187 results about "Green wave" patented technology

The invention relates to the technical field of signal devices, and discloses a traffic signal control method and system based on vehicle-road cloud multi-modal data fusion, and the method comprises the steps: collecting multi-modal traffic data synchronously in real time through a vehicle-end sensor, road-side sensing equipment and a cloud Internet platform; fusing the heterogeneous data by adopting a space-time alignment algorithm, and constructing a standardized space-time feature matrix; traffic flow prediction is carried out based on a multi-layer space-time diagram neural network trained by a federated learning mechanism, and a signal control instruction is generated through reinforcement learning and a multi-objective optimization model; and issuing the green wave parameter, the dynamic timing scheme and the cross-domain coordination strategy to a roadside signal machine through the cloud edge coordination architecture to execute control. The problems that in the prior art, low-delay private network communication cannot be achieved, the data fusion efficiency is low, unmanned driving is not supported, and the deployment cost is high are solved, and the purposes of low-delay communication, high reliability and low risk are achieved.

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

The invention discloses an intelligent traffic signal device and a remote control method, and relates to the technical field of intelligent traffic control. The method is used for solving the problems of low emergency traffic efficiency and global and local control imbalance under sudden traffic events. The method comprises the following steps: firstly, fusing an emergency vehicle navigation path, accident point vehicle motion abnormal parameters and pedestrian aggregation distribution data, constructing a multi-dimensional event evolution feature vector through a space-time encoder, and accurately describing a traffic situation; an event influence domain boundary is delimited based on vehicle trajectory and flow direction consistency analysis, a dynamic evolution model is constructed in combination with an acceleration abrupt change propagation path, and a space-time conflict probability matrix is generated; then executing a hierarchical control strategy, dynamically adjusting an intersection signal period starting time difference in a global level to form an emergency green wave coordination band, and inserting an adaptive full red phase in a local level according to a balance relation between a vehicle arrival rate and a dissipation rate; and finally, reversely correcting model parameters through an actual pass error, updating a conflict probability generation rule, and forming a closed-loop feedback mechanism.

The invention provides an intelligent traffic dynamic green wave control method and system, a storage medium and a program product, and relates to the field of traffic control systems.The method comprises the steps that real-time traffic data of all lanes and emergency data published by a traffic management department are obtained; obtaining traffic flow prediction results of different directions of each road section in a future preset time based on a neural network model; outputting the congestion level and the congestion duration of each road section in combination with the emergency situation data and a traffic flow theoretical model, and further determining an adjustment sequence; and on the basis of the traffic flow prediction result, calculating the optimal green wave bandwidth of the signal lamp at each intersection in each target road section by using a particle swarm optimization algorithm to obtain an optimal timing combination scheme of the signal lamp in each target road section, and performing real-time timing control. By implementing the method, the traffic flow can be dredged more efficiently, the parking frequency and delay time of vehicles on the road are reduced, the overall traffic capacity of the urban road is improved, and energy consumption and environmental pollution are reduced.

The invention discloses a road right sharing configuration method and a traffic control system, and relates to the technical field of special vehicle road right sharing, and the method comprises the steps: obtaining the real-time position and speed information of a common vehicle and a special vehicle at an intersection; determining the traffic flow condition of the current intersection by using a traffic flow prediction model; according to the real-time position and speed of the special vehicle and the traffic flow condition of the current intersection, the period and phase duration of the signal lamp are dynamically adjusted through a bidirectional dynamic green wave algorithm, a signal lamp control strategy is obtained, and it is ensured that the special vehicle can pass through the intersection without stopping; configuring intersection signal lamps according to the signal lamp control strategy; according to the invention, through combination of prediction of the traffic flow condition of the intersection and the real-time position of the special vehicle, the period duration and the phase duration of the signal lamp at the intersection are automatically adjusted, the influence on common vehicles at other phases is reduced as much as possible while the special vehicle is ensured to pass through the intersection without stopping, thereby realizing dynamic distribution and sharing of the road right of the special vehicle, and improving the traffic safety. And the utilization rate of special lane resources is furthest improved.

The invention relates to the technical field of traffic control, and provides a regional green wave coordination control method and system for traffic safety early warning. The method comprises the following steps: traversing a target traffic area to collect traffic data, and constructing a regional dynamic traffic characteristic matrix; performing green wave coordination analysis based on the matrix, and generating a sub-region division scheme; traffic flow characteristics of the subareas are extracted, and green wave coordination control parameters are calculated; formulating and executing an initial control scheme according to parameters, and collecting feedback data; and compensating the optimization parameters according to a feedback result to obtain a final regional coordination control scheme. The technical problem that efficiency and safety are difficult to cooperate due to the fact that traditional green wave control only statically optimizes traffic efficiency and cannot dynamically respond to traffic safety risks is solved, deep coupling of safety early warning and green wave control is achieved through real-time traffic data feedback and dynamic parameter compensation, and the traffic safety is improved. And the traffic efficiency is ensured, and the accident risk is obviously reduced.

The invention discloses an intelligent traffic light coordination control method and system for traffic flow optimization, and relates to the technical field of intelligent control. The method comprises the following steps: collecting traffic flow data of lanes in all directions in a target area in real time through heterogeneous sensor networks deployed at all intersections; identifying the traffic flow data to obtain tidal traffic flow features; performing dynamic intensity evaluation based on the tidal traffic flow characteristics to obtain a tidal direction intensity index; a multi-agent cooperative network is constructed, and a continuous intersection green light phase offset sequence is generated based on cooperative calculation of the multi-agent cooperative network; and when the tide direction intensity index is greater than a preset threshold value, inputting the continuous intersection green light phase offset sequence into a traffic signal lamp control system so as to start a tide green wave mode. The technical problems that in the prior art, traffic flow control is not flexible enough, and traffic lights cannot be intelligently adjusted according to the traffic flow condition are solved, and the technical effects of improving the road passing efficiency and reducing congestion are achieved.

The invention provides a multi-path main road signal cooperative control method considering speed fluctuation, and the method comprises the steps: firstly collecting traffic data, including road network geometric data, traffic flow data and signal control data, and determining a plurality of main paths according to the traffic flow data; then, based on the running speed of each vehicle on each main path, the vehicle running average speed and the speed fluctuation quantity on each main path are calculated; and finally, constructing a signal control optimization model: constructing a target function by taking weighted green wave bandwidth maximization of all paths as a target, adding a loop integer constraint, an interference constraint, a speed fluctuation space-time constraint, a highest speed fluctuation constraint and a lowest speed fluctuation constraint, solving the signal control optimization model, and obtaining an optimized signal period and green wave bandwidth. And outputting the optimized signal timing strategy. The signal timing generated by the method has a good prospect in the aspect of reducing travel delay and parking times, and a better multi-path collaborative effect can be realized.

The invention provides a traffic signal lamp intelligent regulation and control system based on big data, and relates to the technical field of traffic signal lamp regulation and control systems, and the system comprises a multi-channel data collection module which is responsible for collecting traffic data of a plurality of channels, guaranteeing the comprehensiveness and accuracy of the data, and transmitting the traffic data to a cloud server; a geomagnetic sensor, a radar sensor and a camera device on a road are used for collecting the flow, speed and occupancy information of vehicles in real time, the change of the traffic flow can be sensed timely and accurately through multi-source data collection and real-time analysis, and a signal lamp timing scheme is adjusted rapidly according to the change condition, so that the traffic light timing efficiency is improved. Regulation and control of signal lamps are more in line with actual traffic demands, vehicle waiting time is effectively reduced, road passing efficiency is improved, and through green wave band control and trunk line coordination control means, overall traffic operation efficiency of a region is improved, road network traffic flow is balanced, congestion diffusion of local road sections is avoided, and overall operation stability of urban traffic is improved.

The invention discloses a parking lot virtual green wave guiding method and related equipment. The core is to construct a space-time resource map, plan a path according to the space-time resource map, reserve a time window to form a virtual green wave band, issue an instruction to a vehicle terminal and an intelligent barrier gate, monitor vehicle position deviation, and trigger local re-planning if a threshold value is exceeded. According to the scheme, the defects in the prior art are accurately overcome, global space-time resource overall planning is achieved through a space-time resource map, an exclusive time window is reserved for vehicles, multi-vehicle path conflicts are eliminated from the source, and the defects of lack of global view and active scheduling are overcome; static road network navigation is replaced by a space and time coordinated virtual green wave band, vehicles are actively guided to pass orderly, microscopic traffic congestion is effectively solved, and the in-field passing efficiency is greatly improved; through a real-time monitoring and local re-planning mechanism, emergency situations such as vehicle faults can be rapidly handled, congestion diffusion is avoided, traffic flow stability is guaranteed, and finally seamless and non-stop passing of the vehicle from an entrance to a parking space is achieved.

The invention discloses a layered energy-saving driving speed optimization method, computer equipment and a storage medium, and relates to the technical field of data processing.The method comprises the steps that a longitudinal dynamic model is constructed according to the relation between the vehicle state and the driving mileage; constructing an energy consumption model according to the relationship among the vehicle speed, the driving torque and the instantaneous battery output power; constructing a green wave window according to the energy consumption model so as to generate the optimal passing time and the entrance and exit speed of each road section; constructing and optimizing a fillet parallelogram envelope line based on the green wave window according to the maximum speed, the maximum acceleration and the maximum deceleration of the vehicle so as to construct a space-time boundary of the green wave window; and generating an energy-saving speed curve according to the longitudinal dynamic model, the energy consumption model, the space-time boundary of the green wave window, the optimal passing time and the entrance and exit speed. By adopting the method, the speed track with an excellent energy-saving effect can be quickly calculated and obtained.

The embodiment of the invention provides a traffic signal-based vehicle path planning method and related equipment, and belongs to the technical field of automatic driving. The method comprises the following steps: acquiring traffic light data of each intersection of a target driving path; according to the green light time interval of the traffic light data, time constraint calculation of multi-intersection continuous passing is carried out, and a time constraint of continuous green wave passing is obtained; performing speed calculation on each road section according to the time constraint to obtain a speed interval of each road section; performing quadratic programming on the speed interval by taking the energy consumption and the driving time as optimization objectives to obtain a global speed sequence; and performing multi-dimensional constraint and multi-target optimization on a speed controller of the vehicle according to the global speed sequence so as to control the vehicle to run on the target running path. According to the embodiment of the invention, the vehicle can be guided to realize continuous green light passing, and the driving efficiency and the driving comfort are improved while the driving energy consumption of the vehicle is reduced.

The invention provides a trunk line green wave control method for dynamically adjusting green light duration based on real-time sensing data, and relates to the technical field of green wave control, and the method comprises the steps: obtaining a fusion feature set at each intersection of a trunk line; determining an initial green wave period, an initial phase difference and an initial green light duration; a continuous passing zone is formed; dynamic compensation is carried out on the steering phase green light duration, and linkage correction is carried out on the steering phase green light duration and a straight-going phase adjustment result; the pedestrian phase is incorporated into the phase structure of the current period in an insertion mode, and timing calibration with the next period is executed; performing micro-amplitude correction on the phase difference of the adjacent intersections to obtain a phase difference reference distribution scheme; periodically evaluating the control effect to obtain a green wave period reference value; and performing adaptive updating on the green wave period reference value and the phase difference reference distribution scheme. According to the invention, the technical problem of low trunk line green wave control efficiency in the prior art can be solved, and the technical effect of improving the trunk line green wave control efficiency is achieved.

The invention discloses an adaptive signal control method for an urban trunk road in combination with Transform and element reinforcement learning. According to the method, a multi-agent cooperation structure for classified training and decentralized decision making is provided, green wave waiting vehicles are defined, a Transform module capable of capturing potential green wave requirements through historical states is designed, a reward function for balancing and coordinating direction benefits and intersection overall benefits is designed in a Markov decision making model for establishing a single agent, and the method has the advantages of being simple in structure, convenient to operate and high in practicability. A double-layer element learning Bi-MAML framework is provided, a multi-agent PPO algorithm training process based on Bi-MAML is designed, and adaptive signal control of the urban trunk road is realized. The trunk road adaptive signal control method provided by the invention has the advantages that potential green wave requirements can be captured, the coordination direction benefit and the intersection overall benefit are considered, the training cost is reduced, the pertinence and effectiveness of the intelligent agent are ensured, and the problem of poor mobility of a multi-intersection model is solved; the self-adaptive signal control is realized, and meanwhile, the problem of high training cost of multiple intersections of urban arterial roads is effectively solved.

The invention relates to the technical field of traffic signal control, and discloses an arterial road variable bandwidth green wave coordination method and system for multiple critical paths, and the method comprises the following steps: obtaining real-time arterial road structure data, intersection signal timing parameters and critical path information; inputting the collected data into a pre-constructed trunk road green wave optimization model, solving the trunk road green wave optimization model, outputting optimized signal control parameters, and coordinating green waves according to the optimized signal control parameters; the trunk road green wave optimization model comprises an objective function and constraint conditions; and obtaining a target function of the trunk road green wave optimization model by taking the weighted sum of the green wave bandwidth of the maximized key path as a target. The method can be applied to an urban trunk road scene, green wave bands are provided for multiple key paths, the green wave bands can be flexibly changed in different road sections, and the traffic flow passing efficiency of the urban trunk road is improved.

The invention relates to the field of traffic planning, and discloses an urban road network multi-path coordination control method and system considering multi-dimensional dynamic characteristics, and the method comprises the steps: obtaining the traffic flow data, congestion indexes and bidirectional green wave bandwidth difference data of each path in a road network at an intersection; calculating the multi-dimensional dynamic weight of each path at the intersection according to the traffic flow data, the congestion index and the bidirectional green wave bandwidth difference data; constructing a target function by taking the maximum weighted green wave bandwidth sum as a target, and embedding a multi-dimensional dynamic weight into the target function; constructing a collaborative optimization system comprising a high-weight path guarantee constraint, a dynamic adaptability constraint and an auxiliary constraint; and solving an optimal signal timing parameter based on the objective function and the collaborative optimization system, and generating a multi-path coordination control scheme. According to the method, through high-weight path guarantee constraint, dynamic adaptability constraint, flexible breakpoint constraint and the like, the length of a green wave band is increased, and the continuity of green waves and the anti-interference capability of a road network are improved.

The present application provides a green wave traffic control method, device and equipment, the method comprising: after the first longitudinal distance between the self-vehicle and the nearest target traffic light on the navigation route is less than the preset distance, regularly determining the target vehicle speed according to the first longitudinal distance, the state of the target traffic light and the road speed limit, wherein the target vehicle speed is used to control the self-vehicle to reach the target traffic light during the green light period, and the target vehicle speed is less than or equal to the road speed limit; if the self-vehicle speed is less than the target vehicle speed, and there are vehicles on the current lane that hinder the acceleration of the self-vehicle, then detecting whether there is a target lane, wherein the target lane is a safe lane for lane change, and there are no vehicles on the target lane that hinder the acceleration of the self-vehicle; if there is a target lane, then controlling the self-vehicle to change lanes to the target lane and controlling the self-vehicle to accelerate. Through the present application, the speed of the self-vehicle is gradually increased until it reaches the target speed, thereby improving the success rate of green wave traffic lights.

The invention relates to the technical field of traffic lights, and particularly discloses a traffic light signal real-time dynamic adjustment processing method based on electronic police data, which comprises the following steps: acquiring traffic monitoring data of a target traffic light intersection, analyzing and processing the data, generating period-level basic flow data, and dynamically adjusting a traffic light signal scheme according to the flow data. Algorithm data only needs traffic flow collected by an electronic police, a front-end sensing device does not need to be additionally installed, data processing and calculation are directly carried out in an existing traffic light signal machine, a rear-end big data calculation server is omitted, and therefore zero investment of technology application is basically achieved. According to the technology, all traffic signals connected to a signal machine are dynamically adjusted in real time by changing the whole signal control scheme mode, and the technology comprises green light duration adjustment E1, release mode adjustment E2, variable lamp panel adjustment E3, variable lane adjustment E4, intersection induction screen adjustment E5, green wave scheme adjustment E6 and pedestrian crossing control adjustment E7, so that the maximum application scene is achieved.

The invention belongs to the technical field of traffic flow green wave control, and particularly relates to a green wave control method based on traffic flow prediction driving. The method comprises the following steps: acquiring multi-dimensional real-time data in a distributed manner; performing abnormal value detection and data interpolation based on space-time neighborhood analysis to generate high-quality time sequence traffic flow data; modeling a road network into a dynamic graph fusing static connectivity and real-time traffic flow coupling degree, and constructing a double-time-scale time sequence diagram sequence; inputting an improved space-time diagram neural network, extracting time features by optimizing Bi-GRU, capturing spatial association by a multi-head attention mechanism, and outputting an accurate road section-level traffic flow prediction result through residual fusion; and finally, constructing a multi-objective optimization model based on prediction, solving by using an improved multi-objective particle swarm algorithm, obtaining a signal timing scheme, and issuing and executing the signal timing scheme. The method can dynamically adapt to traffic changes, improves the traffic efficiency, and avoids green wave failure and congestion.

The invention relates to the technical field of traffic control, and discloses a green wave path identification method and device, and the method comprises the steps: obtaining a selectable green wave path which is composed of at least one segment which is composed of an upstream intersection and a downstream intersection, obtaining the traffic signal lamp state data of each intersection on the selectable green wave path, and obtaining the traffic signal lamp state data of each intersection on the selectable green wave path; the method comprises the steps of obtaining signal data of traffic signal lamps corresponding to preset directions of all intersections, confirming sections of which the signal data of the upstream intersection and the signal data of the downstream intersection meet a preset signal period and a phase difference condition as selectable sections, and generating a target green wave path corresponding to the selectable sections based on the selectable sections. According to the method, the period duration and phase difference information implied in the traffic signal lamp state data are fully mined by using the traffic signal lamp state data to identify the green wave path, the barrier of a signal control platform is broken, and the efficiency of obtaining the green wave path scheme is improved.

The invention provides a method and a system for adaptive cruise based on a traffic light state. The method comprises the following steps: receiving an intersection passing speed suggested by a green wave vehicle speed guidance (GLOSA) function of a vehicle at least based on the state of a signal lamp in front of the vehicle; receiving a following speed calculated by an adaptive cruise control (ACC) function of the vehicle based on the preceding vehicle condition; and determining an adaptive cruise action to be taken by the vehicle based at least in part on a fusion of the received vehicle following speed and the intersection traffic speed. The adaptive cruise action includes adjusting a speed of the vehicle without incessant use of the ACC function.

The invention belongs to the field of intelligent traffic, and provides a multi-deep reinforcement learning agent cooperative regional traffic signal control system and method based on a green wave band theory, and the method comprises the following steps: (1) carrying out the overall architecture design of a multi-agent cooperative control system, a multi-center hierarchical multi-DDPG intelligent agent cooperative control system based on periodic control and trunk line green wave control is constructed; (2) state spaces, action spaces, reward functions and exploration modes of three types of agents in the hierarchical multi-agent control system are defined, and control algorithms are selected; and (3) performing multi-agent cooperative training optimization on the design by adopting four technologies of output standardization, Ornstein-Uhlenbeck exploration noise, experience playback and expert strategy, solving the problem that the system is difficult to converge when a single agent faces moving target learning, and timely responding to the dynamic change of the traffic flow through the agent system architecture design and the single agent design. And a signal timing scheme is optimized for real-time control, so that the driving delay at the intersection is reduced, and the passing efficiency at the intersection is improved.

The invention relates to the technical field of advanced auxiliary driving systems, in particular to a green wave vehicle speed guiding method based on a vehicle-road cloud cooperative system, and the method comprises the steps: obtaining the vehicle state information of a target vehicle on a current road and / or the road state information of the current road; calculating a subjective lane changing probability value of a human-driven vehicle in the target vehicle and an objective lane changing income value of the target vehicle; the surrounding vehicle behaviors of surrounding vehicles of the target vehicle are predicted, and the queuing dissipation time of the target vehicle is calculated; determining a time boundary condition and a speed boundary condition of the target vehicle meeting the green wave passing condition in the current road; and extracting a speed sequence considering high efficiency and energy conservation to guide the green wave speed of the target vehicle. Therefore, the problems that the requirements of cloud real-time prediction and planning cannot be met, and massive traffic data resources collected by roadside equipment in real time are difficult to efficiently utilize in related technologies, so that the application potential of the traffic data resources in a real traffic scene is limited, and the like are solved.

The invention provides an avoidance path planning method, device and equipment and a storage medium, and the method comprises the steps: obtaining real-time visibility and traffic light states based on a vehicle-mounted sensor; a dynamic elliptical avoidance domain is constructed for the freight vehicle, the long axis radius of the elliptical avoidance domain is determined according to the real-time visibility and the vehicle speed, and the short axis radius of the elliptical avoidance domain is determined according to the real-time visibility and the traffic light state; calculating an avoidance constraint vehicle speed and a green wave band passing vehicle speed for the common vehicle, and taking a smaller value of the two as a recommended vehicle speed; if the overlapping degree of the avoidance domain and the current path exceeds a set threshold value, triggering path re-planning; and performing path re-planning based on the traffic light state, the real-time position of the freight vehicle and the avoidance constraint condition. Reliable data are acquired through the vehicle-mounted sensor, the dynamic elliptical avoidance domain adaptive to the environment and the traffic state is constructed, the safe vehicle speed is recommended for the common vehicle, the path is re-planned when the threshold value is exceeded, the safety and the efficiency are both considered, and the road passing safety and smoothness in the haze day are greatly improved.

The invention discloses a lane problem identification method based on fusion of Internet data and an intersection detector. The method comprises the following steps: determining a problem intersection and an intersection direction needing to be optimized; judging whether the imbalance coefficient of the intersection is smaller than a threshold value; judging whether the entrance lane number is matched with the exit lane number; judging whether the left-turn traffic flow and the straight traffic flow of the entrance lane are matched with the attributes of the entrance lane; judging whether the left-turn traffic flow and the right-turn traffic flow of the entrance lane are matched with the attributes of the entrance lane; judging whether the mixed lane interference condition is greater than a threshold value or not according to the traffic flow state when the green light is over; and calculating the expected queue after the lane number adjustment, and determining a lane number adjustment scheme, thereby determining the problems existing in the lane. According to the method, the road network and the green wave scheme are combined to optimize lane setting, various data sources of the intersection are fused and applied, the found lane problems are comprehensive, and the reasonability of the optimization scheme is improved.

The invention relates to the field of urban traffic management and signal control, and discloses an urban mesoscopic credit control unit division method based on breadth search, which comprises the following steps: collecting traffic data in a road network, calculating an optimal signal timing scheme, and finely adjusting a signal period according to a downstream overflow risk; by calculating the coordination benefit of each steering, effective coordination steering is screened out, and a signal coordination trunk line is generated; generating a credit control unit by using a breadth search algorithm (BFS), and reasonably dividing the credit control unit into a single-point credit control unit, a trunk line credit control unit and a regional credit control unit; finally, by calculating the coordination direction and the phase difference, signal coordination and green wave passing are ensured, and a signal control scheme of a road network is optimized. According to the method, the traffic capacity of urban traffic can be improved, traffic congestion is reduced, and the overall traffic fluency is improved.

The invention discloses a regional green wave scheduling method and system based on traffic flow prediction and a storage medium, and relates to the field of traffic control systems.The method comprises the steps that road vectors, traffic facility data and POI information in a region are extracted, and a road network topological structure is constructed; and road real-time flow is collected based on the road network topological structure, flow prediction is carried out by using the flow prediction model and taking the POI information as input, and road predicted flow is obtained. Data alignment is carried out according to the road real-time flow and the road predicted flow, and a dynamic adjacent weight matrix is constructed to reflect the main trunk-branch trunk priority change. And in combination with a road network topological structure, phase difference and time window adjustment optimization is carried out on the trunk and the branches, and a regional green wave scheduling strategy is obtained. The technical problems that in the prior art, a traffic scheduling system lacks dynamic response to real-time flow and predicted flow, a green wave band strategy cannot be effectively adjusted according to the change of the traffic flow, the traffic efficiency is low in the peak period, and congestion occurs are solved.

The invention discloses a green wave traffic planning method, device and equipment, a medium and a product. The method comprises the following steps: receiving real-time signal lamp information sent by one or more signal lamp devices, wherein the signal lamp information comprises position information of signal lamps, orientation of the signal lamps and real-time phase signals; acquiring real-time position information and a course angle of the vehicle; determining whether target signal lamp equipment of the vehicle exists in the one or more signal lamp equipment or not, and if yes, determining the target signal lamp equipment according to the orientation direction in real-time signal lamp information sent by the target signal lamp equipment and the course angle of the vehicle; and according to the real-time phase signal of the target signal lamp equipment, green wave traffic planning is carried out. By means of direct communication between the vehicle and the signal lamps, the vehicle can identify the target signal lamp from the multiple signal lamp signals in a self-adaptive mode, and then green wave detection is carried out according to the target signal lamp.

The present invention belongs to the technical field of intelligent remote monitoring and dispatching of smart transportation, and relates to an intelligent remote monitoring and dispatching system for smart transportation. The present invention reduces vehicle waiting time, improves road traffic efficiency, and reduces traffic conflicts and congestion by obtaining traffic signal light dispatching information of each time period at each traffic intersection in the target area. By dynamically adjusting the traffic signal light dispatching information of each traffic signal light in the target area to change the traffic signal light dispatching information of the traffic intersection, it helps to adapt to the traffic demand of the current time period and current road conditions, and reduces the risk of traffic conflicts and collisions caused by the inconsistency between the traffic data of the current time period and the historical data. By obtaining the traffic signal light dispatching information of each traffic signal light guiding the intersection in the target area, it helps to achieve coordinated control between signal lights, forming a "green wave" effect, so that vehicles can pass through multiple intersections continuously, reduce the number of stops and starts, and improve traffic fluency.

The invention discloses a traffic signal management and control method and device based on AI edge calculation, and relates to the technical field related to communication, and the method comprises the steps: dividing an intersection passing right into space-time resource slots through real-time interaction between a roadside edge node and a vehicle-mounted terminal; when multiple vehicles request the same slot position, detecting occupation conflicts of the same space-time resource slots, and starting block chain auction fair distribution; and adjacent edge nodes share the result and generate a digitally signed signal control instruction through distributed machine learning collaborative optimization so as to realize signal lamp switching management and control. The technical problems that in traffic signal control, fixed timing is not matched with actual traffic flow changes, trunk line green wave coordination is difficult to adapt to dynamic traffic flow changes, cloud control network delay and fluctuation are caused, and traffic signal disorder is easily caused by vehicle priority passing are solved. The technical effects of improving the traffic efficiency and the real-time reliability of traffic signal management and control when the priority vehicle and the common vehicle are mixed, enhancing the cooperative control capability of the adjacent intersections and reducing the cloud control pressure are achieved.