510 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 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 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 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 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 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 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 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 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.

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.

The invention provides a public transport supply and demand matching method and a related device, and belongs to the technical field of traffic engineering and urban planning. According to the invention, the matching degree of a supply network and a demand network is calculated through a supply-demand matching evaluation algorithm, and a public transport supply-demand matching index of a spatial analysis unit is obtained; and obtaining a public traffic supply and demand matching result based on the public traffic supply and demand matching index. According to the invention, the problem of low accuracy of public transport supply and demand matching is solved.

The invention belongs to the technical field of urban public transport management, and particularly relates to an electric public transport scheduling and charging scheduling method considering source-load double-side fluctuation. The objective of the invention is to solve the problems that a public transportation system has source-load double-side fluctuation due to the intermittency of existing source-side photovoltaic power generation and the randomness of load-side public transportation operation energy consumption, so that the charging demand and the power supply capacity in part of time periods are not matched, the operation task cannot be completed in time, and the energy storage utilization efficiency is low. The invention provides an electric bus scheduling and charging scheduling method considering source-load double-side fluctuation. The method comprises the steps of calculating bus battery remaining capacity based on an optimization time period; calculating the remaining capacity of the energy storage battery based on the remaining capacity of the bus battery; based on the bus battery remaining capacity and the energy storage battery remaining capacity, constructing a chance constraint planning model; and solving the opportunity constraint planning model to obtain an optimal feasible solution which comprises an electric bus scheduling and charging scheduling scheme.

Described herein are systems and methods for validating received encrypted first data including position data and time data for a transportation vehicle. The system receives or accesses second data including second position and second time data of the transportation vehicle. The system determines a validity of the first data by performing operations on the encrypted first data or the encrypted first data and the second data to compare the encrypted first data and the second data. The system assigns a consensus score to the mining device based part on the comparison, and applies a signature function to the encrypted first position and first time data. The system then publishes the encrypted signed valid first position and first time data to a public transportation vehicle ledger.

Disclosed in the present invention is a traffic all-domain safety early warning system based on real-time safety information fusion technology, comprising a traffic all-domain big data cloud center, an onboard intelligent terminal OBU, an intelligent terminal having a client APP installed thereon, and a roadside unit RSU; the onboard intelligent terminal OBU is used for acquiring vehicle self-collected information serving as a first-layer information source; the roadside unit RSU is used for acquiring RSU information serving as a second-layer information source; the intelligent terminal having a client APP installed thereon is used for acquiring all-domain traffic information serving as a third-layer information source; and the traffic all-domain big data cloud center corrects the accuracy of dynamic traffic data comprising the vehicle self-collected information, an RSU information source and the all-domain traffic information, and fuses the corrected data into a unified traffic electronic map. The present invention ensures that all types of individuals entering the public traffic space can be detected in advance, thereby improving the information transparency and operation efficiency of the traffic system, and travel safety.

The invention provides a non-inductive payment method and system, electronic equipment and a storage medium, and belongs to the technical field of public transportation. The method comprises the following steps: performing signature verification on gate signature information received from a target gate to obtain signature verification result information; in response to the condition that the signature verification result information meets a preset verification passing condition, determining a terminal marked timestamp, and performing abstract operation processing on a preset time window and the terminal marked timestamp according to the first key symbol to obtain time abstract data; sending the time summary data and the terminal identification information to a target gate to enable the target gate to generate a gate marking timestamp, and performing data consistency verification based on the time summary data, the terminal identification information and the gate marking timestamp to obtain consistency verification result information; and in response to the consistency verification result information meeting a preset consistency verification passing condition, performing non-inductive payment according to charging information received from the target gate. According to the invention, the security of the non-inductive payment process can be improved.

The invention relates to an intelligent acquisition, analysis, prediction and automatic violation correction method and system based on public traffic violation big data, and belongs to the field of traffic informatization and intelligent management. The objective of the invention is to identify complex and diverse traffic illegal behaviors and analyze massive illegal data. The system comprises data acquisition, processing and storage, analysis and prediction, automatic violation correction and data security. Through multi-source data access, edge calculation and distributed storage, real-time acquisition and efficient processing of illegal behaviors are realized; deep analysis is carried out on the traffic violation data by using deep learning and big data technologies; and through real-time video stream analysis, tracking, recording and evidence obtaining are performed on suspected illegal behaviors. The system adopts a multi-level security protection mechanism, data privacy and security are ensured, and flexible expansion and multi-system collaboration are realized through modular design. Compared with a traditional system, the system has remarkable advantages in the aspects of data acquisition comprehensiveness, processing efficiency, prediction accuracy, violation correction automation, data security and the like.

The invention discloses a bus shift extraction optimization method based on multi-dimensional score driving, and relates to the field of urban public traffic scheduling management, and the method comprises the following steps: initializing shift extraction parameters, determining a shift extraction target, defining time period features, and setting a score weight and a penalty coefficient; constructing a shift extraction optimization mathematical model, including defining decision variables and establishing an objective function and constraint conditions thereof; respectively constructing a multi-dimensional scoring system and a constraint verification strategy by utilizing the objective function and the constraint conditions thereof; generating candidate shift drawing schemes, calculating a comprehensive score and selecting an optimal shift drawing scheme; performing iteration processing on the optimal shift extraction scheme in combination with loop control and a scheme rollback strategy to obtain a final shift extraction scheme; and verifying and counting the final shift drawing scheme, and generating a corresponding shift drawing report. On the premise that operation balance and service integrity are ensured, global optimal selection of the shift drawing scheme is achieved, and the intelligent level and operation efficiency of bus dispatching are remarkably improved.

A smart media player configures interactive media customized for passengers temporarily sharing a common conveyance (e.g., an automobile or other vehicle), using programmed methods. The methods include identifying profile data for each of the passengers and trip data for the common conveyance. Then, the smart media player may select an interactive media title for the passengers as a group based on at least one of the profile data or the trip data, including one or more of a variety of games for playing with other passengers in the vehicle. The interactive media title may be configured for output by the smart media player while the passengers are sharing the common conveyance. The media player plays the interactive media title in the common conveyance enabled for interaction with the passengers during the period.

The invention discloses a city CTC-based running plan morning and evening point real-time adjustment method and device. The device comprises a running chart terminal arranged in a city railway dispatching command system and a city running chart background service. The running chart terminal is used for displaying and operating the running plan and sending running plan operation update to the city running chart background service in real time; and when receiving the train report point reported by the station, the city area working diagram background service performs morning and evening point adjustment processing on the operation plan after the train report point and / or the turn-back plan associated with the operation plan according to the morning and evening point relationship between the train report point and the corresponding operation plan, and updates the operation plan and / or the associated turn-back plan. According to the invention, on the basis of a dispatching command system framework, interconnection and public transportation operation requirements of urban railways are met, and the efficiency of dispatching operation plan morning and evening is improved.

A server is provided to recommend suitable personal mobility based on a recommended route. The server includes a communicator and a controller that determines a first route to an intermediate destination reachable by a vehicle or a public transport based on departure information and destination information received from an external device, and a second route from the intermediate destination reachable by foot or a personal mobility to a final destination. The controller determines a recommended personal mobility based on environment information of the second route and operates the communicator to transmit information about each of the first route, the second route, and the recommended personal mobility to the external device.

The invention discloses a payment method and device based on a multifunctional IC card, a storage medium and a computer program product, and relates to the technical field of intelligent card payment, and the method comprises the steps: receiving a transaction application and a transaction scene triggered by the multifunctional IC card, and the multifunctional IC card is configured with a virtual consumption limit and a third-party payment account; when the transaction scene is a public traffic scene, deducting a consumption amount from a virtual consumption limit of the multifunctional IC card according to the transaction application, and periodically obtaining transaction data according to the consumption amount for batch payment; and when the transaction scene is a non-public traffic scene, deducting a consumption amount from the third-party payment account according to the transaction application for direct payment. According to different transaction scenes, virtual quotas are integrated, periodic payment is carried out, and the cross-domain payment capability of non-card-holding card-passing enterprises is realized under the rigid constraint of zero transformation of terminal equipment.

The invention relates to the technical field of intelligent traffic system (ITS) and urban public traffic scheduling control, in particular to an intelligent bus scheduling method based on multi-source data fusion, which comprises the following steps: S1, a central scheduling platform receives and stores multi-source data; s2, obtaining a fusion feature sequence; s3, sending to a calibrated demand prediction model to obtain a point prediction and demand interval; s4, constructing a transport capacity interval with an upper bound and a lower bound based on the vehicle availability, the driver shift, the maximum passenger capacity of the single vehicle and the road section speed distribution; s5, calculating overlapping ratio = intersection / prediction interval length; when the vehicle number is lower than the threshold value, distributed robust multi-target scheduling optimization of an ambiguity set defined by the Wasserstein radius is solved, the departure interval and the vehicle distribution number are updated, and the target is that the vehicle waiting time condition is weighted in a risk value and an empty driving rate, and the vehicle number, the employees, the departure interval and the insertable control point constraint are met; and S6, issuing an instruction and executing according to a vehicle positioning and control point arrival event. And unified modeling and robust triggering in the same space-time key interval are realized.