Performing-time-series based predictions with projection thresholds using secondary time-series-based information stream
First Claim
1. A method implemented in a computer system for managing traffic flow on a road network, the method comprising:
- receiving, at the computer system, a first time-series data set having one or more values for each time point to be predicted, the first time-series data set comprising traffic occupancy levels obtained from a sensor device associated with a road of said road network;
receiving, at the computer system, a second time-series data set of one or more values per time point with correlation to the first time-series data, the second time-series data set comprising traffic volume levels at the road;
estimating, by the computer system, a functional relationship between the first time-series data and the second time-series data, for each value, over a multiplicity of time points;
determining, at the computer system, an extremal value of the functional relationship of the second time-series data as a function of the first time-series data, said extremal value representing an occupancy level at which a full congested traffic state is reached at the associated sensor device;
modifying, at the computer system, said first time-series data by projecting the occupancy level of the first time series data obtained from the associated sensor device on the extremal value so that first time-series data values that are beyond the extremal value are set to the extremal value;
using, by the computer system, said modified first time-series data in any prediction model to increase accuracy of a future predicted traffic occupancy state; and
regulating a traffic flow of said road network based on said future predicted traffic occupancy state.
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Accused Products
Abstract
A prediction modeling system, method and computer program product for implementing forecasting models that involve numerous measurement locations, e.g., urban occupancy traffic data. The method invokes a data volatility reduction technique based on computing a congestion threshold for each prediction location, and using that threshold in a filtering scheme. Through the use of calibration, and by obtaining an extremal or other specified solution (e.g., maximization) of empirical volume-occupancy curves as a function of the occupancy level, significant accuracy gains are achieved and at virtually no loss of important information to the end user. The calibration use quantile regression to deal with the asymmetry and scatter of the empirical data. The argmax of each empirical function is used in a unidimensional projection to essentially filter all fully congested occupancy level and treat them as a single state.
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Citations
8 Claims
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1. A method implemented in a computer system for managing traffic flow on a road network, the method comprising:
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receiving, at the computer system, a first time-series data set having one or more values for each time point to be predicted, the first time-series data set comprising traffic occupancy levels obtained from a sensor device associated with a road of said road network; receiving, at the computer system, a second time-series data set of one or more values per time point with correlation to the first time-series data, the second time-series data set comprising traffic volume levels at the road; estimating, by the computer system, a functional relationship between the first time-series data and the second time-series data, for each value, over a multiplicity of time points; determining, at the computer system, an extremal value of the functional relationship of the second time-series data as a function of the first time-series data, said extremal value representing an occupancy level at which a full congested traffic state is reached at the associated sensor device; modifying, at the computer system, said first time-series data by projecting the occupancy level of the first time series data obtained from the associated sensor device on the extremal value so that first time-series data values that are beyond the extremal value are set to the extremal value; using, by the computer system, said modified first time-series data in any prediction model to increase accuracy of a future predicted traffic occupancy state; and regulating a traffic flow of said road network based on said future predicted traffic occupancy state. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8)
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Specification