768 results about "Public transport" patented technology

The invention discloses a control strategy model construction method based on big data, and relates to the technical field of intelligent control, a traffic situation deduction and emergency strategy generation module uses a multi-agent traffic simulation technology, simulates a traffic situation in combination with real-time traffic data, evaluates risks and early warns potential crisis by integrating multiple factors, and provides a control strategy model for the traffic situation deduction and emergency strategy generation module. And after an early warning is received, an emergency strategy is generated by using an intelligent algorithm, the strategy is evaluated and optimized by using virtual rehearsal, a result is fed back to a management department, and a synergistic effect with other modules is achieved. Traditional traffic detection equipment data is integrated with multi-source heterogeneous data such as mobile phone signaling, shared bicycle and online hailed bicycle GPS, intelligent vehicle-mounted equipment and public traffic operation, more comprehensive and accurate information such as traffic flow, flow direction, travel behavior track and the like is provided, and rich and accurate basis is provided for traffic management decisions.

To provide an information processing device, an information processing method and a program that can visualize resident's needs to move and actual service conditions of public transportation.SOLUTION: An information processing device 100 comprises: a traffic data storage unit 132 which stores information on stops of public transportation; a movement data storage unit 133 which stores movement data including places of departure, routes and destinations; a reception unit 124 which receives visualization conditions; a totaling unit 125 which totals, for each of mesh sections of a map, departure / arrival counts of the movement data extracted from the movement data storage unit by the use of visualization conditions; and an output unit 126 which outputs a visualized map including stops, and outputs one or more mesh sections in different display modes according to departure / arrival counts.SELECTED DRAWING: Figure 1

The invention relates to the technical field of intelligent traffic, in particular to a vehicle driving path control system and method based on intelligent traffic big data, and the method comprises the following steps: a, collecting the traffic data of the real-time position, the road traffic flow and the congestion condition of a vehicle through a real-time traffic data collection module; b, analyzing the historical traffic data through a big data analysis and prediction module, and predicting peak periods, accident risk areas and traffic bottleneck points; according to the invention, through dynamic acquisition and big data analysis of real-time traffic data, the peak period and the congestion area can be accurately predicted, so that the driving path of the vehicle is optimized, congestion is reduced, in addition, through intelligent regulation and control of a signal lamp and a freight vehicle shunting mechanism, the system avoids interference of freight vehicles to public transportation in the peak period, and the traffic safety is improved. The road traffic efficiency is improved, the system also provides real-time path adjustment suggestion and early warning service, and more convenient and safer travel experience is provided for drivers.

The invention discloses a multi-mode speech enhancement system based on audio and video, and the system comprises a data collection module which collects target audio, background noise and interference sound through a microphone; a camera is used for synchronously collecting environment visual information, including facial expressions and mouth shapes of speakers, and noise and interference sounds are normalized to ensure that the noise and the interference sounds are matched with the dynamic range of a target audio; the data analysis module is used for preprocessing the collected audio, including noise suppression and speech enhancement, and improving the speech definition by applying a deep learning model; through the noise reduction and voice enhancement technology, the interference of background noise can be effectively reduced, and the voice is clearer. Thus, audiences can more easily understand speech contents, especially in noisy environments, such as public transport means or busy streets; and the voice recognition accuracy is improved, and the quality of the voice signal can be improved.

The invention relates to the technical field of public transportation systems, and particularly discloses a bus departure scheduling method, which comprises the following steps of S1, integrating multi-dimensional data; s2, a dynamic prediction model containing machine learning parameters is adopted to calculate the passenger demand in the future period; s3, calculating the number of required vehicles according to the predicted demand, the vehicle capacity and the dynamic load coefficient; s4, constructing a multi-objective function including energy consumption optimization, and solving the optimal departure interval and route; and S5, according to the real-time data, correcting a scheduling scheme, collecting real-time feedback data through a passenger mobile application, analyzing the emotion and demand of the passenger by using a natural language processing technology, based on feedback intention recognition of an emotion analysis model, constructing a passenger demand knowledge base in combination with historical complaint data, and optimizing a dynamic prediction model and a scheduling strategy. Through technology integration and system innovation, the static and single bottleneck of traditional scheduling is broken through, and an intelligent solution considering efficiency, low carbon and user experience is provided for urban buses.

The invention discloses a planning method for collaborative planning of urban and industrial infrastructures, which belongs to the technical field of urban and industrial planning, and comprises the following steps of: establishing a correlation model of an impervious surface and population density, collecting land blocks of a target city and application types of current land blocks, and extracting a densely populated area; a multi-level and diversified traffic system is constructed, station-city integrated public transportation is set, construction and upgrading of public facilities and infrastructures are improved, a data sharing mechanism is established, a joint evaluation system is established, and a public opinion platform is established. According to the method, a population density model estimation method is established based on an impervious surface, through the population distribution condition and the land utilization condition, a GIS system is utilized to optimize traffic lines, public facilities and infrastructure construction is improved, reasonable layout and structural adjustment of the industry are guided, optimization and upgrading of the industrial structure are achieved, and the method is suitable for popularization and application. And sustainable development of the city is promoted through modes of data sharing, planning negotiation, system integration, project docking and the like.

The invention discloses an operation optimization method, system and equipment for cooperative carbon reduction of an intelligent rail line network, and relates to the technical field of energy-saving scheduling and low-carbon operation of urban public transport. A refined section-level energy consumption and carbon emission quantitative model is constructed based on dynamic characteristics of intelligent rail vehicles and carbon emission factors of a power grid; a deep learning network is used for fusing multi-source data to carry out cross-line short-time passenger flow prediction, real-time passenger flow demands, energy consumption and carbon emission indexes and intersection signal phase time window constraints are uniformly incorporated into a multi-target collaborative optimization framework, and solving is carried out through mixed integer linear programming in a rolling vision field. According to the method, combined optimization and dynamic closed-loop control of departure intervals, section operation speeds and road right strategies are realized, so that the total operation energy consumption and carbon emission of the intelligent rail line network are remarkably reduced on the premise of ensuring passenger flow transportation requirements and service quality, and the line network level energy-saving and carbon-reducing operation targets are achieved.

A real-time traffic signal optimization system that includes: (a) a traffic data acquisition module that collects real-time traffic data from sensors, cameras, GPS and vehicle-to-infrastructure communication; b) a machine learning-based traffic prediction engine that analyses historical and real-time data to predict congestion patterns; c) an adaptive signal control unit that dynamically adjusts traffic light switching times based on real-time traffic flow and predictive insights; (d) an emergency and priority vehicle management system that overrides normal signal operation to give priority to emergency vehicles and public transport vehicles; (e) a central traffic monitoring and control dashboard for real-time monitoring, manual interventions and system configuration; and (f) a vehicle-to-infrastructure communication interface enabling data exchange between traffic signals and connected vehicles to improve driver awareness and traffic coordination.

The invention relates to the technical field of intelligent traffic, and discloses a traffic processing method and system, a terminal and a storage medium, and the method comprises the steps: dynamically adjusting traffic lights and public traffic scheduling information based on multi-dimensional traffic data and a first algorithm model; wherein the multi-dimensional traffic data comprises traffic prediction data, environment state data and vehicle running state data; and acquiring navigation path planning information and emergency braking information of each vehicle based on the multi-dimensional traffic data, the traffic light adjustment condition, the public traffic scheduling information and the vehicle operation information. By means of the method, safe and efficient traffic signal adjustment, public traffic scheduling and path planning work can be achieved.

The invention belongs to the technical field of dynamic traffic, and particularly discloses a multi-source data fusion traffic dynamic induction method, which comprises the following steps: S1, traffic information is acquired from multi-source heterogeneous data, and the data comprises real-time traffic information, navigation map data, a road network topological structure and a public traffic timetable; s2, carrying out data fusion processing on the traffic information obtained in the step S1, and generating fused traffic state estimation; s3, on the basis of travel historical records and preferences of the user, constructing a personalized travel guide model, and providing an optimal travel suggestion; and S4, monitoring the traffic condition in real time, performing dynamic adjustment based on user feedback and emergencies, and optimizing the travel path. By integrating real-time information from various data sources, comprehensive perception of road traffic conditions is ensured, traffic changes in space and time can be processed at the same time, the weight is dynamically adjusted according to the reliability of the data sources, and the influence of data islands and information loss is reduced.

The invention relates to a carbon emission measuring and calculating method and system based on urban public transport. The method comprises the following steps: step 1, constructing an urban public transport carbon emission calculation model; step 2, collecting data related to urban public transport carbon emission; step 3, acquiring public database data; step 4, data cleaning and preprocessing; 5, calculating the carbon emission of the urban public transport: calculating the carbon emission in the urban public transport system according to the data in the step 4, and outputting specific emission data; step 6, carbon emission data aggregation and multi-dimensional analysis; step 7, carbon emission trend prediction; and step 8, strategy optimization. According to the method, the accuracy of carbon emission calculation is improved, the carbon emission condition in the public traffic operation process is reflected in real time, and two scientific carbon emission data supports are provided for traffic management departments, enterprises and the public.

The invention belongs to the field of road vehicle control methods, and relates to a bus departure control method based on a master-slave game in an intelligent network connection environment, which comprises the following steps of: (1) setting a control area CZ by taking a harbor type stop station as a center; (2) after getting on and off the bus, triggering a control decision, and sharing information such as real-time position, speed and acceleration of each bus in the bus B and the social vehicle group FV; (3) obtaining a bus driving track when the income of the bus B and the total income of the social vehicle group FV are maximum through an upper layer planning model; obtaining a social vehicle driving track under the condition that the social vehicle group FV selects the maximum individual income based on the decision of the public transport vehicle B through a lower-layer planning model; (4) solving the bilevel planning model through NSGA-II to obtain an optimal bus and social vehicle driving track; and (5) executing the vehicle track. According to the method, the negative influence caused by bus departure can be reduced to the minimum.

The invention discloses a modular bus operation compilation method based on a variable rolling time window, belongs to the technical field of urban public transportation, and particularly relates to a modular bus operation compilation method based on a dynamic time window. The invention aims to solve the problems of long passenger travel time, low module bus operation efficiency and high bus company operation cost caused by the fact that an existing modular bus system cannot deal with mismatching between bus system transport capacity resource supply and passenger flow space-time demand. According to the method, the operation cycle of the bus is decomposed into a plurality of continuous variable time windows, and two-stage interactive optimization control is completed at the starting moment of each time window. Meanwhile, the departure interval information of the compiled scheme is transmitted to the rolling time domain, the first departure interval length obtained by the scheme serves as the variable window length, and the compartment marshalling scheme is executed. And obtaining a departure interval timetable of the departure station and the number of carriages sent by each train at each station until the operation of the total plan cycle is finished.

The invention discloses a short-time rail transit flow prediction method and device based on a decoupling process. The method belongs to the technical field of public transport data analysis and prediction, and comprises the following operation steps: collecting subway AFC card punching data to obtain a passenger OD data set; defining a region to establish a rail transit reality topological structure, and designing a multi-modal convolution feature map through entity subway road measurement data and an OD data set; the method comprises the following steps of: improving an initial Cell structure of an LSTM (Long Short Term Memory), fusing a Swin Transform module into the Cell, and adjusting an original gating structure; and constructing a prediction network by using the IST-LSTM as a new prediction unit and the SBULSTM in parallel. According to the method, AFC clock-in data is collected, a new multi-modal convolution feature map is constructed by using a dynamic time warping method, and a new multi-modal convolution feature map is established by simulating a signal decoupling process; meanwhile, an LSTM unit is improved, and the attention function of Swin Transform is integrated, so that all time features in a time period can be captured, global decoupling information provided by the multi-modal convolution feature map can be enhanced, and the prediction accuracy of the rail traffic flow is improved.

The invention discloses a cooperative organization method for efficient cooperative configuration of public transport hub resources, and relates to the technical field of traffic scheduling, and the method comprises the steps: analyzing the relation between hub transport capacity supply and passenger demands in different time periods according to a space-time path matching result; constructing a transport capacity matching optimization index system, calculating standard indexes, analyzing asynchronous correlation characteristics of various indexes in a time sequence state, and constructing a mapping relation graph between passenger travel characteristics and transport characteristics; analyzing the correlation degree between different mapping elements, constructing a public transportation hub supply and demand matching optimization model, and inputting the feature mapping relation graph into the public transportation hub supply and demand matching optimization model for matching optimization; a data-driven transfer scheduling optimization model is established, multi-mode traffic trip chain connection matching and self-adaptive scheduling are realized in combination with real-time matching measurement information, spatial and temporal mutual influence of multiple traffic demands on a traffic hub is fully considered, and spatial decomposition and collaborative prediction of the traffic demands are realized.

The invention relates to a hybrid power supply electric public transportation system charging scheduling optimization method based on reinforcement learning, belongs to the field of urban public transportation operation management, and particularly relates to a hybrid power supply electric public transportation system charging scheduling optimization method. The invention aims to solve the problems of resource redundancy and waste, high operation energy consumption and high operation cost caused by unreasonable resource allocation of the existing electric public transportation system. The hybrid power supply electric public transportation system charging scheduling optimization method based on reinforcement learning specifically comprises the steps of defining a state space; defining an action space; defining a reward function; establishing a multi-target Markov decision model based on the state space, the action space and the reward function; constructing a reinforcement learning network model based on a multi-target Markov decision model; acquiring a data set; training a reinforcement learning network model based on the data set to obtain a trained reinforcement learning network model, and storing parameters; and based on the trained reinforcement learning network model, obtaining a charging scheme of an actual optimization time period.

A driver protection system installed on a public transit unit, the driver protection system including a glass portion, a base portion connected to the glass portion, a hinge portion connected to the glass portion and base portion, wherein the glass portion and base portion are made from bulletproof materials and / or non-ballistic materials.

The invention belongs to the field of big data processing, and discloses a crowd travel mode identification method and system for multi-view subspace learning based on dynamic and static feature fusion, and the method comprises the steps: carrying out the preprocessing of original signaling data, and cleaning invalid data; extracting a travel track of each user, and processing possible abnormal conditions of ping-pong data and drift data; after track data of a single user in a sample time period is obtained, segmenting the track data, and extracting a single trip chain track; extracting static characteristics such as travel behaviors and travel traffic of a travel chain in combination with multi-source data such as mobile phone signaling data, an OSM road network and public traffic data; extracting time sequence features by using an LSTM model, and modeling dynamic features of a travel track; static and dynamic features are fused, and travel modes are captured from multiple views. According to the method, the complex travel mode can be more accurately captured, data redundancy and noise are reduced, and the accuracy of travel mode identification is remarkably improved.

The invention discloses a rapid connection adaptive scheduling system based on travel feature heterogeneous transfer demand response, and relates to the technical field of traffic scheduling, the system comprises a historical data processing unit, a prediction analysis unit, a real-time monitoring unit and a response scheduling unit; the historical data processing unit is used for acquiring historical transfer passenger flow data and extracting a corresponding travel feature set; the prediction analysis unit is used for training a demand prediction and analysis model by using the travel feature set, and predicting a transfer demand by using the trained model; the real-time monitoring unit is used for collecting the position, speed and passenger capacity information of public transportation in real time and monitoring the passenger flow condition of a transfer station. And the response scheduling unit is used for carrying out scheduling simulation by adopting a genetic algorithm based on the transfer demand and the collected real-time data, and carrying out multi-mode traffic scheduling according to an optimal scheduling strategy. According to the invention, the efficiency and quality of traffic service are improved, and the travel experience of passengers is improved.

The invention provides a mixed traffic cooperative control method based on multi-agent reinforcement learning, which can be applied to the technical field of intelligent traffic and automatic driving. The method comprises the following steps: inputting first state information of a target intersection and second state information of an adjacent intersection into a signal lamp intelligent body obtained based on public traffic priority target training, and outputting a signal lamp phase control action; inputting the signal lamp information and the automatic driving vehicle information of the automatic driving vehicle at the target intersection into a signal lamp fine-tuning intelligent body obtained based on safety target training, and outputting a signal lamp fine-tuning control action; inputting the signal lamp information, the automatic driving vehicle information and the front vehicle driving information into an automatic driving vehicle intelligent body obtained based on safety efficiency cooperative target training, and outputting an automatic driving vehicle driving action; and under the condition that the first signal lamp is controlled to execute the signal lamp phase control action and the signal lamp fine adjustment control action, the automatic driving vehicle is controlled to execute the driving action of the automatic driving vehicle.

The invention relates to the technical field of smart communities, in particular to a future community construction method and system based on government big data, and the method comprises the following steps: collecting power use data, water resource consumption data and public transport utilization rate data in a community, synchronizing the data, and carrying out the standardization processing, and generating a community resource use data set. According to the method, through a time sequence analysis method, the resource demand of a community in a future week can be effectively predicted, a resource flow simulation model is established, prediction data is input in real time, model parameters are adjusted, influences of policies are continuously simulated and evaluated, optimal allocation of community resources is achieved, and the community resource allocation efficiency is improved by constructing a resource allocation diagram and determining an optimal path. The method achieves the high efficiency and fairness of resource distribution, continuously tracks the distribution effect and resident feedback, further guarantees the adaptability and continuity of resource distribution, not only optimizes the community resource management, but also improves the welfare of residents, and enhances the sustainable development of communities.

The invention relates to the technical field of intelligent monitoring, in particular to an intelligent bus kiosk monitoring method and system with an abnormal behavior intelligent monitoring function. The method comprises the following steps: acquiring multi-source monitoring data of a target bus kiosk, and based on the multi-source monitoring data, performing character and article linkage behavior analysis on the multi-source monitoring data to generate an entity interaction feature set; extracting a timestamp sequence and a space coordinate sequence based on the entity interaction feature set, and generating a space-time parameter set based on the timestamp sequence and the space coordinate sequence; based on the entity interaction feature set, according to the space-time parameter set, analyzing whether the current entity interaction feature has an abnormal behavior in the corresponding bus kiosk space-time scene, and if yes, extracting abnormal behavior information; and according to the abnormal behavior information, determining and outputting an abnormal early warning signal. According to the method, the abnormal characteristics of the behaviors under the bus kiosk are analyzed in a multi-dimensional mode, safety early warning is achieved, and the public traffic order is guaranteed.

The invention discloses a subway station air conditioning load adjusting system and method, and the system comprises a load simulation module which is used for simulating the time sequence change of cold loads and heat loads of different types of stations under different meteorological conditions and operation conditions, and generating a system training data set; the passenger travel behavior modeling module is used for establishing a time-period passenger distribution model by using subway historical passenger flow data and urban public transport survey data; outputting passenger flow densities of different areas by using a time period passenger flow distribution model; the machine learning prediction model is used for extracting multi-dimensional features as input features; a system training data set is utilized to learn the mapping relation between the input features and the target cold / heat load, and a load preset value is output; the intelligent load adjusting control module dynamically adjusts the output capacity of the air conditioning system through the building automation system according to the load preset value, and closed-loop control is achieved. According to the invention, TRNSYS simulation, behavior modeling and intelligent control are integrated, and accurate load prediction and energy-saving regulation and control are realized.

The invention discloses an Internet of Vehicles resource scheduling method and system for public transport, and the method comprises the steps: taking an RSU as a leader, taking a bus as a follower, and constructing a Stackelberg game model; and based on the Stackelberg game model, fusing MALPPO multi-agent deep reinforcement learning, and outputting an optimal resource allocation strategy. The system comprises a training game model building module and a multi-agent reinforcement learning module. According to the invention, bandwidth allocation can be optimized, so that a communication system can better adapt to continuously changing communication environments and requirements. The method can be widely applied to the field of communication resource optimization.

The invention relates to the technical field of intelligent traffic management, and discloses a multi-modal time series data analysis-driven public traffic scheduling optimization method, which comprises the following steps of: acquiring multi-modal traffic data, performing time-space alignment preprocessing, and extracting a feature vector; fusing various feature vectors based on a cross-modal interactive learning method; and performing real-time prediction of the public traffic condition based on the multi-modal fusion feature vector to obtain a public traffic flow prediction value, and making a public traffic scheduling strategy based on the obtained public traffic flow prediction value. According to the method, multiple feature vectors are fused based on a cross-modal interactive learning method, the mutual relation between different types of data is effectively captured, the public transport scheduling strategy is formulated based on the particle swarm optimization algorithm, the global search and local search capabilities are effectively balanced, the performance of the PSO algorithm in the public transport vehicle scheduling problem can be effectively improved, and the public transport vehicle scheduling efficiency is improved. And a more efficient scheduling scheme is realized.

The invention provides an urban area dynamic representation learning method based on context representation learning, and belongs to the field of urban function and spatio-temporal data mining, and the method comprises the steps: S1, constructing a continuous dynamic graph according to public transport data; s2, an urban area dynamic representation extractor under multiple time dimensions is constructed, the extractor is used for extracting structure information and semantic information in the continuous dynamic graph, nodes in the continuous dynamic graph are converted into representation vectors of fixed dimensions, and the representation vectors are dynamically updated along with time; s3, pre-training the extractor by using a plurality of prediction tasks, including designing a corresponding loss function to guide the extractor to learn more universal urban region characterization; and S4, utilizing the trained plurality of extractors to obtain the dynamic representation of the urban area, and combining the dynamic representation of the urban area with context representation learning to be applied to a plurality of downstream tasks. According to the method, comprehensive space-time semantic information can be captured, and the dynamic region representation of the city is dynamically captured from multiple time granularities.

The invention relates to the technical field of aircraft wake flow risk analysis, and discloses a mixed operation airport small single-shot training aircraft wake flow encountering risk analysis method. Collecting flight operation programs and information of each subject of training flight, extracting key flight features, and obtaining an average movement track of typical flight tasks of the public transport aircraft and the small training aircraft through clustering analysis; secondly, analyzing a running track and a cross point, carrying out numerical simulation analysis on wake flow of the cross point of the track based on CFD, and constructing an aircraft trailing vortex wake flow dissipation database; finally, an improved strip-shaped belt method is used for calculating extra lift force changes, caused by wake flow vortex fields, of all the parts, then the rolling torque coefficient of the small training aircraft during wake flow encounter is calculated, and the wake flow encounter risk is evaluated. According to the method, the risk that the general single-shot coach aircraft encounters the wake flow of the front civil aviation transport aircraft in the take-off, approach and other stages of the mixed-purpose airport can be accurately evaluated, and the operation efficiency of the mixed-purpose airport is further improved.

The invention relates to a sign language-to-multilingual text speech mutual conversion method based on deep learning and application thereof, belongs to the field of artificial intelligence, man-machine interaction and barrier-free communication, and particularly relates to a sign language recognition, semantic understanding and multilingual speech synthesis method based on deep learning and an application system thereof. The sign language to voice text conversion method comprises the following steps: 1, an image acquisition and preprocessing module, 2, a hand key point detection module, 3, time sequence modeling and sign language recognition, 4, natural language understanding and semantic optimization, 5, a multi-language translation module, and 6, a text and voice synthesis output module. According to the sign language-to-multilingual text voice mutual conversion method based on deep learning and the application thereof, sign languages can be automatically, flexibly and accurately converted into voices and characters, and deaf-mutes are helped to efficiently communicate with common people; the system can be widely applied to hospitals, schools, government affair windows, public transportation and other scenes.

An intelligent analysis method for operational supervision of public transportation industry and a system thereof includes operation information collection, calculation of basic vehicle information and platform passenger flow information, current operation analysis of target vehicles, current data update and calibration of the target vehicles, automatic adjustment of dispatching operation plans for public transportation vehicles within operation areas, and real-time updates of real-time data of online vehicles within the operation areas. The present invention implements vehicle energy consumption monitoring and statistical analysis based on basic information and mileage information of public transportation vehicles, and performs intelligent dispatching and makes preferred planning in terms of road network congestion, charging station operations and site traffic for dispatching and subsequent operations of the entire transportation network based on energy consumption and the location of charging stations on public transport operation lines.

The invention provides a multi-scene-oriented intelligent rail signal timing optimization method and device, and relates to the technical field of intelligent public transportation, and the method comprises the steps: building a basic signal model according to the total number of current arriving vehicles and a signal constraint condition, and carrying out the calculation of each lane operation scene, and obtaining the optimal period duration; calculating the priority buffer time according to the parameters of the lane operation scene and the geometric parameters of the intelligent rail, and constructing the unified period duration through the priority buffer time and the optimal period duration; inputting the unified period duration into the basic signal optimization model, and respectively solving each lane operation scene to generate a signal timing scheme; optimizing signal timing in the remaining time of the current period based on the signal timing scheme to obtain a transition scheme; and performing signal timing switching on the signal timing scheme of the current scene according to the transition scheme to obtain a signal timing result. According to the invention, the problem that the signal timing scheme cannot be smoothly switched in a complex traffic scene is solved.