343 results about "Traffic capacity" patented technology

The invention provides a dynamic path planning method and system in an intelligent traffic system, and relates to the technical field of intelligent traffic. Constructing a dynamic road network traffic capacity model based on a space-time diagram convolutional network; generating a multi-branch trajectory prediction result with a confidence score, adaptively starting an encryption parameter synchronization mechanism in combination with vehicle density, and generating a candidate path set through multi-agent collaborative game optimization; performing multi-dimensional deviation degree evaluation based on actual driving data and planning expectation; synchronously generating a multi-mode cooperative guidance signal; the system comprises a multi-source data fusion unit, a space-time modeling engine, a hierarchical decision-making system, a path verification and execution module, a closed-loop feedback controller, an event response system and a multi-mode man-machine interface. According to the method, the global resource utilization efficiency is improved through cooperation of data-driven modeling and hierarchical game decision, and the real-time performance, the safety and the system adaptability of path planning are improved through a closed-loop feedback and event response mechanism.

A dynamic evaluation method for an extreme rainstorm waterlogging disaster risk for disaster prevention and reduction. The method comprises: investigating and surveying urban system data and disaster prevention and reduction data, using GIS technology to divide disaster-bearing objects into refined risk units on the scale of urban buildings and road networks, and determining the spatial distribution of the disaster-bearing objects; on the basis of an extreme rainstorm waterlogging scene, simulating the disaster influence of a dynamic change process of a flood ponding depth on the disaster-bearing objects; developing refined dynamic evaluation on a waterlogging risk by combining the two methods of waterlogging process simulation and an indicator system; using a spatial complex network and a shortest path plan to calculate a traffic capacity and emergency service accessibility of a road network system; and on this basis, taking into comprehensive consideration the rational allocation of disaster prevention emergency drainage and emergency rescue services to a high-risk area, and proposing dynamic evaluation technology for a waterlogging risk that integrates a disaster evolution process and a disaster prevention response process, and ultimately realizing the dynamic evaluation of the waterlogging risk of each disaster-bearing unit during the waterlogging disaster evolution.

The invention discloses an expressway digital twinborn dynamic construction method, which relates to the technical field of traffic, acquires and preprocesses multi-dimensional data of an expressway system, and constructs a traffic flow model of an expressway based on a deep learning technology and in combination with the multi-dimensional data of the expressway system. The method comprises the steps of mapping multi-dimensional data, collected in real time, of an expressway system to a traffic flow model, dynamically updating a traffic state in the traffic flow model, performing short-term traffic flow prediction by using the traffic flow model, simulating influences when different traffic events occur, and analyzing existing risks. According to the method, the valuable traffic information is extracted from the mass data by capturing and analyzing the traffic flow data in real time and utilizing the machine learning and data mining technology, so that the digital twinborn body can accurately predict the future traffic flow and provide a decision-making basis, the occurrence of traffic congestion and accidents is reduced, and the traffic flow prediction efficiency is improved. And the traffic capacity and the safety of the expressway are improved.

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 multi-intersection signal cooperative control method based on deep reinforcement learning, and the method comprises the steps: firstly constructing a comprehensive reward function which comprises waiting time, queuing length and the influence of adjacent intersections, and constructing a mathematical model of a multi-intersection signal control system based on the reward function, so as to achieve the maximization of a comprehensive reward; and secondly, designing a deep Q network cooperative control algorithm for explicit strategy adjustment, and optimizing and adjusting adjustable strategy function parameters by introducing an explicit strategy adjustment function and utilizing a stochastic gradient descent method. And finally, dynamically adjusting the signal control strategy of each intersection in the road network in a complex and changeable traffic environment by designing a parameter sharing mechanism, thereby realizing collaborative optimization control of multiple intersections. According to the invention, the cooperative control efficiency of a multi-intersection signal system is obviously improved, traffic delay is reduced, and the traffic capacity of a road network is improved.

The invention discloses a traffic signal and direction-variable lane cooperative control method based on multi-agent learning, and the method comprises the steps: making a decision through the iteration of a traffic signal control module and a lane guiding control module, and carrying out the operation at the same time to control the traffic; the lane guiding control module adopts a dual depth Q network algorithm to enhance a learning model, an intelligent agent selects actions based on traffic flow and traffic signal states to maximize the lane utilization rate, and an award is calculated based on lane queuing length difference and the maximum direction demand traffic flow ratio; the traffic signal control module agent selects an action based on the observed lane density and the current lane state and obtains an award according to the traffic flow satisfaction of the selected phase. According to the method, the lane utilization rate is improved by controlling lane guidance, and the dynamically changing traffic flow is controlled in space and time dimensions by using a reinforcement learning framework; higher stability and efficiency are shown, and the traffic capacity of the intersection is improved.

The invention discloses a dynamic expansion management and control method for highway accident congestion based on an ETC portal, and relates to the technical field of traffic management and control, and the method comprises the steps: obtaining accident information; calculating the queuing length of the accident scene; the congestion duration is predicted, wherein the congestion duration is composed of rescue time, obstacle clearing time and congestion dissipation time; dilatation management and control are carried out in different areas, a control area and a buffer area are sequentially divided from the accident occurrence place along the rear, and dynamic dilatation management and control are carried out according to the actual traffic capacity of the accident road section. The method has remarkable advantages in the aspects of quick response, resource adjustment, timely rescue, accurate prediction, plan making and the like, decision scientificity is improved, effectiveness of active traffic management is improved, management strategies of active traffic management are refined, scientificity and rationality of dynamic management and control measures are improved, and the method is suitable for popularization and application. The method is of great significance in improving the emergency processing capacity of the expressway and guaranteeing traffic safety and smoothness.

The invention belongs to the technical field of path planning, and discloses a path planning method and system fusing multi-modal terrain perception and vehicle dynamics, and the method comprises the steps: obtaining and fusing multi-modal geographic data to generate a terrain physical representation, and calculating the terrain complexity based on the terrain physical representation; predicting an overturning index and a stability margin of the vehicle based on the terrain physical representation, and generating a trafficability cost function; taking the trafficability cost function as a constraint condition, selecting a motion element according to terrain complexity to perform trajectory fitting, and generating a vehicle path; executing the vehicle path to obtain a vehicle state, correcting the trafficability cost function by using the vehicle state, and optimizing the vehicle path; and constructing a loss function, training the path planning model, and performing path planning by using the trained model. According to the invention, by establishing a terrain vehicle bidirectional coupling mechanism, safe and adaptive local path planning under a complex unstructured terrain is realized, and the traffic capacity and safety of the system in a dynamic environment are significantly improved.

The invention relates to an intersection lane division and signal timing cooperative control method in an automatic driving environment, and belongs to the field of intelligent traffic. According to the method, lane function division schemes in corresponding directions are published by using lane function adjustment marks, signal timing schemes in corresponding directions are published by using lane-level signal lamps, and personalized suggestion schemes are pushed to network-connected automatic driving vehicles by using a network-connected communication technology, so that lane-level and vehicle-level refined intelligent management and control are realized, and the intelligent management and control efficiency is improved. The intersection traffic efficiency is improved, and intersection road traffic resources are utilized to the maximum extent. According to the invention, the problem of insufficient management refinement of the plane intersection can be overcome, the traffic capacity of the intersection is improved, traffic congestion is reduced, and the utilization rate of road traffic resources is improved.

The invention relates to the technical field of bridge structure health monitoring, and particularly provides a bridge earthquake damage rapid assessment method and system, and the method comprises the steps: carrying out the cross-modal coupling calibration of a time-history sudden change feature and a phase of a space energy spectrum, and generating a high-confidence three-dimensional dynamic response field; inputting the spatial gradient distribution characteristics into a nonlinear material state inversion program, performing path integrity verification in combination with an energy dissipation path, and outputting a potential damage topology boundary of the bridge; dividing an output bridge potential damage topological boundary into discretized damage units, and mapping strain constraints of the discretized damage units into triple decision-making indexes including a traffic capacity attenuation coefficient, an emergency disposal aging threshold value and an aftershock risk level through a bridge structure function attenuation rule. The system comprises a mixed field dynamic response reconstruction module, a damage sensitive domain reverse locking module and a function level damage quantitative mapping module. According to the method, deterministic mapping of the bridge structure damage state and the engineering function index is realized.

The invention discloses a traffic network toughness diagnosis method under flood disasters based on a space-time diagram neural network, and relates to the crossing field of traffic engineering and artificial intelligence. The method comprises the following steps: collecting traffic topology, flood monitoring and traffic flow data, and carrying out space-time alignment; a flood coupling dynamic space-time diagram is constructed, a water depth-traffic capacity response mechanism is introduced, and real-time mapping from a disaster physical state to a network topology is realized by utilizing an attenuation function meeting physical monotonicity constraint and dynamically updating an edge weight of a diagram structure according to real-time water depth; inputting the dynamic graph into a pre-trained space-time graph neural network model, extracting space-time evolution characteristics and outputting a toughness diagnosis result; model training adopts a toughness label generated based on an anti-fact baseline to carry out supervised learning, and introduces a physical constraint loss function. According to the method, the problems of decoupling of disaster features and graph structures and unavailability of toughness labels in the prior art are solved, and the physical consistency and accuracy of diagnosis are improved.

The invention discloses an interval occupation logic checking method, a train control system, a storage medium and electronic equipment, which are used for automatically blocking a standard railway line, and the method comprises the following steps: acquiring train position information and a train type; acquiring track circuit data, and judging a track section state; and generating a corresponding signal permission for the train based on the train position information, the train type and the track section state, and judging the logic state of the corresponding block partition based on the signal permission. The method has the advantages that the normal train, the fault train and the non-moving block train can be ensured to safely run on the automatic block system railway line at the same time, the normal train can still adopt the moving block system and does not need to be degraded into the automatic block system, the running density of the normal train is improved, and the running efficiency is improved. Therefore, the traffic capacity of the automatic block type railway line is improved.

The invention relates to a traffic signal management technology, in particular to an intelligent traffic signal control system, which comprises a traffic data acquisition module for acquiring data of a traffic sensor in real time; the data processing module is used for analyzing the data collected by the sensor and generating an optimized signal switching strategy; the signal control module is used for controlling the state of the traffic signal lamp according to the instruction of the data processing module and automatically adjusting the signal time according to the change of the traffic flow; the cloud computing platform module is used for remotely monitoring and managing the plurality of signal control units; and the fault diagnosis and maintenance module automatically monitors the operation state of each component of the system. By acquiring the traffic flow and the pedestrian state in real time, the signal switching strategy can be intelligently analyzed and optimized, the vehicle waiting time is shortened, the traffic capacity of the intersection is improved, and the system can adjust the state of the signal lamp in time according to flow analysis and behavior pattern recognition, so that the traffic jam in the peak period is effectively relieved.

The invention relates to the technical field of intelligent traffic control, in particular to a single-intersection signal lamp dynamic timing method and system based on holographic perception, and the method comprises the steps: collecting the information of a single intersection and surrounding traffic participants in real time through a multi-source holographic perception device, and carrying out the preprocessing, thereby obtaining a standardized traffic data set; based on the data set, combining vehicle, pedestrian and non-motor vehicle demand weights, constructing a traffic demand evaluation model, and calculating a demand priority coefficient of each entrance lane; constructing a signal lamp dynamic timing optimization objective function by taking minimization of intersection total vehicle waiting time, minimization of pedestrian crossing waiting time and maximization of intersection traffic volume per unit time as objectives according to the priority coefficient in combination with intersection traffic capacity constraint conditions, solving to obtain an optimal signal lamp timing parameter, and issuing the optimal signal lamp timing parameter to a control terminal; and traffic state feedback data of the intersection are collected in real time, and timing optimization is carried out again if the change exceeds a preset threshold value. The system can wait for a long time, improves the intersection passing efficiency, and relieves traffic congestion.

A base station for operating at least one cell of a wireless communication network and is configured to operate the cell in a capacity mode during a first instance of time and in an energy saving mode during a different second instance of time. In the energy saving mode, the base station provides a lower traffic capacity in the cell when compared to the capacity mode to reduce a level of power consumption in the energy saving mode when compared to the capacity mode.

The invention discloses a flight operation guarantee intelligent collaborative intervention method based on DT and a complex network, and the method comprises the following steps: 1, building a flight ground operation guarantee digital twinborn model based on a data driving method, and achieving the virtual-real synchronization; step 2, establishing a flight ground operation guarantee complex network model; and step 3, flight operation guarantee intelligent collaborative intervention based on the digital twin model and the complex network model. Compared with the prior art, the method has the positive effects that the method has great value and practical significance for improving the traffic capacity of the airport and enhancing resource guarantee. Along with continuous development of the aviation transportation industry and complication of flight guarantee work, the method has wide application prospects, can be applied to various large airports, facilitates optimization of flight guarantee work procedures and improvement of operation efficiency, and meanwhile plays a positive role in promoting modernization management and intelligent development of the aviation guarantee industry.

A test method for a variable speed limit (VSL) strategy in a mixed traffic flow considering driver compliance is provided. A road scenario is built, and multiple mixed traffic flows formed by human-driven vehicles (HDVs) and connected and automated vehicles (CAVs) are set, which vary in CAV penetration rate. The VSL strategy is established, and a speed limit of an upstream of the bottleneck area is calculated based on the VSL strategy. A HDV compliance is established, and in combination with the speed limit, the HDV drivers are classified, and a proportion of the HDV drivers in individual types in the road scenario is determined. Based on a traffic flow scenario in which the HDV drivers and CAV drivers are located, indicator data reflecting traffic capacity is calculated. A test system for performing the test method is further provided.

The invention discloses a traffic light control optimization method and system based on multi-source data interaction, and relates to the technical field of intelligent traffic, and the method comprises the steps: obtaining a real-time traffic information data set of a target road section; constructing a real-time traffic twin model based on the real-time traffic information data set, and embedding a traffic prediction model for traffic jam prediction to obtain a jam result; dynamically determining a vehicle passing priority, and generating a traffic light control target based on the congestion result and the vehicle passing priority; randomly simulating different signal timing schemes through a real-time traffic twin model, determining a traffic efficiency index evaluation result set, and selecting an optimal control strategy according to the traffic efficiency index evaluation result set; and adjusting traffic light signals of the target road section based on the optimal control strategy. The technical problems that traffic light signal timing is not flexible and traffic flow changes cannot be adapted in real time in the prior art are solved, and the technical effects of improving the traffic flow efficiency, reducing traffic congestion and improving the priority traffic capacity of emergency vehicles are achieved.

The invention specifically relates to a tunnel group road section traffic capacity assessment method based on intelligent network connection, which relates to the technical field of traffic engineering, and comprises the steps of constructing an AI-driven dynamic assessment model based on fusion data and a digital twinborn model in combination with tunnel engineering constraints, and calculating the traffic capacity, congestion risk and bottleneck position of each road section of a tunnel group in real time. In the invention, a multi-source data acquisition system is combined with an edge and cloud two-stage fusion architecture to construct an LSTM-XGBoost double-model architecture; the LSTM model can calculate and predict the traffic capacity of four time nodes in the future 10 minutes in real time, the XGBoost model accurately locates the bottleneck position and quantifies the contribution degree of four types of causes, meanwhile, the precision is ensured through multi-dimensional verification, the tunnel group whole domain can be covered, instantaneous traffic changes can be captured, and a panoramic decision basis of real-time data, prediction trend and bottleneck causes is provided for traffic management.

The invention provides a deep learning-based method and device for evaluating the influence of an avalanche disaster on a road, and relates to the technical field of disaster coping, and the method comprises the steps: obtaining the historical avalanche monitoring data of a monitoring region; establishing an avalanche disaster influence prediction model according to the topographic parameters and the physical parameters of the snow body before avalanche; according to the avalanche disaster influence characteristics, the road positioning data and the post-avalanche road influence parameters, establishing an avalanche disaster road influence prediction model; acquiring current data of the monitoring area; obtaining current avalanche disaster influence characteristics according to the avalanche disaster influence prediction model; and predicting the influence of the avalanche disaster on each road section according to the avalanche disaster road influence prediction model. According to the method, key parameters of an avalanche occurrence area can be acquired in real time, and the occurrence probability of an avalanche disaster and the influence degree of the avalanche disaster on a road can be accurately predicted and early warned by means of a deep learning algorithm, so that the safety and traffic capacity of the road are effectively improved.

The invention discloses an expressway reconstruction and extension traffic diversion method, system and device and a medium, and the method comprises the steps: obtaining multi-source dynamic traffic data, and constructing a road network origin-destination matrix through data fusion and demand deduction; a traffic flow distribution result is obtained by constructing a system optimal traffic distribution model; identifying the first congestion time period, and calculating the traffic flow exceeding the residual traffic capacity in the congestion time period to obtain the traffic flow to be shunted; solving an optimal shunting path under the target of minimizing the total travel time of the system through a hybrid intelligent algorithm to obtain an optimal shunting path scheme; and verifying the optimal shunting path scheme by using microscopic traffic simulation software, updating traffic data based on a simulation result, and carrying out iterative optimization to obtain a differentiated traffic organization scheme. According to the invention, the traffic flow passing efficiency during the highway reconstruction and extension period is effectively improved.

The invention discloses a road culvert inner wall damage image detection and traffic capacity evaluation method and system, and relates to the technical field of road engineering detection and computer vision, and the method comprises the steps: collecting multi-source data through an annular camera, a laser section scanner and a mileage encoder carried by a detection robot, and carrying out the panoramic expansion and time-space registration; using a multi-scale attention panoramic segmentation network to identify damages such as lining cracks, joint slab staggering, damage and chipping, steel bar exposure and water leakage; calculating the clearance size and the deposition thickness of the culvert by adopting an ellipse fitting method; according to the method, the culvert detection speed reaches 50 m / h, and the damage detection accuracy reaches 89% or above.

The invention discloses an urban road collapse risk calculation method under an extreme flood condition, and the method comprises the following steps: S1, determining the damage condition of an underground structure, and determining the simulation parameters of a road, a soil body, underground water and a flood scene; s2, establishing a three-dimensional road structure model based on DEM-CFD bidirectional coupling according to the road structure and the particle flow microscopic parameters and hydrodynamic parameters of the soil body, and simulating a cavity development process caused by a damaged underground structure under a long-time extreme flood condition; and S3, according to the simulation result, applying a dynamic vehicle load and an FWD load, and evaluating the road collapse risk and traffic capacity. The method simulates the interaction of the road structure and the underground structure under the whole flood process, can recognize the potential collapse risk of the urban road under the flood condition, evaluates the traffic capacity of the urban road after flood, improves the safety of the urban road, can more accurately give the traffic control opinion of the urban road after flood disaster, and improves the traffic control effect of the urban road after flood disaster. And a certain reference is provided for post-disaster road traffic management and repair.

The invention discloses a capacity expansion management and control method for predictable easily-congested road sections, and relates to the technical field of traffic management and control, historical data of each ETC portal in a near period of time on an expressway are collected, and historical traffic operation states of each road section in the monitoring range of each ETC portal are calculated; according to the historical traffic operation state of each road section, easily-congested road sections are screened, easily-congested routes are formed, and the daily average traffic volume of the easily-congested routes is calculated; and formulating a capacity expansion control strategy according to the daily average traffic volume of the easily-congested route. The method is used for solving the problems of large traffic flow and low traffic efficiency of predictable easily-congested road sections, capacity expansion can be carried out in a way of adding lanes and the like for regular and long-term easily-congested road sections so as to improve the traffic capacity, the method is a mesoscopic periodic strategy, conventional microcosmic management and control are improved to mesoscopic management and control, and the traffic efficiency is improved. Optimized configuration and efficient utilization of traffic resources can be realized, and the operation efficiency and the service level of the whole traffic system can be improved.

The invention discloses a dynamic planning method and system for smart city traffic, and relates to the technical field of smart cities and intelligent traffic, and the method comprises the steps: collecting multi-source traffic data, and carrying out the fusion preprocessing; constructing a dynamic weight model to calculate a priority weight; a signal lamp timing scheme is generated based on the weight and is adjusted through a rolling optimization mechanism; outputting an optimization strategy by combining historical and real-time feedback evaluation effects; and a manager is notified through the acousto-optic prompt device and a report is generated. According to the invention, through the dynamic weight model and the rolling optimization mechanism, the overall delay time of the intersection is reduced, the traffic capacity of the traffic flow is improved, the Kalman filter is introduced to fuse multi-source data, and the data processing precision is improved; the LSTM network is adopted to predict the future traffic flow, a scientific basis is provided for a signal lamp timing scheme, and the workload of traffic management personnel is reduced through the acousto-optic prompt device and the automatic report generation module.

The invention relates to the technical field of rail bridge post-earthquake traffic, in particular to a method, medium and equipment for calculating post-earthquake response and traffic capacity of a rail bridge system. The method comprises the following steps: establishing a nonlinear simulation model of a high-speed railway system; the post-earthquake track residual irregularity is calculated, a post-earthquake track residual irregularity characteristic curve is constructed, and fast calculation of post-earthquake response is achieved; establishing a wheel-rail power system nonlinear mapping model based on the convolutional neural network; and performing Monte Carlo simulation on the track residual irregularity quantitative index, inputting the track residual irregularity quantitative index into a wheel-rail power system nonlinear mapping model, and calculating a train power response index to obtain the post-earthquake traffic capacity of the train. A reasonable calculation model is established according to the structural characteristics of the track system, and the time and economic cost is reduced; post-earthquake dynamic response is calculated through a wheel-rail power system nonlinear mapping model, the relation between post-earthquake track residual deformation and train dynamic response is established, and the post-earthquake traffic capacity of the track bridge system under different safety levels is obtained.

The invention relates to the technical field of intelligent traffic, in particular to an expressway dynamic intelligent capacity expansion method and system based on a supply-demand relationship, and the method comprises the steps: carrying out the traffic control unit division of an expressway; collecting real-time traffic data, and performing level division on traffic states according to the traffic data; according to the historical data and the real-time traffic data, carrying out supply and demand analysis based on a machine learning algorithm, and calculating the traffic capacity and short-time traffic flow prediction of the current section road; according to the supply and demand prediction result, the gap traffic volume of the traffic control nodes is calculated, and the traffic management strategy of each traffic control node is formulated and adjusted; and monitoring the implementation effect of the traffic management strategy based on the real-time traffic data, and if an expected target is not reached, optimizing the traffic management strategy. According to the invention, the problems of high cost and long period of a traditional expressway capacity expansion mode are effectively solved, the road traffic capacity is obviously improved, the congestion is reduced, and the traffic management efficiency is optimized.

The invention relates to the technical field of smart traffic, in particular to a smart capacity expansion method and system for an expressway operation period, and the method comprises the steps: collecting real-time traffic flow and speed data, and obtaining traffic jam information through calculating the weighted average flow and speed; analyzing congestion data between adjacent radars according to the traffic congestion information, and combining continuous congestion points to obtain a congestion section; according to the congestion section, through a demand prediction algorithm and a traffic capacity prediction algorithm, calculating a current VC ratio and a future VC ratio; and according to a calculation result, carrying out prediction analysis on a congestion trend, and according to a prediction analysis result, adjusting traffic control measures in real time. According to the invention, the problems of response lag and insufficient data accuracy when a traditional traffic management system deals with real-time traffic changes of the expressway are effectively solved, accurate prediction and dynamic management of traffic congestion are realized, and the traffic congestion problem of the expressway is effectively relieved.

A traffic signal timing method, system, product and medium. The method comprises the following steps: collecting real-time traffic data; according to the real-time traffic data and the preset driving direction, whether the vehicle driving direction is abnormal or not is judged; if the abnormal vehicle is detected, calculating all passable abnormal paths by using topographic data; acquiring real-time driving data of the abnormal vehicle, inputting the real-time driving data and the abnormal paths into a preset dynamic behavior model one by one, and predicting the driving probability of each path; selecting the path with the highest driving probability as a future abnormal driving path; the traffic light timing of each intersection in the future abnormal driving path is adjusted by combining real-time traffic data, so that the red light is turned on when the abnormal vehicle passes; meanwhile, the actual traffic capacity and capacity reduction proportion of each intersection in a future abnormal path are calculated, the green light duration is dynamically adjusted based on the reduction proportion, and the normal traffic circulation efficiency is ensured. By implementing the technical scheme provided by the invention, the passing efficiency of the normal traffic flow under the condition of processing the abnormal driving direction of the vehicle is improved.

The invention discloses an entrance and exit ramp traffic organization method and system based on traffic flow characteristics, and relates to the field of traffic organization, and the method comprises the steps: collecting traffic flow data and passing time data of all entrance and exit ramps in a target traffic area; calculating a congestion index of each entrance / exit ramp according to the traffic flow data and the passing time data; calculating a pressure index of each entrance / exit ramp according to the congestion index, the traffic capacity of the entrance / exit ramp and the total length of the ramp; dividing each entrance / exit ramp into a conventional ramp and a control ramp according to the pressure index; compiling a ramp design scheme for each conventional ramp and each control ramp, wherein the ramp design scheme comprises a conventional lane line layout and a gradient virtual and solid line layout; and integrating each ramp design scheme into a regional overall design scheme. By implementing the method, the road passing efficiency of the entrance and exit ramps can be improved.