TRAFFIC STATE ESTIMATION WITH INTEGRATION OF TRAFFIC, WEATHER, INCIDENT, PAVEMENT CONDITION, AND ROADWAY OPERATIONS DATA
First Claim
10. A traffic state estimation system, comprising:
- a computer processor; and
at least one computer-readable storage medium operably coupled to the computer processor and having program instructions stored therein, the computer processor being operable to execute the program instructions to model one or more estimations of a traffic state within a plurality of data processing modules, the plurality of data processing modules including;
a plurality of data assimilation components configured to ingest input data relative to traffic flow, the plurality of data assimilation components at least including a traffic data aggregation module, a weather data aggregation module, and a roadway operations data aggregation module, wherein the input data relative to traffic flow includes traffic data, observed and predicted weather data, incident data, pavement conditions data, and roadway operations data;
a cell transmission model configured to integrate the input data relative to traffic flow with road link data representative of a segmented roadway network;
a module configured to apply a regression analysis to at least one of the traffic data, the observed and predicted weather data, the roadway operations data, pavement condition data and predictions, and the incident data to separate recurrent and non-recurrent traffic conditions causing delay to identify and explain at least one reason for the delay at specific road links of the segmented roadway network; and
a filter configured to apply weighting coefficients to generate an ensemble of new roadway network traffic states in one or more traffic prediction modules, the ensemble of new roadway network traffic states representing a probability distribution of predicted future traffic states for specific road links in the segmented roadway network.
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Abstract
An integrated traffic state estimation framework ingests information from multiple input data sources having an impact on traffic flow and correlated to specific road links in a segmented roadway network, and generates output data representative of predictive traffic states. The predictive traffic states are then modeled to generate routing information for traffic on a particular section of roadway. These multiple input data sources include general traffic data collected from one or more sensors or third parties, weather data, incident data, pavement condition data, and roadway operations data, each of which includes data relevant to traffic congestion. The input data is weighted and modeled with data processing modules configured to integrate known and predicted information to produce accurate routing information for particular roadway segments for media, telematics, and consumer uses.
72 Citations
25 Claims
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10. A traffic state estimation system, comprising:
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a computer processor; and at least one computer-readable storage medium operably coupled to the computer processor and having program instructions stored therein, the computer processor being operable to execute the program instructions to model one or more estimations of a traffic state within a plurality of data processing modules, the plurality of data processing modules including; a plurality of data assimilation components configured to ingest input data relative to traffic flow, the plurality of data assimilation components at least including a traffic data aggregation module, a weather data aggregation module, and a roadway operations data aggregation module, wherein the input data relative to traffic flow includes traffic data, observed and predicted weather data, incident data, pavement conditions data, and roadway operations data; a cell transmission model configured to integrate the input data relative to traffic flow with road link data representative of a segmented roadway network; a module configured to apply a regression analysis to at least one of the traffic data, the observed and predicted weather data, the roadway operations data, pavement condition data and predictions, and the incident data to separate recurrent and non-recurrent traffic conditions causing delay to identify and explain at least one reason for the delay at specific road links of the segmented roadway network; and a filter configured to apply weighting coefficients to generate an ensemble of new roadway network traffic states in one or more traffic prediction modules, the ensemble of new roadway network traffic states representing a probability distribution of predicted future traffic states for specific road links in the segmented roadway network. - View Dependent Claims (1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18)
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15-1. The system of claim 10, wherein the plurality of data assimilation components ingests at least one of the traffic data, weather data, incident data, pavement condition data, and the roadway operations data from one or more crowd-sourced observations generated by users of the specific road links on the segmented roadway network.
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19. A method of estimating a traffic state, comprising:
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predicting an initial traffic state from a cell transmission model configured with input data representing one or more characteristics of traffic flow and integrated with road links representing a segmented roadway network, the input data including traffic data, observed and predicted weather data, incident data, pavement conditions data, and roadway operations data; separating recurring and non-recurring traffic conditions causing delay in a regression analysis configured to identify and explain at least one reason for the delay at specific road links representing the segmented roadway network; and estimating a future traffic state by filtering output data from the regression analysis by applying weighting coefficients to generate an ensemble of new roadway network traffic states representing a probability distribution of predicted future traffic states for the specific road links in the segmented roadway network. - View Dependent Claims (20, 21, 22, 23, 24, 25)
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Specification