294 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.

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 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.

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.

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.

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 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.

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 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.