717 results about "Smart transportation" patented technology

The present invention relates to a ‘smart traffic ticket device’ with means to 1) determine the identity of a vehicle; 2) determine the identity of the offending driver; 3) issue traffic tickets according to applicable traffic laws. The smart traffic ticket device comprises of; 1 ) an interrogator in the form of a radiofrequency (RF) reader; 2) communication means comprising of a wireless transceiver, modem and communication ports; and 3) a central processing unit comprising of a processor and memory chip that controls the functioning of the smart traffic ticket device. Transponders, comprising of radiofrequency (RF) tags, are mounted on vehicles; and contain vehicle identification information. Similarly, transponders, comprising of radiofrequency (RF) tags, are present on the driver's license; and contain driver identification information. The RF reader of the smart traffic ticket device has means to interrogate the RF tag on vehicles and the RF tag on the driver's license from a safe distance; both while the said vehicle is at rest and while the said vehicle is in motion. The information thus obtained is used to issue a traffic ticket bearing; 1 ) vehicle identification information; 2) driver identification information and; 3) nature of violation and; 4) the associated fine. According to another aspect of the present invention, the smart traffic ticket device is equipped with a traffic violation sensing device, such as a speed sensor, to form a ‘smart speeding ticket device’ . The smart speeding ticket device has means to 1) identify a vehicle driving over allowed speed limit; 2) determine the identity of the said speeding vehicle; 3) determine the identity of the driver of the said speeding vehicle; and 3) issue a speeding ticket to the said speeding vehicle according the applicable traffic laws.

A large-scale real-time traffic flow prediction method of the present invention is based on fuzzy logic and deep LSTM, which relates to a technical field of urban intelligent traffic management. The method includes steps of: selecting an urban road network scene to collect color images of real-time traffic flow congestion information; obtaining congestion levels of multiple intersections according to the color images, which are used in a data training set; and forming a data sensing end of FDFP through a fuzzy mechanism; establishing a deep LSTM neural network, performing deep learning on the training data set, and constructing a prediction end of the FDFP; construct a graph of road intersections and formulate a k-nearest neighbors-based discounted averaging for obtaining congestion on the edges; and inputting real-time traffic information received from a server into an FDFP model.

The embodiment of invention discloses a method, a device and a system of intelligent traffic control, wherein the method is that according to resource needs of a current task of an application platform, corresponding resource nodes are called by the intelligent traffic control device from a cloud computing basic resource pool, the corresponding resource nodes which are called is configured to form a server cluster, the cloud computing basic resource pool comprises computing resource nodes, storage resource nodes and network resource nodes, the current task is broken up into a plurality of subtasks by the intelligent traffic control device, then the plurality of subtasks are respectively configured to the server cluster and are concurrently processed, and then a plurality of subprocess results are obtained, the plurality of subprocess results are aggregated through the intelligent traffic control device, and a processed result of the current task is obtained and is returned to the application platform. According to the intelligent traffic control method, device and system, resources are shared among all the application platforms, resource utilization rate is improved, and event processing efficiency is improved.

The invention discloses a deep learning and intelligent street lamp-based traffic signal lamp intelligent control system, and belongs to the field of intelligent public traffic. The system comprises a roadside unit and an intelligent traffic signal lamp, wherein the roadside unit and the intelligent traffic signal lamp are arranged at a traffic road intersection. The roadside unit comprises an image acquisition module, a vehicle detection module, a congestion judging module and a communication module. The image acquisition module collects road traffic images in each intersection direction and sends the images to the vehicle detection module. In this way, the vehicle flow in each intersection direction is obtained. Meanwhile, the congestion judging module determines the congestion level in each intersection direction in combination with the vehicle flow, and transmits the congestion level to the intelligent traffic signal lamp through the communication module. The intelligent traffic signal lamp adjusts and displays the transit time in real time based on the congestion level. According to the technical scheme of the invention, the road traffic condition is detected in real time. For different road sections, different congestion level threshold values are set flexibly. Therefore, the congestion condition can be automatically judged and the transit time in each direction at the intersection is adjusted. As a result, traffic signal lamps are more flexible and more intelligent to use.

The invention discloses an intelligent traffic system based on light emitting diode (LED) visible light communication, and belongs to the field of intelligent traffic systems. The intelligent traffic system comprises LED traffic lamps, a light emitter and a light receiver, wherein the light receiver and the light emitter are arranged on one vehicle; the light receiver on a back vehicle receives a signal sent by the light emitter on a front vehicle; signals sent by the LED traffic lamps comprise a road condition and traffic lamp conditions; the signal sent by the light emitter comprises a distance between the front vehicle and the rear vehicle and a warning signal; and the light receiver receives the signals sent by the LED traffic lamps and the light emitter. Due to the method, a visible light intelligent traffic network is formed, so that road condition information, the distance between the front vehicle and the rear vehicle, the warning signal and the traffic lamp conditions which are sent by the LED traffic lamps can be received immediately; and therefore, the traffic safety is guaranteed, and the probability of traffic accidents is reduced.

The invention belongs to the field of short-time intelligent traffic control, and particularly relates to a short-time traffic flow control method based on deep learning and spatio-temporal data fusion. The method comprises the steps of: obtaining a data source; analyzing the time and space relevance of the historical data of an intersection; respectively establishing a time dimension GRU model and a space dimension CNN regression model according to the historical data of a prediction checkpoint and the related intersection thereof; fusing output results of the time dimension GRU model and thespace dimension CNN regression model to obtain an adaptive spatio-temporal data fusion model; and counting the prediction result of the adaptive spatio-temporal data fusion model, and sending the prediction result to a traffic department. According to the method, on the space-time level, the space-time dependence of a road traffic flow in a complex road network is analyzed, the network representation learning is used for screening the traffic data, and the screened data is used for inputting the model, so that the accuracy of the traffic flow prediction result of the intersection is improved.

The invention belongs to the field of intelligent traffic, and relates to an urban road vehicle running speed forecasting method based on road network characteristics. The urban road vehicle running speed forecasting method can be applied to forecasting the vehicle running speed on an urban road during a period of time, a model is improved on the basis of a k-nearest neighbor nonparametric regression method, relations between road sections in a road network are considered, and a matched time series state vector is enlarged into a multi-dimensional space-time state matrix. A sample database is built through collected historical data, real-time data are collected for serving as a template to be matched with a sample, and the vehicle speed of a next time series of a target road section in the obtained neighbor sample serves as the forecasted vehicle speed. The gaussian function is used in the model two times for setting weights for the state matrix and forecasting results in an integrated mode so that the forecasting accuracy can be improved. The model provided in the urban road vehicle running speed forecasting method has the advantages that the data of the small road network with the road section to be detected as a center serve as the state matrix; compared with a prior forecasting model which only gives consideration to data of a current forecasted road section, the model provided in the urban road vehicle running speed forecasting method is higher in accuracy of multi-step forecasting; in addition, the method can offset real-time data or can be used for forecasting under the condition that a road section speed detector breaks down.

The invention discloses an intelligent traffic signal control system based on a high-definition video technology, which comprises external station equipment, a communication network and central system equipment, wherein the external station equipment uses each intersection as a unit, and all directions of each intersection are respectively provided with high-definition cameras; the cameras are connected with a high-definition video detecting host machine and installed in a signal case; a signal lamp is connected into a traffic signal control machine; network equipment in an intersection signal machine is intercommunicated and interconnected with the central system; the communication network comprises three parts, namely the network connection from the intersection signal machine to a signal control communication unit, a communication unit and the internal network connection of the central system equipment; and the central system equipment is divided into traffic signal control system equipment, traffic violation processing system equipment, traffic video monitoring system equipment and an operating terminal according to the function. By utilizing the high-definition video technology, the intelligent traffic signal control system can realize the detection of dynamic traffic flow data, traffic data acquisition and traffic enforcement function.

The invention belongs to the field of intelligent transportation, and in particular relates to a method for predicting urban traffic flow based on time-space correlation. The method includes the following steps: 1) forecasting model training: generate corresponding forecasting models according to different time forecasting granularities; 2) real-time traffic forecasting: consistent with the model training process, the latest collected traffic will be used for traffic flow forecasting The data is added to the input end of the forecasting model, and then processed by the forecasting model to output the forecasted traffic for the next period. The invention accurately predicts urban road traffic flow, can realize intelligent traffic control and management, traffic information service, and provides real-time data for alleviating urban traffic congestion, and has significant social and economic benefits.

The embodiment of the invention, which relates to the intelligent traffic field, discloses a detection method, apparatus, and system of an oversized vehicle, thereby improving detection accuracy of an oversized vehicle based on simplification of hardware complexity. According to the embodiment of the invention, when occurrence of a vehicle licence plate in a current view is identified, two shooting units of a binocular camera are triggered to carry out synchronous shooting on a same to-be-detected vehicle and two image frames that are obtained by synchronous shooting are obtained; contours of the to-be-detected vehicle in the two image frames are identified, and a three-dimensional contour model of the to-be-detected vehicle under the world coordinate system is constructed according to the identified contours as well as a relative pose between the two shooting units of the binocular camera; statistics of a distribution situation of all pixel points under the world coordinate system in the three-dimensional contour model is carried out, and the size of the to-be-detected vehicle is determined according to the statistical distribution situation; and when the size exceeds a preset threshold value, the to-be-detected vehicle is determined to be an oversized vehicle. Therefore, the problems in the prior art can be solved.

The invention provides a dynamic graph convolution traffic speed prediction method which comprises the steps: step 1, matching vehicle GPS trajectory data into an urban road network, and obtaining a traffic speed time sequence of each road section; step 2, regarding road sections of the urban road network as graph nodes, regarding intersections of the urban road network as connecting edges of a graph, constructing a road network graph, and obtaining an adjacent matrix between the road sections; step 3, calculating the traffic speed similarity between adjacent road sections according to the traffic speed time sequence of each road section, and obtaining a real-time adjacent road section similarity matrix; and step 4, inputting the traffic speed time sequence of each road section and the adjacent road section similarity matrix into a graph convolution network for training to obtain a future road section traffic speed prediction result. According to the invention, spatial dependence and time dependence between road sections can be learned in real time, the change rule of the traffic speed can be captured, the speed of future urban roads can be predicted more accurately, and the methodcan be applied to intelligent traffic and smart city construction.

The invention belongs to the field of traffic safety and intelligent traffic management control, in particular to a real-time estimating method for high-grade road traffic flow running risks. The real-time estimating method for the high-grade road traffic flow running risks considers the problem that a high-grade road short of fixed-point traffic flow collecting facility is incapable of acquiring the road traffic flow rate, occupancy and the like traffic parameters. The real-time estimating method for high-grade road traffic flow running risks includes that using traffic flow speed data acquired through different traffic information collecting technologies to build a real-time accident forecasting model, using a dynamic Bayesian network model to consider the speed state data of several time periods, building relationships between the traffic flow state and accident risks, and estimating the accident in real time so as to early warn or regulate a vehicle to avoid an accident. The real-time estimating method for the high-grade road traffic flow running risks has good forecasting precision for the high-grade road accident risks through speed data, and the real-time estimating method for the high-grade road traffic flow running risks has broad practical application value.

The invention discloses an intelligent traffic path planning method based on federated learning and digital twinning. The method comprises the following steps of: S1, carrying out vehicle registration in a vehicle system, and verifying the identity information of vehicles; S2, according to the local historical data of the vehicles, participating in federated learning, and training a local model; S3, aggregating the local models of all the vehicles to obtain an aggregation model; S4, judging whether the aggregation model converges to preset precision or exceeds a time limit or not, if yes, entering the step S5, and otherwise, returning to the step S2; S5, establishing an Internet-of-vehicles global digital twinning model; S6, periodically updating the Internet-of-vehicles global digital twinning model; and S7, initiating a path finding request to a roadside unit, and updating the optimal path and the local prediction model in real time. The planning method provided by the invention is applied to the field of Internet of vehicles, so as to solve the problems of low flow prediction and path planning accuracy, high time delay and privacy leakage risk in the current road traffic system.

The invention relates to a method and a system for automatically generating an urban road traffic guidance strategy. According to the method and the system, by applying the intelligent traffic theory, the spatial autocorrelation theory, the fuzzy mathematic theory, an statistical method and the computer technology, real-time traffic flow data and historical data are researched and processed, and therefore, under the condition that a traffic incident occurs in a road traffic network, the traffic guidance strategy which can be released in time through VMS (variable message signs) on a road is automatically generated. According to the method and the system, by taking historical artificial guidance information and the real-time traffic flow data as input data, on the basis that historical guidance empirical laws are analyzed and real-time traffic flow features are excavated, a final guidance release strategy is determined by applying a traffic state spatial inequality analysis method provided by the invention. A proposal and strategy scheme can be provided for the quick release of the guidance information of an urban traffic management department when certain traffic incidents occur, such as road accidents, congestion, severe weather, management and control, and the like.

The invention relates to the field of intelligent transportation forecasting and in particular relates to a short-term traffic flow forecasting method. The short-term traffic flow forecasting method comprises the steps of 1), collecting data of flows and speeds of historical records and presetting traffic states according to combinations formed by the flows and the speeds; 2), building a forecasting model; 3), inputting the traffic states of two historical moments x(t-1) and x(t) corresponding to actual forecasting traffic states, scheduling the forecasting model for forecasting and outputting a forecasting state; and 4), recording an actual state, calculating an error between the forecasting state and the actual state, and recording and saving an error result. The short-term traffic flow forecasting method has the beneficial effects that the method is low in calculation complexity and capable of reflecting road condition changes in real time; meanwhile, the historical data of 10 minutes near the future moment are only taken into account by the algorithm; the method is good in real-time performance; a 5-minute forecasting time duration meets delay of transmission and processing and is applicable to real-time display of a traffic induction screen; the forecasting model is simple and quick to build, applicable to traffic road conditions with various complexities and high in forecasting precision.

The invention discloses an intelligent traffic simulation system and method for an intelligent vehicle, and the system comprises a three-dimensional virtual driving environment module which is used for simulating the driving state of the intelligent vehicle; a training data acquisition module which is used for acquiring training data from the three-dimensional virtual driving environment; a training model module which is used for training a driving model according to the training data; and the three-dimensional virtual driving environment module is provided with an external control interface for controlling the intelligent automobile. According to the invention, the three-dimensional virtual driving environment module is used for simulation training, and compared with real vehicle trainingin a real scene, the trial and error cost is reduced, and the development safety is improved. The method can be widely applied to the technical field of computer simulation.

The invention discloses an expressway entrance ramp confluence control system and method, and belongs to the field of intelligent traffic control and management. The traffic state of a ramp confluence area can be rapidly and effectively collected and analyzed by utilizing the characteristics of all-day and all-weather working capability, high detection precision and the like of millimeter wave radar; intelligent holographic guidance is carried out on vehicles in a confluence area, so that the driving efficiency and safety of the expressway are improved; according to the invention, a noise target is eliminated by using a noise elimination algorithm and a vehicle driving track prediction algorithm based on Kalman filtering, pure driving data information of a vehicle is obtained, and a good noise filtering effect is achieved; based on a ramp confluence area accident prediction comprehensive algorithm, quantitative discrimination indexes of ramp vehicle conflicts under different conditions are formed, a final dangerous condition is determined according to a data fitting result, and an LED gives an early warning prompt, so that the vehicle-road cooperation capability and the confluence efficiency are greatly improved.

The invention discloses an unmanned vehicle control method based on over-the-horizon synergetic cognition and an unmanned vehicle control system based on over-the-horizon synergetic cognition, and belongs to the field of intelligent traffic. The control system comprises an unmanned vehicle and an unmanned driving network supporting platform. The unmanned vehicle comprises an individual situation cognition system and a driving decision generation system. The unmanned driving network supporting platform comprises a global situation cognition system, a local situation cognition system and a driving decision generation system. The control method applying the control system comprises the steps that firstly the individual situation cognition system forms micro driving situation cognition; then the driving decision generation system receives information and processes the information and then issues the information to the driving decision generation system, and the driving decision generationsystem generates the final driving operation; and finally the unmanned vehicle receives and performs the final driving operation through the driving actuator. Real-time cognition of the traffic environmental situation is performed on the network terminal so that safe, efficient and reliable unmanned driving can be realized and the operability and the effectiveness of the unmanned vehicle controlsservice can be enhanced.