Patent ID: 9111442
Filing Date: 2015-08-18
Classification: G06Q,G08G

Abstract:
1. A method for incident duration prediction carried out by a computing device via a computer-aided dispatch system module, a traffic measurement detector system module, a spatial-temporal module, and a regression model module, 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, wherein said obtaining incident data is carried out by the computer-aided dispatch system module executing on the computing device; obtaining traffic data in real-time for the selected geographic area from a system of one or more traffic measurement detectors; 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, wherein said spatially and temporally associating is carried out by the spatial-temporal module executing on the computing device; and predicting incident duration of at least one additional traffic-related incident using one or more regression models, wherein said predicting is 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 via implementing a hybrid predictive model that combines an algorithm for recursive partitioning based on at least one permutation test with a quantile regression model by employing a regression tree wherein a regression model is fitted to each final node of the tree to provide predicted values of incident duration, and wherein said predicting is carried out by the regression model module executing on the computing device.