139results about "Traffic control supervision" patented technology

The invention relates to the technical field of intelligent traffic equipment management, in particular to a signal equipment health state monitoring method and system, and the method comprises the steps: receiving operation, external field sensing and communication link data of signal equipment through a protocol conversion interface, converting the data into a unified format, and storing the data in a data medium; after abnormal value filtering and missing value completion, operation characteristic parameters are extracted, a layered health degree evaluation model is constructed, equipment-level, intersection-level and trunk-level health degrees are calculated in sequence, and early warning is triggered if the health degrees are lower than a preset threshold value; constructing a device-link-scene association map, positioning a fault type and a fault source in combination with a fault feature matching algorithm, and analyzing a fault influence range; and according to the fault influence range, the device priority and the operation and maintenance resource real-time state, generating an operation and maintenance work order and a resource scheduling suggestion, and pushing early warning information, a fault positioning result and a health degree report to an operation and maintenance terminal. Full-level health perception and operation and maintenance resource optimization can be realized, the fault handling period is shortened, and the traffic passing efficiency is guaranteed.

The invention relates to the field of intelligent traffic and vehicle-road cooperation, in particular to a traffic operation double-control intelligent system. Comprising a multi-mode roadside sensing module used for obtaining traffic operation state data and environment state data of a road section; the double-control cooperation unit is used for jointly generating variable speed limit setting and signal control adjustment quantity based on the traffic operation state data and the environment state data; the signal control module is used for carrying out on-line adjustment on intersection phase timing or trunk line phase difference according to the signal control adjustment amount; the road information broadcasting module is used for broadcasting road information to the vehicle; the consistency verification module is used for carrying out consistency comparison on the road information, the actual signal lamp state and the phase remaining time; and the safety domain management module is used for applying double-hysteresis safety domain constraint to the variable speed limit setting and the signal control adjustment amount. According to the invention, the cooperation of two links is realized, the vibration suppression steady state is consistent with the release, and the driving behavior is stably and verifiably guided.

The invention is suitable for the technical field of traffic management, and provides an urban traffic dynamic command method and system based on digital twinning. According to the method, the high-quality data set is generated through direct multi-source data acquisition, restoration and integration; hierarchical dynamic storage is carried out, and block chain security management is carried out; carrying out stored multi-source data acquisition and processing, and constructing a real-time dynamic traffic twin model; based on the real-time dynamic traffic twinborn model, decision optimization of traffic control is carried out, evaluation feedback is carried out through edge calculation, and model parameters of the real-time dynamic traffic twinborn model are corrected; a three-level data storage system is adopted, a data management center is constructed, and data life cycle management is carried out. According to the method, the real-time dynamic traffic twin model can be constructed by fusing multi-source data, traffic flow simulation, congestion prediction and dynamic optimization are realized, a scientific basis is provided for smart city traffic management, traffic congestion is effectively relieved, and the urban traffic efficiency is improved.

A traffic information providing method and a system thereof are provided. The traffic information providing method according to an embodiment of the present disclosure is executed by a computing system, and may comprise the steps of: inputting a plurality of traffic mode data and real-time traffic information, which are generated in advance, into a traffic prediction model; outputting an error of each piece of traffic mode data in the plurality of pieces of traffic mode data at the first time point as an input result; and generating traffic information from a departure place to a destination using first traffic mode data with the smallest error among the plurality of traffic mode data, where the traffic mode data is a combination of mode speeds for each road segment, which is a smallest unit of a road.

This invention provides a highway traffic spatiotemporal service platform and method based on multi-source heterogeneous data fusion. The platform includes: a data acquisition module; a data fusion management module; a base map creation module for generating a highway traffic spatiotemporal service base map with highway intersections as key nodes; a traffic situation analysis module for generating a traffic situation prediction map; and a decision support and personalized service module for providing real-time, visualized traffic situation displays, traffic guidance strategies, and personalized travel services to traffic managers and traffic participants based on the traffic situation prediction map. This invention not only provides real-time, visualized decision support for traffic managers but also allows for customized personalized travel plans for traffic participants, thereby achieving comprehensive perception and precise control of highway traffic operation status and greatly improving the level of intelligent traffic operation management.

The invention relates to a signal control problem diagnosis method for an urban trunk road, and the method comprises the steps: taking the average vehicle control delay of the urban trunk road as an evaluation index, quantifying the benefits of different signal control schemes, and obtaining a benefit matrix; based on the scene to be diagnosed, defining a cost function, and obtaining a cost matrix; based on the benefit matrix and the cost matrix, generating a recommendation system special value matrix, and performing complementation and deviation correction on the recommendation system special value matrix; quantifying the similarity between the scene to be diagnosed and each typical traffic demand scene in the recommendation system characteristic value matrix, and calculating the reference value weight of the typical traffic demand scene to the scene to be diagnosed according to the similarity proportion; and based on the reference value weight, calculating unreasonable setting probabilities of various signal control parameters in the scene needing to be diagnosed, and realizing signal control problem diagnosis according to the unreasonable setting probabilities. Compared with the prior art, the urban trunk signal control problem can be diagnosed only by depending on small sample historical data.

The invention provides an omnibearing intelligent safety monitoring and driving early warning method fusing multiple devices, and the method is characterized in that the method comprises the steps: a multi-source data collection step: collecting motorcycle body data, surrounding environment data, driver state data and vehicle-road cooperation data in real time; a risk assessment step of obtaining a stability risk level and a collision risk level based on the ontology data, the surrounding environment data and the vehicle-road cooperation data, the stability risk level reflecting a vehicle falling risk, and the collision risk level reflecting a collision risk between the vehicle and other traffic participants; and an early warning step of deciding an early warning level and an optimal early warning mode according to the stability risk level and the collision risk level and based on the driver state data.

The invention discloses a traffic light fault real-time identification and alarm method based on improved Yolov5, and relates to the technical field of intelligent traffic, and the method comprises the steps: S1, building a traffic light fault data set in advance, and collecting traffic light images and video data under different scenes, illumination conditions and weathers; step S2, constructing an improved Yolov5 model; and S3, extracting images of a real-time video stream to be detected according to frames, inputting the images into the trained improved Yolov5 model, outputting position information, light color states and fault probabilities of traffic lights, and analyzing and judging fault types in combination with a preset threshold value and a multi-frame time sequence. Real-time video stream processing and multi-frame time sequence analysis are combined, the high-frame-rate real-time detection requirement can be met, time sequence faults can be accurately judged, and the limitation that manual inspection is low in efficiency and poor in real-time performance is broken through; the fault response and handling period is greatly shortened, and powerful technical support is provided for guaranteeing normal operation of the traffic lights and improving urban traffic management efficiency.

The application provides a traffic signal lamp fault emergency management system and method based on an Internet of Things large model, and relates to the field of urban traffic signal lamp supervision. The method is executed by an emergency supervision management platform of the traffic signal lamp fault emergency management system based on the Internet of Things large model, and comprises the following steps: determining a state evaluation result of a signal lamp based on first monitoring data and second monitoring data of the signal lamp; determining a target signal lamp based on the state evaluation result; cutting off the power supply of a fault signal lamp and issuing a detour suggestion to target personnel; determining a fault prediction situation of an abnormal signal lamp based on the first monitoring data, the second monitoring data and the state evaluation result of the abnormal signal lamp; and determining a maintenance parameter based on the fault prediction situation and sending the maintenance parameter to maintenance personnel. The application can accurately diagnose the state of the signal lamp, cut off the power supply and issue a detour suggestion when a fault occurs, predict a fault and determine a maintenance parameter when an abnormality occurs, realize rapid response and active intervention, and improve operation and maintenance efficiency.

The invention belongs to the field of urban rail transit emergency command, and particularly relates to an urban rail transit emergency command method based on digital twinning, which comprises the following steps: collecting multi-source data and setting an emergency trigger condition; deploying a lightweight self-adaptive adjustment twinborn model; according to the rail transit guidance method, the problem of how to estimate the flow direction of crowds and issue an evacuation strategy under the condition that a fire occurs in a scene of urban rail transit and a camera is shielded by smoke or powered off is solved; according to the invention, the method can achieve the estimation of the flow direction of the crowd under the condition of no sight line, and gives an evacuation command accordingly.

The invention discloses a multi-source data fusion and traffic mechanism driven checkpoint equipment closed-loop evaluation method, relates to the technical field of checkpoint equipment evaluation, and solves the technical problem of poor evaluation effect of existing checkpoint equipment. The method comprises the following steps: constructing an upstream equipment mapping table of the bayonet equipment based on road network information and position information of the bayonet equipment; verifying the historical traffic mode of the gate based on the historical traffic data, calculating the real-time accuracy of the gate device based on the floating car trajectory data, and finally comparing the traffic conditions of the to-be-evaluated gate device and the upstream gate device through the upstream device mapping table to judge whether the traffic is conserved or not; determining a mistakenly shot license plate number according to a license plate verification rule; the real license plate number of the mistakenly shot license plate number, the association algorithm and the hardware information are determined by using similar vehicles in the upstream and downstream vehicles, factors which can influence the equipment effect are analyzed, and improvement measures are adopted in a targeted manner; according to the method, environment interference, algorithm output characteristics and hardware state parameters are associated to form a closed-loop feedback mechanism.

The invention relates to the technical field of expressway safety warning, in particular to a segmented flashing warning lamp system triggered at any point and a dynamic indexing method.The system comprises a central monitoring layer, a core control layer, an area control layer and a field equipment layer, and a PLC of the core control layer numbers warning lamps in sequence; after any trigger point i is triggered, the index 300 adjacent warning lamps are turned on, and the specific index number can also be set according to site requirements. The flash is divided into three sections according to the number and is divided into six times per second, four times per second and two times per second, indexes are updated for new trigger points in the same range, warning sections are newly added for new trigger points in different ranges, and resetting can be carried out after faults are removed; according to the segmented flicker warning lamp system triggered at any point and the dynamic indexing method, the first-time warning of an accident can be achieved, a driver can judge the distance from the accident point through the flicker frequency and rapidly brake, major serial traffic accidents are effectively restrained, casualties and economic losses are reduced, and the segmented flicker warning lamp system is suitable for multiple accident scenes and high in reliability.

The invention discloses a highway ice and snow covered pavement traffic safety risk grade prediction method. The method comprises the following steps: firstly, collecting vehicle operation data, meteorological data and road information data; then, dynamic response generated in the braking or accelerating process of the vehicle is analyzed, key parameters such as the speed, the acceleration and the braking force of the vehicle are combined, and the numerical value of the road surface friction coefficient is estimated; afterwards, environmental factors such as ice and snow, temperature and snowfall intensity are comprehensively considered, the road adhesion coefficient obtained through estimation is corrected, a numerical value better fitting the actual road condition is obtained, and road adhesion coefficient data finally used for risk assessment are calculated; and then, in combination with the corrected road adhesion coefficient under the ice and snow weather condition, carrying out detailed analysis on the adhesion coefficients of different areas by applying a risk assessment function. According to the method, low-cost, high-frequency and wide-coverage attachment coefficient estimation is realized, and the comprehensive risk condition of the ice and snow covered pavement can be reflected more comprehensively and stereoscopically.

The invention belongs to the technical field of urban traffic early warning, and provides an urban traffic linkage early warning method and system based on intelligent monitoring, and the method comprises the steps: obtaining multi-source traffic monitoring data, carrying out the multi-mode fusion processing of the multi-source traffic monitoring data, and forming a traffic state data sequence of a target road network; and constructing a traffic change curve based on the traffic state data sequence, carrying out change feature separation on the traffic change curve, classifying change features into rapid change components and trend change components, and carrying out quantitative processing on the separated change features. Through principal component analysis and wavelet transform, a component representing rapid disturbance and a component representing slow change accumulation are separated from the mixed traffic data, and a differentiated clustering model and a data-driven trigger rule are respectively constructed for the two modes with different essence. Therefore, the sudden accident and the slow-moving congestion can be distinguished from the source of the time sequence evolution, and the problems of instantaneous noise false report and trend event missing report are optimized.

In various examples, the systems and methods of this disclosure can train and use machine learning models to determine attributes and, in some cases, classifications related to traffic lights in order to establish traffic rules for the operation of a machine (e.g., an autonomous or semi-autonomous machine or vehicle) in an environment. For example, an image representing a traffic light device can be applied to a machine learning model that includes a variety of component heads. Each of the component heads can be trained to recognize different attributes and / or combinations of attributes associated with the traffic light device. Additionally, in some examples, the machine learning model can include a fusion head trained to classify the traffic light device.For example, the fusion head can classify the traffic light device using the detected attributes and / or using a combined feature vector from multiple feature vectors applied to the multitude of component heads.

This application relates to a signal transmission performance testing system, method, and electronic device for traffic lights. The system includes: a simulation module for generating original traffic light simulation data based on a target transmission test task; at least one roadside unit for broadcasting a combined signal generated from the original traffic light simulation data and real traffic light data to a preset signal coverage area after injecting the original traffic light simulation data; a vehicle terminal for extracting actual traffic light simulation data from the combined signal and generating corresponding traffic light recording data based on the actual traffic light simulation data; and an analysis module for analyzing the original traffic light simulation data and traffic light recording data to generate test results for the target transmission test task. This application's embodiments can flexibly simulate test scenarios through the simulation module and overlay simulated data and real data for unified broadcasting, ensuring the normal transmission of real traffic light information while completing performance testing.

The application discloses a traffic light shielding state recognition method and device and electronic equipment, and the method comprises the following steps: acquiring a road image of a current frame and performing target detection by using a preset target detection model to obtain a target detection result of the current frame; acquiring corresponding high-precision map data and a shielding state recognition result of a traffic light of a previous frame; determining a shielding state recognition strategy of the current frame by using the target detection result and the high-precision map data of the current frame based on the shielding state recognition result of the previous frame; and recognizing the shielding state of the traffic light of the current frame by using the shielding state recognition strategy of the current frame. The application determines the recognition strategy of the current frame based on the recognition result of the shielding state of the previous frame, in combination with the target detection result and the high-precision map, fully considers the influence of various errors in the actual scene on the shielding state judgment, realizes accurate and high-robustness shielding state judgment, and assists the planning control system of an autonomous vehicle to make correct decisions.

The invention relates to the field of traffic control, in particular to a regional traffic situation perception system based on fog computing, which comprises a perception acquisition module, an edge analysis module, a regional cognition module and a situation service module. The system acquires data through the sensing acquisition module; the edge analysis module constructs a digital twinborn model, and outputs a structured scene risk signal by using a multi-modal dynamic attention generative adversarial network and an intention reasoning model; the regional cognition module performs parallel simulation in the digital twin sand table and preferentially generates a collaborative strategy; and the system finally realizes closed-loop optimization of the internal model through a complete scene information packet formed by feedback. According to the invention, by constructing a cloud-edge collaborative digital twinning and multi-agent decision framework, intelligent optimization transformation of traffic management from passive response and local control to active pre-judgment and regional collaboration is realized.

According to one aspect, the present description relates to a system for controlling road traffic, the system comprising an intersection controller (110) configured to transmit electrical signals to optical units (21A, 22A, 23A) of traffic lights at the intersection, the electrical signals resulting from control signals, a transmitter (120) configured to transmit, to connected vehicles, messages configured to inform of an assumed state of at least one optical unit, and a safety module (130). The safety module comprises a receiver (135) configured to receive, via wireless link, each message transmitted by the transmitter (120); a measurement unit configured to determine, simultaneously on receiving the message, an actual state of the at least one optical unit; and a processing unit configured to generate an inconsistency signal in the event of a difference between the assumed state and the actual state of the at least one optical unit.

The invention discloses a traffic signal operation state monitoring method and system, and the method comprises the steps: firstly, monitoring the traffic signal operation state through an intrusive mode and a non-intrusive mode, in the non-intrusive mode, judging the fault state of a signal lamp through comparing a reference value of the signal lamp with an actual measurement value, and carrying out the fault detection of the signal lamp. According to the intrusive mode, the fault state of the signal lamp is judged by monitoring the input and output values of the signal lamp, and the two modes are respectively matched with a pose sensor to record the pose state parameters of the signal lamp. Data collected in the three modes are comprehensively compared through the fault monitoring platform to judge the fault condition of the signal lamp, the working state of the signal lamp can be reflected timely and accurately, and intelligent management of the traffic signal lamp is achieved.

The application provides a traffic accident multi-level early warning method and system based on spatio-temporal data mining, relates to the technical field of traffic safety, and comprises the following steps: acquiring spatio-temporal data of a target intersection and extracting a multi-dimensional spatio-temporal feature sequence, constructing a bidirectional time sequence correlation graph network model, analyzing congestion evolution mode and risk propagation characteristics, constructing a space propagation network based on a multi-agent interaction mechanism, calculating risk propagation intensity and generating a risk level prediction result, and dynamically adjusting traffic control measures, so that the accuracy and timeliness of traffic accident early warning are improved, and the traffic accident occurrence rate is reduced.

This application provides a method, device, equipment, and medium for alarming vehicle accidents, relating to the field of accident alarm technology. The method includes: acquiring a vehicle collision signal; if the collision signal exceeds a preset threshold, acquiring the status of the vehicle's communication module; if the vehicle's communication module status is abnormal, determining the status of the occupants and controlling the vehicle's drone to fly to a target observation location to collect external environmental data of the vehicle; controlling the drone's communication module to send vehicle accident alarm information to a target service device; the alarm information includes the collision signal, the status of the occupants, and the external environmental data. The vehicle accident alarm method, device, equipment, and medium provided by this application can enrich alarm information and improve the success rate of vehicle accident alarms.

A solar intelligent collision early warning mobile signal lamp relates to the field of traffic safety. In order to solve the technical defect that a signal lamp capable of realizing effective monitoring and timely early warning is not disclosed in the prior art, the technical scheme provided by the utility model is as follows: the signal lamp comprises a signal lamp main body; the solar cell panel is used for converting solar energy into electric energy; the LED signal lamp module is used for displaying traffic indication signals; the battery compartment is used for storing electric energy converted by the solar cell panel, and an intelligent signal control machine, a sensor data acquisition module and a communication module are arranged in the electronic control cabin; the sensor module comprises a temperature sensor, an acceleration sensor and a GPS positioning module which are respectively arranged at different positions of the main body; the sensor module sends acquired monitoring data to the sensor data acquisition module. The method is suitable for mobile traffic signal lamp safety control work requiring real-time monitoring and remote management.

The method for adjusting a distortion of a field of view of a camera in an intelligent transportation system comprises: receiving a target image, generating a first extraction result by extracting a predefined target object within the target image and a second extraction result by extracting the target object within a reference image, determining whether a distortion of the field of view of the target camera exists, using a first comparison result between the first extraction result and the second extraction result and a predefined threshold, determining a distortion type of the field of view of the target camera, using the first comparison result, when the distortion of the field of view of the target camera exists, and adjusting the distortion of the field of view of the target camera by using a different field of view adjustment scheme according to the distortion type of the target camera.

The invention discloses a digital vehicle lamp vehicle-road interaction system based on multi-modal sensing fusion, and relates to the technical field of intelligent driving and vehicle control, a roadside collaborative sensing unit is used for acquiring and processing multi-modal data of a roadside sensor, and generating a local dynamic map containing the position, speed and motion trail of a traffic participant; the vehicle-mounted multi-mode sensing unit is used for acquiring multi-mode data of a sensor of a vehicle and outputting vehicle-mounted sensing data; and the conflict detection and arbitration triggering unit is used for fusing the local dynamic map and the vehicle-mounted sensing data so as to identify traffic participants and predict passing intentions of the traffic participants. According to the invention, by introducing an intention uncertainty evaluation and dynamic negotiation mechanism, the problems of static and rigid interaction modes in the prior art are effectively solved. A traditional scheme cannot distinguish subtle intention differences of traffic participants and often causes invalid projection or communication loss, but the system can actively recognize conflict scenes with fuzzy intentions and trigger intelligent arbitration.

The invention discloses a traffic control, prompt and display method and a related device, which are applied to the field of intelligent driving. After the intelligent driving system is started, the intelligent driving system determines that a charging lane where the vehicle enters the toll station is a target lane and indicates the vehicle to pass through the target lane based on the type of the target lane. Wherein the type of the charging lane at least comprises an artificial lane and an ETC lane. Thus, through the intelligent driving system, passing of vehicles under different types of charging lanes in a toll station scene can be achieved, the driving burden of a user is reduced, and the passing efficiency of the vehicles in the toll station scene is improved.

The invention provides an emergency command and dispatch processing method and system applied to an expressway, and relates to the field of expressway emergency management.The method comprises the steps that firstly, scene correlation information of expressway emergency scenes is collected, and the scene correlation information comprises road operation, emergency events, emergency cooperation subjects, road network correlation and other information; performing multi-subject collaborative demand mapping processing based on the information to obtain scene adaptation, resource supply and action coordination demand information of each collaborative subject, then constructing a scene resource subject collaborative link, and determining corresponding relationships between scene demands and resource supply, between resource supply and subject actions, and between subject actions and scene changes; and generating a dynamic collaborative scheduling scheme according to the collaborative link and the scene association information updated in real time, covering the execution instruction, the resource flow path and the scene adaptation adjustment information of each collaborative main body, finally synchronizing the scheme to an execution terminal, dynamically updating the collaborative link and adjusting the scheduling scheme according to the scene change, and pushing the scheduling scheme. Coordination and dynamic adaptability of emergency command and dispatch of the expressway can be improved, and safety and smoothness of the road are guaranteed.

The invention discloses a traffic confluence area large-flow congestion early warning method and system, and the method comprises the steps: collecting key traffic flow parameters of a confluence area, and calculating a comprehensive congestion factor based on the parameters; performing unsupervised automatic classification on the historical congestion index data by applying a K-means clustering algorithm, dividing traffic states into three levels of'unblocked ', 'moderate congestion' and'severe congestion ', and determining corresponding classification thresholds; the method comprises the following steps: firstly, decomposing a congestion index sequence into three components, namely a tendency component, a seasonal component and a residual error component by using an STL algorithm; and then, for different characteristics of each component, respectively introducing a frequency domain adaptive normalization layer and an Informer model for prediction: for each component, filtering first K dominant frequency components in a Fourier domain by the FAN layer, adaptively reducing residual non-stationarity in the component, obtaining a more stable representation, and then inputting the representation into the Informer model for high-precision prediction, the method is suitable for a traffic control platform, and achieves the conversion from passive response to active intervention.

The invention relates to an equipment fault detection method based on time-space multi-source traffic data fusion, and the method comprises the steps: obtaining checkpoint vehicle passing data, an intersection equipment association relationship and intersection basic information, determining vehicle passing record information according to the checkpoint vehicle passing data and the intersection equipment association relationship, and constructing an alternative path information library according to the intersection basic information; performing data cleaning processing on the vehicle passing record information based on the license plate number to obtain final vehicle passing record information of each vehicle; aiming at the final vehicle passing record information of each vehicle, performing trajectory analysis by adopting a road network reachability basic diagram, determining two intersections with reachable paths, and verifying travel information between the two intersections in combination with an alternative path information base and a road speed limit condition to obtain suspected abnormal equipment; and determining the abnormal association strength of the suspected abnormal device and each associated device, and determining a fault device according to the abnormal association strength. According to the invention, the problems of low fault diagnosis and identification accuracy and low real-time performance in the prior art are solved.

The application relates to the technical field of a data model driving-based road infrastructure digital twin simulation prediction method, and particularly discloses a data model driving-based road infrastructure digital twin simulation prediction method. The method comprises the following steps: constructing a three-dimensional digital base model integrating road geometry, traffic facilities and environmental elements; dynamically slicing and returning in a lightened mode through a cloud rendering engine; realizing spatiotemporal dynamic updating of the digital twin by accessing multi-source real-time and historical data; coupling multi-agent traffic simulation and infrastructure degradation model to deduce future operation states; and generating maintenance priority and control optimization suggestions for a decision platform. Through the technical scheme, the road system is changed from static visualization to dynamic simulation prediction, and the traffic operation efficiency and the whole life cycle management level of the infrastructure are improved.