236 results about "Traffic capacity" patented technology

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 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 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 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 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 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 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.

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 relates to the technical field of unmanned aerial vehicle nests, and particularly discloses a traffic accident intelligent restoration system and method based on an unmanned aerial vehicle nest, and the system is provided with a response priority division module, a selected unmanned aerial vehicle nest matching module, an optimal route planning module, an accident vehicle intelligent search module and an accident scene restoration map generation module. The problems that a traditional processing mode needs long road closing time and affects traffic capacity, vehicles, personnel, scattered objects and the like exist in accident sites and are prone to being damaged, and the traditional mode mostly aims at local two-dimensional scenes, accident information is difficult to collect in an omnibearing mode, accurate identification and responsibility division requirements cannot be met, traffic jam is prone to being caused, and the like are solved. And the secondary accident risk is also increased.

The invention relates to a community low altitude-oriented aircraft path planning and traffic capacity evaluation system. The system comprises an airspace facility modeling module, a facility relation judgment module, a hierarchical airspace segmentation module, a noise-speed-safe distance constraint calculation module, a dynamic obstacle marking module, a multi-target path planning module and a bottleneck identification and traffic capacity evaluation module. The method comprises the following steps: modeling airspace facilities, and judging a facility relationship based on spatial geometric attributes and function use requirements of the facilities; performing hierarchical grid division on the low altitude of the community, calculating a safety distance along the movement direction and a safety distance perpendicular to the flight direction, and performing obstacle marking on grids around the building based on the safety distances and the flight direction; and performing path search based on an improved A * algorithm, identifying a bottleneck region, extracting parameters, and calculating the maximum allowable speed of the bottleneck region and the total traffic capacity of the bottleneck region. The system can relieve environmental troubles such as safety compliance dispute and noise pollution caused by operation of the low-altitude aircraft.

The invention discloses a U-turn control method and device of an automatic driving vehicle and the automatic driving vehicle. The method comprises the steps that a first prediction track of a traffic participation object is acquired, and meanwhile, a second prediction track of a target automatic driving vehicle is acquired; when it is determined that a coincident trajectory exists between the first prediction trajectory and the second prediction trajectory based on a comparison result of the first prediction trajectory and the second prediction trajectory, obtaining first arrival time when the traffic participant arrives at each prediction coincident point on the coincident trajectory; evaluating the first arrival time to obtain the influence degree of the traffic participant on the turning-around operation; adjusting the original turning-around strategy according to the influence degree to obtain a target turning-around strategy; and controlling the target autonomous vehicle to execute a turning-around operation according to the target turning-around strategy. According to the invention, the technical problem that the traffic capacity of the autonomous vehicle is limited due to the obvious insufficiency of the autonomous vehicle turn-round path planning in the aspects of dynamic traffic flow influence processing and adaptive adjustment in the related technology is solved.

The invention belongs to the technical field of optimal planning and site selection of electric vehicle charging stations, and particularly relates to an electric vehicle charging station planning and site selection method, which comprises the following steps: S1, constructing an uncertainty set containing road and node traffic capacity loss; constructing a vehicle starting point-terminal point travel scene set in combination with the historical travel data of the vehicle; s2, constructing a network equalization model considering the arrival time fairness of the electric vehicle users according to the passing time cost difference of the electric vehicle users on the traffic road when the electric vehicle users go to the charging station from the starting point so as to solve regional traffic flow distribution; and S3, based on the traffic flow distribution in the S2, constructing a charging station planning model considering the user arrival time fairness, and determining an optimal site selection and capacity configuration scheme of the charging station. According to the method, the time fairness of the electric vehicle user arriving at the charging station can be ensured while the traffic uncertainty of the traffic network road section is effectively dealt with, and the method has a relatively good market application prospect.

The invention discloses a method for evaluating the use performance and driving comfort of a bridge after an explosion load effect. The method comprises the following steps: establishing a finite element model of the bridge under the explosion effect, and extracting the residual area of the section of the bridge after the explosion and the elevation of the bridge floor after the explosion; calculating distribution of bridge section damage degree and bridge deck irregularity level; and the traffic capacity of the bridge is evaluated accordingly. For a bridge which can still be used, a result file of a bridge part after explosion is imported, then an axle coupling model after explosion is established, and unevenness after explosion is formed; and extracting the calculated and analyzed vertical acceleration of the vehicle, and calculating the root mean square value of the acceleration to evaluate the driving comfort of the vehicle. According to the method, through coupling evaluation of the damage degree and the bridge floor irregularity level, the composite influence of explosion on the bridge safety and the driving comfort is comprehensively quantified, and the misjudgment risk caused by one-sided evaluation is avoided; and meanwhile, the economic cost is greatly reduced through a simulation analysis and calculation mode, and the safety risk of post-disaster scene survey is avoided.

The present application is applicable to the technical field of traffic control. Provided are an adaptive traffic signal control method and apparatus, a device and a medium. The adaptive traffic signal control method comprises: on the basis of a background plan and an optimal timing plan which are pre-configured for a target intersection, establishing a plurality of control policies for controlling the traffic light duration; on the basis of the discharged traffic volume, the intersection traffic capacity and the number of queued vehicles at the target intersection at a current moment, determining a current traffic state of the target intersection; on the basis of the current traffic state of the target intersection and the relevance between the target intersection and an intersection adjacent to the target intersection, selecting a target control policy from the plurality of control policies; and on the basis of the target control policy, controlling the target intersection. The adaptive traffic signal control method of the present application can improve the traffic efficiency of intersections.

The invention relates to the technical field of rail bridge engineering, and particularly discloses a rail-bridge system residual traffic capacity assessment method, medium and equipment, and the assessment method comprises the steps: constructing a rail-bridge system seismic response model and a train-rail-bridge system coupling vibration model of a research object; constructing an earthquake-induced irregularity sample library; establishing a simplified calculation model of the shock-actuated irregularity equivalent coefficient; performing curved surface fitting to obtain a shock-induced composite unsmooth fitting curved surface; putting forward earthquake-induced irregularity thresholds in different driving performance index states, and establishing a linear mapping relation between the earthquake-induced irregularity thresholds and the driving speed; and establishing a mapping relationship among the seismic oscillation intensity, the structure natural vibration period and the post-earthquake driving speed limit value in different driving performance index states, and outputting a residual traffic capacity evaluation result according to the post-earthquake driving speed limit value. According to the method, the problem that in an existing method, the quantitative mapping relation between the current rail surface irregularity after the earthquake and the train operation performance is not clear is effectively solved.

The invention discloses an intersection signal control method based on minimum blocking probability and highest traffic capacity, and relates to the technical field of intelligent traffic systems. A bilevel planning optimization model is constructed, an upper layer model takes minimization of intersection blocking probability as a core target, a blocking probability function is established through dynamic quantification of traffic flow random volatility, and a sequential quadratic programming algorithm is fused to optimize a signal period and effective green light time parameters of each phase; the lower layer model is based on a Webster delay model, and feeds back the traffic state in real time by taking the minimization of the total delay of the vehicle as a target. The upper layer model and the lower layer model realize collaborative optimization through a closed loop mechanism of parameter adjustment-delay feedback-iterative optimization, and meanwhile, a dynamic weight coefficient is introduced to flexibly coordinate multi-target conflicts of the blocking probability, delay and traffic capacity. According to the method, the dynamic response capability of signal timing to the real-time traffic demand is remarkably improved, the peak delay of the intersection is effectively reduced by 20%, the average peak delay is effectively reduced by 15%, and the road network toughness is enhanced.

The invention discloses a multi-agent traffic signal control method based on traffic flow dynamic prediction, and the method comprises the steps: constructing an independent reinforcement learning agent for each intersection, precisely depicting a causal mechanism of signal decision to congestion propagation through an intersection-level queue evolution model, and carrying out the prediction of a traffic signal on the basis of a simple traffic state index. According to the method, the comprehensive traffic state fusing the traffic capacity of vehicles at the intersection and the future queue length is established, the method is used for dynamic prediction of each intersection based on the traffic flow state, an intelligent agent can select the action enabling the queue length to be reduced fastest, and therefore a better control effect is achieved. And a neural network model is constructed based on transformer, and optimal traffic phase control based on a reinforcement learning algorithm is realized by using time sequence characteristics and spatial characteristics of multi-intersection traffic flow. The method can effectively utilize the current signal phase and the vehicle state and distribution to predict the queue change after phase switching, and can be used for traffic agent design of a large-scale road network.

The invention relates to a layered collision detection and path search method for a sweeper structure. According to the method, based on the vehicle body structure characteristic that the sweeper is wide in the upper portion and narrow in the lower portion, environment point cloud data are subjected to hierarchical processing according to height, and at least low-layer obstacles and high-layer obstacles are distinguished; and determining the width difference of the vehicle in the high-layer space and the low-layer space in combination with the vehicle equivalent widths corresponding to different heights, and implementing differential expansion processing on the obstacles with different heights in the grid map. On the basis of completing layered expansion, a unified layered Voronoi diagram is constructed, the layered Voronoi diagram is introduced into a path search process, and the driving path of the sweeper is constrained or guided, so that the effective passing space below the blind compensation radar is fully utilized on the premise of ensuring the obstacle avoidance safety, and the blind compensation efficiency is improved. And the traffic capacity and the path planning efficiency of the sweeper in a complex and narrow scene are improved.

The invention provides a safety-oriented subway station passenger flow dynamic control method and system, and relates to the technical field of rail transit, and the method comprises the steps: calculating the time utility of each directed path and the selection probability of stairs and ladders according to real-time detection data, simulating the pedestrian walking process based on a microscopic pedestrian simulation model, and constructing a subway station simulation model; performing gridding processing on the preset risk detection area, constructing a multi-level comprehensive risk degree evaluation model based on the subway station simulation model in combination with the congestion level, the space risk, the train operation condition and the traffic capacity, and determining the comprehensive risk degree; constructing a prediction model; and with the minimization of the difference between the predicted risk degree and a set safety reference value as a target, optimizing the pull-in service delay time through closed-loop control to obtain the pull-in service delay time. The invention provides a metro passenger flow control method oriented to microscopic crowd safety. The metro passenger flow control method overcomes the defect that in the prior art, only the macroscopic service level or congestion is concerned, and local dynamic risks are ignored.

The invention relates to the technical field of bridge post-earthquake driving safety, in particular to a high-speed railway bridge post-earthquake driving speed threshold calculation method, a medium and equipment. The method comprises the following steps: establishing a track-bridge system model and a train-track-bridge coupling system model; a post-earthquake track irregularity quantitative index is put forward, the relation between the post-earthquake track irregularity quantitative index and a post-earthquake driving performance index is established, and the fitting parameter relation between the driving speed and the mapping relation is established; and the driving performance index limit value and the post-earthquake driving speed threshold value of the safety margin are quantified and guaranteed. According to the method, the post-earthquake track irregularity quantitative index TICrms suitable for the two track types is provided, and the index has good applicability in high-speed railway bridge tracks; a train running speed threshold calculation formula meeting the post-earthquake traffic safety requirement is provided, so that the post-earthquake traffic capacity calculation of the high-speed railway bridge is simple, the speed threshold calculation result has reasonable safety margin, and a basis is provided for post-earthquake safe operation and maintenance of the high-speed railway bridge.

The invention discloses a two-way multi-lane highway traffic flow dynamic control method and system based on traffic simulation, and the method comprises the steps: firstly constructing a virtual sensor network covering two-way multiple lanes through a traffic simulation platform, carrying out the total factor perception of a traffic flow, and obtaining the collection data; secondly, abnormal values in the collected data are filtered in a mode of combining a double-sliding-window mechanism and an IQR algorithm, and filtered data are obtained; according to the invention, a function of performing real-time dynamic control on the expressway traffic flow by constructing a closed-loop optimization system of perception, decision, control and evaluation is realized, and accurate perception, intelligent decision and cooperative control can be performed on the traffic flow by fusing dynamic traffic simulation and a real-time data driving technology. Meanwhile, the traffic flow evolution trend pre-judgment capability can be improved through a time-space coupled traffic state prediction model, the overall traffic capacity of the expressway is improved, the life and property safety of road users is guaranteed, and the method is suitable for being widely popularized and used.

Techniques for load balancing secure network traffic are disclosed. A system, process, and / or computer program product for load balancing secure network traffic includes monitoring network traffic for one branch of a plurality of branches for an enterprise network, and splitting the network traffic of the one branch into a plurality of network segments based on a determination that the network traffic exceeds traffic capacity of at least one security processing node (SPN) of a plurality of SPNs using a network load balancer (NLB) in communication with a plurality of Network Processing Nodes (NPNs), the plurality of monitored branches being distributed to the plurality of SPNs via a plurality of tunnels.

The invention discloses a highway passenger and freight mixed traffic flow modeling method based on METANET model optimization. The method comprises the following steps: constructing a dynamic critical density correction function based on a freight car proportion; establishing a differentiated lane changing behavior decision-making mechanism, respectively setting lane changing probabilities based on a Logit utility function and a suppression coefficient for cars and trucks, and enhancing the dynamic simulation capability of lane transverse flow distribution; and constructing a ramp interaction model coupled with the truck proportion, and analyzing a traffic capacity attenuation mechanism of the confluence area. According to the method, the macroscopic modeling advantage of the METANET model is kept, and the method has good structural compatibility and generalizability. According to the method, the prediction precision of the flow, the speed and the density in a multi-vehicle mixed traffic scene is superior to that of a traditional model, and the method is suitable for modeling, simulation and control of an intelligent high-speed system.

The invention discloses a data-driven network traffic modeling and simulation method based on dynamic modulus decomposition. The method comprises the following steps: collecting traffic data through sensors arranged in urban roads; filling missing values in the traffic data by adopting a data reconstruction method based on equilibrium state hypothesis; reconstructing and predicting the nonlinear dynamics of the space average speed in the traffic data based on the Koopman operator theory; calculating an expected time headway corresponding to a space average speed predicted by dynamic modulus decomposition KMD based on time headway distribution estimated offline, and calculating an outflow rate, namely traffic capacity, according to the expected time headway; and predicting the traffic density change based on a cellular transmission model and the traffic capacity calculated in the previous step. According to the method, the time headway (which not only represents the dynamic characteristics of traffic capacity in a congestion state, but also is a core parameter of a CTM model) is introduced, and a dynamic outflow flow rate estimation method based on speed constraint is constructed, so that accurate modeling of a flow density nonlinear relation is realized.

The invention discloses an intersection traffic capacity assessment method and system based on multi-source data fusion. The method comprises the steps of obtaining intersection static model data and delay index data; counting the number of effective lanes in each flow direction based on the intersection static model data, determining the maximum number of lanes in each phase, and calculating the basic green time ratio of the corresponding phase; calculating a flow direction comprehensive delay index based on the real-time delay index data and the flow direction lane number, and taking the ratio of the flow direction comprehensive delay index to the intersection comprehensive delay index as a flow direction delay green time ratio; time period division is carried out based on historical delay index data, and fusion weights of a basic green time ratio and a flow direction delay green time ratio in each time period are distributed; calculating a final green time ratio of each flow direction; and evaluating the traffic capacity of each flow direction of the intersection according to the final green signal ratio and the theoretical traffic capacity. According to the method, the basic lane configuration data and the real-time delay data are combined, intersection traffic capacity evaluation is carried out under the condition of not depending on intersection equipment arrangement and flow data, and the accuracy and universality of traffic capacity evaluation are remarkably improved.

The invention discloses a river-sea combined transport ship collaborative scheduling method and system based on multi-objective optimization, and the method comprises the steps: collecting and fusing dynamic data, static data, scheduling application data and channel environment data of river ships and sea ships, and obtaining fused multi-source heterogeneous data; constructing a channel digital model, and calculating the dynamic traffic capacity of each channel according to the fused multi-source heterogeneous data; constructing a multi-objective optimization model by taking the minimization of the total scheduling time and the total waiting time of all ships as an objective; solving the multi-objective optimization model based on an improved DE-NSGA-II algorithm to obtain a Pareto optimal solution set; and selecting a final scheduling scheme from the Pareto optimal solution set and executing the final scheduling scheme. The ship operation cost is remarkably reduced, carbon emission is reduced, and green port construction is assisted. And the waiting time and the total voyage of the ship are reduced, so that the fuel consumption and the operation cost of the ship are directly reduced.