435 results about "Traffic conditions" patented technology

Provided are a method and an apparatus for constructing a road congestion prediction model, a device, a medium, and a product. A road traffic network is defined as a directed weighted graph. Historical dynamic traffic features of each road segment in the road traffic network are obtained as sample data, including recent dynamic traffic features and periodic dynamic traffic features. The sample data is input into a mixture of adaptive graph learners (MAGL) model for learning, and a probability prediction vector is output. The sample data is input into a trend expert model, and a trend distribution vector of a predicted probability of future traffic conditions is output. The periodic dynamic traffic features are fused to determine a periodicity prediction vector. An aggregated logit vector is obtained. An objective function is determined based on the aggregated logit vector. Congestion prediction training is performed to obtain a road congestion prediction model.

The invention discloses a tunnel ventilation control system and method based on artificial intelligence, and relates to the technical field of ventilation control, and the method comprises the steps: in a tunnel design stage, building an unsteady-state computational fluid dynamics model based on tunnel three-dimensional linear parameters, calculating turbulence structures under different traffic conditions through a large eddy simulation method, and calculating an unsteady-state computational fluid dynamics model; determining an optimal space configuration scheme of the fan group according to the distribution of the velocity field and the pressure field; in the tunnel construction stage, multiple types of sensor arrays are arranged along the vault and the side wall of a tunnel in a layered mode; in the tunnel operation stage, real-time vehicle tracks and speed distribution information of a traffic monitoring system are obtained; establishing a ventilation demand dynamic prediction model based on space-time correlation analysis, constructing a fan cooperative control model considering airflow organization optimization, and solving an optimal operation strategy by adopting a multi-target adaptive weight distribution algorithm; when a fire characteristic signal is monitored, the multiple sets of fans are coordinated to form a relay type smoke exhaust airflow organization.

The invention discloses a traffic planning method and device, a storage medium and a program product, and relates to the technical field of automatic driving. The method comprises the following steps: acquiring vehicle driving data and a real-time traffic condition; according to the vehicle driving data and the real-time traffic condition, a plurality of particles are initialized, and each particle represents an acceleration sequence of the vehicle in a future preset time period; iteratively updating the particles until convergence, and obtaining a preliminarily optimized acceleration sequence; and a quadratic programming algorithm is applied to smooth the preliminarily optimized acceleration sequence, a target acceleration sequence is obtained, and the target acceleration sequence is used for controlling the vehicle to run. The technical problem that a rule-based speed planning method in the prior art lacks flexibility and is difficult to deal with a complex traffic environment is solved.

A system includes a processor that is configured to acquire traffic video, analyze the acquired traffic video to determine traffic conditions, and optimize traffic signal timing based on the traffic conditions, and apply the optimized signal timing to a traffic signal.

The invention discloses a long-distance maneuvering path planning method and system fused with DOP information, and belongs to the technical field of path planning and navigation. The method comprises the following steps: collecting special vehicle information, GNSS DOP information, geographic information and traffic information; based on the DOP value and the traffic condition, the roads are preliminarily screened, and the gradient, the slope direction, the height limit, the load bearing, the turning radius and DOP information are integrated; road condition information is converted into constraint conditions, and DOP information serves as an optimization target and is fused into a heuristic cost function of an improved algorithm; a path with the best navigation effect is obtained through path searching, visual display is carried out on a geographic information system map, and DOP information changes of all key points on the path are displayed in real time. According to the method, a GNSS signal blind area can be effectively avoided, the feasibility and navigation precision of path planning are improved in combination with vehicle dynamics constraints, and the method is suitable for long-distance maneuvering tasks of special vehicles.

The invention relates to the technical field of intelligent traffic and smart city waste management, and discloses a path planning method and system for a garbage transport vehicle based on multi-source data, and the method comprises the steps: generating a reference path based on historical collection and transportation data, the method comprises the following steps: performing data cleaning on pre-acquired garbage quantity data, vehicle state data, real-time traffic data, hydroelectric data and fuel consumption data, performing multi-source time sequence feature fusion anomaly detection on a cleaned data set, performing trajectory inversion on the cleaned data set, performing target constraint through a mixed integer programming model, and performing multi-source time sequence feature fusion anomaly detection on the cleaned data set. Then gradient descent parameter optimization is carried out on the initial optimization model, elastic resource scheduling is carried out on an optimized path scheme based on a large event plan library, a path instruction set is generated, dynamic re-optimization evaluation is carried out on the path instruction set based on garbage amount change and traffic condition data fed back in real time, and an updated path is generated; according to the invention, the path planning efficiency of the garbage transport vehicle based on the multi-source data can be improved.

The application discloses a highway traffic monitoring system based on unmanned aerial vehicle aerial photography, which comprises a camera arranged on a holder, the holder being arranged on an unmanned aerial vehicle, the unmanned aerial vehicle flying at a low speed above a designated highway lane, and the unmanned aerial vehicle driving towards vehicles on the highway lane; the camera is used for photographing a road surface image of the designated highway lane based on the unmanned aerial vehicle and sending the road surface image to an image recognition unit; the image recognition unit recognizes the vehicles in the road surface image; and a road surface monitoring unit determines whether traffic congestion occurs on the designated highway lane based on the positions of the vehicles in adjacent frames.

The invention provides a quantum enhanced real-time multi-mode traffic network optimization method and system, and belongs to the technical field of intelligent traffic control and optimization. Comprising the following steps: carrying out edge calculation and preprocessing on multi-source motion data in a traffic network to generate traffic state characteristic data; a quantum calculation model is adopted to generate signal schemes for different traffic control targets; constructing a digital twinborn model to verify the signal scheme; converting the verified signal scheme into a traffic signal control instruction, and issuing the traffic signal control instruction to a traffic signal controller; and continuously monitoring the traffic condition, and feeding back the monitoring result to the quantum calculation model so as to adjust the signal scheme in real time. According to the method, the multi-mode traffic flow can be coordinated efficiently in real time in a large-scale urban network, the emergency quick response and closed-loop self-adaption capabilities are achieved, and the actual requirements of the future smart city and vehicle road collaborative development can be met.

The invention provides a traffic dynamic cooperative control method and system based on multi-source heterogeneous data fusion, and relates to the technical field of traffic control, and the method comprises the steps: obtaining a multi-source heterogeneous data flow of a management and control region through a ubiquitous perception terminal cluster, carrying out the fine-grained data fusion, and outputting a space-time fusion state matrix; performing blind area state completion to generate a panoramic traffic state map; performing dynamic demand prediction based on the space-time deep learning model, and outputting a pressure distribution matrix; dynamically delimiting a traffic control boundary; performing multi-agent collaborative decision, and outputting a collaborative strategy packet; and according to the real-time dissipation rate deviation, performing closed-loop updating of the traffic coordination strategy. The technical problems that traffic data in the prior art mainly depends on a single data source, data missing or coverage blind areas are prone to occurring, real traffic conditions cannot be comprehensively reflected, traffic flow prediction errors and bottleneck prediction errors are caused, and finally traffic congestion is aggravated are solved.

The utility model relates to a vibration device for simulating new and old bridge splicing seam construction, and the device comprises a vibration rack which comprises a pedestal, a vibration table connected to the pedestal through a floating supporting mechanism, and vibration equipment for vibrating the vibration table; the fixed rack is located on one side of the vibration rack, and the top surface of the fixed rack is flush with the top surface of the vibration table; and the test piece mold is positioned at the tops of the vibration table and the fixed rack and is detachably connected with the vibration table and the fixed rack. A test piece mold is fixed to the top of a vibration rack and the top of a fixed rack, one part of the test piece mold is fixed to a vibration table, and the other part of the test piece mold is fixed to the fixed rack. According to the invention, the working condition of vibration interference of a vehicle in a traffic state on a new and old bridge splicing seam is simulated. The concrete test piece in the test piece mold simulates the concrete setting process of a construction site, and the interference condition of vehicle vibration on the concrete strength is obtained, so that the purpose of verifying the feasibility of the concrete mix proportion is achieved.

The embodiment of the invention discloses a traffic condition identification method and device and a computer program product. The method comprises the following steps: acquiring trajectory data of a vehicle in a target area; determining vehicle behavior characteristics of each road section unit in the target area based on the trajectory data; performing spatial-temporal clustering on abnormal behaviors in the vehicle behavior characteristics, and determining candidate road section units where traffic events occur in the target area based on a spatial-temporal clustering result; and determining a detection interval based on the candidate road section units, and identifying the traffic condition of the detection interval based on the vehicle behavior characteristics of each road section unit in the detection interval. According to the invention, accurate identification of the traffic condition can be realized, and the timeliness and the coverage degree are significantly improved.

The application discloses a kind of induction coordination control methods based on intersection saturation and phase constraint, according to intersection real-time saturation, traffic condition is graded, and six different implementation modes are provided for different grades.In busy situation, give way mode is used, coordination phase can be released in advance and extended, but give way to any non-coordinated phase;In moderate condition, permission mode is used, coordination phase can be released in advance and extended, but give way to non-coordinated phase in order;Idle condition, according to phase constraint, permission give way, permission ignore, order ignore or full induction coordination mode can be flexibly selected to realize the release of all phases in advance and extension under its constraint.The application optimizes intersection release to the greatest extent by dynamically adjusting phase release timing and length to meet the induction coordination control needs of intersection under different scenarios.

A system is provided.SOLUTION: A system comprising: means for real-time analysis of traffic information collected from cameras and sensors; means for generating a traffic simulation using a generative AI model; means for calculating optimal traffic routes and signal light timing; means for sending instructions to signal lights and digital signage based on the calculation results; means for continuously monitoring and optimizing traffic conditions; and means for notifying users of traffic information.SELECTED DRAWING: Figure 1

The present invention provides a driver assistance system and vehicle capable of predicting the likelihood of pedestrians or light vehicles crossing the road before a crosswalk in specific traffic conditions. [Solution] A driving assistance device according to one embodiment of the present disclosure is capable of predicting the time it will take for a target traffic participant to reach a crosswalk when, under predetermined conditions at a first time, the signal for pedestrians or light vehicles at a crosswalk begins to switch from indicating "proceed" to indicating "no proceed" at a second time. Furthermore, based on the predicted time, this driving assistance device can calculate the probability that the target traffic participant will cross the road on which the vehicle is traveling, and determine whether or not to provide driving assistance and the content of the driving assistance according to the calculated probability.

The invention relates to the technical field of highway construction, in particular to a highway construction area dynamic safety guidance system for a complex traffic environment, which comprises a traffic flow monitoring module, a space change identification module, a risk partition judgment module, a dynamic guidance strategy module and an information release coordination module. According to the method, a dynamic guide information generation mechanism is constructed by combining real-time traffic flow monitoring data and construction area space change information, so that the change of a traffic state can be reflected in real time, and the guide information in the construction area is ensured to be consistent with the actual traffic flow density and lane space change; different construction areas are guided differentially according to risk levels and traffic conditions, high-risk areas are clearly divided, the accuracy and timeliness of information release are improved, the release frequency and content of guide information are dynamically adjusted, the traffic order and traffic safety of the construction areas are guaranteed, and the static and lagged information release problem in a traditional scheme is solved.

A method for constructing a communication system for microwave rainfall monitoring includes: acquiring regional status data of the target monitoring area, including geographical data, traffic data, and communication network data; generating system condition data based on the regional status data, including environmental constraint data generated from geographical data, key concern data generated from traffic data, and link resource data generated from communication network data; determining existing available links based on the link resource data, and determining permissible expansion links in conjunction with the environmental constraint data; determining actual expansion links in conjunction with the key concern data and permissible expansion links; and determining microwave communication rules based on existing available links and actual expansion links. This invention provides a method for constructing a communication system for microwave rainfall monitoring, which can rapidly deploy new microwave communication links in conjunction with traffic conditions, accurately monitor rainfall, and is beneficial for estimating potential severe disasters.

The invention discloses an anti-interference multi-label webpage identification method and system under an encrypted traffic condition, and the method comprises the steps: constructing a dynamic association graph through a burst transmission structure of the encrypted traffic, precisely deconstructing aliasing traffic through an entropy weight punishment mechanism and a node aggregation algorithm, and outputting an independent substream sequence; extracting non-stationary time-frequency features for the substreams, fusing the non-stationary time-frequency features with graph topology features extracted based on a graph structure in a tangent space, generating disturbance samples by using an adversarial training environment, adjusting feature weights, and generating composite fingerprints; executing long time sequence modeling on the Lie group manifold by using a Bi-Mama network, reserving a modulation interface, and outputting a preliminary recognition probability; and auditing causal consistency through a webpage loading logic state machine, converting an abnormal state into geodesic line correction in a Lie algebra tangent space, and correcting network parameters on line through a reserved interface. According to the method, the problems that aliasing traffic is difficult to deconstruct, fingerprint robustness is insufficient and an identification result lacks logic consistency constraint are solved.

This invention belongs to the field of airport public transportation allocation technology, and discloses an airport public transportation pre-allocation processing system and method based on a big data model. The method includes: acquiring historical data; using big data processing technology and machine learning and deep learning models to infer passenger demand and public transportation usage from historical data, generating a data projection model; inputting real-time weather, time, and passenger data into the data projection model, employing intelligent optimization algorithms, and dynamically adjusting public transportation resource allocation in conjunction with real-time traffic conditions; monitoring the public transportation resource allocation results in real time, continuously optimizing and dynamically adjusting the results using a feedback mechanism, and pushing the latest adjusted results to downstream third-party systems. This invention utilizes real-time data analysis and intelligent optimization algorithms to achieve a more efficient traffic scheduling scheme, and has significant practical implications and broad application prospects.

This invention relates to the field of intelligent transportation and road engineering technology, specifically disclosing a method and system for splitting and assembling modular vehicle platoons on mountainous highways. First, it acquires real-time road element parameters, platoon perception characteristic parameters, and operational status parameters. Then, it calculates the available line-of-sight (CASD) for platoon cooperation, establishing a multi-factor nonlinear platoon drivability index (CDI) model with CASD as the core constraint to complete the spatiotemporal partitioning of road segment risks. Subsequently, it constructs a marginal benefit decision-making model to achieve forward-looking decision-making for splitting and assembling. Finally, it formulates a splitting strategy matching the cooperative perception boundary and a drivability recovery-oriented assembly strategy to achieve dynamic adaptation of the platoon structure to the traffic conditions of mountainous roads. This invention solves the shortcomings of existing technologies, such as poor scenario adaptability, delayed passive response, and failure to consider platoon perception characteristics. It can be directly applied to the engineering practice of autonomous driving freight platooning on mountainous highways, balancing driving safety and traffic economy.

This design support device allows for the simulation of realistic traffic conditions by accommodating behavioral changes that align with the user's objectives. [Solution] A design support device comprising: a storage unit for storing service plan information and environmental information; an action purpose input unit for inputting action purpose information; a new facility information input unit for inputting new facility information; a generation unit that generates a behavior change model that changes the behavior of users within a domain using environmental information and action purpose information; a first calculation unit that calculates a first traffic state, which is the traffic state of the service mobile and each user in an environment where the service mobile and new facilities exist, using service plan information, environmental information, new facility information and the behavior change model; and a second calculation unit that calculates a second traffic state, which is the traffic state of each user in an environment where the service mobile and new facilities do not exist, using environmental information and the behavior change model, wherein the first calculation unit calculates the traffic state of the service mobile and each user after changing the behavior of users due to the influence of the new facilities.

The present application relates to the technical field of vehicle automatic driving, in particular to a kind of intelligent Agent trajectory planning control method and system based on HDMap;The method mainly includes four steps of the initialization of vehicle Agent, formal path query, entry mode confirmation and automatic driving according to path;High-precision semantic map and the idea of intelligent Agent are used in the present scheme, and a kind of vehicle Agent implementation scheme for flexible operation along lane is presented;In the entry mode of vehicle Agent, the present scheme designs a kind of ingenious vehicle Agent entry mode from the angle of time and space;The present scheme utilizes lane center line segment information in map, regulates the motion trajectory of vehicle, and provides flexible acceleration setting interface, so that the speed of vehicle Agent can change in real time according to the demand of user or traffic condition while automatically driving along lane, which reserves interface for the access of subsequent vehicle anti-collision strategy.

This invention discloses a method for predicting traffic conditions in large-scale urban road networks based on graph neural networks. The method first acquires road network topology and traffic time-series data, extracts road hierarchy information, and constructs graph data. The core lies in building a spatiotemporal graph neural network model that integrates dedicated road hierarchy components: a road hierarchy embedding layer maps discrete levels to continuous vectors to enhance feature semantics; a hierarchical global context module aggregates the global states of similar roads with linear complexity; and a dynamic cross-level attention module models the dynamic interaction dependencies between roads of different levels. This method suppresses noise interference from low-level roads through a hierarchical differentiation strategy and uses a separate weighted loss function for training. This invention effectively solves the problems of insufficient global perception, missing interaction modeling, and significant noise interference caused by neglecting road hierarchy semantics in existing technologies, significantly improving the accuracy and robustness of traffic condition prediction for large-scale urban road networks.

The invention relates to a computer-implemented method (100) for controlling a vehicle (200), comprising: Receiving (101) environmental sensor data (201) from at least one environmental sensor (203) of the vehicle (200) by a control unit (205) of the vehicle (200), wherein the environmental sensor data (201) at least partially represent an environment of the vehicle (200); Determine (103) a potentially hazardous traffic situation (207) based on the environmental sensor data (201) by a Traffic situation assessment module (209) of the control unit (205); and provision (105) of control signals (211) by the control unit (205) to execute a signal braking, wherein the signal braking has a degressive deceleration profile (213).

The invention discloses an intelligent logistics distribution platform and a working method thereof, and relates to the technical field of intelligent logistics. The intelligent logistics distribution platform comprises a logistics order processing module, a logistics resource monitoring module, a logistics environment sensing module, a distribution scheme planning module and a logistics communication control module, and seamless cooperation and dynamic switching of a rider, an unmanned aerial vehicle and an unmanned vehicle can be realized through the communication connection relation and mutual cooperation of the modules. And continuously optimizing a global optimal distribution relay scheme comprising at least one distribution mode in the whole distribution process according to internal and external data such as order attributes, real-time resource positions, real-time traffic conditions and real-time weather conditions, so that the distribution relay can be continuously optimized on the premise of ensuring the reliability. The maximum end-to-end distribution time efficiency and the minimum overall cost are achieved, the core competitiveness of the overall logistics distribution system is improved, and practical application and popularization are facilitated.

The invention discloses a multi-source data fusion and traffic mechanism driven checkpoint equipment closed-loop evaluation method, relates to the technical field of checkpoint equipment evaluation, and solves the technical problem of poor evaluation effect of existing checkpoint equipment. The method comprises the following steps: constructing an upstream equipment mapping table of the bayonet equipment based on road network information and position information of the bayonet equipment; verifying the historical traffic mode of the gate based on the historical traffic data, calculating the real-time accuracy of the gate device based on the floating car trajectory data, and finally comparing the traffic conditions of the to-be-evaluated gate device and the upstream gate device through the upstream device mapping table to judge whether the traffic is conserved or not; determining a mistakenly shot license plate number according to a license plate verification rule; the real license plate number of the mistakenly shot license plate number, the association algorithm and the hardware information are determined by using similar vehicles in the upstream and downstream vehicles, factors which can influence the equipment effect are analyzed, and improvement measures are adopted in a targeted manner; according to the method, environment interference, algorithm output characteristics and hardware state parameters are associated to form a closed-loop feedback mechanism.

This invention discloses an intelligent goods delivery route optimization method, relating to the field of intelligent logistics and delivery technology. The method comprises the following components: S1, metaverse space construction; S2, user role creation and access; S3, immersive collaborative route planning; and S4, route optimization algorithm and data-driven approach. By constructing a logistics and delivery metaverse space, this invention achieves real-time synchronization between the real world and the virtual space, including synchronized updates of order information, vehicle location, and traffic conditions. This helps logistics dispatchers perform more accurate route planning in the virtual environment. Delivery personnel can also view and provide feedback on route planning schemes through virtual roles before executing tasks, thereby achieving multi-party collaborative immersive route planning. This method can significantly improve the efficiency and accuracy of goods delivery, reducing delivery delays or errors caused by information asymmetry or poor communication.

This invention discloses a vehicle-road cooperative information interaction method and system based on intelligent transportation, belonging to the field of vehicle-road cooperative technology. The method includes S1: multi-source data acquisition, S2: 3D environment modeling, S3: structured analysis of traffic conditions, S4: multi-source collaborative fusion, S5: vehicle-end collaboration, and S6: adaptive optimization configuration. This invention establishes a multi-source clock synchronization benchmark, accurately measures and compensates for network transmission delay, eliminates time deviations caused by link asymmetry, and achieves motion distortion correction of laser point clouds and spatiotemporal alignment of multi-sensor data. This effectively eliminates motion distortion of lidar in dynamic traffic scenarios, enabling high-density point clouds to accurately reflect the instantaneous state of the road, balancing perception accuracy and real-time processing efficiency, improving perception reliability under adverse weather conditions, and achieving lane-level precise cooperative control and high-precision vehicle positioning capabilities.

The present invention relates to a method for controlling a vehicle in a traffic situation, wherein the vehicle comprises a control unit for controlling the vehicle, at least one environment sensor, and a prediction planner which can predict future trajectories for at least one detected object, wherein several trajectories are predicted for the detected object, a probability is determined for each predicted trajectory as to whether the object will follow this trajectory or not, and the vehicle is controlled on the basis of a trajectory plan which takes into account at least one predicted trajectory of the object, and wherein several planning options are determined for the trajectory planning of the vehicle, wherein the planning options differ in that only trajectories selected on the basis of their probability are taken into account, and an evaluation of the planning option is carried out.whether a control intervention is necessary at the current time, whereby a planning option for controlling the vehicle is selected from the determined planning options based on the evaluation, by selecting a planning option that performs such an intervention if a control intervention is necessary at the current time, and selecting a planning option that changes the current control of the vehicle as little as possible if no control intervention is necessary at the current time.

The application discloses a traffic robust knowledge distillation training prediction method and device, equipment and a storage medium, the method comprises the following steps: acquiring traffic flow historical observation data, dividing the traffic flow historical observation data into source domain training data and out-of-distribution verification data, and constructing a knowledge distillation architecture comprising a teacher model and a student model; according to the source domain training data and the out-of-distribution verification data, performing meta-learning double-layer optimization training on the knowledge distillation architecture, using the source domain training data in the inner loop to guide the student model to imitate the teacher model, using the out-of-distribution verification data in the outer loop to evaluate the generalization performance of the student model and correct the parameters of the teacher model in reverse, until a robust target student model is obtained; according to the target student model, receiving real-time input traffic flow data and performing forward reasoning, and outputting a prediction result of future traffic conditions, which can significantly improve the generalization ability and accuracy of traffic prediction, and realize efficient and robust prediction in complex traffic environment.

The invention relates to the technical field of crowd flow prediction, discloses a crowd OD flow prediction method and related equipment, and effectively overcomes the defects in fine-grained city internal OD flow prediction in the prior art by constructing an urban crowd flow prediction model fusing multi-dimensional key factors. Spatial correlation among geographic units is captured by means of a spatial adjacency encoder, regional function complementarity and geographic distance characteristics are mined through a land utilization complementation encoder, traffic reachability information is integrated by means of a traffic connection encoder, and a characteristic pattern cooperatively output by a multi-dimensional encoder provides comprehensive support for prediction. According to the design, the comprehensive influence of spatial positions, land utilization and traffic conditions on urban travel is fully considered, the defects that a traditional model ignores spatial dependence and an existing machine learning and deep learning model is insufficient in multi-factor integration are overcome, and the prediction accuracy and the fitting degree are remarkably improved.