Estimating Incident Duration
First Claim
1. A method for incident duration prediction, wherein the method comprises:
- obtaining incident data for at least one traffic-related incident in a selected geographic area, wherein said incident data comprises incident type, one or more incident characteristics, and spatio-temporal information corresponding to the at least one traffic-related incident;
obtaining traffic data for the selected geographic area;
spatially and temporally associating the at least one traffic-related incident with the traffic data to generate incident duration data for the at least one traffic-related incident; and
predicting incident duration of at least one additional traffic-related incident based on the incident duration data for the at least one traffic-related incident and incident type, one or more incident characteristics, and spatio-temporal information corresponding to the at least one additional traffic-related incident;
wherein at least one of the steps is carried out by a computer device.
1 Assignment
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Accused Products
Abstract
A method, an apparatus and an article of manufacture for incident duration prediction. The method includes obtaining incident data for at least one traffic-related incident in a selected geographic area, obtaining traffic data for the selected geographic area, spatially and temporally associating the at least one traffic-related incident with the traffic data to generate incident duration data for the at least one traffic-related incident, and predicting incident duration of at least one additional traffic-related incident based on the incident duration data for the at least one traffic-related incident.
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Citations
25 Claims
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1. A method for incident duration prediction, wherein the method comprises:
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obtaining incident data for at least one traffic-related incident in a selected geographic area, wherein said incident data comprises incident type, one or more incident characteristics, and spatio-temporal information corresponding to the at least one traffic-related incident; obtaining traffic data for the selected geographic area; spatially and temporally associating the at least one traffic-related incident with the traffic data to generate incident duration data for the at least one traffic-related incident; and predicting incident duration of at least one additional traffic-related incident based on the incident duration data for the at least one traffic-related incident and incident type, one or more incident characteristics, and spatio-temporal information corresponding to the at least one additional traffic-related incident; wherein at least one of the steps is carried out by a computer device. - View Dependent Claims (2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18)
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4. (canceled)
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19. A method for incident duration prediction, wherein the method comprises:
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obtaining incident data for at least one traffic-related incident, wherein said incident data comprises incident type, one or more incident characteristics, and spatio-temporal information corresponding to the at least one traffic-related incident; predicting incident duration of at least one additional traffic-related incident based on the incident data for the at least one traffic-related incident and incident type, one or more incident characteristics, and spatio-temporal information corresponding to the at least one additional traffic-related incident by implementing a predictive model that combines an algorithm for recursive partitioning based on at least one permutation test with a quantile regression model; and calibrating the quantile regression model without traffic data to predict the incident duration of the at least one additional traffic-related incident without traffic data; wherein at least one of the steps is carried out by a computer device.
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20. An article of manufacture comprising a computer readable storage medium having computer readable instructions tangibly embodied thereon which, when implemented, cause a computer to carry out a plurality of method steps comprising:
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obtaining incident data for at least one traffic-related incident in a selected geographic area, wherein said incident data comprises incident type, one or more incident characteristics, and spatio-temporal information corresponding to the at least one traffic-related incident; obtaining traffic data for the selected geographic area; spatially and temporally associating the at least one traffic-related incident with the traffic data to generate incident duration data for the at least one traffic-related incident; and predicting incident duration of at least one additional traffic-related incident based on the incident duration data for the at least one traffic-related incident and incident type, one or more incident characteristics, and spatio-temporal information corresponding to the at least one additional traffic-related incident. - View Dependent Claims (21, 22)
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23. A system for incident duration prediction, comprising:
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at least one distinct software module, each distinct software module being embodied on a tangible computer-readable medium; a memory; and at least one processor coupled to the memory and operative for; obtaining incident data for at least one traffic-related incident in a selected geographic area, wherein said incident data comprises incident type, one or more incident characteristics, and spatio-temporal information corresponding to the at least one traffic-related incident; obtaining traffic data for the selected geographic area; spatially and temporally associating the at least one traffic-related incident with the traffic data to generate incident duration data for the at least one traffic-related incident; and predicting incident duration of at least one additional traffic-related incident based on the incident duration data for the at least one traffic-related incident and incident type, one or more incident characteristics, and spatio-temporal information corresponding to the at least one additional traffic-related incident. - View Dependent Claims (24, 25)
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Specification