46 results about "Adaptive traffic control" patented technology

A multiobjective management system for saturated traffic road networks comprising: green wave coordination of locally adaptive traffic control units, traffic movement optimization and live traffic route guidance. Current traffic congestion measurements on intersections are generated from local traffic cameras and remote air-borne conventional cameras and thermal sensing imaging cameras or satellite radar such as SAR / ISAR using optical image brightness analysis. At the first stage of traffic optimization, individual local intersection green times are computed based on current traffic congestion level. At the second stage optimization, the central traffic server uses a multiobjective approach to coordinate the current locally-optimized green times of the first stage and create input constraints for green-way coordination of plurality of traffic lights. The server updates dynamically current cycle start and green times on all network-connected traffic light controllers and also broadcasts recommended travel times, green times and green waves to all on-line client vehicle navigation units. Traffic server and individual client guidance units utilize novel time-dependent modifications of an A*-type algorithm to update current travel and recommended travel times and to execute fastest route searches.

The invention discloses a multi-strategy and multi-object self-adaptation traffic control method. The multi-strategy and multi-object self-adaptation traffic control method is characterized by comprising the following steps that (1), traffic state inspection equipment collects traffic flow data in real time, obtains the traffic state information and sends the traffic state information to an intersection signal machine, and the intersection signal machine uploads the traffic flow data to a signal center control system; (2), the intersection signal machine calculates the traffic intensity of an intersection, judges the traffic states of the intersection and adjusts the control plan of the current intersection in real time according to the different traffic states; (3), according to the intersection traffic flow data uploaded in real time, the signal center control system optimizes the intersection signal control scheme from the global angle of a road network. According to the control method, the road network grade global optimization self-adaptation signal control method is provided through judgment and analysis of the traffic states of the road network, and artery coordination control applicable to any traffic state is achieved.

The invention relates to a cooperative self-adaptive cruise control system algorithm based on traffic information. The traffic information serves as a condition capable of being obtained, prediction control is carried out through a model, and the riding comfort, the fuel economy, the safety and the car following performance serve as to-be-optimized targets; meanwhile, vehicle capacity limitation is added and respectively converted into corresponding optimal targets and system limitation, and as a cost function is built and minimized, a optimal solution sequence is obtained, and a first value is taken and applied to the system to optimize targets. According to the cooperative self-adaptive cruise control system algorithm, on the scene that signal lamps and front waiting motorcades of a CACC system exist in city working condition, more pieces of interference are treated to achieve that the speed of the vehicles is more actively adjusted, unnecessary speed keeping or large speed reduction is reduced so that the fuel economy and the riding comfort can be improved to achieve multi-objective optimization, the condition that as the vehicles follow some radical drivers, the driving behaviors of the vehicles are also radical is reduced, and optimal speed adjustment can be achieved according to the periphery environment.

The invention relates to an urban intelligent traffic signal control system and method. According to the present invention, the real-time hierarchical control over the area is achieved on the basis ofa hierarchical reinforcement learning method, the adaptive flow control and the data feedback of the intersection are achieved on the basis of the deep reinforcement learning method, the abnormal flow monitoring is achieved, the calculation efficiency is greatly improved, and the intelligent control, adaptive flow and emergency situations of the intersection in the area are achieved. By dependingon the data to generate a reliable scheme, a closed loop of the traffic signal control key three-factor data, the scheme and the signal equipment is formed, so that the requirements of the travelersfor the traffic control that the traffic flow change can be responded real-timely and the red light waiting time is shortened, can be realized.

The invention relates to a self-adaptive control system for traffic intersection signal lamps. The control system comprises multiple cameras, multiple signal lamp controllers and a far-end monitoring platform. The cameras are in one-to-one correspondence with the signal lamp controllers. Each camera is correspondingly connected with one signal lamp controller and used for shooting the intersection where the signal lamp controlled by the corresponding signal lamp controller is located and obtaining intersection images. The signal lamp controllers conduct compression encoding on the intersection images, and package intersection image coded data into a UDP packet so as to output intersection image network data. The far-end monitoring platform is connected with the multiple signal lamp controllers through a 4G network so as to obtain the image network data of multiple intersections and conduct self-adaptive control over the multiple signal lamps controlled by the signal lamp controllers respectively on the basis of the image network data of the multiple intersections. The self-adaptive control system can determine the lasting time of traffic lights of the signal lamps at the multiple intersections in a self-adaptive mode according to specific traffic conditions of the multiple intersections on the same road section, thereby achieving reasonable dispatching of traffic flow.

An adaptive traffic control subarea dividing method based on spatial data mining belongs to the field of traffic technology. The method aims to settle a problem of relatively low outgoing efficiency of a control area which is divided by an existing control subarea dividing method. The method comprises the steps of firstly performing characteristic extraction on a peak time period, a valley time period, a high flow threshold, a middle flow threshold and a low flow threshold, selecting a traffic information characteristic which affects dividing of the control subarea; then establishing an influence weight calculating model based on a time-space characteristic, obtaining the weight of a characteristic r, and finally realizing traffic control subarea based on a weighted commodity detection algorithm divided by a dynamic module degree. The adaptive traffic control subarea dividing method is suitable for dividing the traffic control subarea.

The invention discloses a system for simulating and training the program control of an air traffic control. The system comprises a simulation server, a comprehensive application server, a switchboard, a technology monitoring position, a training master control position, at least one training unit, an inner telephone and a display device. By using the system disclosed by the invention, the control on-duty environment can be realistically simulated, the united simulation training of an area, a zone and an airport can be realized, the working procedure of each link of the program control can be fully trained, the control process can be comprehensively understood by a control person, and the control program can be comprehensively mastered by the control person. According to the system disclosed by the invention, a network adopts a hierarchical design, the network design is simplified, the detection and the removing of primary fault are realized; and a dual-network design of a voice network and a data network is adopted, the training command transmission is reliable, the data traffic of the data network is simultaneously reduced to a large extent, and the low-delay and less-vibration performance of voice transmission is solved by the independent voice network. The invention also provides a training method of a single training mode, a control on-duty mode and a united control on-duty mode of the system for simulating and training the program control of the air traffic control.

The present invention relates to an adaptive traffic control system and its method. It is characterized by that its core technique adopts traffic signal engineering technique, and adopts mixed traffic control algorithm, and provides an algorithm for controlling motor vehicle, pedestrain and bicycle so as to form actual control function. Said invention can be used for making mixed traffic control.

The present invention discloses a multi-model-based servo system adaptive control system which belongs to the electromechanical control field. The system comprises a multi-mode parameter estimation module, a switching mechanism module and an adaptive controller, wherein a servo system is controlled by the adaptive control system. The multi-mode parameter estimation module comprises n fixing modules, an identification model and an adaptive model, and each module in the multi-mode parameter estimation module outputs a set of model parameters aiming at the servo system and inputs to the switching mechanism module. The switching mechanism module selects a set of model parameters making a cost function to be minimum from the multiple sets of model parameters as the optimal model parameters, and inputs to the adaptive controller, and the adaptive controller carriers out the adaptive control law calculation on the inputted optimal model parameters to obtain an adaptive controlled quantity, and inputs to the controlled object. The system can obtain the estimation parameters which can represent the system dynamic characteristics best without needing to obtain the priori knowledge about the system uncertainty.

The invention relates to a signal control system and signal control method based on traffic simulation. The signal control system comprises a data acquisition module, a simulation and forecast merging module, an intelligent traffic signal lamp control module and a traffic guidance module. The data acquired by a last intersection acquisition module are sent to the simulation and forecast merging module, the traffic flow data of a current intersection and the data acquired at the last intersection are sent to the intelligent traffic signal lamp control module to carry out self-adaptation traffic control, accordingly, a detector set at the current intersection is replaced, half detectors in a road section can be replaced and removed, the purchasing and maintaining cost are greatly saved, the intersections of upstream and downstream are corelated, the signal timing dial is uniformly coordinated and adjusted, the intercommunication of a road network is enhanced, a scheme capable of improving the traffic capacity of the road section can be designed, and the problem of traffic jam in cities at present can be relieved.

The invention relates to an intelligent traffic cloud control system for onsite big data collection, centralized analysis and control of a traffic system. Being different from the working mode of a traditional traffic guidance control system that data are separately collected and transmitted by different sensing devices, and a central system summarizes, analyzes and processes the information, in the intelligent traffic cloud control system provided by the invention, a field control server for broadband bus communication based on an IP address performs centralized analysis and processing of big data, and achieves adaptive traffic control, traffic law enforcement, locating and tracking, cooperative control and other functions and services by means of the fused edge computing and cloud computing of a plurality of adjacent file control servers. The invention provides a solution for realizing high integration of high reliability, high instantaneity and control functions of big data processing of the intelligent traffic cloud control system.

The invention discloses an adaptive tracking control method and an adaptive tracking control system with output constraints for sea-surface all-drive ships. The adaptive tracking control method includes acquiring sea-surface all-drive ship system dynamic models; receiving expected target trajectories and output constraint conditions which are set by users; computing ship control parameters according to the output constraint conditions and the all-drive ship system dynamic models and outputting the ship control parameters to ship control systems. The output constraint conditions include trajectory errors and ship swaying velocity errors. The adaptive tracking control method and the adaptive tracking control system have the advantages that movement trajectory errors of the ships can be accurately controlled on the basis of the adaptive tracking control method, and accordingly the ships can assuredly move according to given target trajectories.

Systems and methods for adaptive control are disclosed. The systems and methods can control uncertain dynamic systems. The control system can comprise a controller that employs a parameter dependent Riccati equation. The controller can produce a response that causes the state of the system to remain bounded. The control system can control both minimum phase and non-minimum phase systems. The control system can augment an existing, non-adaptive control design without modifying the gains employed in that design. The control system can also avoid the use of high gains in both the observer design and the adaptive control law.

An electric scooter with a lighting system that includes a rear mounted light that projects light upward and toward the front of the scooter to illuminate the back of a rider, and side lights that project light to illuminate the sides of a scooter. The lighting system employs a multi-faceted approach to vary intensity and effects for improved visibility in traffic. The light system can be configured to automatically illuminate or change effects in response to road or environmental conditions, or in response to existing or future roadway infrastructure such as autonomous traffic infrastructure or adaptive traffic control systems. The light system may also be integrated with the braking system for signaling a slow down or stop. The system may be controlled by communication between an onboard processor and a personal computing device such as a smart phone that can be docked on the scooter and provide display of information and a means of input.

The invention relates to the field of data collection and traffic control, in particular to a system monitoring data collecting method with dynamically controllable traffic. The system monitoring data collecting method with the dynamically controllable traffic mainly comprises three parts, namely monitoring data asynchronous collection and processing, traffic control starting detection and traffic control data combination. According to the monitoring data asynchronous collection and processing method, by calling a system thread interface, an independent monitoring data processing thread is started to take charge of multi-target processing output of system data after a monitoring module is initialized; the total period of dynamitic traffic control can be divided into two parts, namely a traffic control detection part Td and a traffic control execution part Te. Compared with the prior art, the system monitoring data collecting method with the dynamically controllable traffic has the advantages that through a traffic speed weight control algorithm, stable operation of a key real-time system is guaranteed, the collecting requirement of the monitoring data of different monitoring targets are effectively met, and the quality of ground data is guaranteed through a combination method of feature data.

The invention discloses a tractor self-adaptive control system, and relates to the field of application and energy conservation of escalator power devices. Related running parameters of a tractor aremonitored in real time according to a multi-sensor sensing principle, and signals collected by sensors are received in real time through a PID controller to control the escalator tractor in real time.The running parameters and other related feature information of the tractor are extracted; a neural network model is established according to the related feature information to describe the influences of the running parameters of the tractor on the escalator tractor, and the weight of a neural network is optimized through a differential evolutionary algorithm on the basis of the established neural network model; self-adaptive control is conducted on the escalator tractor according to the established optimization model. By means of the method, the running parameters are automatically recognized and corrected through the controller, and the adaptability of the tractor to the running environment is enhanced.

A current or forecasted weather condition at an intersection or roadway may be identified using cameras and / or other weather sensors connected with traffic infrastructure installed at the intersection or roadway. Adverse traffic events typically associated with weather conditions of the same type as the identified weather condition may be determined, for example based on historical records of correlations between weather and traffic. The traffic infrastructure may adjust an option, for example by adjusting traffic signal timings, to mitigate the adverse traffic event in response to identifying the weather condition.

The invention provides a self-adaptive traffic control system, which includes a traffic central controller, an area controller and traffic indicators, wherein the traffic central controller includes a traffic condition predicator and a central sychrotimer; the area controller includes a traffic condition calculator and a self-adjusting processor; the traffic indicator includes a wireless transceiver, a signal adjusting processor and a pulse data collector; and the traffic central controller, the area controller and the traffic indicators are connected through a wireless system. Furthermore, the traffic condition predicator processes the data collected by the traffic indicators in the wireless network through the pulse data collectors, so as to predict the traffic condition. The self-adaptive traffic control system is improved from the existing scats traffic management system, facilitates the traffic flow, and becomes more practical by adding T-road processors.

The invention discloses a flat-peak adaptive traffic control method and apparatus. In the method, in a flat-peak state, a first motorcade entering a light-controlled intersection is detected by a traffic control device arranged at the current light-controlled intersection, when there is no vehicle or pedestrian generating conflict with the first entering motorcade in the light-controlled intersection, a signal light in a light control direction where the first motorcade is located can be controlled in a green light state, and the signal light of vehicles and pedestrians in a vertical directionof the light control direction is controlled in a red light state; and the current traffic condition of the light-controlled intersection is self-adapted to ensure the traffic safety of the vehiclesand pedestrians in the vertical direction of the light control direction where the first motorcade is located, meanwhile, discrete vehicles are induced to drive into the first motorcade from the outside of the first motorcade, in order to ensure that the first motorcade can quickly pass through the current light-controlled intersection within a green light time, thereby reducing the times of the green light, and no remote transmission of traffic data is needed, thereby shortening the data transmission time and improving the traffic efficiency.

The invention relates to an SCATS (Sydney coordinated adaptive traffic system) detector data abnormal value on-line detection method. The method includes the following steps that: (1) SCATS data are extracted from a database, and are arranged according to intersection serial numbers, detector serial numbers, and chronological orders; (2) zero data judgment is performed on the extracted road traffic flow data, zero data which are continuously zero for a time span which exceeds a time interval in the road traffic flow data are classified as abnormal data, and the other data in the road traffic flow data are classified as normal data; and (3) abnormal data judgment is performed on the normal data which are obtained after zero data judgment, the normal data are subjected to traffic flow threshold test, traffic flow parameter consistency test, and traffic flow statistical rule test, and data which are in accordance with test standards are normal data, and data not in conformity with the test standards are abnormal data. With the method of the invention adopted, the validity and accuracy of detection data are ensured, the functions of a SCATS can be performed normally, and the operationeffect of the system can be improved.

Embodiments of the disclosure provide systems and methods for optimizing a traffic control plan. The system may include at least one storage device configured to store instructions and at least one processor configured to execute the instructions to perform operations. The operations may include receiving traffic system log data and parsing the traffic system log data to obtain a first set of traffic performance parameters. The operations may also include receiving trajectory data relating to a plurality of vehicle movements and parsing the trajectory data to obtain a second set of traffic performance parameters. The operations may further include determining relationships between vehicle delays and degrees of saturation based on the first and second sets of traffic performance parameters.In addition, the operations may include optimizing the traffic control plan based on the relationships.

The invention discloses a signal lamp self-adaptive control system and method for large park traffic safety and overcomes the defect that the fixed timing control method of large- and medium-sized park traffic signal lamps cannot flexibly adapt to park pedestrian flow change. The method includes the steps of firstly, using camera devices distributed on various road sections of a park to perform video collection, and processing images according to a set human body recognition algorithm to obtain pedestrian flow information so as to achieve pedestrian flow detecting; secondly, by a control module, judging road section busy states according to corresponding pedestrian flows, and providing corresponding signal lamp control signals; thirdly, by the signal lamps, responding to the received control signals from the control module, and controlling passing rights. The method has the advantages that the pedestrian flows of the various road sections in the park are judged, the passing rights of the road sections are controlled in real time by controlling the switching of the signal lamps at the intersections of the park, pedestrian passing requirements are satisfied effectively, and road safety is guaranteed effectively.

The embodiment of the invention provides a traffic control system and method. The traffic control system may include a communication interface (310) configured to receive vehicle trajectory data (302)acquired by a sensor and traffic control data (304) from a traffic signal controller (106). The traffic control system may also include at least one processor (320). The at least one processor (320)may be configured to detect an abnormal traffic condition. The at least one processor (320) may be further configured to optimize an online traffic control scheme by adjusting a green signal ratio ofthe at least two phases based on the vehicle trajectory data (302). The at least one processor may also be configured to provide the optimized online traffic control scheme to a traffic signal controller (106) in real time to generate a traffic control signal.

The invention discloses a smart city construction road traffic signal equipment fault analyzing processing method and system, and a computer storage medium. The smart city construction road traffic signal equipment fault analyzing processing method comprises the following steps: obtaining fault alarm information corresponding to a city traffic signal lamp; judging the authenticity of the fault alarm information; performing preprocessing according to the judging result of the fault alarm information corresponding to the early warning traffic signal lamp; analyzing the received fault alarm information corresponding to the early warning traffic signal lamp; acquiring basic information corresponding to the urban traffic management site; acquiring an adaptive traffic control station corresponding to the early warning traffic signal lamp; and performing information sending on a fault information analyzing result. The problem that an existing traffic signal equipment fault analyzing and processing method cannot guarantee the accuracy and authenticity of a traffic signal equipment fault monitoring result is effectively solved, and the fault elimination efficiency of the traffic signal equipment is greatly improved.