77 results about "Urban transportation" patented technology

The embodiment of the application discloses a kind of traffic network scheduling control method, system based on space-time big data, electronic equipment, belong to the technical field of electronic data processing, method steps include: the following steps are executed in edge side: receiving and analyzing terminal raw data, filtering and trajectory generation using the rules issued by cloud end;Call fusion engine to perform space-time alignment and matching on multi-source trajectory, generate fusion trajectory;Based on fusion trajectory, traffic flow, average speed are calculated synchronously, and input to event risk assessment model to output risk probability value;After comparing risk probability value with threshold, event level is generated and corresponding alarm is triggered.In cloud side, server converges multi-dimensional information uploaded by each node for macro analysis and model iteration optimization, and periodically issues updated strategy and configuration to edge node to dynamically adjust its processing logic, so as to realize intelligent traffic control from cloud end global optimization to edge real-time execution.

The utility model discloses a magnetic attraction piece and a magnetic attraction assembled three-dimensional track. The magnetic attraction assembled three-dimensional track comprises the magnetic attraction piece, a track piece and a toy car. The magnetic attraction piece is provided with a middle through opening, the middle through opening is provided with a rail butt joint groove, and inserting holes used for being connected with a vehicle rail piece are formed in the four corners of the magnetic attraction piece. The magnetic attraction splicing three-dimensional track further comprises a track piece and a toy car, inserting columns are arranged at the two ends of the track piece, the inserting columns are used for being connected with the inserting holes of the magnetic attraction pieces in an inserted mode, and guide rail parts corresponding to the track butt joint grooves of the magnetic attraction pieces are arranged at the track piece. By adopting the structural design of combining the magnetic attraction pieces and the vehicle rail pieces, not only can a richer, more real and layered urban traffic scene be built, but also the hand-eye coordination and logical thinking ability of children can be exercised in the splicing process, the imagination and creativity of the children can be fully expanded, the openness and playability of the toy can be improved, and the toy is worthy of popularization and application. The construction requirements of children of different age groups are met.

The application provides a traffic signal lamp fault emergency management system and method based on an Internet of Things large model, and relates to the field of urban traffic signal lamp supervision. The method is executed by an emergency supervision management platform of the traffic signal lamp fault emergency management system based on the Internet of Things large model, and comprises the following steps: determining a state evaluation result of a signal lamp based on first monitoring data and second monitoring data of the signal lamp; determining a target signal lamp based on the state evaluation result; cutting off the power supply of a fault signal lamp and issuing a detour suggestion to target personnel; determining a fault prediction situation of an abnormal signal lamp based on the first monitoring data, the second monitoring data and the state evaluation result of the abnormal signal lamp; and determining a maintenance parameter based on the fault prediction situation and sending the maintenance parameter to maintenance personnel. The application can accurately diagnose the state of the signal lamp, cut off the power supply and issue a detour suggestion when a fault occurs, predict a fault and determine a maintenance parameter when an abnormality occurs, realize rapid response and active intervention, and improve operation and maintenance efficiency.

The application discloses a kind of attention mechanism trajectory generation and simulation methods for sparse data completion, belong to intelligent transportation and trajectory prediction field.The application fills in trajectory missing value using high-precision sensor and map information, combines graph attention network (GAT) and multi-modal fusion technology;Adopt the attention module based on distance (D-GAT) and based on view (V-GAT), capture the interaction between vehicles, improve the understanding of complex traffic scene;Through prediction supervision generator and multi-modal trajectory generator, combine LSTM and Gaussian mixture model (GMM) to generate multiple possible trajectories, and use Kalman filter for online adjustment, ensure the accuracy and real-time of trajectory.The application realizes the intelligent completion of sparse traffic data and the accurate generation of trajectory, provides reliable data support and decision basis for intelligent transportation system, helps the efficient operation and sustainable development of urban traffic planning and management.

The present application belongs to the intelligent traffic technical field, specifically is a kind of trajectory restoration system based on urban traffic road network.The system of the present application includes traffic trajectory data acquisition module, GPS trajectory feature extraction module, GPS trajectory restoration learning module;Wherein traffic trajectory data acquisition module collects the latitude and longitude data of the vehicle driving on urban road in driving process, GPS trajectory feature extraction module extracts space-time feature to the low sampling rate trajectory point input neural network, and GPS trajectory restoration learning module inputs the output of last stage into new neural network model to predict the latitude and longitude information of missing sampling point in input low sampling rate trajectory.The present application can greatly improve the utilization of low sampling rate trajectory data;At the same time, combined with the topological structure of urban road network, the restored GPS trajectory is more in line with the real trajectory, effectively improves the utilization efficiency of trajectory data.

The present invention relates to the technical field of target detection. Provided are a small target detection method and system based on an improved YOLOv7 algorithm, and a storage medium and an electronic device. In the technique, the performance and effect of a YOLOv7 model in an urban traffic road-end detection scenario is improved from two perspectives: introducing an adaptive bounding box loss weight without changing the network structure of the model, thereby increasing the detection accuracy of the model with regard to a small target; and adding a detection head for a small target with only a slight increase in the inference time, which also increases the detection accuracy of the model with regard to the small target. In the present invention, the detection accuracy of a YOLOv7 target detection algorithm with regard to a small target is improved with almost no increase in the detection cost, thereby ensuring both the speed and accuracy of small target detection.

The application discloses a kind of based on high-efficiency structure diagram clustering realizes the method for urban traffic network planning, first using the characteristics that core intersection node in high threshold under SCAN algorithm and collaborative traffic function area are necessarily contained in low threshold result, the traffic area division result under different intersection correlation degree threshold and traffic area scale threshold is pre-organized into a kind of urban traffic road network hierarchical index model;Second, in online query stage, by parameter analysis and index positioning processing to the intersection correlation degree threshold input by user, quickly locate to corresponding target traffic hierarchical tree;Subsequently, using the hierarchical relationship and threshold screening processing of target traffic hierarchical tree, replace traditional complex full road network traversal operation, quickly extract traffic subnet node set;Finally, based on the initial clustering member list extracted, accurately determine pivot node and outlier by label mapping and extraction processing in closed neighborhood structure, to realize the efficient clustering of large-scale urban road network data.

The application relates to the technical field of intelligent traffic signal control, and discloses a multi-level city traffic signal lamp global linkage dynamic control method based on AI, which comprises the following steps: S1, collecting multi-source traffic data of a global road network in real time, performing space-time fusion and cleaning on the multi-source traffic data, and constructing a global traffic state feature library; the application realizes global road network cooperative control through a three-level AI architecture, can avoid local congestion diffusion, can make the global traffic efficiency increase by 30-60%, can make the average waiting time of vehicles reduce by more than 50%, can make the congestion conduction between regions reduce by more than 70%, and the system has multiple scene capabilities such as emergency priority and tidal traffic flow adaptation; the system realizes lifelong self-evolution relying on data closed-loop self-training, the control effect is continuously optimized, the level decoupling architecture guarantees that a single point fault does not affect global operation, and the system is stable; meanwhile, the system is compatible with multi-source equipment access, has low transformation cost, and is convenient for large-scale popularization.

This invention discloses a method for dynamic grouping and passenger pick-up scheduling of private cars at urban transportation hubs. The urban transportation hub includes a private car storage area for temporary parking, a passenger waiting area for passengers, and laneside areas for passenger boarding. Navigation and information dissemination facilities are installed in the private car storage area, passenger waiting area, and laneside areas. Private car information collection facilities are also installed in the private car storage area and laneside areas. A transportation hub dispatch server is connected and communicates with all navigation and information dissemination facilities, all private car information collection facilities, all passenger mobile communication terminals, and all private car mobile terminals. The transportation hub dispatch server receives each passenger's instruction and dispatches the corresponding private car to the laneside for passenger pick-up. This invention achieves more dynamic and intensive scheduling of passenger private cars in transportation hubs, and has high fault tolerance.

This invention provides a collaborative control method for multiple bottleneck ramps and connecting road networks on expressways based on feedback control. This method treats the entire expressway mainline and each sub-region of the urban road network as continuous entities that can be represented by a macroscopic basic graph model, constructing a feedback control system based on real-time traffic detection data. The entire expressway mainline adopts dynamic pre-set point proportional-integral (PI) feedback control based on the macroscopic basic graph, while each urban road network unit adopts classical PI feedback control. The method includes the following steps: S1, construction of the research object and control unit; S2, traffic state parameters and model assumptions; S3, construction of the upper-level control framework; S4, feedback control decision system based on the macroscopic basic graph; S5, allocation and execution of the lower-level control total flow in individual control objects. The method proposed in this invention utilizes a simple and highly robust macroscopic basic graph to efficiently achieve collaborative control of large-scale expressways and urban road networks, thereby improving the overall traffic efficiency of the transportation system and providing a feasible new approach for urban traffic congestion management.

The application provides a traffic signal indicator adjustment method, device and server, and relates to the technical field of intelligent transportation.The method comprises the following steps: obtaining historical monitoring data of a target road section and real-time road condition information of the target road section; predicting road traffic data conforming to the target road section based on the historical monitoring data and the real-time road condition information; and generating an adjustment scheme of a traffic signal indicator corresponding to the target road section according to the road traffic data, so as to adjust the traffic signal indicator of the target road section.The traffic signal indicator adjustment method, device and server provided by the application can adjust the traffic signal indicator of the target road section, and the whole adjustment process can be automatically completed without manual intervention, which not only saves time and effort, but also helps to alleviate the problem of urban traffic congestion.

This invention relates to the field of road management, specifically to a data-driven road traffic risk control system and method, comprising: a road monitoring module, a vehicle management module, an accident analysis module, a traffic focusing module, and a dispatching and allocation module. The road monitoring module is used to establish a road network model; the vehicle management module is used to plan the optimal driving route; the accident analysis module is used to determine the type of accident; the traffic focusing module is used to optimize patrol routes; and the dispatching and allocation module is used to build an exchange service platform for command and dispatch. This invention can improve traffic operation efficiency, reduce accident risks, build an intelligent traffic management system, enhance the perception and management level of urban traffic, improve the accuracy of traffic accident target detection, improve traffic infrastructure construction, enhance road network safety, optimize the allocation of human and material resources, and reduce traffic management costs.

The application provides a kind of city road network data resource directory generation method and system combined with service atlas, it is related to city traffic data management technical field, first access city road network service association conduction source and road network basic data generate service data association conduction pool;Then, based on the service association conduction logic deduces the road network data association derivative relationship, forms data association derivative network;According to the dynamic adaptation of data association derivative network, the dynamic directory unit is constructed by combining road network data;Based on the service conduction attribute of dynamic directory unit, the association conduction link is generated;Finally, the dynamic directory unit and the association conduction link are integrated to form a closed-loop city road network data resource directory that can be dynamically updated, which effectively improves the management efficiency and utilization value of city road network data resources.

The application provides a kind of urban vehicle carrying capacity scale optimization method and system based on mixed prediction, it is related to traffic transport engineering technical field, the method steps include obtaining city, traffic, running track, travel and point of interest data, and pre-processing;Supply capacity and travel demand mixed two-way prediction model is constructed, based on the upper limit of physical capacity of urban road and parking resources, combined with the reasonable sharing rate of taxicab in traffic structure to calculate the supply capacity, by fusing deep learning and traffic planning theory to simulate the space-time network distribution of origin-destination travel, obtain the initial carrying capacity scale based on travel demand;The initial carrying capacity scale is corrected based on the model of multimodal traffic development trend and estimated and adjusted based on horizontal city comparison, to obtain the recommended carrying capacity scale after optimization;Set up multi-dimensional monitoring index system, real-time monitoring and evaluation of the operation state of taxicab market, execute corresponding control response measures.

1. Name of the product in this design: Urban traffic guardrail cleaning vehicle. 2. Purpose of this design: For use in urban traffic guardrail cleaning vehicles. 3. The key design features of this product are the combination of shape and pattern. 4. The image or photograph that best illustrates the design's key points: 3D view 1.

The present invention relates to the technical field of urban traffic management, and in particular to a pavement defect monitoring and prediction method and system. The method comprises the following steps: constructing a pavement digital twin model of a target pavement; constructing a historical pavement monitoring dataset, and acquiring pavement monitoring indicator precise data and pavement high-definition image data in real time; on the basis of the pavement digital twin model and the pavement monitoring dataset, using a dual-source weight fusion method to obtain an indicator weight of each pavement monitoring indicator; and on the basis of the pavement monitoring indicator precise data, the indicator weight, and the pavement high-definition image data, updating the pavement digital twin model to achieve real-time monitoring and prediction of a target pavement defect, and formulating a pavement maintenance strategy. The present invention is based on the pavement digital twin model, and takes into account the degree of influence of each pavement monitoring indicator on a pavement defect, achieving real-time, accurate, and efficient monitoring and prediction of the pavement defect, thereby formulating the pavement maintenance strategy in a timely manner to ensure the road traffic capacity and safety.

The present application relates to the technical field of bridge engineering, and more particularly to a shallow-buried elastic composite steel damping seamless expansion joint, which comprises two platforms arranged in the expansion joint, the inner side of each platform being equipped with an elastic expansion mechanism, the elastic expansion mechanism comprising a fixed connecting plate, a pressing plate, connecting bolts and an auxiliary assembly, and the top of each platform being fixed with the fixed connecting plate and the pressing plate through the connecting bolts, the structure design of the elastic expansion mechanism making the device convenient to replace, the pouring of the first elastic filler between the platform and the first omega-shaped steel plate achieving good traffic comfort and waterproof effect, the present application being applicable to the replacement and installation of the 80-320mm expansion amount of the installation slot with a depth less than 200mm, and realizing good anti-seismic damping, multidirectional displacement performance, beam end damping and anti-falling beam effect, and the replacement being completed conveniently and quickly by only removing the elastic filler during maintenance and replacement.

The application belongs to the field of traffic flow prediction, and provides a city traffic flow prediction method, system and product under complex road network conditions, and the technical scheme is as follows: time mixing features are extracted based on city road network traffic flow time series data, intersection information between different traffic features is captured based on the time mixing features, mixed feature representation is obtained by fusing the time mixing features and the intersection information between different traffic features; global dependence in a long sequence is captured by selective state space modeling based on the mixed feature representation, and dynamic time series features are output; after the dynamic time series features are enhanced, a trailing time dimension is projected to obtain prediction values of each feature variable of the traffic time series data. In city traffic flow prediction, data can be dynamically processed according to real-time traffic conditions, multivariate information can be effectively utilized, and efficient long sequence modeling can be performed, and the prediction accuracy of time series data is significantly improved.

This invention proposes a map data interaction method and system based on urban road networks, comprising: acquiring open-source geographic data; identifying topological relationships between road entities using a computational geometry-based topological relationship reasoning algorithm; constructing an urban road knowledge graph; anchoring semantic nodes through spatial indexing, extracting their neighborhood association subgraphs, and using viewport adaptive computation and force-directed layout algorithms to focus the association subgraphs on the knowledge graph view, establishing a mapping relationship from map to knowledge graph; parsing the geometric attributes of semantic nodes, using a viewport fitting algorithm to drive map positioning and zooming, and establishing a mapping relationship from knowledge graph to map; receiving the user's original query statement, performing intent recognition and structured query generation through a large language model, and combining the knowledge graph retrieval results with the current intent recognition results to generate predictive decision support information; this method improves the semantic understanding depth, human-computer interaction efficiency, and decision-making level of urban traffic.

The application discloses a large-scale trajectory generation modeling algorithm and platform system supporting multi-scene migration, relates to the technical field of urban traffic and mobile travel, and comprises the following steps: S1, parallel preprocessing of large-scale trajectory data; S2, cross-scene adaptive trajectory generation modeling; S3, multi-task joint training; and S4, generation of an interpretable trajectory prediction result. The application not only realizes a breakthrough in the scale of urban-level trajectory data processing, but also makes significant progress in the core capability, adaptability and interpretability of the model, provides an end-to-end solution with high performance, high availability and high credibility, effectively overcomes the limitations of the prior art, provides solid technical support for various application scenarios of intelligent traffic, can easily cope with further growth of future data volume, and meets the rigid demand for real-time or quasi-real-time processing of massive mobile data under the background of smart cities.

The application provides a kind of urban multi-mode traffic network post-disaster repair strategy efficiency evaluation method and system, belongs to the technical field of urban traffic operation management, constructs multi-mode traffic network structure model using road network and bus network open source data;Using traffic travel demand data to simulate urban traffic distribution, construct multi-mode traffic network function model;Based on multi-mode traffic network function model, according to the spatial distribution characteristics of disaster and post-disaster network repair strategy, construct multi-mode traffic network structure recovery model, evaluate the efficiency of structure recovery;Based on multi-mode traffic network structure recovery model, reload travel demand, construct multi-mode traffic network function recovery model, evaluate the efficiency of function recovery.The application can comprehensively consider various traffic modes and the complex dynamic game between travelers, provide more accurate and more comprehensive repair strategy evaluation, can enhance the resilience of urban integrated transport system, so that it can better cope with various disasters and emergencies.

The application discloses a building fire escape path planning method and device based on global information fusion, which fuses building physical information system, building external fire safety rescue system, city traffic scheduling system, building BIM system and city 3D geographic information system and other data, and performs real-time evaluation on the building fire escape path, so that the escape path based on the optimal personal safety coefficient is determined according to the principle of the shortest and non-crowded. According to the building internal condition and the real-time dynamic information of the rescue of the fire station around the building, the optimal escape path is solved, disaster control, personnel escape and rescue implementation global information are fused, the best escape path strategy is determined, the efficiency and quality of building fire disposal are improved, the maximum safety space and escape path real-time planning can be obtained from the whole life cycle evolution of the fire, and personnel congestion or being trapped caused by information islands or one-sided information can be avoided.

This invention discloses an adaptive pavement structure design system and method for urban road traffic loads, relating to the field of road engineering technology. It includes a load data construction module, a mechanical estimation module, and a pavement structure generation module. The load data construction module is configured to construct a dynamic load spectrum based on a hidden Markov model. The mechanical estimation module is configured to calculate the mechanical parameters of the pavement structure at different locations by combining the dynamic load spectrum and road segment information. The pavement structure generation module is configured to map the mechanical parameters onto pavement design parameters to generate pavement structure schemes. The pavement structure generation module includes a collaborative optimization unit, which includes a multi-level digital twin model to simulate the dynamic evolution of pavement performance. This invention can proactively adapt to constantly changing traffic load characteristics to suit complex urban traffic environments.

This invention discloses a traffic prediction method based on an adaptive semantically enhanced spatiotemporal graph network. First, an initial spatial graph is constructed using traffic sensor data and standardized. Then, an adaptive node embedding method is introduced to dynamically learn the spatial dependencies between nodes in the traffic network. Further, a dual-branch structure is adopted, using a parallel gated network (PGN) to capture long-term traffic patterns and a linear hierarchical aggregation to capture short-term local patterns. Finally, spatial and temporal features are fused for end-to-end training to optimize the prediction model. This method effectively improves the accuracy and robustness of traffic prediction and is suitable for intelligent management and decision optimization in complex urban traffic environments.

The application provides an electric light truck urban cruise control method and system, which comprises the following steps: based on a federal learning architecture, the global parameters of an urban traffic cloud platform are updated cooperatively through a federal average algorithm; a working condition characteristic vector library covering global urban roads is constructed according to the global parameters, a migration learning algorithm is used to migrate the speed trajectory planning experience under the historical optimal working condition to the inside of the working condition characteristic vector library, and a source domain-target domain adaptation model is constructed in combination with the remaining battery capacity and power system state of the electric light truck; a speed trajectory initial framework corresponding to the electric light truck is output through the source domain-target domain adaptation model, and a corresponding optimization objective function is constructed according to the speed trajectory initial framework; preset constraint conditions are called out, and the optimal speed trajectory is calculated according to the constraint conditions and the optimization objective function, so that the corresponding urban cruise is completed according to the optimal speed trajectory. The application can effectively improve the cruise control efficiency of the electric light truck.

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

This invention discloses a boundary signal control method based on traffic elasticity theory, belonging to the fields of network elasticity science and urban traffic management. The method includes: acquiring the urban traffic network topology, traffic flow information, and signal control data; establishing a capacity multigraph of the traffic network based on its topology, defining a finite set of roads, and dividing it into multiple traffic sub-regions; constructing boundary signal phase models for the boundary regions of each traffic sub-region, and constructing a global signal phase matrix based on the signal phases at each node, thus establishing a control signal set; establishing elasticity state curves for each traffic sub-region and calculating the elasticity values ​​of the traffic sub-regions; constructing an incremental model of the road network traffic flow system based on traffic flow information and elasticity values, establishing an output vector prediction expression, establishing an error prediction model, and designing a model predictive controller. This invention can improve the traffic efficiency of the road network and solve the problem of existing signal control schemes being insufficiently timely and effective.

The present invention discloses an urban traffic velocity estimation method based on multi-source crowd sensing data. This method, based on roadside pedestrian data and road navigation data collected by smart phones, obtains a final estimated velocity through the steps of missing data filling, self-view velocity aggregation and multi-view velocity fusion. This fine-grained large-scale urban traffic velocity estimation method can achieve velocity estimation on all types of roads, including suburban road sections and paths, instead of just focusing on main roads in a city center. According to the present invention, based on data driving, the urban traffic velocity estimation method does not need to install additional devices on roads, and is low in cost and high in universality. Compared with the prior art, the urban traffic velocity estimation method has higher practicability, theoretical property and applicability, and is of great significance for improving traffic management and planning.