109 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 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 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 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 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 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 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 a 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 at least comprises signal timing schemes of all the intersections on the trunk road; for any intersection on the trunk road, the signal timing scheme of the intersection is optimized through the optimization function; and controlling the intersection according to the optimized 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.

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 invention discloses a traffic signal broad-spectrum green wave control method. The method comprises the following steps: S1, starting: configuring a proportional signal mode and acquiring the length d of a road section between intersections and traffic time; s2, acquiring real-time traffic information; s3, calculating a broad-spectrum green wave configuration time difference tgw according to the mode instruction or the length q of a waiting fleet in front of an intersection; s4, after the respective intersection transition period of each intersection is completed, the respective ratio mode is operated; s5, whether differential control is started or not is determined according to the mode starting instruction or the situation of a motorcade head sensor; s6, judging whether the state is a differential state or not; if yes, returning to S5, otherwise returning to S3 to execute.

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.

The invention discloses a green wave optimization method based on a cellular automaton under BIM-GIS-MLP, and relates to the field of road digitization, and the method comprises the steps: segmenting a road according to a BIM and GIS platform; the terminal point information of the multiple journeys of each vehicle is counted based on the IOT, and corresponding traffic light calibration information and access information of the subsequent vehicles through the IOT are fed back to the subsequent vehicles; the BIM and GIS platform analyzes the average speed range passing through the multiple sections of continuous travel and calculates the sum of the duration of the green wave section; and calculating the optimization problem of the sum of the duration of the green wave section through linear programming, and solving the maximum value of the target function. According to the method, data networks of the Internet of Vehicles and the Internet of Things are accessed to realize data fusion and intercommunication, an MLP model is accessed to analyze individuals, and then the maximum value of the target function about the green wave road section time is obtained through linear programming, so that the traffic smoothness is improved, the energy loss and exhaust emission are reduced, and the urban environment quality is 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.

This invention discloses a trunk road green wave control method and system based on the KCF algorithm, belonging to the field of traffic control technology. It includes the following steps: S1: traffic flow detection and tracking; S2: green wave control; S3: signal control. This invention uses the KCF algorithm to achieve real-time traffic flow detection and tracking, providing more accurate and real-time traffic flow information, thereby achieving more refined trunk road green wave control, further improving road capacity, and alleviating traffic congestion. Simultaneously, it can adapt to changes in traffic flow and road conditions, exhibiting better adaptability and practicality.

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 present invention discloses a multi-path signal coordination control method for bus priority, comprising the following steps: S1, collecting relevant information about trunk intersections and bus routes in the trunk; S2, dividing the multi-path bus signal control sub-areas based on the stop locations of each bus route and combining dummy variables; S3, establishing a multi-path signal coordination control optimization model for bus priority with the optimization goal of maximizing the weighted green wave bandwidth of each bus route, and then establishing intra-control sub-area group constraints, inter-control sub-area group constraints, and travel time constraints for each path; S4, solving the optimization model, obtaining the green wave bandwidth of each bus route, and outputting the optimized signal control parameters for each intersection in the trunk route. When applied to the bus signal coordination control of urban trunk routes, the present invention can provide green wave bandwidth for multiple bus routes, significantly improve the traffic efficiency of buses on urban trunk roads, and alleviate traffic congestion.

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 application provides an optimization method, device, system and program product of ground traffic signal, the optimization method comprises: obtaining the vehicle flow image collected by the low-altitude unmanned aerial vehicle; using an AI image recognition module to identify the number and position of vehicles in the vehicle flow image, and superimposing the identification result on a road segment structure to generate a vehicle flow space-time heat map; based on the vehicle flow space-time heat map, predicting the vehicle flow density after Δt seconds at a prediction time t i In the prediction time t, the vehicle flow density after Δt seconds is predicted, and when the probability that the vehicle flow density is greater than a predetermined congestion critical density threshold is greater than a predetermined probability threshold, the green wave optimization is started, including: determining the optimal green wave passing speed and the phase difference between adjacent signals; the determined optimal green wave passing speed and phase difference are sent to the signal control unit of the corresponding intersection, and the corresponding regulation and control are performed. By using the above technical scheme, through dynamic modeling, trend prediction and formation of multi-intersection linkage optimization and green wave control closed loop of traffic flow, the optimization of traffic signal control is realized.

The application discloses a green wave control scheme generation method based on a time-distance diagram, and comprises the following steps: S100, acquiring all intersection information of coordinated direction intersections in a target region; S200, determining the green light time range that can be occupied by each intersection coordinated direction, and the maximum and minimum values of the positive and reverse green wave bandwidths through the intersection information; S300, taking the green wave occupied green light time t i ≤ the green light time range T i of each intersection coordinated direction as a constraint condition, sequentially traversing the reverse phase difference of the starting intersection, the reverse green wave bandwidth, and the positive green wave bandwidth, and determining the maximum positive and reverse green wave bandwidths and the optimal starting intersection phase difference that satisfy the constraint condition; and S400, calculating and generating the intersection phase sequence, phase time and phase difference according to the maximum positive and reverse bandwidths and the positive and reverse phase differences of the starting intersection. The application realizes the rapid determination of the maximum bandwidth of the green wave and the starting intersection phase difference based on the characteristics of the green wave time-distance diagram and through the simulation of manual debugging.