145 results about "Transportation forecasting" patented technology

The invention belongs to the field of intelligent transportation, and discloses a traffic prediction method based on an attention temporal graph convolutional network. The method includes the following steps that: firstly, an urban road network is modeled as a graph structure, nodes of the graph represent road sections, edges are connection relationships between the road sections, and the time series of each road section is described as attribute characteristics of the nodes; secondly, the temporal and spatial characteristics of the traffic flow are captured by using an attention temporal graph convolutional network model, the temporal variation trend of the traffic flow on urban roads is learned by using gated cycle units to capture the time dependence, and the global temporal variation trend of the traffic flow is learned by using an attention mechanism; and then, the traffic flow state at different times on each road section is obtained by using a fully connected layer; and finally,different evaluation indexes are used to estimate the difference between the real value and the predicted value of the traffic flow on the urban roads and further estimate the prediction ability of the model. Experiments prove that the method provided by the invention can effectively realize tasks of predicting the traffic flow on the urban roads.

In the present invention a traffic information providing apparatus makes a vehicle location and a running date a starting point, and predicts arrival time to each link included in a designated area, based on current traffic information provided by a traffic information center; meteorological forecast information provided by a meteorological information center; and statistical traffic information acquired from a traffic information data base where past traffic information is stored, thereby produces meteorological consideration traffic prediction information of the link, and sends the produced meteorological consideration traffic prediction information to a car navigation device. Then the car navigation device is configured to receive the produced meteorological consideration traffic prediction information and to search a guidance route, based on the produced meteorological consideration traffic prediction information.

Real-time high-fidelity spatiotemporal data on transportation networks can be used to learn about traffic behavior at different times and locations, potentially resulting in major savings in time and fuel. Real-world data collected from transportation networks can be used to incorporate the data's intrinsic behavior into a time-series mining technique to enhance its accuracy for traffic prediction. For example, the spatiotemporal behaviors of rush hours and events can be used to perform a more accurate prediction of both short-term and long-term average speed on road-segments, even in the presence of infrequent events (e.g., accidents). Taking historical rush-hour behavior into account can improve the accuracy of traditional predictors by up to 67% and 78% in short-term and long-term predictions, respectively. Moreover, the impact of an accident can be incorporated to improve the prediction accuracy by up to 91%.

A method and a traffic prediction and control system (TPCS) for predicting and controlling vehicle traffic flow through a traffic intersection dynamically with proximal traffic intersections are provided. The TPCS dynamically receives sensor data from sensors at a local traffic intersection, determines traffic flow parameters, and determines a traffic flow flux using the traffic flow parameters. The TPCS dynamically receives analytical parameters from sensors at proximal traffic intersections and determines a minimum safe driving distance between leading and trailing vehicles, a traffic free flow density, a synchronized traffic flow density, and a traffic jam density to predict transitions of the vehicle traffic flow across traffic flow phases through the local traffic intersection. The TPCS controls the vehicle traffic flow by dynamically adjusting duration of traffic signals of the local traffic intersection and transmitting traffic signal time adjustment instructions to the proximal traffic intersections to maintain an optimized traffic flow flux.

A method and apparatus for predicting traffic on a transportation network where real time data points are missing. In one embodiment, the missing data is estimated using a calibration model comprised of historical data that can be periodically updated, from select links constituting a relationship vector. The missing data can be estimated off-line whereafter it can be used to predict traffic for at least a part of the network, the traffic prediction being calculated by using a deviation from a historical traffic on the network. The invention further discloses a method for in-vehicle navigation; and a method for traffic prediction for a single lane.

The method is suitable for the technical field of traffic prediction, and provides a traffic flow prediction method and system, and a terminal device. The method comprises the following steps of: acquiring traffic data in a specified time period of a target area, performing graph convolution network training on the traffic data, capturing a topological structure of an urban road network to obtainspatial features, inputting the time sequence of the spatial features into a gating cycle unit model, and obtaining dynamic changes through information transmission between units to capture time features; and establishing a prediction model according to the spatial features and the time features so as to predict the traffic flow of the target area according to the prediction model. According to the invention, a graph convolutional neural network based on an attention mechanism is applied to the prediction problem in the traffic field, high-dimensional features in the road traffic network can be better mined in a complex road structure, the accuracy of traffic flow prediction is improved in cooperation with the improved recurrent neural network, and the improvement effect is obvious especially in medium-term and long-term prediction.

A system, method and computer program product for forecasting a vehicle traffic condition in a near future. The system comprises a traffic prediction tool, a turning percentage prediction module and a simulation tool. The traffic prediction tool estimates a traffic speed and volume in a traffic link. A traffic link refers to a portion of a traffic road where the traffic prediction tool is installed. The turning percentage prediction module estimates a turning percentage in the traffic link based on the estimated traffic speed and traffic volume. The simulation tool computes, based on the estimated turning percentage, the estimated traffic speed and the estimated traffic volume, an expected traffic volume in the traffic link.

A three-dimensional urban traffic road network global state prediction method under a large data environment is disclosed. Aiming at a coupling multi-layer large-scale urban traffic road network, a global traffic mode containing coupling information is defined, a method of extracting a main road network characteristic is provided, a high dimension state time sequence is converted into a discrete state type sequence, and difficulties of high dimension data in processing and prediction aspects are simplified. From aspects of traffic periodicity, delay performance and other characteristics, a historical state transfer information database containing a multielement traffic characteristic is constructed, mass data information and a data driving idea are fully used and a Markov transition probability theory is applied so as to realize multi-step global traffic state prediction. Through the global traffic prediction, a traffic state of each area can be acquired one time. Compared to an existing traffic prediction method, by using the method of the invention, a long-term prediction result is acquired rapidly and a good prediction output effect is possessed.

Disclosed is a system and a method for collecting various road traffic information, road image information, and position information of a position information generation device carried by an object, the position of which is to be tracked, by interworking with an information provision means and using the collected information to provide service subscribers with real-time traffic information, traffic prediction, or position information of the object via wireless Internet, satellite broadcasting, DMB, or cable data broadcasting. The system and method can provide detailed traffic information service regarding a specific region or location on a nationwide scale, predict traffic to properly deal with it, and easily provide position information of an object to be tracked.

The invention discloses a model method of a traffic prediction. The method comprises the following steps of: (1) establishing a road traffic basic facility geographical information database, storing and updating traffic planning schemes, and establishing intersection delay determination; (2) establishing an urban planning construction geographical information database, storing regulatory detailed plans of each area, and utilizing an original unit method to construct traffic generation cases; (3) calling data of the road traffic basic facility geographical information database and the urban planning construction geographical information database, and utilizing a gravity model method to predict parallel distributed matrixes; (4) constructing a multi-element logit model, and determining the probability distribution for a single person selecting a travelling mode in specific travelling information; and (5) utilizing a capacity-limited multi-path distribution method to carry out traffic distribution. The modeling purpose, method to realization are overall planed, modeling parameter selection, basic data sources and calculation program design are coordinated, and the convenient application and operation of a traffic prediction model system are ensured.

The invention discloses a traffic prediction method based on adaptive spatial self-attention map convolution, belongs to the traffic field and the deep learning field, and provides an adaptive spatial self-attention graph convolution network (ASSAGCN) for traffic prediction. The ASSAGCN is formed by stacking two residual error blocks. Each residual block is composed of a graph convolution module (GCN), a multi-head spatial self-attention module (MHSSA), a gating fusion module (GF) and a multi-receptive-field cavity causal convolution module (MRDCC), Wherein the GCN performs modeling on local spatial correlation of a road network based on connectivity; the MHSSA is used for capturing implicit spatial correlation of a road network and aggregating information of each node globally; the GF fuses the output of the GCN and the output of the MHSSA; and the MRDCC is used to model temporal correlation. An input layer adopts a simple full-connection layer to map input to a high-dimensional space to improve the expression ability of the model, and an output layer adopts two 1 * 1 convolutional layers. The method can capture the potential spatial correlation in the road network, and adapts to the dynamic change of the road network structure.

The invention discloses a traffic prediction method and device based on a dynamic space-time diagram convolution attention model. Comprising the following steps: modeling a road traffic network into a graph structure; modeling a dynamic space-time diagram convolution attention model by using the diagram structure and the diagram signal; through an encoder unit of the dynamic space-time diagram convolution attention model, capturing dynamic space correlation among sensor nodes at different moments, capturing time correlation of step lengths at historical moments and time correlation among multi-step prediction, and obtaining a hidden state including each prediction step length; and enabling the decoder unit to receive the hidden state of each prediction step length, and obtaining a prediction result of recording speed values of all sensor nodes in the next one hour by adopting a scheduling sampling strategy. Experiments prove that the method of the invention can effectively obtain a traffic prediction result in the next one hour, and the model of the method of the invention exceeds SOTA.

The invention discloses a traffic prediction method of a spatio-temporal graph convolution model based on attribute enhancement. The method comprises the following steps: constructing an adjacent matrix A based on road network data; constructing an attribute enhancement matrix Kt = [Xt, p, Bt] at the moment t based on the feature matrix X, the interest point information vector p and the weather information matrix B; inputting the attribute enhancement matrixes of the n historical moments and the adjacent matrix of the road network into a spatio-temporal diagram convolution model for learning and training, calculating a traffic flow hidden state, and obtaining a traffic prediction value. According to the method of the invention, on the basis of spatial features when a spatio-temporal diagram convolution model is used for modeling, multi-source fragmented city data are fused to capture the relationship between external factors influencing traffic and traffic flow, the perception of the spatio-temporal diagram convolution model on the external influence factors is enhanced, and therefore, more efficient and accurate traffic prediction can be realized.

The invention discloses a quality control method for crowd-sourcing trajectory data. The method comprises the first step of data cleaning, the second step of abnormal trajectory data recognition and processing, the third step of data set trajectory pre-generation and adjusting and the fourth step of complete trajectory displaying and historical database updating. A solution is provided for fault data and missing data in traffic data of the crowd-sourcing trajectory data, the completeness of the trajectory data is ensured, and accurate data support is provided for traffic planning and traffic predicting.

The invention discloses an expressway traffic flow forecasting method based on time series. The expressway traffic flow forecasting method includes the steps of selecting one time scale, and carrying out statistics to build the traffic flow time series Q=(x); setting the value range of the number p of autoregression items and the number q of moving average items according to the selected time scale; solving the number p of the autoregression items and the number q of the moving average items; fitting the optimal number p of the autoregression items and the optimal number q of the moving average items through the maximum likelihood estimation in cooperation with the traffic flow time series Q to obtain an optimal ARMA model, and obtaining weight parameters of historical measured values and weight parameters of historical error values; solving the traffic flow forecasting series (please see the specifications) under the different time scales. By means of the expressway traffic flow forecasting method, an obtained time series model can better meet the requirement for forecasting various kinds of flow of an expressway, and the forecasting universality is improved; operation is simple, the forecasting efficiency is improved, the forecasting speed is increased, and the engineering requirement of traffic forecasting of the expressway is met.