146 results about "Transportation forecasting" patented technology

A method is provided for predicting load of traffic of vehicles that are travelling according to non reference route plan, provided with Dynamic Route Guidance capability of their PMMS, in a Forward Time Interval related Route Segment and according to a predetermined protocol between mobile systems and a non mobile system platform of a SODMS. Using mobile units, a traffic prediction query is receiving according to a predetermined differential traffic load match process. A match process is performed by each of the mobile units and, according to a match, a predetermined response procedure is enabled, wherein a response procedure in each mobile unit uses a predetermined random process to select an allocated slot in which to transmit a predetermined signal, which provides an improved way to predict traffic in conjunction with off line database statistics, preferably with such that are being adaptively corrected by prior data and method to predict traffic which do not include, or lack sufficient erratic traffic information.

The invention relates to the field of traffic forecasting. A road traffic forecasting method comprises the steps that information collecting is carried out, namely vehicle driving related information and traffic forecasting auxiliary information are collected; driving path analyzing is carried out, so as to calculate the driving path, which takes a road as an element, of each vehicle to acquire driving path data; road traffic threshold calculation and vehicle behavior chain analysis are carried out, wherein road traffic threshold calculation is used for calculating the traffic threshold of each road in different flow states, and vehicle behavior chain analysis is used for analyzing and calculating the behavior chain of each vehicle; vehicle behavior mode analysis is carried out to analyze and calculate the travel mode of each vehicle; vehicle travel forecasting is carried out to forecast the travel purpose, the starting point, the driving path, the probability and the time of each vehicle in future; road traffic forecasting is carried out to forecast the flow, the flow patterns, the vehicle sources and the destinations of different roads at different times in future; and information releasing is carried out on a road traffic forecasting result.

The invention discloses a traffic prediction and control system for vehicle traffic flows at traffic intersections. 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.

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%.

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.

A system and method for traffic prediction based on space-time relation are disclosed. The system comprises a section spatial influence determining section for determining, for each of a plurality of sections to be predicted, spatial influences on the section by its neighboring sections; a traffic prediction model establishment section for establishing, for each of the plurality of sections to be predicted, a traffic prediction model by using the determined spatial influences and historical traffic data of the plurality of sections; and a traffic prediction section for predicting traffic of each of the plurality of sections to be predicted for a future time period by using real-time traffic data and the traffic prediction model. An apparatus and method for determining spatial influences among sections, as well as an apparatus and method for traffic prediction, are also disclosed. With the present invention, a spatial influence of a section can be used as a spatial operator and a time sequence model can be incorporated, such that the influences on a current section by its neighboring section for a plurality of spatial orders can be taken into account. In this way, the traffic condition in a spatial scope can be measured more practically, so as to improve accuracy of prediction.

The invention relates to the technical field of traffic prediction, in particular to a traffic operation state prediction method and system for a congested area. The method comprises the following steps: S1, acquiring regional traffic data information; S2, generating a plurality of congestion subareas according to the regional traffic data information; S3, performing road section speed predictionon each road section in each congestion subarea according to the prediction time period to obtain a road section prediction speed of the corresponding prediction time period; S4, carrying out the OD prediction according to the obtained prediction speed of the road section to obtain OD prediction parameters; S5, simulating the regional traffic operation state according to the road section prediction speed and the OD prediction parameters of the congestion subarea so as to predict the regional traffic operation state. According to the method, the dynamic characteristics of the road section association relationship are reserved by constructing the congestion subareas, the problem that a previous traffic simulation area is fixed is solved, the problem of simulation flow parameter calibration under data loss is solved by utilizing the generalization performance of the meta-learning model, and the traffic operation state of the congestion area can be better predicted.