3277 results about "Road traffic" patented technology

The invention provides a road traffic anomaly detection method and related equipment based on holographic perception, and the method comprises the steps: carrying out the time-space registration and preprocessing of video streams, point clouds, vehicle motion states, traffic parameters and environment data collected by multi-source sensor equipment of a target road network, and obtaining a multi-source data stream; performing feature acquisition and fusion processing through a preset target detection model and a feature extraction network to obtain target feature data and scene semantic information; establishing a space-time diagram data structure according to the target feature data and the scene semantic information, and performing target trajectory analysis through a space-time attention mechanism; and carrying out association analysis processing in combination with the analysis result and the environment data to obtain target behavior characteristics and interaction relation data, and realizing road traffic anomaly detection through the target behavior characteristics and the interaction relation data. Through multi-source data fusion and a deep learning model, the accuracy of environment perception is improved, and multi-dimensional information in a complex road network is captured for road traffic anomaly detection.

The invention discloses a road traffic flow prediction method based on a space-time mixed attention network, and the method breaks through the limitation of a conventional time sequence model and a single deep learning architecture based on the systematic analysis of urban road traffic flow space-time heterogeneity, periodic non-stationarity and road network topological relevance, constructs the space-time mixed attention network, and achieves the prediction of road traffic flow. Spatial heterogeneous correlation of road network nodes is captured through a graph convolution network, dynamic time sequence evolution characteristics of traffic flow are modeled by adopting a hybrid architecture, a residual attention mechanism is introduced to realize layer-by-layer refining of multi-scale spatio-temporal characteristics, and the overall architecture of the method has remarkable advantages in the aspects of spatial topology modeling and time dynamic capture compared with a traditional model. Feature decoupling learning is carried out on multi-source heterogeneous data such as weather and events, adaptive integration of environment sensitive features is realized through a parameterized gating fusion strategy, and the prediction error fluctuation amplitude in an extreme weather scene is reduced by 34.8%.

The invention provides a real-time map updating method and system based on multi-source geographic information data fusion. Wherein a three-dimensional model is constructed by integrating traffic flow data, remote sensing images and road network topology, the road traffic pressure is quantified, and a feature correlation index is established. And obtaining a vehicle displacement vector and a speed gradient in combination with laser point cloud and video monitoring data, generating a dynamic road feature data set, and establishing a mapping relationship with the index table. On the basis, a road state prediction model is constructed, and through correlation analysis of traffic pressure data and a real-time feature data set, deep correlation between vehicle motion features and road states is established. And dynamically adjusting a road network connection structure, synchronously updating mapping parameters and predicting model precision, and realizing real-time adaptive adjustment of the road network topology. According to the technical scheme provided by the invention, the real-time performance of map updating and the road condition prediction accuracy are remarkably improved, and the urban traffic congestion index can be reduced.

The present invention relates to a road traffic speed prediction method fusing a multi-feature neural network, comprising: performing feature data pre-processing and using a data set to train a multi-feature input based MF-Informer traffic speed prediction model: input normalization of a multi-source traffic speed prediction model is realized by means of feature data pre-processing; and a data set is used to train a multi-feature input based MF-Informer traffic speed prediction model to implement prediction, wherein the MF-Informer traffic speed prediction model comprises a location information embedding module, an encoder module, and a decoder module. A prediction result is outputted by means of a fully connected layer, an error between the prediction result and a real value is calculated, reverse gradient propagation is performed, and the model is continuously optimized; finally, the trained model is used to perform traffic speed prediction on a prediction time period, the prediction result is outputted for denormalization to obtain output data, and traffic speed prediction model evaluation is completed. According to the present invention, multi-dimensional feature fusion is realized by combining floating vehicle data with meteorological data and air pollution data, thereby effectively improving the accuracy of traffic speed prediction.

The invention relates to the technical field of traffic management, in particular to a smart city intelligent traffic management system based on big data, which comprises a multi-source data acquisition module, a data fusion module, a dynamic feature generation module, a situation prediction module, a multi-target optimization module and an instruction execution module, the multi-source data acquisition module acquires multi-source data in real time; the data fusion module generates a standardized traffic data flow and outputs a three-dimensional data cube; the dynamic feature generation module outputs a traffic feature matrix; the situation prediction module generates a dynamic situation map; the multi-objective optimization module outputs an optimization instruction set; and the instruction execution module issues a control instruction to the traffic signal controller, the variable information board and the vehicle-mounted terminal. According to the invention, traffic jam can be relieved, road traffic efficiency can be improved, bus priority scheduling can be optimized, vehicle delay can be reduced, and emergency response speed can be improved.

The invention discloses a road traffic signal control system and method, and the method comprises the steps: collecting real-time data through a sensor, a camera and a geomagnetic device, combining historical data with emergency analysis, and predicting a flow mode and a change trend; green light time distribution is dynamically optimized, a fixed timing, inductive and adaptive control strategy is adopted, and traffic light duration and priority adjustment is achieved; through regional cooperation and linkage control, green wave bands and branch intersection red light optimization are set, and peak period traffic signals are intelligently dispatched. A machine learning model is used to predict a flow peak value, environment and accident detection data are combined, a signal lamp period is dynamically adjusted, vehicle waiting time, traffic efficiency, carbon emission and traffic satisfaction are evaluated, and a traffic control strategy is comprehensively optimized. According to the road traffic signal control system and method, the traffic flow in different time periods is predicted by adopting the flow prediction model based on big data analysis and machine learning, and the signal period is adjusted in advance.

The invention relates to the field of road traffic, in particular to a multi-feature fusion fatigue driving detection method and a fatigue driving detection system. The method comprises the following steps: extracting facial features from a face image of a driver; extracting vehicle features from the vehicle driving parameters of the vehicle driven by the driver; and fusing the facial features and the vehicle features to judge whether the driver is in fatigue driving. Extracting facial features by designing a CNN model; extracting basic convolution features; extracting local convolution features; extracting global convolution features; performing pooling operation; aggregating global features; and carrying out dimensionality reduction mapping. A self-encoder is designed to extract vehicle characteristics; a symmetric deep neural network structure is adopted, and high-dimensional time sequence data is compressed to a low-dimensional potential space through nonlinear mapping; through combination and matching of the CNN model and the auto-encoder, the technical defects of feature redundancy, noise interference, information loss and suboptimal decision existing in an existing multi-feature fusion fatigue driving detection system are thoroughly solved.

The invention provides an intelligent distribution decision-making method and system for production materials, and the method comprises the steps: firstly obtaining the material demand information, including material types, demand priorities and demand time windows, of all production nodes in a preset period of a target factory, and then combining the real-time traffic and storage layout data of an area where the target factory is located; the method comprises the following steps: determining dynamic environment parameters including a road traffic state and a storage node capacity, then generating an initial distribution path set according to a demand priority and a time window, inputting the initial distribution path set and the dynamic environment parameters into a pre-trained path optimization model after each path is associated with a transportation tool and a material distribution scheme, and outputting a target delivery path and a dynamic scheduling instruction used for adjusting the loading capacity and the driving speed of the transportation tool, and finally controlling the transportation tool to deliver materials according to the target delivery path and the dynamic scheduling instruction, and re-planning the path on the way based on the real-time dynamic environment parameters to realize intelligent and efficient delivery.

The invention relates to the technical field of traffic flow regulation and control, and discloses an electronic information traffic flow automatic regulation and control system based on a wireless sensor network. The multi-source sensing module collects road network multi-dimensional traffic data through a wireless sensing network, and traffic situation characteristics are generated through a specific data fusion method. The spatio-temporal feature fusion module utilizes a spatio-temporal attention mechanism to associate cross-modal features, and the collaborative decision-making module inputs fusion features into a pre-trained distributed decision-making model to generate a regulation and control instruction set. The dynamic game optimization module constructs a multi-target game optimization model, and traffic signal parameters are optimized by adopting a dynamic game strategy decomposition algorithm. And the hierarchical execution module executes regulation and control instructions in a distributed manner through a three-level control architecture of a central decision-making layer, a regional coordination layer and an intersection execution layer. The system can comprehensively collect and fuse traffic data, realizes scientific decision making and accurate regulation and control, effectively improves the road passing efficiency, balances the road network load, and relieves traffic jam.

Provided are a method and an apparatus for constructing a road congestion prediction model, a device, a medium, and a product. A road traffic network is defined as a directed weighted graph. Historical dynamic traffic features of each road segment in the road traffic network are obtained as sample data, including recent dynamic traffic features and periodic dynamic traffic features. The sample data is input into a mixture of adaptive graph learners (MAGL) model for learning, and a probability prediction vector is output. The sample data is input into a trend expert model, and a trend distribution vector of a predicted probability of future traffic conditions is output. The periodic dynamic traffic features are fused to determine a periodicity prediction vector. An aggregated logit vector is obtained. An objective function is determined based on the aggregated logit vector. Congestion prediction training is performed to obtain a road congestion prediction model.

The invention discloses an intersection entrance lane mixed vehicle team cooperative guidance control method, which overcomes the problems of large calculation amount and difficulty in achieving a global optimal state in the prior art, and comprises the following steps: 1) obtaining road information; 2) dividing intelligent agents; and 3) establishing a multi-agent ecological cooperative control model of the intersection entrance lane mixed vehicle fleet: firstly, establishing a single-agent speed trajectory planning model, calculating an optimal passing trajectory of a single agent, and then establishing a multi-agent cooperative control model by taking the overall operation efficiency of the vehicle fleet as an optimization target on the basis of the optimal passing trajectory of the single agent, so as to realize the multi-agent ecological cooperative control of the intersection entrance lane mixed vehicle fleet. Cooperative control among a plurality of agents is realized, and a model is trained through a reinforcement learning method after model construction is completed; and 4) the controllable agents execute the optimized trajectory: issuing the established and trained model to each controllable agent, adopting a rolling time domain updating strategy, calculating the speed trajectory of each agent passing through the intersection by using road traffic information, and inputting the speed trajectory to a CAV controller for execution.

The invention relates to an urban road carbon emission prediction and optimization control method and system, and belongs to the technical field of road traffic carbon emission monitoring, and the method comprises the steps: obtaining real-time monitoring data of a to-be-detected road, and carrying out the preprocessing; performing space-time alignment on the preprocessed real-time monitoring data, extracting dynamic traffic features and static road features, and generating a space-time feature matrix; inputting the spatial-temporal characteristic matrix into a pre-trained carbon emission prediction model to obtain a prediction result matrix of the to-be-detected road; on the basis of the prediction result matrix, identifying a road grid of which the carbon emission intensity prediction value exceeds a preset threshold value as a high emission area; acquiring real-time traffic state data of the high emission area; and based on the real-time traffic state data and the prediction result matrix of the high emission area, constructing a multi-objective optimization function and carrying out solving to obtain an optimization control strategy of the high emission area. According to the invention, traffic carbon emission optimization control and real-time traffic condition synchronization can be realized.

The invention relates to the technical field of intelligent traffic management, and discloses a road traffic signal lamp management system based on big data, which comprises a data acquisition and fusion module for acquiring traffic sensor data, visual data, GPS track data and external environment data, generating unified traffic state data through space-time alignment, data filtering and multi-modal data fusion; and the flow prediction module is used for predicting the traffic flow of a single intersection and a region by using a time sequence model and a graph neural network based on the traffic state data generated by the data acquisition and fusion module. Through multi-modal data acquisition and space-time alignment, Kalman filtering, Bayesian inference and other technologies, deep fusion of different data sources is realized, the comprehensiveness and accuracy of data are significantly improved, dynamic changes of a complex traffic scene can be truly reflected, high-quality basic data are provided for subsequent flow prediction and optimization control, and a core technical support is formed.

The invention discloses a road intelligent induction and dynamic early warning method and system integrated with meteorological perception, and relates to the technical field of intelligent traffic and road safety. The method comprises the following steps: acquiring real-time weather, traffic and road data, performing multi-source data fusion by adopting an improved Kalman filtering and attention mechanism, and generating unified state estimation; dynamic risk assessment is carried out in combination with Bayesian reasoning and a Markov model, and speed-limiting adaptive adjustment is realized based on safety, traffic efficiency and energy consumption multi-objective optimization; and further calculating the length and position of the dynamic early warning area, and controlling devices such as intelligent spikes to issue induction information. The system comprises a data acquisition unit, a fusion estimation unit, a risk prediction unit, a speed adjustment unit, an early warning calculation unit and an induction unit. According to the invention, real-time monitoring, risk prediction and intelligent regulation and control of the road traffic environment in complex weather are realized, and the driving safety and the traffic efficiency are improved.

The invention provides a high-precision perception-driven intelligent road network collaborative optimization method and system, and relates to the technical field of intelligent traffic, and the method comprises the steps: collecting road sensing data through multi-sensor fusion, and obtaining multi-source perception data; performing dynamic traffic flow modeling according to the multi-source sensing data to obtain a traffic state prediction result; performing optimization decision on the traffic state prediction result to obtain an intelligent optimization decision; performing road network level cooperative control on the intelligent optimization decision to obtain a road network level cooperative control result; and performing real-time dynamic adjustment by using the road network level cooperative control result to obtain a road network scheduling result. According to the method and the device, the technical targets of global collaborative scheduling, improvement of traffic flow prediction precision, optimization of a real-time dynamic scheduling scheme and improvement of the intelligent level of urban traffic management can be realized, and the technical effects of reducing traffic congestion, improving road traffic efficiency, optimizing traffic resource allocation and improving emergency response capability are achieved.

The invention belongs to the technical field of intelligent monitoring, and particularly relates to a 5G Internet of Things fused video monitoring intelligent early warning method, which comprises the following steps of: acquiring vehicle attribute information and environment perception data, constructing a 5G Internet of Things perception network, fusing a vehicle movement track and road condition environment data through a cross-modal attention mechanism, generating an enhanced environment perception graph, and simultaneously, carrying out early warning on the vehicle movement track and the road condition environment data. Optimizing the transmission efficiency by adopting a dynamic resolution adjustment algorithm; the method comprises the following steps: constructing a dynamic behavior prediction model by using a space-time diagram convolutional network, predicting a vehicle abnormal behavior probability and an evolution trajectory, calculating an early warning level through an adaptive risk quantification algorithm, simulating a risk diffusion coefficient by using a dynamic risk propagation model according to the road section vehicle density, the average vehicle speed and the road traffic capacity, and correcting early warning sensitivity parameters in real time. And transmitting to a command platform, performing situation deduction, triggering a grading early warning instruction, and generating thermodynamic diagram warning information. Therefore, the problems of insufficient positioning precision, weak analysis capability, poor transmission efficiency and the like in the prior art are solved.

The invention discloses an urban road traffic entrance and exit influence evaluation method based on big data, and relates to the technical field of urban traffic management. Constructing an urban road traffic network topological graph based on the traffic feature vectors, proposing a dynamic weight graph embedding algorithm, and establishing a road network association mapping model; applying a graph neural network algorithm based on an attention mechanism to the road network association mapping model, performing road node influence factor evaluation, and quantitatively analyzing the road entrance and exit influence degree; fusing influence factor evaluation results, and constructing a multi-dimensional traffic influence evaluation model by adopting a cross-domain ensemble learning method; and according to a performance evaluation result of the multi-dimensional traffic influence evaluation model, generating urban road traffic entrance and exit optimization decision suggestions through an intelligent recommendation algorithm, and completing accurate scheduling of traffic network nodes. The intelligent recommendation algorithm is developed based on reinforcement learning, and reliable optimization suggestions are provided for traffic management decisions.

The invention relates to the technical field of data analysis, in particular to a road traffic intelligent operation and maintenance method and system based on multi-source data fusion, and the method comprises the steps: constructing a three-dimensional model through a credibility calculation module by integrating three types of factors, namely a road environment, an equipment state and historical verification, generating a dynamically updated credibility index, and correcting a weight in real time based on feedback; the problem that the reliability of the multi-source data fluctuates dynamically due to the influence of environmental interference, equipment aging and historical deviation is solved; the road feature conflict resolution module maps to feature dimensions corresponding to road topology conflict features through dynamic credibility indexes, constructs a road conflict decision matrix, and fuses and generates a road anomaly decision set with credible labels according to the feature dimensions; the resource scheduling module is used for dividing three-level response areas based on credible indexes, executing accurate scheduling of triple constraints in combination with device priorities and credible labels and generating a resource-decision association graph, and self-adaptive optimal configuration of limited maintenance resources according to data reliability is achieved.

The invention relates to the technical field of road maintenance management, and particularly discloses a novel digital road maintenance management method and system based on multi-source data, and the method comprises the steps: carrying out the multi-dimensional analysis of a road crack image through employing an image processing technology based on deep learning; the method comprises the following steps of: identifying a crack type and quantifying key parameters such as width, length and area of the crack, simultaneously, capturing a time sequence change trend and a periodic rule of road traffic flow by performing time sequence analysis on historical traffic flow data of a road, and further fusing a static crack detection result with a dynamic traffic load mode through a time sequence derivation algorithm to obtain a dynamic crack detection result. The expansion trend of the crack under the action of the future traffic load is predicted, and long-term crack development prediction is formed, so that road maintenance management is performed based on the long-term prediction result of the road crack. According to the method, accurate diagnosis and active intervention of the road state can be effectively realized through multi-dimensional data collaborative analysis, so that the efficiency and effect of road maintenance are remarkably improved.

The invention relates to the technical field of intelligent traffic, in particular to a highway traffic incident recognition and early warning method and system, and the method comprises the steps: collecting traffic data in real time through sensing equipment arranged along a highway; performing preliminary identification and classification of conventional traffic events on the preprocessed traffic data through edge computing nodes; inputting a preliminary analysis result and unclassified traffic data into a cloud end, and carrying out secondary analysis and semantic understanding; and merging the logic relationship and the events of the same target to obtain a final event identification result. According to the traffic event attribute and the event identification result, generating early warning information; and inputting feedback data after each event processing into the recognition model for self-learning and optimization, and dynamically optimizing model parameters, event classification standards and early warning rules. According to the invention, the problems of false report and missing report in traffic event identification are effectively solved, the identification precision is improved, the repeated report is reduced, and the method can dynamically adapt to the problems of complex scenes and non-uniform standards.

The invention discloses a complex scene traffic sign detection method and system based on dynamic frequency band focusing and double-domain attention screening. The method comprises the following steps: carrying out data preprocessing and data enhancement on a collected road traffic sign image; a CADPCM module and a CHAttention cross coordination attention mechanism are used to construct a CACHNet backbone network; designing a DWMSN neck network, and establishing a dynamic fusion mechanism of multi-scale features; a CACHNet and a DWMSN neck network are used to construct a CDWN model, and a traffic sign enhancement data set is used to train the CDWN model to determine the optimal model weight thereof. Compared with the prior art, the method has the advantages that the average detection precision is improved by 3.3% while the light weight of the model is maintained by constructing a three-level framework of the feature extraction unit, the attention feature expression enhancement and the dynamic feature fusion, the complex scenes such as illumination variation and shielding can be effectively dealt with, and the method is suitable for popularization and application. And high-precision traffic sign detection support is provided for a vehicle-mounted intelligent auxiliary driving system.

The invention relates to the technical field of intelligent traffic, in particular to a vehicle driving path control system and method based on intelligent traffic big data, and the method comprises the following steps: a, collecting the traffic data of the real-time position, the road traffic flow and the congestion condition of a vehicle through a real-time traffic data collection module; b, analyzing the historical traffic data through a big data analysis and prediction module, and predicting peak periods, accident risk areas and traffic bottleneck points; according to the invention, through dynamic acquisition and big data analysis of real-time traffic data, the peak period and the congestion area can be accurately predicted, so that the driving path of the vehicle is optimized, congestion is reduced, in addition, through intelligent regulation and control of a signal lamp and a freight vehicle shunting mechanism, the system avoids interference of freight vehicles to public transportation in the peak period, and the traffic safety is improved. The road traffic efficiency is improved, the system also provides real-time path adjustment suggestion and early warning service, and more convenient and safer travel experience is provided for drivers.

The invention provides a road traffic space-time service platform and method based on multi-source heterogeneous data fusion. The platform comprises a data acquisition module; a data fusion management module; the base map making module is used for generating a road traffic space-time service base map taking a road intersection as a key node; the traffic situation analysis module is used for forming a traffic situation prediction map; and the decision support and personalized service module is used for providing real-time and visual traffic situation display, traffic guidance strategies and personalized travel services for traffic managers and traffic participating users based on the traffic situation prediction map. According to the invention, real-time and visual decision support is provided for traffic managers, and personalized travel schemes can be customized for traffic participating users, so that comprehensive perception and accurate regulation and control of road traffic operation states are realized, and the intelligent level of traffic operation management is greatly improved.

The invention discloses a multi-dimensional data-driven urban traffic scheduling method and platform, and relates to the technical field of traffic scheduling, and the method comprises the steps: obtaining the distribution information of urban traffic facilities, and determining a traffic key region set; a fixed source detection device is arranged, and a key area multi-dimensional traffic operation data flow set and a road traffic multi-dimensional real-time data flow are obtained; acquiring a traffic operation associated index set, and performing preprocessing and index feature extraction; performing congestion prediction analysis to obtain urban traffic congestion parameters, performing scheduling analysis on the urban traffic congestion parameters, and determining a traffic scheduling strategy scheme set; scheduling simulation and effect comparison optimization are carried out, and traffic scheduling optimization control is carried out. The technical problems of insufficient traffic scheduling and low operation efficiency in the prior art are solved, and the technical effects of accurately mastering the traffic condition and improving the traffic scheduling and operation efficiency are achieved.

The invention discloses a graph convolution recurrent neural network road traffic flow prediction method considering data missing, and relates to the technical field of intelligent traffic, and the method comprises the steps: data collection and preprocessing; constructing node features based on a Gaussian mixture model; designing a graph convolution layer considering data loss; establishing a space-time dynamic model and optimizing the space-time dynamic model; predicting a future traffic state; establishing a model, and continuously optimizing model hyper-parameters through training by using a stochastic gradient descent algorithm to obtain a traffic state prediction scheme; according to the method, the problem of data missing caused by sparse sensor deployment, data loss, hardware faults and other factors can be solved, meanwhile, the spatial-temporal correlation of traffic data can be effectively captured, and the accuracy and reliability of traffic state prediction are effectively improved.

The invention discloses a road traffic sign target detection method based on YOLOv8, and relates to the field of road target detection, in the method, a CDFF-YOLO model is constructed, and an MPA module is embedded in a feature fusion network of the model to enhance the feature fusion capability of C2f; an MSF module is constructed, GSConv and CARAFE up-sampling operators are embedded to realize a multi-dimensional sequence information fusion effect, and the detection precision of the model is improved while the parameter quantity and the calculation quantity of the model are reduced as much as possible. A DFF module is constructed, two-dimensional Fourier transform and inverse transform methods are adopted to enable feature information to be enhanced in a frequency domain and a space domain at the same time, and the detection performance of a network model for shielded small targets and small targets under uneven illumination is effectively improved. The three modules not only enhance the detection effect of the small target model, but also enable the network to stably cope with the detection scene of the small target. According to the method, the detection performance of the small road traffic sign target is excellent, the processing speed is high, and deployment of mobile terminal equipment is facilitated.

The invention discloses an electric vehicle battery thermal management method and system under intelligent network connection. The method comprises the steps that the road traffic flow state, the environment temperature, the real-time temperature of a power battery and vehicle driving state data are obtained; establishing a vehicle driving state prediction model, and predicting the vehicle driving state in the next time period by using the historical driving state data of the vehicle; establishing a thermal management model prediction controller, and predicting the temperature state of the power battery in the next time period; determining the optimal working temperature range of the battery according to the type of the power battery used by the automobile; performing power battery temperature state division according to the power battery type and the predicted power battery temperature state; a corresponding battery thermal management prediction control strategy is adopted according to the temperature state of the power battery; and a power battery emergency heat management strategy is adopted. According to the battery temperature pre-management strategy, the battery temperature control accuracy is high, and the safety and the cruising ability of the battery are guaranteed.

The invention discloses an intelligent driving cooperative collision avoidance system and method based on the Internet of Vehicles, and belongs to the technical field of automatic driving. Comprising a vehicle-mounted unit, a road side unit and a service platform, the vehicle-mounted unit is used for collecting vehicle state information and surrounding environment information and sending the vehicle state information and the surrounding environment information to the road side unit and the service platform. The road side unit is used for collecting road condition information, performing target detection based on the surrounding environment information and the road condition information, and obtaining obstacle information; and the service platform is used for evaluating a collision risk according to the vehicle state information and the obstacle information, predicting an optimal collision avoidance strategy based on the collision risk, and sending an instruction to the vehicle-mounted unit to perform collision avoidance control. Potential collision risks can be analyzed based on vehicle state information and road condition information which are shared in real time, and the safety of road traffic is improved. The problem that a traditional collision avoidance system mainly depends on perception and decision of a single vehicle and lacks cooperation with other vehicles and road infrastructures is solved.

The invention discloses an intelligent traffic control system and method based on multi-agent near-end strategy optimization, and belongs to the field of intelligent traffic, Internet of Vehicles and deep reinforcement learning. The method comprises the following steps: firstly, constructing a fog-cloud collaborative three-layer architecture, and realizing real-time monitoring and dynamic regulation and control of traffic flow through cloud global decision and local sensing collaboration of a road side unit (RSU); secondly, designing indexes of'road section overlap ratio 'and'road section time overlap ratio', and solving the problem of secondary congestion caused by rerouting; then, a multi-agent near-end strategy optimization (MAPPO) algorithm is adopted, so that the traffic signal lamp is used as an autonomous agent to dynamically adjust the phase, and the limitation of single-point control is broken; and finally, through integrated optimization of rerouting and adaptive signal control, an original multi-objective optimization problem is converted into a layered multi-agent reinforcement learning problem. According to the invention, vehicle driving time and system energy consumption can be effectively reduced, road traffic efficiency is improved, and active avoidance and dynamic alleviation of urban traffic congestion are realized.

The invention relates to the technical field of expressway monitoring, and provides an expressway abnormal state detection method and system based on a support vector machine, which processes vehicle trajectory data and ETC gantry data through Kalman filtering and adaptive weighted average to improve the data accuracy, introduces a Gaussian kernel function to construct an SVM model, and improves the detection accuracy of the expressway abnormal state. An original feature space is mapped to a high-dimensional space to deal with a nonlinear problem, samples with labels are used to train a multi-classification SVM model, and accurate detection of various abnormal states such as traffic congestion, accidents and vehicle faults is realized through a voting mechanism. According to the method, the accuracy of abnormal state detection is improved, powerful decision support is provided for expressway managers, the traffic flow is optimized, the road traffic capacity is improved, and the development of an intelligent traffic system is promoted.