92 results about "Congestion prediction" patented technology

A scheduling system and method for moving plural objects through a multipath system described as a freight railway scheduling system. The scheduling system utilizes a cost reactive resource scheduler to minimize resource exception while at the same time minimizing the global costs associated with the solution. The achievable movement plan can be used to assist in the control of, or to automatically control, the movement of trains through the system. Alternative routes through congested areas are automatically selected using congestion prediction metrics.

A method of estimating routing congestion between pins in a net of an integrated circuit design, by establishing one or more potential routes between the pins which pass through buckets in the net, assigning a probabilistic usage to each bucket based on any partial blockage of the wiring tracks in each bucket, and computing routing congestion for each bucket using its probabilistic usage. When the net is a two-pin net that is a part of a larger multi-pin net, and a tree is constructed to bridge the two-pin net to another pin of the multi-pin net. The routing congestion for each bucket is computed as a ratio of the bucket usage to bucket capacity. For L-shaped routes (having at least one bend in a bucket), the probabilistic usage is proportional to a scale factor a which is a ratio of a minimum number of available wiring tracks for a given route to a sum of minimum numbers of available wiring tracks for all possible routes. For Z-shaped routes (having at least two bends in two respective buckets), the probabilistic usage is equal to a ratio of a minimum capacity of a given route to a sum of minimum capacities of all routes having an associated orientation with the given route. Assignment of the usage values may entail the creation of a temporary usage map of the net buckets with an initial value of zero usage in every temporary usage map bucket, thereafter storing usage values in corresponding buckets of the temporary usage map, and deriving a final usage map from the temporary usage map.

A scheduling system and method for moving plural objects through a multipath system described as a freight railway scheduling system. The scheduling system utilizes a cost reactive resource scheduler to minimize resource exception while at the same time minimizing the global costs associated with the solution. The achievable movement plan can be used to assist in the control of, or to automatically control, the movement of trains through the system. Alternative routes through congested areas are automatically selected using congestion prediction metrics.

The invention discloses a short-time road traffic congestion prediction method based on a CS-SVR algorithm, and the algorithm belongs to the technical field of road congestion prediction. The method comprises the following steps that (1) original data is acquired and processed; (2) all parameters are initialized by adopting a Cuckoo Search (CS) algorithm; (3) a target function is built, and initial fitness calculation is carried out; (4) random walk is carried out, and a new bird nest position is calculated; (5) the optimal objective function value is updated; (6) whether a host bird changes or reserves the bird nest position or not when an external bird egg is found is judged, the global optimum position is selected, moreover, whether the number of iterations is reached or not is judged,and if the maximum iteration parameter is not reached, the operation is returned to the step S03, and iteration algebra is plus 1; and (7) if the maximum iteration algebra is reached or the precisionrequirement is met, SVR is used for carrying out traffic congestion prediction on a test sample.

The invention relates to an intelligent traffic signal light transformation method, which is applied to the field of traffic signal light control at intersections and solves a problem that the green light is still on when the front phase is congested and a problem of how to control a signal light algorithm according to video vehicle data. The intelligent traffic signal light transformation methodcomprises a congestion prediction algorithm, a signal light combination selection algorithm, a preferential green light algorithm and a delayed green light algorithm. The congestion prediction algorithm is adopted, the naive Bayesian, decision-making tree, logistic regression, K-nearest neighbor, random forest, AdaBoost and gradient lifting methods in a statistical machine learning method are applied, historical data collected from video serves as a training data set, four characteristic attributes such as the intersection leaving distance, the average speed, the speed variation and the average density and the congestion condition are enabled to serve as training data, the average speed, the speed variation and the average vehicle density corresponding to the intersection leaving distancerange in the current real-time video of the road are enabled to serve as characteristic attributes, the congestion probability at a certain position after a certain time is predicted by applying the statistical machine learning method, the transformation of the signal lights is controlled according to the congestion probability, and judging the best phase combination, the preferential green lightphase and the green light delay time according to the number of vehicles inspected and calculated by the real-time video of the road.

In a case where a CPU 11 provided in an information distribution center 3 has received a request command for requesting predicted traffic information transmitted from a navigation apparatus 2 as well as vehicle information such as a navigator identifying ID, coordinate data of a vehicle position, or coordinate data of a destination, the CPU 11 searches a basic route based on update-purpose map information 14A, and sets a distribution range serving as a transmission target of traffic information 16A, 16B, and 16C. Then, the CPU 11 generates the predicted traffic information 16C such as congestion prediction information corresponding to the distribution range, extracts the predicted traffic information 16C of each link having a difference in the generated predicted traffic information 16C and the current traffic information 16A, and distributes the extracted predicted traffic information 16C to the navigation apparatus 2 together with a link ID of a corresponding link.

The invention discloses a traffic jam prediction method and system based on a jam portrait. The method comprises the steps of obtaining traffic data, and obtaining the jam portrait data of a traffic bottleneck point in the traffic data according to the traffic data, wherein the jam portrait data reflects the road condition state of the traffic bottleneck point; determining congestion portrait feature parameters of the traffic bottleneck point according to the congestion portrait data of the traffic bottleneck point; and performing congestion prediction on the traffic data according to the congestion portrait feature parameters to obtain a prediction result which is used for traffic management. According to the invention, the traffic data can be predicted based on the congestion portrait, the obtained prediction result can be used for active traffic guidance management, the police basis of the traffic police can be determined according to the prediction result, a basis can be provided for optimization timing of signals and the like, and a basis can also be provided for travel route planning.

The invention discloses a traffic jam prediction method and device and electronic equipment. The method comprises the steps: performing road condition similarity processing between every two lanes on obtained road feature data of traffic road conditions to acquire lane similarity data, wherein the road feature data comprises TMC data of multiple periods before a to-be-predicted period; processing the lane similarity data by adopting a first preset network model, extracting lane space topological structure features from the lane similarity data, and outputting to obtain a first processing result; processing the first processing result by adopting a second preset network model, and outputting to obtain a second processing result; and determining a congestion prediction result of at least one lane in the road in the to-be-predicted period according to the second processing result. According to the technical scheme, the accuracy of traffic jam prediction can be improved.

In a congestion prediction using measurement data which is acquired by an on-road sensor or a probe car, and which includes none of explicit information about bottleneck points, with respect to time-sequence data on congestion ranges accumulated in the past, data on congestion front-end positions are summarized into plural clusters by the clustering. Representative value in each cluster is assumed as position of each bottleneck. A regression analysis, in which day factors are defined as independent variables, is performed with congestion length from each bottleneck point selected as the target. Here, the day factors refer to factors such as day of the week, national holiday / etc. It then becomes possible to precisely predict a future congestion length.

The invention relates to a traffic jam prediction method and system. The method comprises the following steps: acquiring historical trajectory data of all vehicles in a road network to be predicted, wherein the to-be-predicted road network is composed of a plurality of road sections; for any road section, obtaining the average speed of the road section in a set time period according to the historical trajectory data of all the vehicles in the set time period of the road section; determining the average speed of each road section in all time periods as a congestion feature matrix of the road network to be predicted; obtaining an adjacent matrix of the to-be-predicted road network according to the position relation of all the road sections in the to-be-predicted road network; and inputting the congestion feature matrix of the road network to be predicted and the adjacent matrix of the road network to be predicted into a congestion prediction model to obtain a congestion prediction result. According to the invention, the prediction condition of traffic jam can be improved.

The present invention discloses a congestion prediction method and device. The method comprises the steps of: receiving first data information sent by a first control sensor, and obtaining a first characteristic parameter value in the first data information; determining an associated second monitoring area according to a first monitoring area and preset associated sets; receiving second data information sent by a second control sensor, and obtaining a second characteristic parameter value in the second data information; and determining congestion information of the first monitoring area at a first time length being subjected to preset delayed at a current time period according to the first characteristic parameter value, the second characteristic parameter value and a monitoring model completing training in advance. In the embodiment of the invention, one associated set is stored in each monitoring area, so that the congestion information of the first monitoring area is determined according to the first monitoring area, characteristic parameter values of associated second monitoring area and the monitoring model when congestion prediction is performed. Therefore, the accuracy of congestion prediction is improved.

The invention discloses a sensor-network-based novel intelligent transportation model design method. The method comprises: according to an indicator function, road traffic volume collection, intersection delay time, and vehicle congestion prediction value are calculated to realize intelligent transportation guidance scheduling. According to the invention, the method has the following advantages: the system model is guided and scheduled by using a transportation performance indicator function; with introduction of the indicator function, credibility and guidance of system implementation are improved; and thus the method has the certain study value in realizing sensor-network-based intelligent transportation.