Abstract: Single loop inductive sensors are widely deployed in infrastructure for traffic data collection, however, these loops currently provide little more than vehicle detection. A system and method are provided that enable single loop inductive sensors to be used for vehicle classification (e.g., identification as motorcycle, passenger car, bus, etc.). Classification may be done using the Federal Highway Administration's 13 class system. Initially a signature library is built from vehicle signatures with known classifications. Vehicle signature waveforms of unknown classification obtained from inductive loop sensors are analyzed to identify specific features in the waveform including the number of “peaks”, the first peak location and its magnitude. A classifier (e.g., K-nearest neighbor) uses a representation of the vehicle signature and the features to determine from the signature library the classification of the vehicle.

Abstract: A system and method are disclosed for using vehicle signature data from a single inductive loop sensor to estimate vehicle speed. Two regressors are calculated from the vehicle signature: the “inverse of the duration” and the “slew rate”. A point is found by normalizing the vehicle signature in amplitude and determining the earliest point in the normalized signature to cross a set threshold. The slew rate is the slope of the normalized vehicle signature at this point. Regression models are generated from empirical data for several vehicle categories. Using the regression model for the category of vehicle and the two regressors vehicle speed is estimated. The regression models have been demonstrated to be robust eliminating the need for site-specific calibration or estimation.

Abstract: A system and method are disclosed for using vehicle signature data from a single inductive loop sensor to estimate vehicle speed. Two regressors are calculated from the vehicle signature: the “inverse of the duration” and the “slew rate”. A point is found by normalizing the vehicle signature in amplitude and determining the earliest point in the normalized signature to cross a set threshold. The slew rate is the slope of the normalized vehicle signature at this point. Regression models are generated from empirical data for several vehicle categories. Using the regression model for the category of vehicle and the two regressors vehicle speed is estimated. The regression models have been demonstrated to be robust eliminating the need for site-specific calibration or estimation.

Abstract: Single loop inductive sensors are widely deployed in infrastructure for traffic data collection, however, these loops currently provide little more than vehicle detection. A system and method are provided that enable single loop inductive sensors to be used for vehicle classification (e.g., identification as motorcycle, passenger car, bus, etc.). Classification may be done using the Federal Highway Administration's 13 class system. Initially a signature library is built from vehicle signatures with known classifications. Vehicle signature waveforms of unknown classification obtained from inductive loop sensors are analyzed to identify specific features in the waveform including the number of “peaks”, the first peak location and its magnitude. A classifier (e.g., K-nearest neighbor) uses a representation of the vehicle signature and the features to determine from the signature library the classification of the vehicle.

Abstract: A system and method are disclosed for using vehicle signature data from a single inductive loop sensor to estimate vehicle speed. Two regressors are calculated from the vehicle signature: the “inverse of the duration” and the “slew rate”. A point is found by normalizing the vehicle signature in amplitude and determining the earliest point in the normalized signature to cross a set threshold. The slew rate is the slope of the normalized vehicle signature at this point. Regression models are generated from empirical data for several vehicle categories. Using the regression model for the category of vehicle and the two regressors vehicle speed is estimated. The regression models have been demonstrated to be robust eliminating the need for site-specific calibration or estimation.