56results about "Detection of traffic movement" patented technology

The invention relates to the field of intelligent traffic, in particular to a traffic signal lamp positioning, recognizing and classifying method based on the video analysis. The color change regularity of traffic signal lamps is utilized, in other words, all traffic signal lamps are turned on and turned off regularly and last for a period of time, the color change rule of related pixel points in an image is tracked, the area position of the signal lamps in a surveillance video is recognized, then the position of the signal lamps is further confirmed, and the signal lamps are classified according to the image characteristics of the signal lamps. The method has the advantages that the operation is convenient, the signal lamps can be automatically detected and classified, and the method has good robustness and adaptability and provides the support for the automatic detection of the traffic law violation and signal lamp fault.

An intelligent static traffic management system comprises a first parking information collection unit, a traffic information management platform, a first monitoring unit and an access unit, wherein the traffic information management platform comprises an integrated management platform; the integrated management platform comprises a parking area basic information management unit, a parking area record management unit, a parking space early warning unit, a parking information release and data collection unit and a parking data optimization analysis unit; the parking area basic information management unit is used for recording parking area basic information; the parking area record management unit is used for recording parking area record information; the parking space early warning unit is used for analyzing the real-time parking condition of the parking area and giving early warning information when the amount of parking is close to saturation; the parking information release and data collection unit is used for collecting the working state of a parking device and displaying parking service information in real time; and the parking data optimization analysis unit is used for analyzing the utilization condition of a parking space and releasing a parking index.

The invention discloses a bidirectional LSTM network-based vehicle behavior identification method and system; the method comprises the following steps: invoking offline acquired traffic video data; detecting and tracking vehicles in the traffic video data, and extracting vehicle driving tracks; extracting features from preprocessed vehicle driving tracks, and building a training data set and a test data set; using a bidirectional LSTM recurrent neural network to make model training for the training data set, and forming a vehicle behavior identification model; inputting the test data set intothe vehicle behavior identification model to make precision assignment; inputting the traffic video data acquired online in real time in the vehicle behavior identification model with the precision assignment, identifying vehicle behaviors, and outputting an identification result. The method uses the vehicle behavior identification model based on the bidirectional LSTM network to identify the vehicle driving tracks in the traffic video data, thus determining the vehicle driving behaviors with a high identification precision.

The invention discloses a front-vehicle road condition display system. The front-vehicle road condition display system comprises a plurality of first vehicular units, second vehicular units and roadside units; the first vehicular units are used for collecting safety information of a first vehicle in real time through a vehicle terminal, and sending the safety information to the roadside units, wherein the safety information at least comprises a road condition video image in front of the first vehicle; the roadside units are used for receiving the safety information sent by the first vehicular units, and forwarding the safety information to the second vehicular units; the second vehicular units are used for receiving the safety information forwarded by the roadside units and sent by the first vehicular units, and at least displaying the road condition video image on a second vehicle. The invention further discloses a front-vehicle road condition display method. By adopting the front-vehicle road condition display system, road condition information of a front vehicle can be seen in a vehicle following process to improve the driving safety of the vehicles.

The invention relates to a dynamic V2V link delay predicting method in VANETs to solve the problems that the link delay cannot accurately and practically predicted, the vehicle-mounted network environment and the application requirement cannot be met through the method based on the MANETs, it is very hard for a driver to find out the universal law of automobile node velocity change, and then great difficulty is brought to link delay prediction. The method comprises the steps that firstly, the relative velocity value delta v of two automobiles is calculated; secondly, the relative displacement value delta S of two automobile heads is calculated; thirdly, the average speed difference value u and the variance sigma 2 of the formula seen in the specification are worked out; fourthly, the relative distance L is calculated; fifthly, the mathematical expectation value ET of link delay is calculated. The method is applied to the field of dynamic V2V link delay prediction in VANETs.

The invention relates to the field of artificial intelligence, discloses a traffic jam prediction method, device and equipment and a storage medium. The method is used for solving the technical problem that a traffic jam condition cannot be predicted in an existing urban intelligent traffic system. The method comprises the following steps: according to a traffic jam prediction request, obtaining traffic flow data at each traffic gate to obtain a traffic flow data set; analyzing the parameters of the traffic checkpoints in the traffic flow data set by using a big data analysis technology to obtain node parameters; calculating the relationship among the traffic checkpoints to obtain edge parameters; and generating a traffic map in a preset time period based on the node parameters and the edge parameters through a pre-constructed road network knowledge graph, and inputting the traffic map into a traffic prediction model pre-established based on a graph convolutional neural network for road condition prediction processing to obtain a predicted value of the congestion degree of each traffic gate corresponding to the traffic congestion prediction request. In addition, the invention also relates to a block chain technology, and the related information of the traffic flow data can be stored in a block chain.

The invention relates to a path dynamic grading method of an intersection group surrounding an express way entrance ramp. The method comprises the following steps: 1) determining an intersection groupsurrounding the entrance ramp; 2) computing traffic time sequence similarity of an approach of each intersection in the intersection group and the entrance ramp; and 3) and clustering the paths in the intersection group by taking the traffic time sequence similarity of each intersection and the entrance ramp, the distance between the intersection and the entrance ramp, and the intersection traffic as the feature vectors of the intersection approach, classifying path level as the traffic control reference base. Compared with the prior art, the limitation that the intersection group surroundingthe ramp is unconsidered since the collaborative control on the ramp and the ground intersection only consider the joint intersection is broken, a more comprehensive control range is proposed for theintersection group adjacent to the ramp; the grading control is adopted on the path in the region on the partition basis, a more precise control policy is proposed; the original data is analyzed based on de-noising, and the short-time change feature of the traffic stream is highlighted.

The invention discloses a traffic data restoration method based on a graph convolution time sequence generative adversarial network. The method comprises the following steps: obtaining an original traffic data set collected by traffic equipment, and carrying out abnormal value processing on the obtained original traffic data set by employing a unary Gaussian distribution outlier screening method;selecting a data set in a period of time from the data set subjected to abnormal value processing as a complete real data set, and randomly deleting the real data set according to different proportions to obtain a plurality of to-be-restored data sets; constructing a generative adversarial network model with restored traffic data by utilizing a generative network and a discrimination network; inputting the to-be-restored data sets into the generative network to obtain a reconstructed data set; then inputting the reconstructed data set and the real data set into the discrimination network together to complete dynamic adversarial training of the generative network and the discrimination network, so that the discrimination network cannot distinguish the reconstructed data set and the real data set; and carrying out traffic data restoration on the trained generative adversarial network.

A networked system for providing public space data on demand, including a plurality of vehicles driving on city and state roads, each vehicle including an edge device with processing capability that captures frames of its vicinity, a vehicle-to-vehicle network to which the plurality of vehicle are connected, receiving queries for specific types of frame data, propagating the queries to the plurality of vehicles, receiving replies to the queries from a portion of the plurality of vehicles, and delivering matched data by storing the matched data into a centralized storage server, and a learner digitizing the public space in accordance with the received replies to the queries.

The invention relates to a traffic jam detection system, method and apparatus based on an automobile electronic sign. The traffic jam detection system based on an automobile electronic sign includes first automobile electronic sign card readers, second automobile electronic sign card readers and a controller, wherein the first automobile electronic sign card readers are arranged over all the entrance lanes of a preset road section, and are used for reading the first automobile electronic sign quantity of vehicles passing the entrance lanes; the second automobile electronic sign card readers are arranged over all the exit lanes of the preset road section, and are used for reading the second automobile electronic sign quantity of vehicles passing the exit lanes; and the controller is connected with the first automobile electronic sign card readers and the second automobile electronic sign card readers, and is used for determining the jam state of the preset road section according to the received the quantity, of the vehicles passing the entrance lanes and the exit lanes, read by the first automobile electronic sign card readers and the second automobile electronic sign card readers.

The invention discloses a system and a method of monitoring illegal traffic parking in a yellow grid line area. The system comprises a high-definition camera, a light supplement lamp, an industrial control computer, network equipment and a computer monitoring system. The camera and the light supplement lamp are arranged on a portal frame of a monitored road section. The industrial control computer is arranged inside a roadside power supply box. The high-definition camera and the light supplement lamp are electrically connected with the industrial control computer through network cables. The industrial control computer is electrically connected with the computer monitoring system through the network equipment via a network cable. The industrial control computer receives shooting data of the high-definition camera and meanwhile identifies the license plates of illegally parking vehicles. The system has the beneficial effects that interference of trees, pedestrians and other motor vehicles is effectively filtered by selecting a binary morphology algorithm during an image segmenting and identifying process; and great anti-noise performance is achieved. By adopting an optimized image processing method, the system can monitor illegal parking behaviors in yellow grid line areas of four lanes simultaneously. The system achieves a high recognition rate, high accuracy and high real-time performance.

The invention belongs to the technical field of vehicle monitoring technology and relates to a self-adaption mobile network bus video monitoring terminal. The self-adaption mobile network bus video monitoring terminal comprises a system core module, a data acquisition module, a human-computer interaction module, a USB (universal serial bus) concentrator module, a wireless communication module, a mobile hard disk storage module, a CAN (controller area network) bus module and an SD (security digital) card storage module. The terminal is combined with embedding technology, H.264 network video compression technology and human body identification and tracking technology, can videotape drivers and conductors in buses, front doors and rear doors and inside condition of the buses during a whole journey, and calculates the number of passengers, provides support for multiple networks, dynamically selects bearing networks according to network condition, and provides smooth real-time videos and vehicle information of passenger number, GPS (global positioning system) positioning information and so on. Therefore, by the aid of the self-adaption mobile network bus video monitoring terminal, effective monitoring to safe running of the buses and normal work of the drivers and the conductors is provided, and particularly real-time condition inside the buses can be quickly known under the condition of emergencies.

The invention discloses a vehicle-mounted card type highway traffic flow statistics and traffic jam forecasting method. A vehicle-mounted card (1) and a plurality of electronic base stations (2) mounted on each section of a highway are used in the method. The electronic base stations (2) communicate with the passing vehicle-mounted card (1) through short-distance telecommunication, and the card number, the vehicle type, the driving direction and the passing time of the vehicle-mounted card are obtained, subjected to statistics and sent to a superior center through a long-distance radio data transmission circuit in a timed, quantified or remote control manner to serve as the bases for a traffic jam forecasting signal; the electronic base stations are low in power consumption and small in size, can be conveniently mounted on each section of the highway according to the traffic flow monitoring requirements, and can work in all weathers without weather environment influence; the superior center can timely obtain or take the traffic flow of each monitoring point and send out a traffic jam forecast to a highway administration department according to a set traffic flow threshold.

The invention discloses a deep learning method for predicting urban traffic jam based on GPS, and relates to the field of intelligent traffic management. Through the deep learning method, a unsupervised large-scale urban network traffic jam prediction method (CRC3D) is innovatively constructed based on GPS real-time data on urban taxies. The method is characterized in that a recursion neural network (RNN), a convolutional neural network (CNN) and a three-dimensional convolutional neural network (C3D) are combined. The recursion neural network and the three-dimensional convolutional neural network can efficiently capture time-varying characteristics of continuous traffic states from different angles. The method is good in effect and high in practicability.

The invention provides an identification and screening method of frequently congested road sections. The method comprising the following steps of: acquiring a historical congestion data, counting thecumulative congestion time duration of a certain road section, determining a probability distribution of the obeyed cumulative congestion time duration of the road section, calculating the reliabilityof the cumulative congestion time duration of the road section, and judging that the road section is a frequently congested road section when the reliability exceeds a set threshold epsilon 1; and under the condition of a given reliability, calculating the cumulative congestion time duration of a given reliability of the road section according to the probability distribution obeyed to the cumulative congestion time duration of the road section, and judging that the road section is a frequently congested road section when the cumulative congestion time of duration a given reliability exceeds the set threshold epsilon 2. According to the identification and screening method of frequently congested road sections, determining whether to be a congested road section by calculating the reliability of the road section accumulated congestion beyond the expected threshold T0 or calculating the cumulative congestion time for a given reliability, and an accurate and powerful scientific basis is provided for the traffic management department to screen the congested road sections.

The invention discloses a method for measuring an interval between highway detectors. The method comprises the following steps of: 1, acquiring the basic information indexes of a highway, wherein the unidirectional lane number n, the service level, the designed vehicle speed v and the maximum service flow rate k1 of the highway are taken as the basic information indexes; 2, acquiring the normal state flow rate k2 and the abnormal state flow rate k0 of vehicles on the highway; 3, detecting the time t0 of sensing the abnormal event of the highway by the detectors; and 4, measuring the interval between the highway detectors. By the method, the number of the detectors can be reduced and input cost can be lowered under the condition of fully ensuring the detecting effect of the detectors.

The invention discloses an urban road traffic jam degree evaluation method and a system and a storage medium, and the method comprises the steps: selecting a target road section, dividing the target road section into a plurality of standard segments, and calculating the corresponding road condition grade indexes of the standard segments; based on the road condition grade indexes of the continuousstandard small segments, setting a traffic jam threshold value, and searching a traffic jam source by utilizing a traffic jam source calculation algorithm; calculating a traffic jam damping coefficient of the traffic jam source according to the road condition grade index of the traffic jam section; based on the traffic jam damping coefficient calculated in real time, calculating an average blocking value of the road section in the time period; and after accumulating historical data for a certain time, calculating a historical maximum average blocking value of the road section, and calculatinga blocking index. According to the invention, the resistance to the traffic section caused by the discontinuous state due to the traffic jam source in the road section due to traffic jam can be recorded in real time, the traffic jam degree of the road and the process of spreading, diffusing and disappearing the traffic jam along with the time change can be visually reflected, and a decision basisis provided for the formulation of a traffic jam control strategy.

The embodiment of the invention discloses a method and a device for detecting a stationary vehicle state based on GPS data. The method comprises the following steps: receiving GPS data stream from a vehicle; acquiring multiple GPS coordinates of the vehicle in the specified time period; calculating the center point coordinate of the multiple GPS coordinates and the distance and direction distribution from each GPS coordinate to the center point coordinate; and determining the stationary state of the vehicle based on the distance and direction distribution. According to the method and device disclosed by the embodiment, the characteristics of the GPS data in the stationary state can be mined, so that the stationary vehicle state is detected based on the GPS data, the mileage calculation error in taxi taking software is greatly reduced, and the travel experience of users is improved. Meanwhile, the method and the device can be widely applied to the fields of urbanization calculation and internet of things.

The invention provides an abnormality detection method for bayonet equipment. The method comprises the steps: obtaining vehicle passing sensing data of a plurality of parts of bayonet equipment in a preset time and a preset range, and the vehicle passing sensing data comprises vehicle recognition information, bayonet position information and vehicle passing time information; according to the vehicle recognition information, analyzing the vehicle passing perception data of each vehicle in the plurality of bayonet devices, and obtaining vehicle passing state information of each vehicle in the to-be-detected bayonet device; According to the vehicle passing state information of each vehicle, obtaining a possible abnormal value of the to-be-detected gate device. According to the anomaly detection method for the bayonet equipment, the accuracy of the information acquired by the bayonet equipment can be reversely deduced through the vehicle sensing data acquired by the bayonet equipment, so that the anomaly condition of the bayonet equipment to be detected is judged, and the detection efficiency of the anomaly detection equipment on the bayonet equipment is effectively improved.

The invention relates to a method for realizing intersection lane index fusion processing based on a traffic flow and occupancy information. The method comprises the steps: inputting basic data, and collecting the flow of a lane group and the average occupancy of the lane group through an electronic police; converting the lane group flow into saturations of corresponding lanes, converting the saturations into traffic indexes according to road service levels, and converting the occupancy into the traffic indexes; and calculating and judging a ratio of the traffic indexes converted from saturations to the traffic indexes converted from the occupancy, and calculating the final traffic index according to different conditions. The method for realizing the intersection lane index fusion processing based on the traffic flow and the occupancy information is adopted, and the method belongs to a traffic state research and judgment method of urban lamp control intersections. A good effect is achieved in practical engineering project application, a fusion index model based on the flow and the occupancy as the indexes is established in different traffic states, and a problem that the traffic state is inaccurately researched and judged due to a fact that quality of the occupancy collected by a traffic information collection system is not high is avoided.

The invention belongs to the technical field of traffic control, and provides a coordination control method for multiple MFD sub-area boundaries based on a random distributed control algorithm. The problem about the uneven distribution of traffic resources in the morning and evening peak hours is solved. The traffic control method comprises the following steps of: S1, modeling and collecting data;S2, estimating the vehicle number probability distribution; S3, establishing a basis function representation model of a vehicle number probability distribution density function; S4, establishing a nonlinear prediction model between the input variable and the first n-1 weight vectors; and S5, solving the output of the control quantity. The output control is the green-time-rate matrix of traffic lights of each area boundary, and the number of vehicles in each area is adjusted by changing the duration of the traffic lights at the area boundaries. According to the coordination control method forthe multiple MFD sub-area boundaries based on the random distributed control algorithm, the problem of unreasonable distribution of the vehicles on urban road network can be minimized, and the utilization efficiency of the urban road network is improved.

The invention provides a traffic monitoring method and a traffic monitoring system based on unmanned aerial vehicle image recognition, and the traffic monitoring method comprises the following steps: S1, providing an unmanned aerial vehicle aerial traffic picture as a sample picture, and carrying out the vehicle marking of the sample picture through image marking software LabelImg, and forming a sample set; s2, preprocessing the sample set by adopting a data enhancement method to form a training set; s3, constructing a YOLO v3 model, training the YOLO v3 model through the training set, and obtaining the trained YOLO v3 model; and S4, receiving an aerial traffic image of the unmanned aerial vehicle in real time, inputting the aerial traffic image into the trained YOLO v3 model to monitor and analyze the vehicle, and calculating to obtain real-time traffic monitoring data. According to the traffic monitoring method and the traffic monitoring system, macroscopic and accurate monitoring data can be obtained in real time, and the traffic situation can be studied, judged and decided in time.

A traffic monitoring apparatus capable of determining a cause of traffic congestion more definitely is provided. A traffic monitoring apparatus (10) includes a vehicle information acquisition unit (11), an additional information acquisition unit (12), a congestion determination unit (13), and a cause determination unit (14). The vehicle information acquisition unit (11) acquires vehicle information regarding a travelling state of a vehicle from data received from a detection apparatus (20). The additional information acquisition unit (12) acquires additional information regarding objects that are other than the travelling vehicle and are present in the vicinity of the travelling vehicle. The congestion determination unit (13) determines, for each of a plurality of lanes on the road, whether or not congestion is occurring based on the vehicle information. The cause determination unit (14) determines, for the lane that has been determined to be congested, a cause of congestion using at least the additional information.

The invention discloses an automatic reminder system for vehicle violations. The system includes: a first collection unit used for real-time collection of vehicle driving information; a second collection unit for real-time collection Road violation regulation information; a judging unit, configured to determine whether a vehicle violates regulations based on the driving information and the violation regulation information, and the type and reason of the violation; a reminder unit, configured to judge The judgment result of the unit is displayed on the monitor, and the judgment result is transmitted to the vehicle owner in real time, which realizes a reasonable system design, good real-time reminder of the system, and real-time monitoring of the technical effect of the vehicle.