69 results about "Green wave" patented technology

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 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 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.

The application discloses a trunk green wave parking intersection recommendation method, which overcomes the problem that the existing technology cannot recommend parking intersections for a trunk line that cannot realize bidirectional green waves, and comprises the following steps: calculating the distance between two adjacent intersections on the trunk line to obtain an intersection pair recommendation sequence Z; calculating the average lane number difference between the coordinated direction and the non-coordinated direction of each intersection to obtain an average lane number intersection recommendation sequence A; calculating the average lane flow difference between the coordinated direction and the non-coordinated direction of each intersection to obtain a lane average flow intersection recommendation sequence B; calculating the flow similarity between intersections to obtain an intersection flow trend intersection recommendation sequence; obtaining a final output list from the obtained intersection recommendation sequence, and outputting corresponding recommendation reason labels. The trunk green wave parking intersection recommendation method can recommend a downlink direction parking intersection recommendation list and a recommendation reason for a trunk line that cannot realize bidirectional green waves, and improves the intelligent level of traffic control.

The invention discloses a multi-type intersection mixed traffic control method based on multi-agent reinforcement learning, and the method comprises the steps: setting CAV special lanes and common lanes for different types of intersections, and configuring different signal lamp phases for all types of intersections; a double-layer control structure is constructed, an upper-layer controller selects an optimal signal phase by adopting a multi-agent reinforcement learning algorithm according to the state information of each intersection, and the signal lamp state of each intersection is dynamically adjusted; and when the CAV special phase is activated, the lower-layer controller adopts a reinforcement learning algorithm to carry out passing right distribution and cooperative control on the CAV on the CAV special lane, and carries out speed guidance and track cooperation on the CAV fleet. By constructing a double-layer control framework and fusing a collaborative decision-making mechanism of multi-agent reinforcement learning, the green wave passing time of vehicles on a main road is prolonged, and the passing efficiency is improved; the overall throughput of a road network is improved, and the bearing and dispersion capabilities of a regional traffic system are enhanced.

The application provides a data processing system for controlling traffic lights, comprising a first adjusting module, a first traffic light ID list, a second adjusting module, a second traffic light ID list, a processor and a memory storing a computer program, when the computer program is executed by the processor, a first time point and a second time point are acquired, a first target time point list and a second target time point list are acquired according to the first time point, the second time point and a relative phase difference, the green light start time of a first traffic light corresponding to a first traffic light ID is adjusted to a first target time point through the first adjusting module, the green light start time of a second traffic light corresponding to a second traffic light ID is adjusted to a second target time point through the second adjusting module, and the adjustment of the green light start time of the traffic lights in the green wave section is not affected whether the networks are interconnected or not, which is beneficial to improving the traffic efficiency of the road.

The invention relates to the field of traffic signal control, in particular to a trunk line adaptive dynamic green wave traffic signal control method, which comprises the following steps of: establishing constraint conditions according to core parameters such as a fixed trunk line coordination public period, a coordination phase and a phase difference, and fusing single-intersection adaptive control and trunk line green wave coordination control; a corrected traffic state vector is generated based on real-time and historical entrance lane queuing data, and dynamic regulation and control of green wave bandwidth and green light duration of each phase are realized through stage sequence optimization driven by pressure, green time distribution optimization based on shared contribution degree, and distributed decision stage release sequence and green light duration of each intersection. Finally, the technical problems that in the prior art, green wave timing is fixed, and traffic flow randomness cannot be dynamically adapted are solved, traffic flow in the coordinated direction passes without stopping, the waiting time of traffic flow in the non-coordinated direction is shortened, and the overall passing efficiency of all intersections of the trunk line is greatly improved.

Embodiments of the present application disclose a traffic signal control method, device and equipment, and a storage medium. The method comprises: performing isolated optimization processing on the basis of historical traffic data of N isolated intersections by an isolated intersection optimization model to obtain N traffic signal information corresponding to the N isolated intersections, wherein the traffic signal information comprises a display duration of a traffic signal light allowing traffic and a signal cycle; one isolated intersection corresponds to one traffic signal information; and performing traffic signal control optimization processing in the direction of maximum road network green wave bandwidth on the basis of the N traffic signal information by a road network green wave coordination optimization model to obtain a traffic signal coordination control scheme of the road network. The embodiments of the present application can accurately determine the cycle and duration of the traffic signal light, and effectively relieve traffic congestion.

This invention discloses a method and terminal for intelligent coordinated control of traffic lights. The method includes: upon receiving a request for passage from an intelligent driving vehicle at a target intersection, real-time collection of pedestrian passage requests, traffic conditions in conflicting directions, and traffic conditions in the target direction; determining whether to trigger a constant green strategy based on the traffic conditions in the conflicting directions and the pedestrian passage request; if not triggered, calculating the next cycle phase sequence and duration scheme of the traffic lights using a signal control algorithm based on the intelligent vehicle passage request, the traffic conditions in the target direction, the traffic conditions in conflicting directions, the pedestrian passage request, and the default timing strategy; adjusting the traffic lights at the target intersection according to the next cycle phase sequence and duration scheme; and adjusting the traffic lights at the next preset intersection according to the driving route in the intelligent vehicle passage request and the traffic lights at the target intersection to form a green wave segment. This method can effectively improve intersection traffic efficiency and meet the passage needs of intelligent driving vehicles.

The invention discloses a green wave time period dynamic division and adaptive optimization method. The method comprises the following steps: acquiring a coordination phase and a corresponding coordination direction of each intersection in a line; performing intersection state judgment based on the historical flow data, and performing time period division according to different intersection states; screening intersections on the line in each time period to obtain a plurality of intersections as starting points of the green wave line; calculating the intersection signal period of each intersection in the line and the public period of the line, and determining the green light time of each phase of the intersection; identifying an overflow risk road section in the line, and performing green light time adjustment on a terminal intersection of the overflow risk road section; and performing intersection signal adjustment on the intersections in the green wave line according to the phase difference between the adjacent intersections. According to the method, the traffic states of different time periods are considered, the green light signal scheme of each time period is determined according to the specific time period, and adjustment is further performed according to the overflow state of the intersection, so that the traffic control scheme is more accurate, and the traffic efficiency is improved.

The invention provides an intelligent green wave band control method and system based on Internet of Vehicles data and related equipment, and relates to the technical field of intelligent green wave band control, and the method comprises the steps: obtaining the initial formation parameters of an initial motorcade when it is monitored that the initial motorcade composed of a plurality of Internet of Vehicles exists in a target road section, continuously acquiring real-time formation parameters of the initial motorcade in the process of driving the initial motorcade to the downstream intersection of the target road section; the real-time fleet length and the real-time average speed of the initial fleet are determined according to the real-time formation parameters, the real-time fleet length is compared with the initial fleet length of the initial fleet, the fleet length change rate is obtained, and the formation dispersion index of the initial fleet is determined according to the fleet length change rate; performing dispersion degree grading analysis on the initial motorcade by utilizing the formation dispersion index, and determining a motorcade control mode of the initial motorcade; and executing green wave band linkage control operation on the signal lamps at the downstream intersection by using the motorcade control mode.

The invention discloses an emergency path control system and method based on data interaction, and relates to the field of traffic control, and the method comprises the steps: collecting an image of a to-be-monitored region in real time, precisely recognizing the type and severity level of an event through a mapping model, dynamically dividing the number of layers of evacuation nodes, and guaranteeing the timeliness and accuracy of event response; a traffic green channel is automatically opened up by detecting an emergency vehicle in combination with an event type, a continuous green wave band is formed, it is ensured that the emergency vehicle passes through an event occurrence place in time, and the emergency response speed and safety are improved; vacant parking spaces behind emergency vehicles and vehicle coordinates are detected through a to-be-monitored network diagram, a detouring path is planned, the vehicles are guided to complete evacuation in a standby parking lot in advance, and traffic paralysis on an event site is avoided; and the initial evacuation speed is dynamically adjusted to be iteratively optimized to be below a threshold value, so that the high efficiency and the resource utilization rate of the evacuation process are ensured, and delay and resource waste caused by congestion are avoided.

The invention relates to the technical field of intelligent traffic control, in particular to a phase difference dynamic transition control method and system based on a holographic sensing technology, and the method comprises the steps: collecting the lane-level traffic index data of each intersection of a trunk line in real time; obtaining new and old coordination schemes for coordinating all intersections of the trunk line, and calculating phase difference offsets between the schemes; according to the obtained phase difference offset of each intersection of the trunk line, calculating the number of periods required by phase difference transition and the duration of each period, and generating a transition scheme according to the phase green time ratio of the new scheme; according to the real-time traffic index data of each intersection of the trunk line, dynamically controlling the phase duration and the signal control instruction of each intersection; the signal control instruction is issued to a signal machine for operation, and dynamic transition control is executed; dynamic smooth transition of the phase difference is achieved, stability of the transition process is guaranteed, traffic flow changes are rapidly responded, normal operation of green wave control is ensured, the overall traffic efficiency is remarkably improved, and the traffic capacity of a road network is effectively exerted.

The invention belongs to the technical field of intelligent traffic, and discloses an IVCPS intelligent quantitative evaluation method based on a road section scale, and the method comprises the steps: carrying out the quantitative evaluation of the complexity of a road section scale scene, and calculating the complexity score S of the road section scene through the four dimensions of a road section traffic environment, a road attribute weight, a traffic state and a traffic event; road section scale vehicle function quantitative evaluation: calculating a function expression score V of the vehicle on the road section by evaluating a vehicle lane level guiding function and a green wave passing function; and quantitatively evaluating the matching performance of the road scene and the vehicle function, and calculating a final matching degree score based on the ratio of the vehicle function score V to the road scene score S and by introducing a scene difficulty coefficient K and a normalization coefficient F. The method can scientifically and quantitatively evaluate the performance and adaptive capacity of the intelligent networked automobile information physical system on road sections with different complexity degrees, and provides a key basis for system optimization and deployment.

This invention belongs to the field of digital cloud scheduling technology for road networks, specifically relating to a method and system for digital cloud scheduling of road networks based on vehicle-road cooperation. The method includes: collecting traffic flow data using roadside units and identifying vehicle information; analyzing the vehicle information to obtain the vehicle spatial interference index and vehicle behavior interference index of unconnected vehicles, and combining these to generate a penetration impact index for unconnected vehicles; using the penetration impact index of unconnected vehicles to obtain a green wave attenuation index through green wave traffic interference analysis; using the green wave attenuation index to obtain road saturation through road capacity assessment; and invoking the corresponding green wave coordination strategy based on the road saturation. This invention achieves quantitative and dynamic differentiated scheduling of interference from unconnected vehicles, effectively improving the efficiency of green wave traffic and the overall operational efficiency of the road network in mixed traffic flow environments.

The invention provides a multi-vehicle green wave traffic control method and system based on vehicle-road cloud cooperation. The multi-vehicle green wave traffic control method comprises the steps of obtaining global traffic state data of a target area; generating a global comprehensive potential energy field of the target area according to the global traffic state data; constructing a first dimension particle swarm according to the global comprehensive potential energy field; based on the first dimension particle swarm, multi-vehicle cooperative green wave passage trajectory optimization is carried out to obtain first optimized trajectory data; trajectory planning conflicts among individuals can be reduced, the group passing efficiency is improved, and multi-vehicle green wave passing is achieved.

The application discloses a green wave vehicle speed guiding method based on GA-PSO, realizes low-delay information interaction and unified queuing coordination between vehicles through a bidirectional navigation topological structure, effectively alleviates queuing oscillation under high-density traffic, and significantly improves intersection traffic throughput and queuing stability; a UV-TSA model is used to process the acceleration and deceleration process in stages, so that the speed conversion is continuous and smooth, the driving comfort is greatly improved, and the vehicle fuel consumption and exhaust emission are reduced; the GA-PSO hybrid algorithm combines the advantages of genetic operation and particle swarm optimization, enhances the population diversity, realizes efficient global optimization of the speed trajectory under multiple constraint conditions, solves the problem that traditional green wave vehicle speed guiding depends on single vehicle decision and has poor adaptability, and effectively alleviates urban intersection congestion and environmental pollution.

The embodiment of the specification discloses a green wave passing-based hybrid vehicle energy management method, device and medium, relates to the technical field of hybrid vehicle energy management, and the method comprises the following steps: acquiring current motion state data of a hybrid vehicle, acquiring signal lamp phase timing information of a current intersection through a vehicle-road communication module to determine a guide speed characteristic vector; extracting an information receiving time delay from the signal lamp phase timing information to determine a multi-modal observation state vector; inputting the multi-modal observation state vector into a probabilistic action network in a pre-trained soft actor-critic network to output a mean parameter and a standard deviation parameter, calculating a confidence coefficient according to the standard deviation parameter to generate a target equivalent factor; constructing an evaluation index function of instantaneous equivalent energy consumption, solving an engine target torque instruction and a motor target torque instruction that minimize the evaluation index function in a powertrain physical constraint feasible set to realize hybrid vehicle energy management.

The application discloses a layered energy-saving driving speed optimization method, a computer device and a storage medium, relates to the technical field of data processing, and comprises the following steps: constructing a longitudinal dynamics model according to the relationship 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 to generate the optimal passing time of each road section and the entrance and exit speed; constructing and optimizing a round-cornered parallelogram envelope based on the green wave window according to the maximum speed, the maximum acceleration and the maximum deceleration of the vehicle to construct the space-time boundary of the green wave window; and generating an energy-saving speed curve according to the longitudinal dynamics model, the energy consumption model, the space-time boundary of the green wave window, the optimal passing time and the entrance and exit speed. According to the application, the energy-saving effect of the speed trajectory can be quickly calculated and obtained.

The present disclosure provides a green wave vehicle speed determination method and device, electronic equipment and readable storage medium, which relates to the field of artificial intelligence, and especially relates to the technical field of intelligent transportation, automatic driving and the like. The implementation scheme is: obtaining light state information of two continuous downstream intersections at which the vehicle travels at the current time, the distance therebetween, a navigation path and a reference green wave vehicle speed; determining the start time and the end time of the green wave zone at the two continuous intersections respectively based on the light state information of the two continuous intersections, the distance therebetween and the reference green wave vehicle speed; determining whether the vehicle will travel into the green wave zone in space based on the navigation path; determining whether the vehicle meets the condition of traveling into the green wave zone in time based on the start time and the end time, the reference green wave vehicle speed and the current time; and determining the recommended green wave vehicle speed in response to determining that the vehicle will travel into the green wave zone in space and meets the condition of traveling into the green wave zone in time.

This invention discloses a green wave coordination control method for non-closed road networks with coordinated path selection function, comprising the following steps: Step 1, classifying intersections in the non-closed road network; Step 2, constructing a green wave coordination control model for the non-closed road network with coordinated path selection function; Step 3, determining the green wave coordination control scheme for the non-closed road network. This invention considers the inherent correlation between path selection and green wave coordination, and can not only optimize phase difference and intersection phase sequence, but also simultaneously optimize coordinated paths, achieving synchronous optimization of path selection and green wave coordination in non-closed road networks.

This invention discloses a method for adaptive coordinated control of arterial roads based on indefinite periods, comprising the following steps: collecting intersection information; establishing an indefinite-period arterial road coordinated control timing optimization model; solving the indefinite-period arterial road coordinated control timing optimization model; establishing an indefinite-period phase difference coordinated control optimization model; solving the indefinite-period phase difference coordinated control optimization model; and outputting an optimized arterial road coordinated timing scheme based on indefinite periods. This invention constructs a maximum green wave band model applicable to indefinite periods, determines the allocation ratio based on the traffic flow ratio, making it applicable to different periods on arterial roads, and solves it using a genetic algorithm, achieving fast convergence speed and high computational accuracy. The indefinite-period arterial coordinated control established by this invention can be dynamically adjusted according to real-time traffic data, enabling better understanding of traffic conditions and improving the efficiency and accuracy of traffic management. This invention can provide an accurate adaptive coordinated control scheme for arterial roads with high reliability.

The embodiment of the invention provides a trunk line green wave optimization method and device, equipment and a storage medium, and the method comprises the steps: obtaining an initial absolute phase of each intersection in each direction of a trunk line according to a signal control scheme of each intersection of the trunk line and an initial phase difference of each pair of adjacent intersections; obtaining a set of phase adjustment combinations according to the plurality of selectable phase adjustment amounts of each intersection, each phase adjustment combination comprising a selected selectable phase adjustment amount of each intersection; according to each phase adjustment combination, adjusting the initial absolute phase, and obtaining a green wave width corresponding to each phase adjustment combination in each direction of the trunk line; and obtaining the optimal bidirectional green wave fraction of the trunk line according to each green wave width, and obtaining the phase difference of the adjacent intersections after trunk line green wave optimization according to the optimal bidirectional green wave fraction. According to the technical scheme, the two directions of the trunk line are searched separately, the bidirectional green waves are optimized according to the bidirectional green wave score, and the method is suitable for trunk line green wave optimization in any scene.

The invention relates to an intelligent traffic road condition information monitoring and predicting method and system based on artificial intelligence. The method comprises the following steps: acquiring multi-source data related to traffic road conditions on a road in real time; based on the multi-source data, carrying out real-time monitoring on an edge computing node by adopting a target and tracking algorithm based on deep learning, and identifying a target and a corresponding abnormal event as well as a continuous motion track of the target; based on the multi-source data, carrying out data fusion by adopting a dynamic weighted fusion method, and generating a road-level comprehensive traffic state vector in real time; and performing short-time traffic prediction based on the road-level comprehensive traffic state vector, and dynamically adjusting signal timing, green wave coordination and induction signals by adopting a multi-agent deep reinforcement learning method based on a prediction result. Compared with the prior art, the method has the advantages of improving the traffic operation and maintenance efficiency and the like.

The invention provides a vehicle control method and device, electronic equipment, a storage medium and a vehicle, and the method comprises the steps: detecting whether a green wave vehicle speed interval is received from a navigation system of a target vehicle or not when the target vehicle runs according to a planned path; if the green wave vehicle speed interval is received from the navigation system of the target vehicle, judging whether the target vehicle has a collision risk with the front vehicle when driving according to the green wave vehicle speed interval, and correspondingly controlling the target vehicle according to a risk judgment result; and if the green wave vehicle speed interval is not received from the navigation system of the target vehicle, determining whether the current state of the traffic signal lamp at the intersection in front of the target vehicle and the residual duration of the current state of the traffic signal lamp are obtained or not, and performing corresponding control on the target vehicle according to the obtained result. By the adoption of the technical scheme, energy consumption caused by red light waiting or invalid starting and stopping of the vehicle is reduced, and therefore the energy-saving effect is achieved on the premise that passing efficiency and driving safety are guaranteed.

The application provides a kind of intelligent green wave band control method, system and related equipment based on Internet of Vehicles data, it is related to intelligent green wave band control technical field, the method comprises: when monitoring the initial vehicle group formed by multiple Internet of Vehicles vehicles exists in target section, the initial formation parameter of initial vehicle group is acquired, in the process that initial vehicle group travels to the downstream intersection of target section, the real-time formation parameter of initial vehicle group is continuously acquired;According to real-time formation parameter, the real-time vehicle group length and real-time average speed of initial vehicle group are determined, the initial vehicle group length is compared with the initial vehicle group length of initial vehicle group, and the vehicle group length change rate is obtained, and the formation dispersion index of initial vehicle group is determined according to vehicle group length change rate;The dispersion degree classification analysis of initial vehicle group is carried out using formation dispersion index, and the vehicle group control mode of initial vehicle group is determined;Green wave band linkage control operation is executed on the signal lamp of downstream intersection using vehicle group control mode.

This invention relates to a multi-level monitoring and evaluation method for green wave traffic on arterial roads. It solves the problem of poor coordinated control of green wave traffic on urban arterial roads in existing technologies. It includes the following steps: S1, acquiring traffic operation data and basic road network information data for urban green wave arterial roads; S2, constructing a multi-dimensional indicator system; S3, calculating the results based on the multi-dimensional indicator system; S4, analyzing problems with the green wave traffic on arterial roads based on the calculation results, evaluating the operational efficiency level, and coordinating control parameters; S5, outputting the coordinated control parameters of the green wave traffic on arterial roads as signals and transmitting them to the traffic signal controllers at intersections, thereby controlling and adjusting the traffic lights at the corresponding intersections. The advantages of this invention are: constructing a multi-dimensional indicator system at three levels—micro, meso, and macro—and calculating, monitoring, and evaluating the operational status of green wave traffic on arterial roads, promptly identifying problems and proposing optimization solutions, thereby improving the operational efficiency of green wave traffic on arterial roads, reducing traffic delays and parking rates, and optimizing road traffic.

The embodiment of the application provides a trunk coordination control method and device, a storage medium and an electronic device. The method comprises the following steps: obtaining a target trunk and an initial trunk scheme, then determining a first intersection and first intersection configuration information according to the initial trunk scheme, then determining a forward green wave bandwidth and a reverse green wave bandwidth between the first intersection and a second intersection according to first intersection attributes and second intersection attributes, and performing red wave optimization on the first intersection according to the forward green wave bandwidth and the reverse green wave bandwidth to obtain a target trunk scheme. Through the application, the technical problem that the applicability of the trunk coordination control scheme is low in the related art, resulting in low efficiency of trunk coordination control can be solved.

The invention discloses a green wave coordination control method and device and electronic equipment, and belongs to the technical field of traffic. The method comprises the steps that operation constraint conditions and coordination directions of all intersections on the trunk road are determined, a trunk road coordination control scheme meeting the coordination directions of all the intersections is determined under the operation constraint conditions, and the coordination control scheme comprises a public signal period of the trunk road and an initial signal timing scheme of all the intersections on the trunk road; according to the public signal period, determining a period control mode of each intersection; if the cycle control mode is multi-cycle control, determining a coordination signal cycle of the intersection according to the public signal cycle; updating the initial signal timing scheme of the intersection based on the coordination signal period to obtain an updated signal timing scheme of the intersection; and traffic signals of the intersection are controlled according to the updated signal timing scheme so as to realize green wave coordination control of the trunk road. According to the invention, the control effect of green wave coordination control on the trunk road is improved.