295 results about "Traffic model" patented technology

The invention discloses a bus congestion degree analysis method based on real-time data of a bus GPS (Global Position System) and IC (Integrated Circuit) cards. The bus congestion degree analysis method comprises step 1, establishing a historical quality database according to data of the bus GPS and historical data of the IC cards, performing real-time data screening and recovery and mining effective data; step 2, performing data fusion and establishing a bus trip real-time information table of residents; step 3, calculating the real-time bus route passenger flow OD distribution according to a traffic model; step 4, performing partition on threshold values of the bus congestion state and performing the bus congestion degree analysis on bus stops and bus routes respectively based on the obtained real-time bus route passenger flow OD distribution. According to the bus congestion degree analysis method based on the real-time data of the bus GPS and the IC cards, the validity, reliability and accuracy of the data can be improved and the important data basis is provided for the improvement of the utilization rate of the public transport.

An embodiment of the invention provides an interactive type visual simulation system under the vehicle road collaborative environments.The system comprises a real traffic simulation three-dimensional model, a virtual driving vehicle, a driver-vehicle interactive simulation unit and a traffic flow model, and the traffic flow model considers driver factors and combines a vehicle starting and stop module.The system is based on the three-dimensional simulation environments, an interactive type dynamic mapping model corresponding to the virtual vehicle, different typical scene simulation and the like, the traffic flow model considering the driver behavior is designed, the behavior and the effect of a cooperative vehicle infrastructure system can be displayed to a user from multiple angles, at the same time, the acceptance degree of the driver on vehicle road collaboration certain functions and the design rationality of certain functions are analyzed, and a virtual driving environment with sense of reality and sense of immersion can be provided for the driver.

The embodiment of the invention discloses a target value area analysis method and device, and apparatus based on big data and a medium. The method comprises the steps of obtaining network operation and maintenance O-domain big data and operation analysis B-domain big data; performing association analysis on the O-domain big data and the B-domain big data, positioning a target value area, and obtaining a target network grid corresponding to the target value area; based on the O-domain big data and the B-domain big data, performing user behavior analysis on the target value area, and constructing a user portrait; constructing a user flow model based on O-domain and B-domain big data; and performing service traffic prediction on the user traffic model based on the target network grid and theuser portrait, rasterizing the predicted service traffic, identifying a target value user from the traffic grid, and performing network planning on the target value area according to the target network grid and the target value user. By utilizing the embodiment of the invention, O-domain and B-domain big data can be integrated, and network planning optimization and marketing can be better supported.

The invention provides a traffic signal light control method and system based on vehicle queuing length measurement. Vehicle flow information is divided into steady flow and combined flow by building a city road combined traffic flow model, and actual vehicle flow information can be accurately simulated. Pixel points in an original map of a digital graph can serve as road coordinates by carrying out digital graph collecting on a road, a sampling time interval is set, digital graph information of the road is collected on a sampling position again when it is detected that a vehicle on the road does not move forward any more in a road go-through cycle, a congestion starting position is obtained, a real-time vehicle queuing length can be calculated according to the distance from the congestion starting point to a detection point by means of a vehicle queuing length computational formula, the operation state of an upstream intersection signal light is controlled according to the real-time vehicle queuing length, and accordingly upstream intersection traffic congestion caused due to the fact that the upstream intersection vehicle enters the congested road is avoided.

An encoder and corresponding method are disclosed for performing video rate control to meet network traffic model restrictions, including pre-encoding the sequence of pictures for each of a plurality of quantization parameter values, selecting for each picture of the sequence one of the plurality of quantization parameter values responsive to the quantization parameter values and bitrate operating points of the neighboring pictures in the sliding time window, and encoding each picture of the sequence using the quantization parameter value selected for that picture.

The invention relates to an abnormality detecting method based on industrial control system network traffic and belongs to the field of information security. The industrial control system network traffic is collected, by analyzing the traffic features, the fact that normal traffic and abnormal traffic samples are evidently different in terms of power spectral density is discovered when a digital signal processing method is used to convert traffic signals from a time domain to a frequency domain, a low-frequency power sum critical value is found by analyzing the difference features in a large amount of historical data, if the low-frequency power of a to-be-detected sample is larger than the critical value, the sample traffic is taken as the abnormal traffic. The method includes a data preprocessing module, a traffic modeling module and an abnormality detecting module, wherein the data preprocessing module is used for processing early data traffic, the traffic modeling module is used for building normal models and abnormal models according toe low-frequency power and distribution of normal traffic and abnormal traffic, and the low-frequency power sum critical valve can be calculated. The abnormality detecting module is used for detecting abnormality. The false alarm rate of the method is 6.1% and the alarm missing rate of the method is 9.3%.

The loss of packets in a communication system can be minimized in an optimal manner by adapting a set of error correction (EC) parameters in response to a calculated probability of packet loss. The calculated probability is obtained from derived algorithms that are applied to a set of communication parameters. Algorithms are derived from Bernoulli-distributed traffic models and constant bit rate (CBR) traffic models of the communication system. A collapsed-state model is used to derive a very efficient algorithm that calculates an approximate probability of packet loss. Alternate applications for the algorithms are also disclosed.

The present disclosure is directed to methods and systems for reciprocal generation of watch-lists and traffic models characteristic of malicious network activity. In some aspects, the described methods and systems relate to maintaining data for recognition of malicious network activity. In general, the methods include monitoring network traffic; comparing endpoint data from monitored data packets to endpoints in a watch-list of network endpoints and comparing packet data from monitored data packets to traffic models in a catalog of traffic models characterizing malicious network activity; and determining, based on the comparisons, that a set of data packets comprise suspect network activity. The methods include adding a network endpoint to the watch-list when the determination is based on comparing packet data to a traffic model or adding a traffic model to the catalog when the determination is based on comparing endpoint data.

An embodiment generally relates to systems and methods for enhanced cellular automata (CA) models. A processing module can generate a traffic model that comprises a set of vehicles. The traffic model can be processed according to the enhanced CA model. In particular, a perceived gap between vehicles in the set of vehicles can be identified. Further, a subsequent velocity of a following vehicle can be calculated based on metrics such as a maximum velocity, the perceived gap, and various time components.

A computer implemented method measures a performance of surveillance system. A site model, a sensor model and a traffic model are selected respectively from a set of site models, a set of sensor models, and a set of traffic models to form a surveillance model. Based on the surveillance model surveillance signals are generated. Performance of the surveillance system is evaluated according to qualitative surveillance goals and the surveillance signals to determine a value of a quantitative performance metric of the surveillance system.

A method for improving network performance using a virtual queue is disclosed. The method includes measuring characteristics of a packet arrival process at a network element, establishing a virtual queue for packets arriving at the network element, and modeling the packet arrival process based on the measured characteristics and a computed performance of the virtual queue.

A traffic generator generates a plurality of traffic flows, with each of the traffic flows being associatable with one or more of a plurality of output interfaces of the traffic generator. In an illustrative embodiment, each of the output interfaces may have any desired combination of the traffic flows associated therewith. The traffic flows may be generated based on user selection of at least one of a protocol encapsulation, a packet size distribution model, a packet arrival time distribution model, a traffic model, and a packet payload description. Information characterizing one or more of the traffic flows may be stored as a traffic file in a memory associated with the traffic generator.

The invention relates to a GIS traffic model-based method of an optimization analysis on a wireless network. The method comprises the following steps: preparing data; measuring an area of a wireless coverage area according to GIS, wherein the area is set by taking a wireless cell as a basic point; calculating a traffic of a per unit area of the set area and a traffic of a per unit area of the wireless cell; calculating a resource utilization rate of the set area and a resource utilization rate of the wireless cell; expressing a total traffic, a total resource utilization rate, traffics of services of various types and resource utilization rates of services of various types of the wireless cell on an electronic map with different layers; and classifying sizes of traffics and resource utilization rates as well as setting layer levels and corresponding colors for presentation in a same layer. Imbalance between services can be presented by contrasts of different layers. According to the invention, characteristics of traffic behaviors of users of the cell can be obtained; on the basis of presentation of different layers on a GIS platform, a comprehensive analysis is made by combining data including interference, hardware failure, and parameter, so that an optimized solution scheme of a wireless network is provided.

A computer implemented method measures a performance of surveillance system. A site model, a sensor model and a traffic model are selected respectively from a set of site models, a set of sensor models, and a set of traffic models to form a surveillance model. Based on the surveillance model surveillance signals are generated. Performance of the surveillance system is evaluated according to qualitative surveillance goals and the surveillance signals to determine a value of a quantitative performance metric of the surveillance system.

The invention discloses an apparatus for testing network performance parameter, a system thereof, and a method thereof. The method comprises the following steps: acquiring information of all user terminals with different interface forms, wherein, the user terminals are connected with a unified interface platform; supporting a flexible telephone traffic model set function, and forming an AT instruction packaged with a test service parameter; outputting the AT instruction to the user terminals which are connected with the unified interface platform, and controlling the user terminals to carry out a test according to a set telephone traffic model; according to the received AT instruction, carrying out a network performance parameter test by the terminals, and feeding back test data information; receiving the test data information returned by the user terminals by an automatic calling server, carrying out processing to obtain a network performance test parameter, and automatically generating a KPI statistics form and a test report. By applying the apparatus, the system and the method of the invention, high capacity pressure and performance tests of a communication network can be realized, and reliability of test data and test efficiency are raised.

The loss of packets in a communication system can be minimized in an optimal manner by adapting a set of error correction (EC) parameters in response to a calculated probability of packet loss. The calculated probability is obtained from derived algorithms that are applied to a set of communication parameters. Algorithms are derived from Bernoulli-distributed traffic models and constant bit rate (CBR) traffic models of the communication system. A collapsed-state model is used to derive a very efficient algorithm that calculates an approximate probability of packet loss. Alternate applications for the algorithms are also disclosed.

The invention provides a chip verification method, device and system. The method comprises the following steps of: generating a verification platform, wherein the verification platform comprises a model layer and an interface layer, the model layer comprises a traffic model and a register model, and the interface layer comprises a data bus interface agent and a CPU bus interface agent; verifying a chip to be tested by using the verification platform. The chip verification method, device and system solve low reusability of the platform caused by lack of hierarchy of the verification architecture of the UVM, improve the reusability of the verification architecture, and improve verification efficiency.

A traffic forecasting system configured to forecast a travel time of a route is disclosed. The traffic forecasting system includes a model-training module, a model-selecting module and a forecasting module. The model-training module builds multiple candidate models. Each of the candidate models corresponds to one of a plurality of road section and one of a plurality of mathematical models. The model-selecting module selects an estimated model corresponding to the road section from the candidate models matching the road sections of the route. The forecasting module calculates estimated vehicle speeds of each road section according to the estimated models of each road section of the route. The model-selecting module selects one of the candidate models corresponding to the road section with the smallest error as the estimated model of the road section.

The invention discloses a traffic flow modeling method of an unmanned vehicle and belongs to the field of unmanned driving. The method comprises steps that on the basis of analyzing factors that affect the traffic of the expressway, a traditional traffic model based on the differential equation is established; the relation map of the number of lanes affecting the average daily traffic flow in the average period and the peak period is made; the unmanned vehicle hypothesis and the classical Gipps safety distance rule are introduced; the NaSch model is improved; the automatic driving cellular automaton traffic flow model is proposed based on the safe distance; the new control method based on the spring damper is used to analyze the cooperation between the multiple vehicles; and finally the sensitivity detection is performed by increasing the reaction time of the unmanned vehicle from 0.1s to 1s. The traffic flow modeling method of the unmanned vehicle is introduced to improve the traffic safety and solve the traffic jam.

The invention discloses a fault simulation analysis method for an SP Guru-based electric power dispatching data network. The method comprises the following steps of: (1) establishing a network model of the electric power dispatching data network through an SP Guru platform according to configuration information; (2) establishing a traffic model of the electric power dispatching data network through the SP Guru platform according to traffic data; (3) carrying out network fault simulation based on a dispatching data network simulation model formed by the network model and the traffic model; (4) obtaining service quality and performance of the dispatching data network under a fault condition according to the fault type of the network fault simulation and the traffic data of the traffic model when the simulation network fault happens; (5) comparing configuration information of a network device after an actual network fault happens with configuration information of the network device in normal operation before the simulation network fault happens, so as to position and analyze the network device having the fault. The method can realize simulation analysis to the network fault of the dispatching data network.

A method (and structure) for predicting an impact of an incident on a system. Incident properties and traffic conditions of at least one historical incident are received, to calibrate one or more parameters of a traffic model, as executed by a processor on a computer. Current traffic conditions, a prediction of recurrent traffic conditions, and an indication of a current incident on the system are received. A duration of the current incident and traffic conditions at a location at which the current incident occurs are predicted. Predicted traffic conditions in the system are calculated, based on the calibrated model parameters, the current traffic conditions, the prediction of recurrent traffic conditions, and the predicted duration of the current incident and traffic conditions at the current incident location.

The invention relates to the field of artificial intelligence, and provides a traffic information processing method and device, which can provide a more reasonable and reliable traffic control strategy and can improve the traffic service quality. The method comprises the following steps: determining a target traffic model from a plurality of candidate traffic models by adopting historical trafficdata, wherein each candidate traffic model comprises at least one of a driver model, a road propagation model or a road network evaluation model, and the candidate traffic models correspond to historical traffic data, parameters of the target traffic model are adjusted according to the current traffic data, and the parameters of the target traffic model are used for describing the current trafficoperation state; and generating a traffic control strategy based on the adjusted parameters of the target traffic model, the traffic control strategy comprising at least one of driver navigation information, traffic signal control information or road network boundary control information.

The invention discloses a stable modeling method for a Payne-Whitham (PW) macroscopic traffic flow model. The method is used for solving the technical problem that the conventional PW macroscopic traffic flow model cannot directly handle a traffic jam problem. The invention adopts the technical scheme that: on the basis of the PW macroscopic traffic flow model, a relationship between the traffic jam problem and system stability is obtained by establishing a new traffic model, so the occurrence of traffic jam or other abnormal phenomena can be determined macroscopically from the angle of global stability, basic foundation is provided for traffic control and decision, and the method is directly applicable to handling of the traffic jam problem.

The invention relates to an urban traffic carbon emission measuring and calculating method based on target urban traffic model data. According to the invention, local traffic model data of a target city is used as a basis to establish a local, more accurate urban traffic carbon emission measuring and calculating method meeting boundary accounting requirements of global protocol for community-scalegreenhouse gas emissions inventories (GPC). The traffic carbon emission measuring and calculating method is more suitable for measuring and calculating the traffic carbon emission of adjacent cities,especially all cities in the Pearl Delta city group area, and is more suitable for measuring and calculating the carbon emission on the microcosmic level.

The invention discloses a scrambling code optimizing method based on a measurement report, which comprises the following steps: establishing an interference matrix among cells by extracting measurement report information from IUB interface signaling and establishing a traffic model; using a strong correlation synthesis method to obtain scrambling code relativities under different time delays, carrying out synthesis on the premise of considering the relativity of downlink synchronous codes under different time delays; and finally, allocating the scrambling codes by using a genetic algorithm toobtain the final scrambling code optimizing scheme of the cells. The method can sufficiently reflect the perception of users, gives rational suggestions for the allocation of scrambling codes in the present network, and enhances the rationality of scrambling code optimization.

The present invention discloses a public transit passenger flow trajectory real-time tracking method based on big data. The present invention aims to provide a method facing to a public transit system to detect a moving trajectory of a passenger swiping a card on a bus in a low-cost, complete-road-network, low-false-alarm, less-time-delay and real-time mode. In order to achieve the above aims, the technical scheme of the present invention provides a method for real-time detection of public transit passenger flow congestion proficiency information based on public transit payment data and GPS data. In recent years, public transit vehicles popularize public transit IC card payment terminals and GPS terminals gradually, and accumulate a large amount of data after a long time of operation. A traffic model is set up by comprehensive analysis of big data comprising the public transit payment data (comprising IC card swiping data and coin data, the same below), public transit GPS data and the like, can monitor riding moving trajectory of the passenger swiping the card in the public transit vehicles effectively, accurately and reliably, and can warn a big passenger flow in time. The method provided by the present invention lays a solid data foundation for increasing scientificity of pubic transit operation management, and improving public crisis response levels.

A method for optimizing storage configuration for future demand and a system applying the method are disclosed. The system includes a monitoring module, a storage recording module, a traffic modeling module, a rule-based decision module, and a storage management module. With performance values and utilization values provided from the monitoring module, a traffic status of data access in a particular time in the future can be generated. Then, a storage configuration with the workload requirement according to some rules can be available. The storage configuration is implemented to fulfill the requirement of the traffic status of data access.

The invention discloses a global path planning method and system based on energy consumption economy. The method comprises the following steps: establishing a vehicle energy consumption model according to relevant parameters of vehicle dynamic property in advance, and establishing a real-time traffic model according to section parameters, map information and real-time traffic information; establishing an energy consumption map according to the vehicle energy consumption model and the real-time traffic model; determining a path according to the energy consumption map, a starting point and a target point. The path planned through the method is the lowest in path, and relatively optimal duration and / path length can be guaranteed.

The invention relates to an iterative dynamic linearization and self-learning control method of an express way traffic system, and belongs to the technical field of express way traffic control. The method comprises the following steps that (1) a spatial discrete traffic model of the express way traffic system is established; (2) the spatial discrete traffic flow model is expressed in the form a general nonlinear discrete time system; (3) a general nonlinear discrete time model is converted into a dynamic linear data model; and (4) learning control and parameter update rules of the dynamic linear data model are established. The provided LDM-AILC method can be used to process a nonlinear system needless of a known linear parameter structure, and serves as a data driving control method, and design and analysis of a controller are only dependent on I / O data. In addition, under the condition that a random initial state and a tracking object of iterative change can be repeated in a non-strict way, the provided LDM-AILC method can achieve a complete tracking performance.

The invention discloses a GSM cell parameter optimization method based on traffic modeling and traffic prediction. The method comprises collecting communication information of a cell according to the scene type of the cell; constructing a self-adaptive three-dimensional matrix of the cell parameter according to the communication information of the cell, the actual traffic, the half-rate parameter setting, and the TCH (traffic channel) congestion number of the cell to obtain a cell traffic model; predicting traffic of the cell; calculating the predicted traffic of the cell; and inputting the predicted traffic of the cell and the cell channel configuration information into the cell traffic model to obtain a half-rate threshold set value of the cell. The method provided by the invention can realize the self-adaptive optimization of the cell parameters through traffic modeling and traffic prediction, thereby effectively improving the equilibrium problem of the network capacity and quality, and improving the network quality.