of a pedestrian; determining the length of the upstream and downstream judgment area of the pedestrian crossing based on the road design speed and the street crossing time; monitoring the number of pedestrians and the longest waiting time in the waiting area, and judging whether a first triggering condition is met or not by combining a reasonable waiting time threshold value and a capacity threshold value; judging whether a second triggering condition is met or not by comparing the theoretical driving time of the vehicle with the pedestrian crossing time in the judgment area; and when the first and second trigger conditions are met simultaneously, starting a pedestrian green light phase, otherwise, maintaining the current signal state. According to the invention, the waiting time of pedestrians and vehicles can be effectively reduced, and the intersection passing efficiency and safety are improved.

The invention discloses a multi-mode urban traffic prediction system, belongs to the technical field of traffic, and solves the problems that a tunnel structure is not early warned in time due to hidden damage under the action of multiple coupling, and finally, a river-crossing tunnel bursts water suddenly and is forced to be closed under the triggering of peak traffic flow vibration, so that the tunnel structure cannot be early warned. Therefore, the problem of chain paralysis of the traffic system of the whole city is solved. Comprising a multi-modal data acquisition module, a damage evolution modeling module, a traffic influence analysis module, a collaborative optimization control module and a dynamic plan generation module. According to the method, the sensing capability is constructed by fusing multi-source monitoring data, hidden structure damage is identified and predicted by means of a multi-physics field coupling model, a traffic collaborative optimization strategy is generated based on adaptive dynamic planning, and then whole-process prevention and control from risk to emergency are realized through a dynamic plan and meta-learning. Therefore, the vicious circle of structural damage-traffic jam-rescue blocking is blocked, and regional paralysis is avoided.

The embodiment of the invention discloses a traffic instruction conflict detection method and system and a computer storage medium. The traffic instruction conflict detection method comprises the steps that instruction core information is extracted from a received traffic instruction; querying an instruction conflict rule in an instruction conflict database according to the instruction core information to obtain a query result; performing instruction conflict hierarchical detection based on the query result to obtain an instruction conflict type corresponding to the traffic instruction; according to the instruction conflict type, performing conflict processing on the traffic instruction to obtain a conflict processing result; and feeding back the conflict processing result to the instruction conflict database, and updating an instruction conflict rule.

The invention relates to the technical field of smart traffic, and discloses a smart traffic monitoring system and method based on multi-source data fusion, and the system comprises a multi-source data collection module, a spatial-temporal feature fusion engine, a hierarchical decision core, a decision execution module, and an online learning module. The method corresponds to the system. According to the method, heterogeneous traffic data are unified under the same time-space reference through a multi-modal fusion technology, and comprehensive and accurate road network state feature representation is constructed; the hierarchical decision-making core forms a closed-loop decision-making link from event identification to control strategy generation through close cooperation of an event sensing layer and a regional optimization layer, and realizes rapid response and accurate control of traffic abnormity; on-line learning continuously optimizes decision logic based on complete historical operation data so as to adapt to a continuously changing traffic environment; finally, the accuracy of traffic state perception and the timeliness of decision response are improved, and reliable technical guarantee is provided for efficient management and control of intelligent traffic.

The invention provides a mixed traffic cooperative control method based on multi-agent reinforcement learning, which can be applied to the technical field of intelligent traffic and automatic driving. The method comprises the following steps: inputting first state information of a target intersection and second state information of an adjacent intersection into a signal lamp intelligent body obtained based on public traffic priority target training, and outputting a signal lamp phase control action; inputting the signal lamp information and the automatic driving vehicle information of the automatic driving vehicle at the target intersection into a signal lamp fine-tuning intelligent body obtained based on safety target training, and outputting a signal lamp fine-tuning control action; inputting the signal lamp information, the automatic driving vehicle information and the front vehicle driving information into an automatic driving vehicle intelligent body obtained based on safety efficiency cooperative target training, and outputting an automatic driving vehicle driving action; and under the condition that the first signal lamp is controlled to execute the signal lamp phase control action and the signal lamp fine adjustment control action, the automatic driving vehicle is controlled to execute the driving action of the automatic driving vehicle.

The embodiment of the invention provides a traffic signal-based vehicle path planning method and related equipment, and belongs to the technical field of automatic driving. The method comprises the following steps: acquiring traffic light data of each intersection of a target driving path; according to the green light time interval of the traffic light data, time constraint calculation of multi-intersection continuous passing is carried out, and a time constraint of continuous green wave passing is obtained; performing speed calculation on each road section according to the time constraint to obtain a speed interval of each road section; performing quadratic programming on the speed interval by taking the energy consumption and the driving time as optimization objectives to obtain a global speed sequence; and performing multi-dimensional constraint and multi-target optimization on a speed controller of the vehicle according to the global speed sequence so as to control the vehicle to run on the target running path. According to the embodiment of the invention, the vehicle can be guided to realize continuous green light passing, and the driving efficiency and the driving comfort are improved while the driving energy consumption of the vehicle is reduced.

The invention discloses a dynamic planning method and system for smart city traffic, and relates to the technical field of smart cities and intelligent traffic, and the method comprises the steps: collecting multi-source traffic data, and carrying out the fusion preprocessing; constructing a dynamic weight model to calculate a priority weight; a signal lamp timing scheme is generated based on the weight and is adjusted through a rolling optimization mechanism; outputting an optimization strategy by combining historical and real-time feedback evaluation effects; and a manager is notified through the acousto-optic prompt device and a report is generated. According to the invention, through the dynamic weight model and the rolling optimization mechanism, the overall delay time of the intersection is reduced, the traffic capacity of the traffic flow is improved, the Kalman filter is introduced to fuse multi-source data, and the data processing precision is improved; the LSTM network is adopted to predict the future traffic flow, a scientific basis is provided for a signal lamp timing scheme, and the workload of traffic management personnel is reduced through the acousto-optic prompt device and the automatic report generation module.

The invention discloses a traffic jam prediction management method and system based on artificial intelligence, and a medium, and relates to the technical field of intelligent traffic systems, and the method comprises the steps: collecting multi-source traffic data to construct a space-time traffic data set, carrying out the traffic state classification based on the space-time traffic data set, and generating a multistage jam probability distribution diagram; performing congestion prediction in combination with the multi-stage congestion probability distribution diagram and the real-time traffic event data, and determining a congestion evolution path; and carrying out traffic management according to the congestion evolution path, generating a dynamic dispersion instruction set, and sending the dynamic dispersion instruction set to traffic control equipment of the target road section to carry out traffic congestion management. The technical problems that a traditional traffic management means cannot adapt to complex and changeable traffic conditions, and accurate congestion prediction and efficient management are difficult to achieve are solved, accurate prediction of traffic congestion is achieved, a dynamic dispersion strategy is efficiently generated and executed according to the real-time traffic conditions, and the traffic congestion prediction efficiency is improved. Therefore, the traffic management efficiency and the road traffic capacity are improved.

A system for mapping road segment free spaces for use in autonomous vehicle navigation. The system includes at least one processor programmed to: receive from a first vehicle one or more location identifiers associated with a lateral region of free space adjacent to a road segment; update an autonomous vehicle road navigation model for the road segment to include a mapped representation of the lateral region of free space based on the received one or more location identifiers; and distribute the updated autonomous vehicle road navigation model to a plurality of autonomous vehicles.

The invention discloses an intelligent decision-making system construction method for traffic signal control. The method comprises a model training and deployment stage and an application and evolution stage, and specifically comprises the following steps of: S1, generating a pairing training sample set of traffic state structured data and conflict-free signal control instructions based on a pre-stored road traffic conflict rule in the model training and deployment stage; utilizing the paired training sample set to supervise and finely adjust a large language model to obtain a basic model; s2, accessing the basic model into a traffic simulation environment for reinforcement learning training; and in each training step, the basic model outputs a signal control instruction according to the current traffic state, performs safety verification on the instruction according to the road traffic conflict rule, generates a safety reward signal and the like. The traffic signal intelligent decision-making system which is safe, credible, sustainable in evolution and suitable for edge independent deployment is constructed.

The invention discloses an automatic driving control method and device and a vehicle, and relates to the technical field of automatic driving, and the method comprises the steps: obtaining the signal lamp indication information of a traffic signal lamp in front of the vehicle and the current position of the vehicle; under the condition that the signal lamp indication information indicates the first passing instruction, in response to the situation that the current position is located in a preset scene range, extracting wearing features of personnel in the preset scene range, determining traffic management personnel based on the wearing features, and in response to successful authentication of the traffic management personnel, determining gestures of the traffic management personnel; and in response to the gesture indicating the second passing instruction and the first passing instruction and the second passing instruction being mutually exclusive, controlling the vehicle to run based on the second passing instruction. Therefore, the automatic driving vehicle is more intelligent, the congestion is effectively relieved, and the traffic efficiency is improved.

The invention belongs to the field of intelligent traffic, discloses an emergency vehicle priority and queuing optimization cooperative control method, and solves the problem of cooperation of emergency priority and social vehicle dispersion at a peak-saturated intersection. The method comprises the following steps: constructing a road network model containing a center intersection and four peripheral intersections, and collecting multi-source traffic data; a data set is established after preprocessing, and an LSTM-Transform double-branch model is trained to predict queuing and emergency tracks; constructing an intelligent agent based on PPO, and combining segmented reward training; and deploying an output signal decision. According to the invention, emergency low delay is guaranteed, social vehicle queuing is optimized, peak intersection efficiency is improved, and the method is suitable for high-saturation scenes; according to the method, the social vehicle congestion is effectively relieved while the rescue vehicles are guaranteed to pass preferentially, and the overall operation efficiency and the emergency response capability of the intersection in the complex traffic environment are remarkably improved.

The invention discloses a traffic drainage signal management method and system based on a Beidou system, and relates to the technical field of Beidou satellite navigation, and the method comprises the steps: obtaining the real-time traffic data of the position and speed of a vehicle at each intersection based on the Beidou system, and transmitting the real-time traffic data to a traffic management platform; carrying out feature matching on the accident identification model trained based on historical emergency data and a deep learning algorithm and real-time traffic data, and identifying an emergency; and constructing a real-time traffic situation map in combination with the identified emergency information and the traffic condition information of the intersections. The vehicle position and speed data are obtained in real time through the Beidou system, emergencies are rapidly recognized and signal lamp timing is dynamically adjusted in combination with a deep learning algorithm, and compared with a traditional fixed timing scheme, traffic flow changes can be responded in real time, frequent start and stop of vehicles are reduced, the road passing efficiency is improved, and the traffic safety is improved. And particularly, in a sudden traffic accident or construction scene, a signal strategy can be quickly optimized, and traffic congestion aggravation is avoided.