1279 results about "Traffic system" patented technology

The invention provides a dynamic path planning method and system in an intelligent traffic system, and relates to the technical field of intelligent traffic. Constructing a dynamic road network traffic capacity model based on a space-time diagram convolutional network; generating a multi-branch trajectory prediction result with a confidence score, adaptively starting an encryption parameter synchronization mechanism in combination with vehicle density, and generating a candidate path set through multi-agent collaborative game optimization; performing multi-dimensional deviation degree evaluation based on actual driving data and planning expectation; synchronously generating a multi-mode cooperative guidance signal; the system comprises a multi-source data fusion unit, a space-time modeling engine, a hierarchical decision-making system, a path verification and execution module, a closed-loop feedback controller, an event response system and a multi-mode man-machine interface. According to the method, the global resource utilization efficiency is improved through cooperation of data-driven modeling and hierarchical game decision, and the real-time performance, the safety and the system adaptability of path planning are improved through a closed-loop feedback and event response mechanism.

The invention belongs to the field of intelligent traffic systems, and relates to a multi-mode space-time traffic flow modeling method supporting large-scale road network real-time prediction, and the method comprises the steps: firstly designing a space-time prediction framework facing a dynamic traffic network, and then carrying out the training to obtain a final prediction model; the space-time prediction framework comprises a data embedding layer, a space-time coding module and a deep modeling and output module based on an expert hybrid mechanism; the data embedding layer comprises two parallel channels of time embedding and spectral domain space embedding and a time-space data fusion module; the space-time coding module comprises a block-level sparse time attention module, a space attention-message passing module and a weighted fusion layer which are parallel; the deep modeling and output module based on the expert hybrid mechanism comprises an MoE dynamic expert modeling module, a full-connection mapping module, a jump connection layer and an output layer; according to the design, the response speed, the prediction precision and the cross-regional adaptive capacity of the model in a high-heterogeneity scene are improved.

Disclosed in the present invention are a multi-agent vehicle-road-cloud integrated collaborative decision-making and control architecture system and method based on federated reinforcement learning. A multi-agent federated reinforcement learning decision-making and control framework having embedded vehicle dynamics characteristics is used, so as to solve the problem of in-depth integration of an intelligent traffic system and intelligent vehicles, and realize autonomous driving with vehicle-traffic in-depth decision-making and control collaboration; a semantic matrix is generated at a road side to serve as an input for vehicle-side reinforcement learning, so as to construct vehicle-side global and local trajectory planning guided by the road side; an integrated reward function for vehicle-side reinforcement learning is designed on the basis of a driving safety field constructed by the road side, so as to realize comprehensive consideration of vehicle-side safety and comfort; on the basis of road-side federated learning, vehicle-side neural network parameters are uploaded by means of V2I communication, so as to solve the problem of vehicle-road information asymmetry caused by privacy awareness; and for different environmental sample distributions, a local optimal policy for a current environment is selected by means of neural network screening, so as to synthesize a shared model benefiting from different environments, thus realizing a balance between sample efficiency and model robustness.

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 provides a non-stationary traffic prediction method based on wave flow decomposition and time delay perception. The method mainly comprises the following steps: constructing a traffic network diagram and inputting historical traffic flow data; constructing a decoupling flow layer, and decoupling the original flow into a wave component and a flow component; constructing a time gating convolution module, and capturing short-time dependency; constructing a space-time causal chain of time-delay perception directed graph attention and processing wave components; constructing an adaptive graph convolutional network, and processing global steady-state features of flow components; constructing a time gating convolution module, and capturing long-term time dependence; constructing an adaptive event fusion module; constructing a full connection layer; and outputting the predicted traffic flow data. The method overcomes the limitation of a traditional method in the aspects of prediction accuracy and coping with a complex traffic network, and effectively solves the problems that a traditional traffic flow prediction method is insufficient in prediction accuracy in an intelligent traffic system, cannot reflect the influence of the traffic network and the like.

The invention belongs to the technical field of vehicle trajectory prediction, and discloses a multi-scale interactive perception highway vehicle trajectory prediction method, which comprises the following steps: jointly modeling short-term burst features and long-term evolution trends through a convolutional neural network and a bidirectional gating cycle unit, and introducing a time sequence attention mechanism to improve the perception ability for key time slices; in combination with a dynamic graph attention mechanism including physical edge features such as relative position, relative speed and relative acceleration, a vehicle interaction relationship is updated in real time so as to improve spatial modeling precision and interpretability; in the decoding stage, the guide vector and the semantic information of the lane are fused, so that the predicted trajectory conforms to the geometric structure of the road in space and keeps smooth and continuous in time. According to the method, the robustness and adaptability of the model in the sparse adjacent vehicle environment of the expressway can be improved while the prediction precision is ensured, a more stable and reliable trajectory prediction result is provided for an intelligent traffic system, and powerful technical support is provided for traffic safety management and operation scheduling of the expressway.

Systems, methods, and devices to detect unauthorized third-party connections within a network infrastructure, such as by analyzing network traffic data using fuzzy matching and machine learning techniques. One aspect includes receiving network traffic data comprising records of communication events involving network identifiers, determining communication relationships between network entities identified by the network identifiers, accessing entity identifiers associated with known third-party systems, and determining associations between the network identifiers and the entity identifiers using a fuzzy matching process. Other aspects include identifying communication relationships involving the third-party systems based on the associations and detecting unregistered or unknown third-party connections within the network infrastructure. Further aspects include normalizing identifiers, computing string similarity metrics, assigning confidence scores, incorporating external data sources, building network association patterns, comparing current patterns to baseline patterns to detect anomalies, and updating security policies or firewall rules in response to detected anomalies. Additional aspects are provided.

The invention relates to the technical field of traffic data processing, and discloses a traffic edge computing data security sharing method and system, and the method comprises the steps: collecting traffic edge computing network multi-source heterogeneous data, and carrying out the classification standardization processing to generate a structured data set; designing a dynamic data sharing security protocol based on a multi-party security computing protocol and a homomorphic encryption algorithm; verifying the authority of a requester in a multi-level manner by using an attribute-based access control model and a zero-knowledge proof mechanism, and generating a dynamic access token; storing data by adopting a fragmentation storage and redundancy encryption strategy, and recording storage information through a hash chain; and dynamically adjusting the encryption strength and the sharing strategy according to the network threat level and the data sensitivity. The method effectively guarantees safe sharing of traffic data, accurately controls access authority, improves storage and sharing efficiency, adapts to complex network environment changes, and provides powerful support for development of an intelligent traffic system.

The invention relates to the technical field of traffic communication control, and discloses a vehicle-road cooperative communication optimization system for intelligent traffic. The system comprises a multi-source data sensing module for collecting multi-source heterogeneous data; the communication feature extraction module is used for analyzing the multi-dimensional features; the space-time fusion modeling module is used for constructing a combined space-time feature space; the double-layer resource scheduling library is used for storing an optimization strategy; and the dynamic collaborative decision module is used for generating a real-time scheme and instruction. The method also relates to the functions of abnormal track detection, path re-planning, communication link stability prediction and the like. According to the system, deep fusion and efficient processing of multi-source data are realized, communication resource scheduling and traffic flow regulation and control are optimized, abnormal behaviors of vehicles can be processed in time, the stability of a communication link is guaranteed, the operation efficiency, safety and communication reliability of an intelligent traffic system are effectively improved, and intelligent traffic development is promoted.

The invention discloses an unmanned aerial vehicle highway intelligent patrol method and system based on patrol demands, and belongs to the field of unmanned aerial vehicle technologies, intelligent traffic systems and artificial intelligence, and the method comprises the steps: obtaining a natural language instruction, and carrying out the semantic analysis to generate a structured task vector; constructing and generating a self-adaptive inspection strategy based on the task vector and fusing real-time data; distributing the strategy to the unmanned aerial vehicle for autonomous inspection, and identifying and generating a classification event alarm; and finally responding to an alarm and triggering a processing mode to generate a closed-loop inspection task report. According to the method, a technical path of combining multi-level reasoning based on a large language model and dynamic behavior strategy synthesis is adopted, and a complete cognition and action framework from semantic intention understanding, behavior decision making to closed-loop response is constructed, so that autonomous generation and intelligent execution of a complex and fuzzy inspection task can be realized; and the automation level, the response speed and the decision-making intelligence of highway inspection are obviously improved.

The invention provides a traffic flow prediction method and system based on an embedded physical information deep neural network, and belongs to the technical field of intelligent traffic system and deep learning crossing. The method comprises the following steps: firstly, carrying out variable grid division on an urban high-density road network, and establishing a physical model integrated with signal control to generate traffic state prediction; then constructing a physical information neural network, jointly inputting historical detection data and a physical model prediction result, and embedding a traffic flow conservation equation and a vehicle transmission rule as physical constraints through a loss function; weighted loss is utilized to optimize network parameters, and short-time density, flow and congestion propagation prediction conforming to the traffic flow theory is achieved. The problems that a traditional model is low in precision and a pure data driving method is insufficient in physical consistency are solved, and the accuracy and reliability of urban complex road network traffic situation prediction are remarkably improved.

The invention relates to a resource allocation system for 5G-A Internet of Vehicles network slices, which belongs to the technical field of intelligent traffic systems and comprises a network slice architecture module, a management and control plane module, a programmable module, a safety and fault-tolerant module, a performance monitoring and optimizing module, a mobility management module, a resource arbitration engine and a cross-domain collaborative interface. And the network slice architecture module is used for dividing Internet of Vehicles communication into a plurality of virtual slices based on a 5G-A network slice technology. According to the resource allocation system and method oriented to the 5G-A Internet of Vehicles network slices, accurate control over end-to-end performance indexes is achieved through the slice definition template library and a QoS dynamic mapping mechanism, the problem that key service performance is degraded due to one-step resource allocation of a traditional network is solved, distributed agent collaboration based on the MAPPO algorithm, and the resource allocation efficiency is improved. LSTM load prediction and a heuristic time delay equalization algorithm are combined, so that the system can respond to business demand changes at a millisecond level, and the resource utilization rate is improved.

The invention belongs to the technical field of intelligent traffic system and artificial intelligence crossing, and particularly relates to a traffic multi-agent simulation decision-making method and system based on a large language model. Road network state data are coded into a three-dimensional feature matrix containing channel dimensions, time dimensions and space dimensions, and joint representation of numerical road network data and text event reports is achieved through a hybrid embedding model. The decision-making layer comprises a dynamic Prompt generator which generates a candidate scheme set based on a four-layer progressive prompt structure; the Monte Carlo tree search multi-objective optimization is executed in cooperation with the decision core module; and the execution layer comprises a cross-language communication bridging device which adopts a gRPC bidirectional stream communication protocol and a Protobuf data serialization scheme to realize millisecond-level data interaction between services and support a hot plug mechanism of a strategy injection interface. According to the invention, dynamic optimization of urban traffic resource allocation and breakthrough improvement of simulation deduction efficiency are realized.

The invention belongs to the field of artificial intelligence and intelligent traffic systems, particularly relates to a vehicle path planning system based on multi-agent collaboration, and aims to solve the problems of path response lag, insufficient global optimization ability and conflict between individuals and group targets in a dynamic traffic environment. The system adopts a three-level collaborative architecture of a central coordination server, a regional scheduling agent and a vehicle-mounted agent, realizes macroscopic regulation and local resource allocation through a global guidance potential field and an improved auction algorithm, and improves the dynamic adaptive capacity by combining event-driven replanning and a robustness verification mechanism. Different scheduling and multi-time scale coupling operation of heterogeneous vehicles are supported, and the road network passing efficiency and the path reliability are effectively improved.

The invention relates to the technical field of intelligent traffic systems, and discloses an expressway multi-source traffic data grading and classification processing system, which comprises a data acquisition standardization module for acquiring and preprocessing multi-source traffic data; the data source quality evaluation module is used for evaluating the credibility of the data source; the scene emergency degree calculation module is used for calculating the flow anomaly degree, the vehicle speed anomaly degree and the meteorological risk score and identifying an emergency event; the data grading module is used for calculating a comprehensive priority score of the data and obtaining a grading data set by adopting a threshold segmentation method; the data classification module is used for carrying out self-adaptive classification on the data; the data fusion module identifies the same traffic parameter, performs conflict detection and performs fusion processing on conflict data; the result output module is used for obtaining a processing result by adopting a grading and classification output and quality feedback mechanism; according to the invention, intelligent refined processing of the highway multi-source traffic data is realized, and the emergency response speed and the data processing accuracy are improved.

According to the urban traffic automatic simulation plug-in based on the large language model and the implementation method, after a user inputs a request for a road network and a request for vehicle routing by using a natural language, the request is transmitted into a full-process automatic simulation module, and the full-process automatic simulation module carries out simulation; the input analysis agent extracts a key value from a natural language to generate a character string in a Json format, then the character string is spliced, a prompt input simulation input construction agent containing detailed simulation information is generated, the simulation input construction agent calls a packaged tool function, a vehicle route xml file and a road network xml file are generated respectively, and the vehicle route xml file and the road network xml file are connected with the simulation input construction agent. And the simulation execution intelligent body opens the SUMO-gu i to start simulation, and returns a result through a graphical interface. According to the method, the technical complexity of traditional traffic simulation is effectively simplified, a simulation tool is promoted to be transformed from professional modeling to intelligent decision support, and an innovative technical path is provided for optimization and management of a dynamic traffic system.

The invention discloses a traffic flow prediction method based on a clustering and space-time fusion graph convolutional network, and belongs to the technical field of intelligent traffic. The method comprises the following steps: firstly, acquiring traffic flow data and resampling, and constructing microscopic and macroscopic traffic flow data sets; then constructing a space-time fusion graph convolutional neural network, wherein the space-time fusion graph convolutional neural network comprises a space-time fusion module and a macro-micro interaction module; training the model by using the data set; and finally, traffic flow prediction is carried out by using the trained model. Compared with the prior art, through a clustering algorithm and a unique network structure, the traffic flow time-space information is effectively integrated, the prediction precision is improved, the adaptability of the model to a complex traffic scene is enhanced, more reliable prediction support is provided for an intelligent traffic system, and optimization of traffic management and alleviation of congestion are facilitated.

The invention discloses an intelligent traffic control method and system in a low-altitude economic environment, and relates to the technical field of intelligent traffic systems, and the method comprises the steps: collecting low-altitude aircraft and ground traffic spatio-temporal data through a multi-modal sensor network, and generating a multi-source heterogeneous data set; constructing a dynamic traffic situation map through spatio-temporal feature fusion; performing three-dimensional path planning to generate a three-dimensional guiding strategy; detecting conflicts and correcting strategies according to a preset rule base, and outputting an instruction set to distribute real-time traffic flow. The technical problem that in the low-altitude economic environment, a traditional traffic management and control method is difficult to meet the cross-domain cooperation requirement of the low-altitude aircraft and the ground traffic is solved, and the technical effects of three-dimensional cooperative management and control of the low-altitude aircraft and the ground traffic and further guaranteeing safe and efficient operation of the traffic in the low-altitude economic environment are achieved.

The invention provides a multi-node collaborative airspace control method and device for urban low-altitude traffic. The method comprises the following steps: acquiring sensing data of a real-time sensing system of an urban low-altitude aircraft body and an urban airspace infrastructure; assigning task scheduling sorting, and determining a local congestion coefficient and a path starting and ending point pair of a current aircraft assignment task; constructing a path cost function to optimize the initial spline path, and generating a path control point sequence and a path total cost; generating a final path of the aircraft during actual execution based on dynamic path adjustment of distributed cooperative control; and mapping the final path to the urban airspace, generating a complete scheduling sequence of the aircraft in the urban airspace, and sending the complete scheduling sequence to an aircraft control system for execution. Under the background that the urban low-altitude airspace is increasingly complex, cooperation among the aircrafts can be guaranteed, the utilization efficiency of airspace resources is improved, meanwhile, flight safety is guaranteed, and therefore a foundation is laid for intelligent development of a low-altitude traffic system.

The invention discloses a pre-training large model traffic flow prediction method based on double-activation domain bridging and space-time self-attention, and the method comprises the steps: designing a space-time feature multi-embedding module, carrying out the fusion of time, space and feature embedding, and constructing the multi-granularity representation of traffic flow data; designing a double-activation field bridging module, and aligning traffic flow data with the pre-training model space representation through a double-path activation mechanism; designing a dynamic gating mechanism, and adjusting the weight of the double activation branches through a softmax function; a LoRA strategy is combined with a partial attention freezing method to carry out fine tuning on the large language model; a pre-training multi-granularity space-time Transform module is designed, a space-time self-attention mechanism is used, and the modeling capability of the model for multi-granularity space-time features in traffic flow data is enhanced; and designing an output regression layer, and outputting a final prediction result by using a DADB module in combination with the convolutional layer. According to the method, the technical gap problem of the pre-training model and the traffic flow data can be effectively solved, and scientific decision support is provided for optimization of a smart city intelligent traffic system.

A method by which a first device performs maneuver driving in an intelligent transportation system (ITS) according to various embodiments may comprise: receiving a vehicle-to-everything (V2X) message related to a maneuver sharing and coordinating service (MSCS) from a second device; and determining whether to perform cooperative driving with the second device on the basis of the V2X message.

The invention discloses a highway event detection algorithm based on data situation analysis, and belongs to the technical field of intelligent traffic systems. According to the method, a dynamic graph structure is constructed by fusing multi-source data of an ETC portal, a toll station, a traffic detector, weather and the like, spatial-temporal characteristics are extracted by utilizing graph convolution and LSTM, and fuzzy distribution prediction of a traffic state is realized; calculating a node score mutation rate based on a prediction result, screening abnormal nodes in combination with a serious congestion probability, and extracting a connected abnormal sub-graph with a compact structure as a potential event region; a multi-factor event scoring function is designed, sudden change intensity, structural compactness and a state offset direction are fused, a risk level is quantified, and an adaptive boundary learning device is introduced to dynamically discriminate an alarm, so that scene limitation of a fixed threshold value is avoided; the detection accuracy and the alarm flexibility are improved, and the method is suitable for intelligent sensing and early warning of highway traffic events.

The invention discloses a multi-signal lamp linkage control method and system for intelligent urban traffic, and relates to the technical field of intelligent traffic, and the method comprises the steps: constructing an urban multi-level response system which covers the response architecture of three levels of intersections, regions and cities; a sensing acquisition network is accessed to collect weather information and traffic information, and traffic abnormal conditions in the response range of each level are identified; integrating the meteorological data, the traffic data and the traffic abnormal information into an urban multi-level response system according to the association of the response levels, and analyzing the traffic signal lamp strategy of each level so as to formulate the traffic control strategy of each level; and performing overall optimization on the traffic control strategy of each level in combination with the correlation characteristics of each level region, and finally forming a global collaborative control strategy. Therefore, the technical effects of fine control and global optimization, and enhancement of the adaptability and the overall operation benefit of the traffic system are achieved.

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 discloses an RFID multi-label information fusion and unmanned vehicle path real-time correction method, system and device based on improved whale optimization, and a storage medium, and belongs to the technical field of the crossing field of the Internet of Things and an intelligent traffic system, and the method comprises the steps: collecting received signal strength indication information and label position information, constructing a label signal propagation model and a positioning probability distribution model; performing fusion processing of multi-label positioning information, adopting a dynamic weighting strategy to adjust signal contribution, obtaining an unmanned vehicle position estimation value, taking the unmanned vehicle position estimation value as state input of path optimization, constructing a path cost function, introducing a whale optimization search mechanism based on dynamic update parameters, performing iterative optimization on a path candidate solution, and obtaining a path optimization result; outputting a path correction result; and a real-time path instruction is issued to the vehicle control module, and the unmanned vehicle is guided to complete path correction and navigation control in a dynamic environment. According to the method, the problems of poor path adaptability, low fusion precision and local optimum in the existing method are solved.

The invention discloses an intelligent traffic flow statistics and prediction platform based on multi-source data fusion, and relates to the technical field related to traffic prediction, and the platform comprises a data collection layer which is used for collecting road traffic flow data, meteorological data, public traffic operation data and data including traffic condition description on a social media platform; a data preprocessing layer; a data fusion layer; a traffic flow calculation module; and the traffic flow prediction module constructs a prediction model framework based on the space-time convolutional neural network, optimizes hyper-parameters of the ST-CNN, and applies the optimized model to traffic flow prediction. According to the invention, data collected by the annular induction coil and the camera focuses on actual traffic conditions of specific roads and intersections at a microscopic level, and meteorological data, public traffic operation data and social media data reflect traffic influence factors of the whole city or the region from a more macroscopic perspective, so that macroscopic and microscopic aspects are combined, and the traffic influence factors of the whole city or the region are reflected. And the operation condition of the urban traffic system can be known more comprehensively.

The invention relates to a highway vehicle trajectory reconstruction method based on a car-following model and dynamic time warping, belongs to the technical field of trajectory reconstruction, and is particularly suitable for networking and automatic driving vehicle (CAV) environments. The method comprises the following steps: firstly, extracting CAV and detected motion characteristics of front and back common vehicles, and reconstructing a candidate track set by using a car-following model and an inverse car-following model; then, calculating the similarity between the candidate trajectory and the known trajectory by adopting a DTW algorithm; and finally, performing weighted fusion according to similarity scores, and finally generating a more accurate reconstruction trajectory. Experiments show that the method can effectively improve the trajectory reconstruction precision under the condition that the CAV permeability is low, and further reduce errors along with the increase of the permeability, thereby providing data support for the application of automatic driving and intelligent traffic systems.

The invention discloses an air-railway combined transportation path optimization method based on XGBoost and a space-time attention network, and particularly relates to the field of intelligent transportation systems.According to the method, a traffic network basis is constructed by integrating flight and train historical data, topological information and weather data, delay time and consumed time of a critical path are predicted by means of an XGBoost model, and the time consumption of the critical path is predicted by means of the XGBoost model; a space-time dependency relationship is modeled through a space-time attention network, and complex space-time association is captured in combination with a space and time attention module and a multi-head mechanism; an optimal path is generated based on a weighted multi-objective function (covering time, economy, reliability and comfort), and users are supported to dynamically adjust weights to adapt to personalized requirements; and meanwhile, real-time adjustment of model parameters is realized by adopting an exponential weighted moving average and self-adaptive updating strategy, so that the prediction precision is remarkably improved, the reliability of the path and the user satisfaction are optimized, the real-time performance is ensured through a lightweight closed-loop updating mechanism, and a high-precision, personalized and real-time response intelligent solution is provided for air-railway combined transportation.

The invention provides an intelligent path planning method and system based on dynamic road condition prediction. The method comprises the steps of firstly obtaining multi-source dynamic data of a target area; then, constructing a weather influence prediction model to predict weather influence parameters in a future time period; secondly, constructing a weather-traffic coupling model, and respectively establishing correlation models of corresponding precipitation, traffic flow density and average vehicle speed according to road types through historical data analysis, so as to estimate the traffic efficiency of each road section under a dynamic weather condition; and finally, generating a plurality of candidate paths according to the passing efficiency, screening out an alternative path set meeting a multi-target optimization condition from the candidate paths, performing simulation evaluation on the alternative path set, and determining an optimal path according to a simulation result. Compared with a traditional static path planning method, the method has the advantages that the responsiveness of a traffic system to meteorological disasters is remarkably improved, and predictable navigation service is provided for intelligent network connection vehicles.

The invention discloses a traffic control and guidance system and method based on an ant colony algorithm, belongs to the technical field of road traffic control, and solves the problems that a neural network model in an existing method mainly focuses on respective route conditions of a plurality of bifurcation routes of a bifurcation on a one-way driving road, and the information fusion degree between traffic elements is low. The method comprises the following steps: identifying dynamic characteristic information of vehicles in a road network, pre-constructing a road network optimization model based on an ant colony algorithm combined with deep learning, and analyzing a pheromone spread function cluster solution; according to the invention, the road network optimization model based on the ant colony algorithm and the deep learning is pre-constructed, the collaborative optimization of traffic signal control and vehicle induction is realized, the traffic signal optimization control strategy and the vehicle optimization path strategy are output, so that the signal timing and the induction path can be matched with each other, and the control accuracy is improved. Therefore, the operation efficiency of the traffic system is improved and the accuracy and effectiveness of the guidance strategy are ensured.