A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Wavelet‐Clustering‐Neural Network Model for Freeway Incident Detection
Abstract: An improved freeway incident‐detection model is presented based on speed, volume, and occupancy data from a single detector station using a combination of wavelet‐based signal processing, statistical cluster analysis, and neural network pattern recognition. A comparative study of different wavelets (Haar, second‐order Daubechies, and second‐ and fourth‐order Coifman wavelets) and filtering schemes is conducted in terms of efficacy and accuracy of smoothing. It is concluded that the fourth‐order Coifman wavelet is more effective than other types of wavelets for the traffic incident detection problem. A statistical multivariate analysis based on the Mahalanobis distance is employed to perform data clustering and parameter reduction to reduce the size of the input space for the subsequent step of classification by the Levenberg–Marquardt backpropagation (BP) neural network. For a straight two‐lane freeway using real data, the model yields an incident detection rate of 100%, false alarm rate of 0.3%, and detection time of 35.6 seconds.
Wavelet‐Clustering‐Neural Network Model for Freeway Incident Detection
Abstract: An improved freeway incident‐detection model is presented based on speed, volume, and occupancy data from a single detector station using a combination of wavelet‐based signal processing, statistical cluster analysis, and neural network pattern recognition. A comparative study of different wavelets (Haar, second‐order Daubechies, and second‐ and fourth‐order Coifman wavelets) and filtering schemes is conducted in terms of efficacy and accuracy of smoothing. It is concluded that the fourth‐order Coifman wavelet is more effective than other types of wavelets for the traffic incident detection problem. A statistical multivariate analysis based on the Mahalanobis distance is employed to perform data clustering and parameter reduction to reduce the size of the input space for the subsequent step of classification by the Levenberg–Marquardt backpropagation (BP) neural network. For a straight two‐lane freeway using real data, the model yields an incident detection rate of 100%, false alarm rate of 0.3%, and detection time of 35.6 seconds.
Wavelet‐Clustering‐Neural Network Model for Freeway Incident Detection
Ghosh‐Dastidar, Samanwoy (author) / Adeli, Hojjat (author)
Computer‐Aided Civil and Infrastructure Engineering ; 18 ; 325-338
2003-09-01
14 pages
Article (Journal)
Electronic Resource
English
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